DataBloom

Posted by Chen Liang and Yifeng Lu, Software Engineers, Google Research, Brain Team

Time series forecasting is an important research area for machine learning (ML), particularly where accurate forecasting is critical, including several industries such as retail, supply chain, energy, finance, etc. For example, in the consumer goods domain, improving the accuracy of demand forecasting by 10-20% can reduce inventory by 5% and increase revenue by 2-3%. Current ML-based forecasting solutions are usually built by experts and require significant manual effort, including model construction, feature engineering and hyper-parameter tuning. However, such expertise may not be broadly available, which can limit the benefits of applying ML towards time series forecasting challenges.

To address this, automated machine learning (AutoML) is an approach that makes ML more widely accessible by automating the process of creating ML models, and has recently accelerated both ML research and the application of ML to real-world problems. For example, the initial work on neural architecture search enabled breakthroughs in computer vision, such as NasNet, AmoebaNet, and EfficientNet, and in natural language processing, such as Evolved Transformer. More recently, AutoML has also been applied to tabular data.

Today we introduce a scalable end-to-end AutoML solution for time series forecasting, which meets three key criteria:

• Fully automated: The solution takes in data as input, and produces a servable TensorFlow model as output with no human intervention.
• Generic: The solution works for most time series forecasting tasks and automatically searches for the best model configuration for each task.
• High-quality: The produced models have competitive quality compared to those manually crafted for specific tasks.

We demonstrate the success of this approach through participation in the M5 forecasting competition, where this AutoML solution achieved competitive performance against hand-crafted models with moderate compute cost.

Challenges in Time Series Forecasting
Time series forecasting presents several challenges to machine learning models. First, the uncertainty is often high since the goal is to predict the future based on historical data. Unlike other machine learning problems, the test set, for example, future product sales, might have a different distribution from the training and validation set, which are extracted from the historical data. Second, the time series data from the real world often suffers from missing data and high intermittency (i.e., when a high fraction of the time series has the value of zero). Some time series tasks may not have historical data available and suffer from the cold start problem, for example, when predicting the sales of a new product. Third, since we aim to build a fully automated generic solution, the same solution needs to apply to a variety of datasets, which can vary significantly in the domain (product sales, web traffic, etc), the granularity (daily, hourly, etc), the history length, the types of features (categorical, numerical, date time, etc), and so on.

An AutoML Solution
To tackle these challenges, we designed an end-to-end TensorFlow pipeline with a specialized search space for time series forecasting. It is based on an encoder-decoder architecture, in which an encoder transforms the historical information in a time series into a set of vectors, and a decoder generates the future predictions based on these vectors. Inspired by the state-of-the-art sequence models, such as Transformer and WaveNet, and best practices in time series forecasting, our search space included components such as attention, dilated convolution, gating, skip connections, and different feature transformations. The resulting AutoML solution searches for the best combination of these components as well as core hyperparameters.

To combat the uncertainty in predicting the future of a time series, an ensemble of the top models discovered in the search is used to make final predictions. The diversity in the top models made the predictions more robust to uncertainty and less prone to overfitting the historical data. To handle time series with missing data, we fill in the gaps with a trainable vector and let the model learn to adapt to the missing time steps. To address intermittency, we predict, for each future time step, not only the value, but also the probability that the value at this time step is non-zero, and combine the two predictions. Finally, we found that the automated search is able to adjust the architecture and hyperparameter choices for different datasets, which makes the AutoML solution generic and automates the modeling efforts.

Benchmarking in Forecasting Competitions
To benchmark our AutoML solution, we participated in the M5 forecasting competition, the latest in the M-competition series, which is one of the most important competitions in the forecasting community, with a long history spanning nearly 40 years. This most recent competition was hosted on Kaggle and used a dataset from Walmart product sales, the real-world nature of which makes the problem quite challenging.

We participated in the competition with our fully automated solution and achieved a rank of 138 out of 5558 participants (top 2.5%) on the final leaderboard, which is in the silver medal zone. Participants in the competition had almost four months to produce their models. While many of the competitive forecasting models required months of manual effort to create, our AutoML solution found the model in a short time with only a moderate compute cost (500 CPUs for 2 hours) and no human intervention.

We also benchmarked our AutoML forecasting solution on several other Kaggle datasets and found that on average it outperforms 92% of hand-crafted models, despite its limited resource use.

Evaluation of the AutoML Forecasting solution on other Kaggle Datasets (Rossman Store Sales, Web Traffic, Favorita Grocery Sales) besides M5.

This work demonstrates the strength of an end-to-end AutoML solution for time series forecasting, and we are excited about its potential impact on real-world applications.

Acknowledgements
This project was a joint effort of Google Brain team members Chen Liang, Da Huang, Yifeng Lu and Quoc V. Le. We also thank Junwei Yuan, Xingwei Yang, Dawei Jia, Chenyu Zhao, Tin-yun Ho, Meng Wang, Yaguang Li, Nicolas Loeff, Manish Kurse, Kyle Anderson and Nishant Patil for their collaboration.

Posted by Jaqui Herman and Cat Armato, Program Managers

This week marks the beginning of the 34^th annual Conference on Neural Information Processing Systems (NeurIPS 2020), the biggest machine learning conference of the year. Held virtually for the first time, this conference includes invited talks, demonstrations and presentations of some of the latest in machine learning research. As a Platinum Sponsor of NeurIPS 2020, Google will have a strong presence with more than 180 accepted papers, additionally contributing to and learning from the broader academic research community via talks, posters, workshops and tutorials.

If you are registered for NeurIPS 2020, we hope you’ll visit our virtual booth and chat with our researchers about the projects and opportunities at Google that go into solving the world’s most challenging research problems, and to see demonstrations of some of the exciting research we pursue, such as Transformers for image recognition, Tone Transfer, large-scale distributed RL, recreating historical streetscapes and much more. You can also learn more about our work being presented in the list below (Google affiliations highlighted in blue).

Organizing Committees

General Chair: Hugo Larochelle

Workshop Co-Chair: Sanmi Koyejo

Diversity and Inclusion Chairs include: Katherine Heller

Expo Chair: Pablo Samuel Castro

Senior Area Chairs include: Corinna Cortes, Fei Sha, Mohammad Ghavamzadeh, Sanjiv Kumar, Charles Sutton, Dale Schuurmans, David Duvenaud, Elad Hazan, Marco Cuturi, Peter Bartlett, Samy Bengio, Tong Zhang, Claudio Gentile, Kevin Murphy, Cordelia Schmid, Amir Globerson

Area Chairs include: Boqing Gong, Afshin Rostamizadeh, Alex Kulesza, Branislav Kveton, Craig Boutilier, Heinrich Jiang, Manzil Zaheer, Silvio Lattanzi, Slav Petrov, Srinadh Bhojanapalli, Rodolphe Jenatton, Mathieu Blondel, Aleksandra Faust, Alexey Dosovitskiy, Ashish Vaswani, Augustus Odena, Balaji Lakshminarayanan, Ben Poole, Colin Raffel, Danny Tarlow, David Ha, Denny Zhou, Dumitru Erhan, Dustin Tran, George Tucker, Honglak Lee, Ilya Tolstikhin, Jasper Snoek, Jean-Philippe Vert, Jeffrey Pennington, Kevin Swersky, Matthew Johnson, Minmin Chen, Mohammad Norouzi, Moustapha Cisse, Naman Agarwal, Nicholas Carlini, Olivier Bachem, Tim Salimans, Vincent Dumoulin, Yann Dauphin, Andrew Dai, Izhak Shafran, Karthik Sridharan, Abhinav Gupta, Abhishek Kumar, Adam White, Aditya Menon, Kun Zhang, Ce Liu, Cristian Sminchisescu, Hossein Mobahi, Phillip Isola, Tomer Koren, Chelsea Finn, Amin Karbasi

NeurIPS 2020 Foundation Board includes: Michael Mozer, Samy Bengio, Corinna Cortes, Hugo Larochelle, John C. Platt, Fernando Pereira

Accepted Papers

Rankmax: An Adaptive Projection Alternative to the Softmax Function
Weiwei Kong*, Walid Krichene, Nicolas Mayoraz, Steffen Rendle, Li Zhang

Unsupervised Sound Separation Using Mixture Invariant Training
Scott Wisdom, Efthymios Tzinis*, Hakan Erdogan, Ron Weiss, Kevin Wilson, John Hershey

Learning to Select Best Forecast Tasks for Clinical Outcome Prediction
Yuan Xue, Nan Du, Anne Mottram, Martin Seneviratne, Andrew M. Dai

Interpretable Sequence Learning for Covid-19 Forecasting
Sercan O. Arık, Chun-Liang Li, Jinsung Yoon, Rajarishi Sinha, Arkady Epshteyn, Long T. Le, Vikas Menon, Shashank Singh, Leyou Zhang, Nate Yoder, Martin Nikoltchev, Yash Sonthalia, Hootan Nakhost, Elli Kanal, Tomas Pfister

Towards Learning Convolutions from Scratch
Behnam Neyshabur

Emergent Complexity and Zero-shot Transfer via Unsupervised Environment Design
Michael Dennis, Natasha Jaques, Eugene Vinitsky, Alexandre Bayen, Stuart Russell, Andrew Critch, Sergey Levine

Inverse Rational Control with Partially Observable Continuous Nonlinear Dynamics
Minhae Kwon, Saurabh Daptardar, Paul Schrater, Xaq Pitkow

Off-Policy Evaluation via the Regularized Lagrangian
Mengjiao Yang, Ofir Nachum, Bo Dai, Lihong Li, Dale Schuurmans

CoinDICE: Off-Policy Confidence Interval Estimation
Bo Dai, Ofir Nachum, Yinlam Chow, Lihong Li, Csaba Szepesvári, Dale Schuurmans

Unsupervised Data Augmentation for Consistency Training
Qizhe Xie, Zihang Dai, Eduard Hovy, Minh-Thang Luong, Quoc V. Le

VIME: Extending the Success of Self- and Semi-supervised Learning to Tabular Domain
Jinsung Yoon, Yao Zhang, James Jordon, Mihaela van der Schaar

Funnel-Transformer: Filtering out Sequential Redundancy for Efficient Language Processing
Zihang Dai, Guokun Lai, Yiming Yang, Quoc Le

Big Bird: Transformers for Longer Sequences
Manzil Zaheer, Guru Guruganesh, Avinava Dubey, Joshua Ainslie, Chris Alberti, Santiago Ontanon, Philip Pham, Anirudh Ravula, Qifan Wang, Li Yang, Amr Ahmed

Provably Efficient Neural Estimation of Structural Equation Models: An Adversarial Approach
Luofeng Liao, You-Lin Chen, Zhuoran Yang, Bo Dai, Zhaoran Wang, Mladen Kolar

Conservative Q-Learning for Offline Reinforcement Learning
Aviral Kumar, Aurick Zhou, George Tucker, Sergey Levine

MOReL: Model-Based Offline Reinforcement Learning
Rahul Kidambi, Aravind Rajeswaran, Praneeth Netrapalli, Thorsten Joachims

Maximum-Entropy Adversarial Data Augmentation for Improved Generalization and Robustness
Long Zhao, Ting Liu, Xi Peng, Dimitris Metaxas

Generative View Synthesis: From Single-view Semantics to Novel-view Images
Tewodros Habtegebrial, Varun Jampani, Orazio Gallo, Didier Stricker

PIE-NET: Parametric Inference of Point Cloud Edges
Xiaogang Wang, Yuelang Xu, Kai Xu, Andrea Tagliasacchi, Bin Zhou, Ali Mahdavi-Amiri, Hao Zhang

Enabling Certification of Verification-Agnostic Networks via Memory-Efficient Semidefinite Programming
Sumanth Dathathri, Krishnamurthy (Dj) Dvijotham, Alex Kurakin, Aditi Raghunathan, Jonathan Uesato, Rudy Bunel, Shreya Shankar, Jacob Steinhardt, Ian Goodfellow*, Percy Liang, Pushmeet Kohli

An Analysis of SVD for Deep Rotation Estimation
Jake Levinson, Carlos Esteves, Kefan Chen, Noah Snavely, Angjoo Kanazawa, Afshin Rostamizadeh, Ameesh Makadia

Direct Policy Gradients: Direct Optimization of Policies in Discrete Action Spaces
Guy Lorberbom, Chris J. Maddison, Nicolas Heess, Tamir Hazan, Daniel Tarlow

Faster Differentially Private Samplers via Rényi Divergence Analysis of Discretized Langevin MCMC
Arun Ganesh*, Kunal Talwar*

DISK: Learning Local Features with Policy Gradient
Michał J. Tyszkiewicz, Pascal Fua, Eduard Trulls

Robust Large-margin Learning in Hyperbolic Space
Melanie Weber*, Manzil Zaheer, Ankit Singh Rawat, Aditya Menon, Sanjiv Kumar

Gamma-Models: Generative Temporal Difference Learning for Infinite-Horizon Prediction
Michael Janner, Igor Mordatch, Sergey Levine

Adversarially Robust Streaming Algorithms via Differential Privacy
Avinatan Hassidim, Haim Kaplan, Yishay Mansour, Yossi Matias, Uri Stemmer

Faster DBSCAN via Subsampled Similarity Queries
Heinrich Jiang, Jennifer Jang, Jakub Łacki

Exact Recovery of Mangled Clusters with Same-Cluster Queries
Marco Bressan, Nicolò Cesa-Bianchi, Silvio Lattanzi, Andrea Paudice

A Maximum-Entropy Approach to Off-Policy Evaluation in Average-Reward MDPs
Nevena Lazic, Dong Yin, Mehrdad Farajtabar, Nir Levine, Dilan Görür, Chris Harris, Dale Schuurmans

Fairness in Streaming Submodular Maximization: Algorithms and Hardness
Marwa El Halabi, Slobodan Mitrović, Ashkan Norouzi-Fard, Jakab Tardos, Jakub Tarnawski

Efficient Active Learning of Sparse Halfspaces with Arbitrary Bounded Noise
Chicheng Zhang, Jie Shen, Pranjal Awasthi

Private Learning of Halfspaces: Simplifying the Construction and Reducing the Sample Complexity
Haim Kaplan, Yishay Mansour, Uri Stemmer, Eliad Tsfadia

Synthetic Data Generators — Sequential and Private
Olivier Bousquet, Roi Livni, Shay Moran

Learning Discrete Distributions: User vs Item-level Privacy
Yuhan Liu, Ananda Theertha Suresh, Felix Xinnan X. Yu, Sanjiv Kumar, Michael Riley

Learning Differential Equations that are Easy to Solve
Jacob Kelly, Jesse Bettencourt, Matthew J. Johnson, David K. Duvenaud

An Optimal Elimination Algorithm for Learning a Best Arm
Avinatan Hassidim, Ron Kupfer, Yaron Singer

The Convex Relaxation Barrier, Revisited: Tightened Single-Neuron Relaxations for Neural Network Verification
Christian Tjandraatmadja, Ross Anderson, Joey Huchette, Will Ma, Krunal Kishor Patel*, Juan Pablo Vielma

Escaping the Gravitational Pull of Softmax
Jincheng Mei, Chenjun Xiao, Bo Dai, Lihong Li*, Csaba Szepesvari, Dale Schuurmans

The Complexity of Adversarially Robust Proper Learning of Halfspaces with Agnostic Noise
Ilias Diakonikolas, Daniel M. Kane, Pasin Manurangsi

PAC-Bayes Learning Bounds for Sample-Dependent Priors
Pranjal Awasthi, Satyen Kale, Stefani Karp, Mehryar Mohri

Fictitious Play for Mean Field Games: Continuous Time Analysis and Applications
Sarah Perrin, Julien Perolat, Mathieu Lauriere, Matthieu Geist, Romuald Elie, Olivier Pietquin

What Do Neural Networks Learn When Trained With Random Labels?
Hartmut Maennel, Ibrahim M. Alabdulmohsin, Ilya O. Tolstikhin, Robert Baldock*, Olivier Bousquet, Sylvain Gelly, Daniel Keysers

Online Planning with Lookahead Policies
Yonathan Efroni, Mohammad Ghavamzadeh, Shie Mannor

Smoothly Bounding User Contributions in Differential Privacy
Alessandro Epasto, Mohammad Mahdian, Jieming Mao, Vahab Mirrokni, Lijie Ren

Differentially Private Clustering: Tight Approximation Ratios
Badih Ghazi, Ravi Kumar, Pasin Manurangsi

Hitting the High Notes: Subset Selection for Maximizing Expected Order Statistics
Aranyak Mehta, Uri Nadav, Alexandros Psomas*, Aviad Rubinstein

Myersonian Regression
Allen Liu, Renato Leme, Jon Schneider

Assisted Learning: A Framework for Multi-Organization Learning
Xun Xian, Xinran Wang, Jie Ding, Reza Ghanadan

Adversarial Robustness via Robust Low Rank Representations
Pranjal Awasthi, Himanshu Jain, Ankit Singh Rawat, Aravindan Vijayaraghavan

Multi-Plane Program Induction with 3D Box Priors
Yikai Li, Jiayuan Mao, Xiuming Zhang, Bill Freeman, Josh Tenenbaum, Noah Snavely, Jiajun Wu

Privacy Amplification via Random Check-Ins
Borja Balle, Peter Kairouz, Brendan McMahan, Om Dipakbhai Thakkar, Abhradeep Thakurta

Rethinking Pre-training and Self-training
Barret Zoph, Golnaz Ghiasi, Tsung-Yi Lin, Yin Cui, Hanxiao Liu, Ekin Dogus Cubuk, Quoc Le

Reinforcement Learning with Combinatorial Actions: An Application to Vehicle Routing
Arthur Delarue, Ross Anderson, Christian Tjandraatmadja

Online Agnostic Boosting via Regret Minimization
Nataly Brukhim, Xinyi Chen, Elad Hazan, Shay Moran*

From Trees to Continuous Embeddings and Back: Hyperbolic Hierarchical Clustering
Ines Chami, Albert Gu, Vaggos Chatziafratis, Christopher Ré

Faithful Embeddings for Knowledge Base Queries
Haitian Sun, Andrew Arnold*, Tania Bedrax Weiss, Fernando Pereira, William W. Cohen

Contextual Reserve Price Optimization in Auctions via Mixed Integer Programming
Joey Huchette, Haihao Lu, Hossein Esfandiari, Vahab Mirrokni

An Operator View of Policy Gradient Methods
Dibya Ghosh, Marlos C. Machado, Nicolas Le Roux

Reinforcement Learning with Feedback Graphs
Christoph Dann, Yishay Mansour, Mehryar Mohri, Ayush Sekhari, Karthik Sridharan

On Completeness-aware Concept-Based Explanations in Deep Neural Networks
Chih-Kuan Yeh, Been Kim, Sercan Arik, Chun-Liang Li, Tomas Pfister, Pradeep Ravikumar

Rewriting History with Inverse RL: Hindsight Inference for Policy Improvement
Benjamin Eysenbach, Xinyang Geng, Sergey Levine, Ruslan Salakhutdinov

The Flajolet-Martin Sketch Itself Preserves Differential Privacy: Private Counting with Minimal Space
Adam Smith, Shuang Song, Abhradeep Thakurta

What is Being Transferred in Transfer Learning?
Behnam Neyshabur, Hanie Sedghi, Chiyuan Zhang

Latent Bandits Revisited
Joey Hong, Branislav Kveton, Manzil Zaheer, Yinlam Chow, Amr Ahmed, Craig Boutilier

MetaSDF: Meta-Learning Signed Distance Functions
Vincent Sitzmann, Eric Chan, Richard Tucker, Noah Snavely, Gordon Wetzstein

Measuring Robustness to Natural Distribution Shifts in Image Classification
Rohan Taori, Achal Dave, Vaishaal Shankar, Nicholas Carlini, Benjamin Recht, Ludwig Schmidt

Robust Optimization for Fairness with Noisy Protected Groups
Serena Wang, Wenshuo Guo, Harikrishna Narasimhan, Andrew Cotter, Maya Gupta, Michael I. Jordan

Learning Discrete Energy-based Models via Auxiliary-variable Local Exploration
Hanjun Dai, Rishabh Singh, Bo Dai, Charles Sutton, Dale Schuurmans

Breaking the Communication-Privacy-Accuracy Trilemma
Wei-Ning Chen, Peter Kairouz, Ayfer Ozgur

Differentiable Meta-Learning of Bandit Policies
Craig Boutilier, Chih-wei Hsu, Branislav Kveton, Martin Mladenov, Csaba Szepesvari, Manzil Zaheer

Multi-Stage Influence Function
Hongge Chen*, Si Si, Yang Li, Ciprian Chelba, Sanjiv Kumar, Duane Boning, Cho-Jui Hsieh

Compositional Visual Generation with Energy Based Models
Yilun Du, Shuang Li, Igor Mordatch

O(n) Connections are Expressive Enough: Universal Approximability of Sparse Transformers
Chulhee Yun, Yin-Wen Chang, Srinadh Bhojanapalli, Ankit Singh Rawat, Sashank Reddi, Sanjiv Kumar

Curriculum By Smoothing
Samarth Sinha, Animesh Garg, Hugo Larochelle

Online Linear Optimization with Many Hints
Aditya Bhaskara, Ashok Cutkosky, Ravi Kumar, Manish Purohit

Prediction with Corrupted Expert Advice
Idan Amir, Idan Attias, Tomer Koren, Roi Livni, Yishay Mansour

Agnostic Learning with Multiple Objectives
Corinna Cortes, Mehryar Mohri, Javier Gonzalvo, Dmitry Storcheus

CoSE: Compositional Stroke Embeddings
Emre Aksan, Thomas Deselaers*, Andrea Tagliasacchi, Otmar Hilliges

Reparameterizing Mirror Descent as Gradient Descent
Ehsan Amid, Manfred K. Warmuth

Understanding Double Descent Requires A Fine-Grained Bias-Variance Decomposition
Ben Adlam, Jeffrey Pennington

DisARM: An Antithetic Gradient Estimator for Binary Latent Variables
Zhe Dong, Andriy Mnih, George Tucker

Big Self-Supervised Models are Strong Semi-Supervised Learners
Ting Chen, Simon Kornblith, Kevin Swersky, Mohammad Norouzi, Geoffrey Hinton

JAX MD: A Framework for Differentiable Physics
Samuel S. Schoenholz, Ekin D. Cubuk

Gradient Surgery for Multi-Task Learning
Tianhe Yu, Saurabh Kumar, Abhishek Gupta, Sergey Levine, Karol Hausman, Chelsea Finn

LoopReg: Self-supervised Learning of Implicit Surface Correspondences, Pose and Shape for 3D Human Mesh Registration
Bharat Lal Bhatnagar, Cristian Sminchisescu, Christian Theobalt, Gerard Pons-Moll

ICE-BeeM: Identifiable Conditional Energy-Based Deep Models Based on Nonlinear ICA
Ilyes Khemakhem, Ricardo P. Monti, Diederik P. Kingma, Aapo Hyvärinen

Demystifying Orthogonal Monte Carlo and Beyond
Han Lin, Haoxian Chen, Tianyi Zhang, Clement Laroche, Krzysztof Choromanski

FixMatch: Simplifying Semi-Supervised Learning with Consistency and Confidence
Kihyuk Sohn, David Berthelot, Chun-Liang Li, Zizhao Zhang, Nicholas Carlini, Ekin D. Cubuk, Alex Kurakin, Han Zhang, Colin Raffel

Compositional Generalization via Neural-Symbolic Stack Machines
Xinyun Chen, Chen Liang, Adams Wei Yu, Dawn Song, Denny Zhou

Universally Quantized Neural Compression
Eirikur Agustsson, Lucas Theis

Self-Distillation Amplifies Regularization in Hilbert Space
Hossein Mobahi, Mehrdad Farajtabar, Peter L. Bartlett

ShapeFlow: Learnable Deformation Flows Among 3D Shapes
Chiyu “Max” Jiang, Jingwei Huang, Andrea Tagliasacchi, Leonidas Guibas

Entropic Optimal Transport between Unbalanced Gaussian Measures has a Closed Form
Hicham Janati, Boris Muzellec, Gabriel Peyré, Marco Cuturi

High-Fidelity Generative Image Compression
Fabian Mentzer*, George Toderici, Michael Tschannen*, Eirikur Agustsson

COT-GAN: Generating Sequential Data via Causal Optimal Transport
Tianlin Xu, Li K. Wenliang, Michael Munn, Beatrice Acciaio

When Do Neural Networks Outperform Kernel Methods?
Behrooz Ghorbani, Song Mei, Theodor Misiakiewicz, Andrea Montanari

Sense and Sensitivity Analysis: Simple Post-Hoc Analysis of Bias Due to Unobserved Confounding
Victor Veitch, Anisha Zaveri

Exemplar VAE: Linking Generative Models, Nearest Neighbor Retrieval, and Data Augmentation
Sajad Norouzi, David J. Fleet, Mohamamd Norouzi

Mitigating Forgetting in Online Continual Learning via Instance-Aware Parameterization
Hung-Jen Chen, An-Chieh Cheng, Da-Cheng Juan, Wei Wei, Min Sun
 
Consistent Plug-in Classifiers for Complex Objectives and Constraints
Shiv Kumar Tavker, Harish Guruprasad Ramaswamy, Harikrishna Narasimhan

Online MAP Inference of Determinantal Point Processes
Aditya Bhaskara, Amin Karbasi, Silvio Lattanzi, Morteza Zadimoghaddam

Organizing Recurrent Network Dynamics by Task-computation to Enable Continual Learning
Lea Duncker, Laura Driscoll, Krishna V. Shenoy, Maneesh Sahani, David Sussillo

RL Unplugged: A Collection of Benchmarks for Offline Reinforcement Learning
Caglar Gulcehre, Ziyu Wang, Alexander Novikov, Thomas Paine, Sergio Gómez, Konrad Zolna, Rishabh Agarwal, Josh S. Merel, Daniel J. Mankowitz, Cosmin Paduraru, Gabriel Dulac-Arnold, Jerry Li, Mohammad Norouzi, Matthew Hoffman, Nicolas Heess, Nando de Freitas

Neural Execution Engines: Learning to Execute Subroutines
Yujun Yan*, Kevin Swersky, Danai Koutra, Parthasarathy Ranganathan, Milad Hashemi

Spin-Weighted Spherical CNNs
Carlos Esteves, Ameesh Makadia, Kostas Daniilidis

An Efficient Nonconvex Reformulation of Stagewise Convex Optimization Problems
Rudy R. Bunel, Oliver Hinder, Srinadh Bhojanapalli, Krishnamurthy Dvijotham

Stochastic Optimization with Laggard Data Pipelines
Naman Agarwal, Rohan Anil, Tomer Koren, Kunal Talwar*, Cyril Zhang*

Regularizing Towards Permutation Invariance In Recurrent Models
Edo Cohen-Karlik, Avichai Ben David, Amir Globerson

Fast and Accurate kk-means++ via Rejection Sampling
Vincent Cohen-Addad, Silvio Lattanzi, Ashkan Norouzi-Fard, Christian Sohler*, Ola Svensson

Fairness Without Demographics Through Adversarially Reweighted Learning
Preethi Lahoti*, Alex Beutel, Jilin Chen, Kang Lee, Flavien Prost, Nithum Thain, Xuezhi Wang, Ed Chi

Gradient Estimation with Stochastic Softmax Tricks
Max Paulus, Dami Choi, Daniel Tarlow, Andreas Krause, Chris J. Maddison

Just Pick a Sign: Optimizing Deep Multitask Models with Gradient Sign Dropout
Zhao Chen, Jiquan Ngiam, Yanping Huang, Thang Luong, Henrik Kretzschmar, Yuning Chai, Dragomir Anguelov

A Spectral Energy Distance for Parallel Speech Synthesis
Alexey A. Gritsenko, Tim Salimans, Rianne van den Berg, Jasper Snoek, Nal Kalchbrenner

Ode to an ODE
Krzysztof Choromanski, Jared Quincy Davis, Valerii Likhosherstov, Xingyou Song, Jean-Jacques Slotine, Jacob Varley, Honglak Lee, Adrian Weller, Vikas Sindhwani

RandAugment: Practical Automated Data Augmentation with a Reduced Search Space
Ekin Dogus Cubuk, Barret Zoph, Jon Shlens, Quoc Le

On Adaptive Attacks to Adversarial Example Defenses
Florian Tramer, Nicholas Carlini, Wieland Brendel, Aleksander Madry

Fair Performance Metric Elicitation
Gaurush Hiranandani, Harikrishna Narasimhan, Oluwasanmi O. Koyejo

Robust Pre-Training by Adversarial Contrastive Learning
Ziyu Jiang, Tianlong Chen, Ting Chen, Zhangyang Wang

Why are Adaptive Methods Good for Attention Models?
Jingzhao Zhang, Sai Praneeth Karimireddy, Andreas Veit, Seungyeon Kim, Sashank Reddi, Sanjiv Kumar, Suvrit Sra

PyGlove: Symbolic Programming for Automated Machine Learning
Daiyi Peng, Xuanyi Dong, Esteban Real, Mingxing Tan, Yifeng Lu, Gabriel Bender, Hanxiao Liu, Adam Kraft, Chen Liang, Quoc Le

Fair Hierarchical Clustering
Sara Ahmadian, Alessandro Epasto, Marina Knittel, Ravi Kumar, Mohammad Mahdian, Benjamin Moseley, Philip Pham, Sergei Vassilvitskii, Yuyan Wang

Fairness with Overlapping Groups; a Probabilistic Perspective
Forest Yang*, Moustapha Cisse, Sanmi Koyejo

Differentiable Top-k with Optimal Transport
Yujia Xie*, Hanjun Dai, Minshuo Chen, Bo Dai, Tuo Zhao, Hongyuan Zha, Wei Wei, Tomas Pfister

The Origins and Prevalence of Texture Bias in Convolutional Neural Networks
Katherine Hermann, Ting Chen, Simon Kornblith

Approximate Heavily-Constrained Learning with Lagrange Multiplier Models
Harikrishna Narasimhan, Andrew Cotter, Yichen Zhou, Serena Wang, Wenshuo Guo

Evaluating Attribution for Graph Neural Networks
Benjamin Sanchez-Lengeling, Jennifer Wei, Brian Lee, Emily Reif, Peter Wang, Wesley Wei Qian, Kevin McCloskey, Lucy Colwell, Alexander Wiltschko

Sliding Window Algorithms for k-Clustering Problems
Michele Borassi, Alessandro Epasto, Silvio Lattanzi, Sergei Vassilvitskii, Morteza Zadimoghaddam

Meta-Learning Requires Meta-Augmentation
Janarthanan Rajendran*, Alex Irpan, Eric Jang

What Makes for Good Views for Contrastive Learning?
Yonglong Tian, Chen Sun, Ben Poole, Dilip Krishnan, Cordelia Schmid, Phillip Isola

Supervised Contrastive Learning
Prannay Khosla*, Piotr Teterwak*, Chen Wang*, Aaron Sarna, Yonglong Tian, Phillip Isola, Aaron Maschinot, Ce Liu, Dilip Krishnan

Critic Regularized Regression
Ziyu Wang, Alexander Novikov, Konrad Zolna, Josh Merel, Jost Tobias Springenberg, Scott Reed, Bobak Shahriari, Noah Siegel, Caglar Gulcehre, Nicolas Heess, Nando de Freitas

Off-Policy Imitation Learning from Observations
Zhuangdi Zhu, Kaixiang Lin, Bo Dai, Jiayu Zhou

Effective Diversity in Population Based Reinforcement Learning
Jack Parker-Holder, Aldo Pacchiano, Krzysztof Choromanski, Stephen Roberts

Memory Based Trajectory-conditioned Policies for Learning from Sparse Rewards
Yijie Guo, Jongwook Choi, Marcin Moczulski, Shengyu Feng, Samy Bengio, Mohammad Norouzi, Honglak Lee

Object-Centric Learning with Slot Attention
Francesco Locatello*, Dirk Weissenborn, Thomas Unterthiner, Aravindh Mahendran, Georg Heigold, Jakob Uszkoreit, Alexey Dosovitskiy, Thomas Kipf

On the Power of Louvain in the Stochastic Block Model
Vincent Cohen-Addad, Adrian Kosowski, Frederik Mallmann-Trenn, David Saulpic

Learning to Execute Programs with Instruction Pointer Attention Graph Neural Networks
David Bieber, Charles Sutton, Hugo Larochelle, Daniel Tarlow

SMYRF – Efficient Attention using Asymmetric Clustering
Giannis Daras, Nikita Kitaev, Augustus Odena, Alexandros G. Dimakis

Graph Contrastive Learning with Augmentations
Yuning You, Tianlong Chen, Yongduo Sui, Ting Chen, Zhangyang Wang, Yang Shen

WOR and p’s: Sketches for ℓ_p-Sampling Without Replacement
Edith Cohen, Rasmus Pagh, David P. Woodruff

Fourier Features Let Networks Learn High Frequency Functions in Low Dimensional Domains
Matthew Tancik, Pratul Srinivasan, Ben Mildenhall, Sara Fridovich-Keil, Nithin Raghavan, Utkarsh Singhal, Ravi Ramamoorthi, Jonathan Barron, Ren Ng

Model Selection in Contextual Stochastic Bandit Problems
Aldo Pacchiano, My Phan, Yasin Abbasi Yadkori, Anup Rao, Julian Zimmert, Tor Lattimore, Csaba Szepesvari

Adapting to Misspecification in Contextual Bandits
Dylan J. Foster, Claudio Gentile, Mehryar Mohri, Julian Zimmert

Leverage the Average: an Analysis of KL Regularization in Reinforcement Learning
Nino Vieillard, Tadashi Kozunoú, Bruno Scherrer, Olivier Pietquin, Rémi Munos, Matthieu Geist

Learning with Differentiable Pertubed Optimizers
Quentin Berthet, Mathieu Blondel, Olivier Teboul, Marco Cuturi, Jean-Philippe Vert, Francis Bach

Munchausen Reinforcement Learning
Nino Vieillard, Olivier Pietquin, Matthieu Geist

Log-Likelihood Ratio Minimizing Flows: Towards Robust and Quantifiable Neural Distribution Alignment
Ben Usman, Avneesh Sud, Nick Dufour, Kate Saenko

Your GAN is Secretly an Energy-based Model and You Should Use Discriminator Driven Latent Sampling
Tong Che, Ruixiang Zhang, Jascha Sohl-Dickstein, Hugo Larochelle, Liam Paull, Yuan Cao, Yoshua Bengio

Sample Complexity of Uniform Convergence for Multicalibration
Eliran Shabat, Lee Cohen, Yishay Mansour

Implicit Regularization and Convergence for Weight Normalization
Xiaoxia Wu, Edgar Dobriban, Tongzheng Ren, Shanshan Wu, Zhiyuan Li, Suriya Gunasekar, Rachel Ward, Qiang Liu

Most ReLU Networks Suffer from ℓ² Adversarial Perturbations
Amit Daniely, Hadas Shacham

Geometric Exploration for Online Control
Orestis Plevrakis, Elad Hazan

PLLay: Efficient Topological Layer Based on Persistent Landscapes
Kwangho Kim, Jisu Kim, Manzil Zaheer, Joon Sik Kim, Frederic Chazal, Larry Wasserman

Simple and Principled Uncertainty Estimation with Deterministic Deep Learning via Distance Awareness
Jeremiah Zhe Liu*, Zi Lin, Shreyas Padhy, Dustin Tran, Tania Bedrax-Weiss, Balaji Lakshminarayanan

Bayesian Deep Ensembles via the Neural Tangent Kernel
Bobby He, Balaji Lakshminarayanan, Yee Whye Teh

Hyperparameter Ensembles for Robustness and Uncertainty Quantification
Florian Wenzel, Jasper Snoek, Dustin Tran, Rodolphe Jenatton

Conic Descent and its Application to Memory-efficient Optimization Over Positive Semidefinite Matrices
John Duchi, Oliver Hinder, Andrew Naber, Yinyu Ye

On the Training Dynamics of Deep Networks with L₂ Regularization
Aitor Lewkowycz, Guy Gur-Ari

The Surprising Simplicity of the Early-Time Learning Dynamics of Neural Networks
Wei Hu*, Lechao Xiao, Ben Adlam, Jeffrey Pennington

Adaptive Probing Policies for Shortest Path Routing
Aditya Bhaskara, Sreenivas Gollapudi, Kostas Kollias, Kamesh Munagala

Optimal Approximation — Smoothness Tradeoffs for Soft-Max Functions
Alessandro Epasto, Mohammad Mahdian, Vahab Mirrokni, Emmanouil Zampetakis

An Unsupervised Information-Theoretic Perceptual Quality Metric
Sangnie Bhardwaj, Ian Fischer, Johannes Ballé, Troy Chinen

Learning Graph Structure With A Finite-State Automaton Layer
Daniel Johnson, Hugo Larochelle, Daniel Tarlow

Estimating Training Data Influence by Tracing Gradient Descent
Garima Pruthi, Frederick Liu, Satyen Kale, Mukund Sundararajan

Tutorials

Designing Learning Dynamics
Organizers: Marta Garnelo, David Balduzzi, Wojciech Czarnecki

Where Neuroscience meets AI (And What’s in Store for the Future)
Organizers: Jane Wang, Kevin Miller, Adam Marblestone

Offline Reinforcement Learning: From Algorithm Design to Practical Applications
Organizers: Sergey Levine, Aviral Kumar

Practical Uncertainty Estimation and Out-of-Distribution Robustness in Deep Learning
Organizers: Dustin Tran, Balaji Lakshminarayanan, Jasper Snoek

Abstraction & Reasoning in AI systems: Modern Perspectives
Organizers: Francois Chollet, Melanie Mitchell, Christian Szegedy

Policy Optimization in Reinforcement Learning
Organizers: Sham M Kakade, Martha White, Nicolas Le Roux

Federated Learning and Analytics: Industry Meets Academia
Organizers: Brendan McMahan, Virginia Smith, Peter Kairouz

Deep Implicit Layers: Neural ODEs, Equilibrium Models, and Differentiable Optimization
Organizers: David Duvenaud, J. Zico Kolter, Matthew Johnson

Beyond Accuracy: Grounding Evaluation Metrics for Human-Machine Learning Systems
Organizers: Praveen Chandar, Fernando Diaz, Brian St. Thomas

Workshops

Black in AI Workshop @ NeurIPS 2020 (Diamond Sponsor)
Mentorship Roundtables: Natasha Jacques

LatinX in AI Workshop @ NeurIPS 2020 (Platinum Sponsor)
Organizers include: Pablo Samuel Castro
Invited Speaker: Fernanda Viégas
Mentorship Roundtables: Tomas Izo

Queer in AI Workshop @ NeurIPS 2020 (Platinum Sponsor)
Organizers include: Raphael Gontijo Lopes

Women in Machine Learning (Platinum Sponsor)
Organizers include: Xinyi Chen, Jessica Schrouff
Invited Speaker: Fernanda Viégas
Sponsor Talk: Jessica Schrouff
Mentorship Roundtables: Hanie Sedghi, Marc Bellemare, Katherine Heller, Rianne van den Berg, Natalie Schluter, Colin Raffel, Azalia Mirhoseini, Emily Denton, Jesse Engel, Anusha Ramesh, Matt Johnson, Jeff Dean, Laurent Dinh, Samy Bengio, Yasaman Bahri, Corinna Cortes, Nicolas le Roux, Hugo Larochelle, Sergio Guadarrama, Natasha Jaques, Pablo Samuel Castro, Elaine Le, Cory Silvear

Muslims in ML
Organizers include: Mohammad Norouzi

Resistance AI Workshop
Organizers include: Elliot Creager, Raphael Gontijo Lopes

Privacy Preserving Machine Learning — PriML and PPML Joint Edition
Organizers include: Adria Gascon, Mariana Raykova

OPT2020: Optimization for Machine Learning
Organizers include: Courtney Paquette

Machine Learning for Health (ML4H): Advancing Healthcare for All
Organizers include: Subhrajit Roy

Human in the Loop Dialogue Systems
Organizers include: Rahul Goel
Invited Speaker: Ankur Parikh

Self-Supervised Learning for Speech and Audio Processing
Organizers include: Tara Sainath
Invited Speaker: Bhuvana Ramabhadran

3rd Robot Learning Workshop
Organizers include: Alex Bewley, Vincent Vanhoucke
Invited Speaker: Pete Florence

Workshop on Deep Learning and Inverse Problems
Invited Speaker: Peyman Milanfar

Crowd Science Workshop: Remoteness, Fairness, and Mechanisms as Challenges of Data Supply by Humans for Automation
Invited Speakers: Lora Aroyo, Praveen Paritosh

Workshop on Fair AI in Finance
Invited Speakers: Berk Ustun, Madeleine Clare Elish

Object Representations for Learning and Reasoning
Panel Moderator: Klaus Greff

Deep Reinforcement Learning
Organizers include: Chelsea Finn
Invited Speaker: Marc Bellemare

Algorithmic Fairness Through the Lens of Causality and Interpretability
Organizers include: Awa Dieng, Jessica Schrouff, Fernando Diaz

Machine Learning for the Developing World (ML4D)
Steering Committee Member: Ernest Mwebaze

Machine Learning for Engineering Modeling, Simulation and Design
Organizers include: Stephan Hoyer

Machine Learning for Creativity and Design
Organizers include: Adam Roberts, Daphne Ippolito
Invited Speaker: Jesse Engel

Cooperative AI
Invited Speaker: Natasha Jaques

International Workshop on Scalability, Privacy, and Security in Federated Learning (SpicyFL 2020)
Invited Speaker: Brendan McMahan

Machine Learning for Molecules
Organizers include: Jennifer Wei
Invited Speaker: Benjamin Sanchez-Lengeling

Navigating the Broader Impacts of AI Research
Panelists include: Nyalleng Moorosi, Colin Raffel, Natalie Schluter, Ben Zevenbergen

Beyond BackPropagation: Novel Ideas for Training Neural Architectures
Organizers include: Yanping Huang

Differentiable Computer Vision, Graphics, and Physics in Machine Learning
Invited Speaker: Andrea Tagliasacchi

AI for Earth Sciences
Invited Speaker: Milind Tambe

Machine Learning for Mobile Health
Organizers include: Katherine Heller, Marianne Njifon

Shared Visual Representations in Human and Machine Intelligence (SVRHM)
Invited Speaker: Gamaleldin Elsayed

The Challenges of Real World Reinforcement Learning
Organizers include: Gabriel Dulac-Arnold
Invited Speaker: Chelsea Finn

Workshop on Computer Assisted Programming (CAP)
Organizers include: Charles Sutton, Augustus Odena

Self-Supervised Learning — Theory and Practice
Organizers include: Barret Zoph
Invited Speaker: Quoc V. Le

Offline Reinforcement Learning
Organizers include: Rishabh Agarwal, George Tucker

Machine Learning for Systems
Organizers include: Anna Goldie, Azalia Mirhoseini, Martin Maas
Invited Speaker: Ed Chi

Deep Learning Through Information Geometry
Organizers include: Alexander Alemi

Expo

Drifting Efficiently Through the Stratosphere Using Deep Reinforcement Learning
Organizers include: Sal Candido

Accelerating Eye Movement Research via Smartphone Gaze
Organizers include: Vidhya Navalpakkam

Mining and Learning with Graphs at Scale
Organizers include: Bryan Perozzi, Vahab Mirrokni, Jonathan Halcrow, Jakub Lacki

*Work performed while at Google

Posted by Ivan Grishchenko and Valentin Bazarevsky, Research Engineers, Google Research

Real-time, simultaneous perception of human pose, face landmarks and hand tracking on mobile devices can enable a variety of impactful applications, such as fitness and sport analysis, gesture control and sign language recognition, augmented reality effects and more. MediaPipe, an open-source framework designed specifically for complex perception pipelines leveraging accelerated inference (e.g., GPU or CPU), already offers fast and accurate, yet separate, solutions for these tasks. Combining them all in real-time into a semantically consistent end-to-end solution is a uniquely difficult problem requiring simultaneous inference of multiple, dependent neural networks.

Today, we are excited to announce MediaPipe Holistic, a solution to this challenge that provides a novel state-of-the-art human pose topology that unlocks novel use cases. MediaPipe Holistic consists of a new pipeline with optimized pose, face and hand components that each run in real-time, with minimum memory transfer between their inference backends, and added support for interchangeability of the three components, depending on the quality/speed tradeoffs. When including all three components, MediaPipe Holistic provides a unified topology for a groundbreaking 540+ keypoints (33 pose, 21 per-hand and 468 facial landmarks) and achieves near real-time performance on mobile devices. MediaPipe Holistic is being released as part of MediaPipe and is available on-device for mobile (Android, iOS) and desktop. We are also introducing MediaPipe’s new ready-to-use APIs for research (Python) and web (JavaScript) to ease access to the technology.

Top: MediaPipe Holistic results on sport and dance use-cases. Bottom: “Silence” and “Hello” gestures. Note, that our solution consistently identifies a hand as either right (blue color) or left (orange color).

Pipeline and Quality
The MediaPipe Holistic pipeline integrates separate models for pose, face and hand components, each of which are optimized for their particular domain. However, because of their different specializations, the input to one component is not well-suited for the others. The pose estimation model, for example, takes a lower, fixed resolution video frame (256×256) as input. But if one were to crop the hand and face regions from that image to pass to their respective models, the image resolution would be too low for accurate articulation. Therefore, we designed MediaPipe Holistic as a multi-stage pipeline, which treats the different regions using a region appropriate image resolution.

First, MediaPipe Holistic estimates the human pose with BlazePose’s pose detector and subsequent keypoint model. Then, using the inferred pose key points, it derives three regions of interest (ROI) crops for each hand (2x) and the face, and employs a re-crop model to improve the ROI (details below). The pipeline then crops the full-resolution input frame to these ROIs and applies task-specific face and hand models to estimate their corresponding keypoints. Finally, all key points are merged with those of the pose model to yield the full 540+ keypoints.

MediaPipe Holistic pipeline overview.

To streamline the identification of ROIs, a tracking approach similar to the one used for the standalone face and hand pipelines is utilized. This approach assumes that the object doesn’t move significantly between frames, using an estimation from the previous frame as a guide to the object region in the current one. However, during fast movements, the tracker can lose the target, which requires the detector to re-localize it in the image. MediaPipe Holistic uses pose prediction (on every frame) as an additional ROI prior to reduce the response time of the pipeline when reacting to fast movements. This also enables the model to retain semantic consistency across the body and its parts by preventing a mixup between left and right hands or body parts of one person in the frame with another.

In addition, the resolution of the input frame to the pose model is low enough that the resulting ROIs for face and hands are still too inaccurate to guide the re-cropping of those regions, which require a precise input crop to remain lightweight. To close this accuracy gap we use lightweight face and hand re-crop models that play the role of spatial transformers and cost only ~10% of the corresponding model’s inference time.

	MEH	FLE
Tracking pipeline (baseline)	9.8%	3.1%
Pipeline without re-crops	11.8%	3.5%
Pipeline with re-crops	9.7%	3.1%

Hand prediction quality.The mean error per hand (MEH) is normalized by the hand size. The face landmarks error (FLE) is normalized by the inter-pupillary distance.

Performance
MediaPipe Holistic requires coordination between up to 8 models per frame — 1 pose detector, 1 pose landmark model, 3 re-crop models and 3 keypoint models for hands and face. While building this solution, we optimized not only machine learning models, but also pre- and post-processing algorithms (e.g., affine transformations), which take significant time on most devices due to pipeline complexity. In this case, moving all the pre-processing computations to GPU resulted in ~1.5 times overall pipeline speedup depending on the device. As a result, MediaPipe Holistic runs in near real-time performance even on mid-tier devices and in the browser.

Phone	FPS
Google Pixel 2 XL	18
Samsung S9+	20
15-inch MacBook Pro 2017	15

Performance on various mid-tier devices, measured in frames per second (FPS) using TFLite GPU.

The multi-stage nature of the pipeline provides two more performance benefits. As models are mostly independent, they can be replaced with lighter or heavier versions (or turned off completely) depending on the performance and accuracy requirements. Also, once pose is inferred, one knows precisely whether hands and face are within the frame bounds, allowing the pipeline to skip inference on those body parts.

Applications
MediaPipe Holistic, with its 540+ key points, aims to enable a holistic, simultaneous perception of body language, gesture and facial expressions. Its blended approach enables remote gesture interfaces, as well as full-body AR, sports analytics, and sign language recognition. To demonstrate the quality and performance of the MediaPipe Holistic, we built a simple remote control interface that runs locally in the browser and enables a compelling user interaction, no mouse or keyboard required. The user can manipulate objects on the screen, type on a virtual keyboard while sitting on the sofa, and point to or touch specific face regions (e.g., mute or turn off the camera). Underneath it relies on accurate hand detection with subsequent gesture recognition mapped to a “trackpad” space anchored to the user’s shoulder, enabling remote control from up to 4 meters.

This technique for gesture control can unlock various novel use-cases when other human-computer interaction modalities are not convenient. Try it out in our web demo and prototype your own ideas with it.

In-browser touchless control demos. Left: Palm picker, touch interface, keyboard. Right: Distant touchless keyboard. Try it out!

MediaPipe for Research and Web
To accelerate ML research as well as its adoption in the web developer community, MediaPipe now offers ready-to-use, yet customizable ML solutions in Python and in JavaScript. We are starting with those in our previous publications: Face Mesh, Hands and Pose, including MediaPipe Holistic, with many more to come. Try them directly in the web browser: for Python using the notebooks in MediaPipe on Google Colab, and for JavaScript with your own webcam input in MediaPipe on CodePen!

Conclusion
We hope the release of MediaPipe Holistic will inspire the research and development community members to build new unique applications. We anticipate that these pipelines will open up avenues for future research into challenging domains, such as sign-language recognition, touchless control interfaces, or other complex use cases. We are looking forward to seeing what you can build with it!

Complex and dynamic hand gestures. Videos by Dr. Bill Vicars, used with permission.

Acknowledgments
Special thanks to all our team members who worked on the tech with us: Fan Zhang, Gregory Karpiak, Kanstantsin Sokal, Juhyun Lee, Hadon Nash, Chuo-Ling Chang, Jiuqiang Tang, Nikolay Chirkov, Camillo Lugaresi, George Sung, Michael Hays, Tyler Mullen, Chris McClanahan, Ekaterina Ignasheva, Marat Dukhan, Artsiom Ablavatski, Yury Kartynnik, Karthik Raveendran, Andrei Vakunov, Andrei Tkachenka, Suril Shah, Buck Bourdon, Ming Guang Yong, Esha Uboweja, Siarhei Kazakou, Andrei Kulik, Matsvei Zhdanovich, and Matthias Grundmann.

Posted by Yun-Ta Tsai¹ and Rohit Pandey, Software Engineers, Google Research

Professional portrait photographers are able to create compelling photographs by using specialized equipment, such as off-camera flashes and reflectors, and expert knowledge to capture just the right illumination of their subjects. In order to allow users to better emulate professional-looking portraits, we recently released Portrait Light, a new post-capture feature for the Pixel Camera and Google Photos apps that adds a simulated directional light source to portraits, with the directionality and intensity set to complement the lighting from the original photograph.

Example image with and without Portrait Light applied. Note how Portrait Light contours the face, adding dimensionality, volume, and visual interest.

In the Pixel Camera on Pixel 4, Pixel 4a, Pixel 4a (5G), and Pixel 5, Portrait Light is automatically applied post-capture to images in the default mode and to Night Sight photos that include people — just one person or even a small group. In Portrait Mode photographs, Portrait Light provides more dramatic lighting to accompany the shallow depth-of-field effect already applied, resulting in a studio-quality look. But because lighting can be a personal choice, Pixel users who shoot in Portrait Mode can manually re-position and adjust the brightness of the applied lighting within Google Photos to match their preference. For those running Google Photos on Pixel 2 or newer, this relighting capability is also available for many pre-existing portrait photographs.

Pixel users can adjust a portrait’s lighting as they like in Google Photos, after capture.

Today we present the technology behind Portrait Light. Inspired by the off-camera lights used by portrait photographers, Portrait Light models a repositionable light source that can be added into the scene, with the initial lighting direction and intensity automatically selected to complement the existing lighting in the photo. We accomplish this by leveraging novel machine learning models, each trained using a diverse dataset of photographs captured in the Light Stage computational illumination system. These models enabled two new algorithmic capabilities:

1. Automatic directional light placement: For a given portrait, the algorithm places a synthetic directional light in the scene consistent with how a photographer would have placed an off-camera light source in the real world.
2. Synthetic post-capture relighting: For a given lighting direction and portrait, synthetic light is added in a way that looks realistic and natural.

These innovations enable Portrait Light to help create attractive lighting at any moment for every portrait — all on your mobile device.

Automatic Light Placement
Photographers usually rely on perceptual cues when deciding how to augment environmental illumination with off-camera light sources. They assess the intensity and directionality of the light falling on the face, and also adjust their subject’s head pose to complement it. To inform Portrait Light’s automatic light placement, we developed computational equivalents to these two perceptual signals.

First, we trained a novel machine learning model to estimate a high dynamic range, omnidirectional illumination profile for a scene based on an input portrait. This new lighting estimation model infers the direction, relative intensity, and color of all light sources in the scene coming from all directions, considering the face as a light probe. We also estimate the head pose of the portrait’s subject using MediaPipe Face Mesh.

Estimating the high dynamic range, omnidirectional illumination profile from an input portrait. The three spheres at the right of each image, diffuse (top), matte silver (middle), and mirror (bottom), are rendered using the estimated illumination, each reflecting the color, intensity, and directionality of the environmental lighting.

Using these clues, we determine the direction from which the synthetic lighting should originate. In studio portrait photography, the main off-camera light source, or key light, is placed about 30° above the eyeline and between 30° and 60° off the camera axis, when looking overhead at the scene. We follow this guideline for a classic portrait look, enhancing any pre-existing lighting directionality in the scene while targeting a balanced, subtle key-to-fill lighting ratio of about 2:1.

Data-Driven Portrait Relighting
Given a desired lighting direction and portrait, we next trained a new machine learning model to add the illumination from a directional light source to the original photograph. Training the model required millions of pairs of portraits both with and without extra light. Photographing such a dataset in normal settings would have been impossible because it requires near-perfect registration of portraits captured across different lighting conditions.

Instead, we generated training data by photographing seventy different people using the Light Stage computational illumination system. This spherical lighting rig includes 64 cameras with different viewpoints and 331 individually-programmable LED light sources. We photographed each individual illuminated one-light-at-a-time (OLAT) by each light, which generates their reflectance field — or their appearance as illuminated by the discrete sections of the spherical environment. The reflectance field encodes the unique color and light-reflecting properties of the subject’s skin, hair, and clothing — how shiny or dull each material appears. Due to the superposition principle for light, these OLAT images can then be linearly added together to render realistic images of the subject as they would appear in any image-based lighting environment, with complex light transport phenomena like subsurface scattering correctly represented.

Using the Light Stage, we photographed many individuals with different face shapes, genders, skin tones, hairstyles, and clothing/accessories. For each person, we generated synthetic portraits in many different lighting environments, both with and without the added directional light, rendering millions of pairs of images. This dataset encouraged model performance across diverse lighting environments and individuals.

Photographing an individual as illuminated one-light-at-a-time in the Google Light Stage, a 360° computational illumination rig.

Left: Example images from an individual’s photographed reflectance field, their appearance in the Light Stage as illuminated one-light-at-a-time. Right: The images can be added together to form the appearance of the subject in any novel lighting environment.

Learning Detail-Preserving Relighting Using the Quotient Image
Rather than trying to directly predict the output relit image, we trained the relighting model to output a low-resolution quotient image, i.e., a per-pixel multiplier that when upsampled can be applied to the original input image to produce the desired output image with the contribution of the extra light source added. This technique is computationally efficient and encourages only low-frequency lighting changes, without impacting high-frequency image details, which are directly transferred from the input to maintain image quality.

Supervising Relighting with Geometry Estimation
When photographers add an extra light source into a scene, its orientation relative to the subject’s facial geometry determines how much brighter each part of the face appears. To model the optical behavior of light sources reflecting off relatively matte surfaces, we first trained a machine learning model to estimate surface normals given the input photograph, and then applied Lambert’s law to compute a “light visibility map” for the desired lighting direction. We provided this light visibility map as input to the quotient image predictor, ensuring that the model is trained using physics-based insights.

The pipeline of our relighting network. Given an input portrait, we estimate per-pixel surface normals, which we then use to compute a light visibility map. The model is trained to produce a low-resolution quotient image that, when upsampled and applied as a multiplier to the original image, produces the original portrait with an extra light source added synthetically into the scene.

We optimized the full pipeline to run at interactive frame-rates on mobile devices, with total model size under 10 MB. Here are a few examples of Portrait Light in action.

Portrait Light in action.

Getting the Most Out of Portrait Light
You can try Portrait Light in the Pixel Camera and change the light position and brightness to your liking in Google Photos. For those who use Dual Exposure Controls, Portrait Light can be applied post-capture for additional creative flexibility to find just the right balance between light and shadow. On existing images from your Google Photos library, try it where faces are slightly underexposed, where Portrait Light can illuminate and highlight your subject. It will especially benefit images with a single individual posed directly at the camera.

We see Portrait Light as the first step on the journey towards creative post-capture lighting controls for mobile cameras, powered by machine learning.

Acknowledgements
Portrait Light is the result of a collaboration between Google Research, Google Daydream, Pixel, and Google Photos teams. Key contributors include: Yun-Ta Tsai, Rohit Pandey, Sean Fanello, Chloe LeGendre, Michael Milne, Ryan Geiss, Sam Hasinoff, Dillon Sharlet, Christoph Rhemann, Peter Denny, Kaiwen Guo, Philip Davidson, Jonathan Taylor, Mingsong Dou, Pavel Pidlypenskyi, Peter Lincoln, Jay Busch, Matt Whalen, Jason Dourgarian, Geoff Harvey, Cynthia Herrera, Sergio Orts Escolano, Paul Debevec, Jonathan Barron, Sofien Bouaziz, Clement Ng, Rachit Gupta, Jesse Evans, Ryan Campbell, Sonya Mollinger, Emily To, Yichang Shih, Jana Ehmann, Wan-Chun Alex Ma, Christina Tong, Tim Smith, Tim Ruddick, Bill Strathearn, Jose Lima, Chia-Kai Liang, David Salesin, Shahram Izadi, Navin Sarma, Nisha Masharani, Zachary Senzer.

---

¹  Work conducted while at Google. ↩

We are thrilled to announce sparklyr 1.5 is now available on

CRAN!

To install sparklyr 1.5 from CRAN, run

install.packages("sparklyr")

In this blog post, we will highlight the following aspects of
sparklyr 1.5:

• Better
  dplyr interface
• 
  4 useful additions to the sdf_* family of functions
• New
  RDS-based serialization routines along with several
  serialization-related improvements and bug fixes

Better dplyr interface

A large fraction of pull requests that went into the sparklyr
1.5 release were focused on making Spark dataframes work with
various dplyr verbs in the same way that R dataframes do. The full
list of dplyr-related bugs and feature requests that were resolved
in sparklyr 1.5 can be found in
here.

In this section, we will showcase three new dplyr
functionalities that were shipped with sparklyr 1.5.

Stratified sampling

Stratified sampling on an R dataframe can be accomplished with a
combination of dplyr::group_by() followed by dplyr::sample_n() or
dplyr::sample_frac(), where the grouping variables specified in the
dplyr::group_by() step are the ones that define each stratum. For
instance, the following query will group mtcars by number of
cylinders and return a weighted random sample of size two from each
group, without replacement, and weighted by the mpg column:

mtcars %>% dplyr::group_by(cyl) %>% dplyr::sample_n(size = 2, weight = mpg, replace = FALSE) %>% print()

## # A tibble: 6 x 11 ## # Groups: cyl [3] ## mpg cyl disp hp drat wt qsec vs am gear carb ## <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> ## 1 33.9 4 71.1 65 4.22 1.84 19.9 1 1 4 1 ## 2 22.8 4 108 93 3.85 2.32 18.6 1 1 4 1 ## 3 21.4 6 258 110 3.08 3.22 19.4 1 0 3 1 ## 4 21 6 160 110 3.9 2.62 16.5 0 1 4 4 ## 5 15.5 8 318 150 2.76 3.52 16.9 0 0 3 2 ## 6 19.2 8 400 175 3.08 3.84 17.0 0 0 3 2

Starting from sparklyr 1.5, the same can also be done for Spark
dataframes with Spark 3.0 or above, e.g.,:

library(sparklyr) sc <- spark_connect(master = "local", version = "3.0.0") mtcars_sdf <- copy_to(sc, mtcars, replace = TRUE, repartition = 3) mtcars_sdf %>% dplyr::group_by(cyl) %>% dplyr::sample_n(size = 2, weight = mpg, replace = FALSE) %>% print()

# Source: spark<?> [?? x 11] # Groups: cyl mpg cyl disp hp drat wt qsec vs am gear carb <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> 1 21 6 160 110 3.9 2.62 16.5 0 1 4 4 2 21.4 6 258 110 3.08 3.22 19.4 1 0 3 1 3 27.3 4 79 66 4.08 1.94 18.9 1 1 4 1 4 32.4 4 78.7 66 4.08 2.2 19.5 1 1 4 1 5 16.4 8 276. 180 3.07 4.07 17.4 0 0 3 3 6 18.7 8 360 175 3.15 3.44 17.0 0 0 3 2

or

mtcars_sdf %>% dplyr::group_by(cyl) %>% dplyr::sample_frac(size = 0.2, weight = mpg, replace = FALSE) %>% print()

## # Source: spark<?> [?? x 11] ## # Groups: cyl ## mpg cyl disp hp drat wt qsec vs am gear carb ## <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> ## 1 21 6 160 110 3.9 2.62 16.5 0 1 4 4 ## 2 21.4 6 258 110 3.08 3.22 19.4 1 0 3 1 ## 3 22.8 4 141. 95 3.92 3.15 22.9 1 0 4 2 ## 4 33.9 4 71.1 65 4.22 1.84 19.9 1 1 4 1 ## 5 30.4 4 95.1 113 3.77 1.51 16.9 1 1 5 2 ## 6 15.5 8 318 150 2.76 3.52 16.9 0 0 3 2 ## 7 18.7 8 360 175 3.15 3.44 17.0 0 0 3 2 ## 8 16.4 8 276. 180 3.07 4.07 17.4 0 0 3 3

Row sums

The rowSums() functionality offered by dplyr is handy when one
needs to sum up a large number of columns within an R dataframe
that are impractical to be enumerated individually. For example,
here we have a six-column dataframe of random real numbers, where
the partial_sum column in the result contains the sum of columns b
through d within each row:

ncols <- 6 nums <- seq(ncols) %>% lapply(function(x) runif(5)) names(nums) <- letters[1:ncols] tbl <- tibble::as_tibble(nums) tbl %>% dplyr::mutate(partial_sum = rowSums(.[2:5])) %>% print()

## # A tibble: 5 x 7 ## a b c d e f partial_sum ## <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> ## 1 0.781 0.801 0.157 0.0293 0.169 0.0978 1.16 ## 2 0.696 0.412 0.221 0.941 0.697 0.675 2.27 ## 3 0.802 0.410 0.516 0.923 0.190 0.904 2.04 ## 4 0.200 0.590 0.755 0.494 0.273 0.807 2.11 ## 5 0.00149 0.711 0.286 0.297 0.107 0.425 1.40

Beginning with sparklyr 1.5, the same operation can be performed
with Spark dataframes:

library(sparklyr) sc <- spark_connect(master = "local") sdf <- copy_to(sc, tbl, overwrite = TRUE) sdf %>% dplyr::mutate(partial_sum = rowSums(.[2:5])) %>% print()

## # Source: spark<?> [?? x 7] ## a b c d e f partial_sum ## <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> ## 1 0.781 0.801 0.157 0.0293 0.169 0.0978 1.16 ## 2 0.696 0.412 0.221 0.941 0.697 0.675 2.27 ## 3 0.802 0.410 0.516 0.923 0.190 0.904 2.04 ## 4 0.200 0.590 0.755 0.494 0.273 0.807 2.11 ## 5 0.00149 0.711 0.286 0.297 0.107 0.425 1.40

As a bonus from implementing the rowSums feature for Spark
dataframes, sparklyr 1.5 now also offers limited support for the
column-subsetting operator on Spark dataframes. For example, all
code snippets below will return some subset of columns from the
dataframe named sdf:

# select columns `b` through `e` sdf[2:5]

# select columns `b` and `c` sdf[c("b", "c")]

# drop the first and third columns and return the rest sdf[c(-1, -3)]

Weighted-mean summarizer

Similar to the two dplyr functions mentioned above, the
weighted.mean() summarizer is another useful function that has
become part of the dplyr interface for Spark dataframes in sparklyr
1.5. One can see it in action by, for example, comparing the output
from the following

library(sparklyr) sc <- spark_connect(master = "local") mtcars_sdf <- copy_to(sc, mtcars, replace = TRUE) mtcars_sdf %>% dplyr::group_by(cyl) %>% dplyr::summarize(mpg_wm = weighted.mean(mpg, wt)) %>% print()

with output from the equivalent operation on mtcars in R:

mtcars %>% dplyr::group_by(cyl) %>% dplyr::summarize(mpg_wm = weighted.mean(mpg, wt)) %>% print()

both of them should evaluate to the following:

## cyl mpg_wm ## <dbl> <dbl> ## 1 4 25.9 ## 2 6 19.6 ## 3 8 14.8

New additions to the sdf_* family of functions

sparklyr provides a large number of convenience functions for
working with Spark dataframes, and all of them have names starting
with the sdf_ prefix.

In this section we will briefly mention four new additions and
show some example scenarios in which those functions are
useful.

sdf_expand_grid()

As the name suggests, sdf_expand_grid() is simply the Spark
equivalent of expand.grid(). Rather than running expand.grid() in R
and importing the resulting R dataframe to Spark, one can now run
sdf_expand_grid(), which accepts both R vectors and Spark
dataframes and supports hints for broadcast hash joins. The example
below shows sdf_expand_grid() creating a 100-by-100-by-10-by-10
grid in Spark over 1000 Spark partitions, with broadcast hash join
hints on variables with small cardinalities:

library(sparklyr) sc <- spark_connect(master = "local") grid_sdf <- sdf_expand_grid( sc, var1 = seq(100), var2 = seq(100), var3 = seq(10), var4 = seq(10), broadcast_vars = c(var3, var4), repartition = 1000 ) grid_sdf %>% sdf_nrow() %>% print()

## [1] 1e+06

sdf_partition_sizes()

As sparklyr user @sbottelli suggested here, one
thing that would be great to have in sparklyr is an efficient way
to query partition sizes of a Spark dataframe. In sparklyr 1.5,
sdf_partition_sizes() does exactly that:

library(sparklyr) sc <- spark_connect(master = "local") sdf_len(sc, 1000, repartition = 5) %>% sdf_partition_sizes() %>% print(row.names = FALSE)

## partition_index partition_size ## 0 200 ## 1 200 ## 2 200 ## 3 200 ## 4 200

sdf_unnest_longer() and sdf_unnest_wider()

sdf_unnest_longer() and sdf_unnest_wider() are the equivalents
of tidyr::unnest_longer() and tidyr::unnest_wider() for Spark
dataframes. sdf_unnest_longer() expands all elements in a struct
column into multiple rows, and sdf_unnest_wider() expands them into
multiple columns. As illustrated with an example dataframe
below,

library(sparklyr) sc <- spark_connect(master = "local") sdf <- copy_to( sc, tibble::tibble( id = seq(3), attribute = list( list(name = "Alice", grade = "A"), list(name = "Bob", grade = "B"), list(name = "Carol", grade = "C") ) ) )

sdf %>% sdf_unnest_longer(col = record, indices_to = "key", values_to = "value") %>% print()

evaluates to

## # Source: spark<?> [?? x 3] ## id value key ## <int> <chr> <chr> ## 1 1 A grade ## 2 1 Alice name ## 3 2 B grade ## 4 2 Bob name ## 5 3 C grade ## 6 3 Carol name

whereas

sdf %>% sdf_unnest_wider(col = record) %>% print()

evaluates to

## # Source: spark<?> [?? x 3] ## id grade name ## <int> <chr> <chr> ## 1 1 A Alice ## 2 2 B Bob ## 3 3 C Carol

RDS-based serialization routines

Some readers must be wondering why a brand new serialization
format would need to be implemented in sparklyr at all. Long story
short, the reason is that RDS serialization is a strictly better
replacement for its CSV predecessor. It possesses all desirable
attributes the CSV format has, while avoiding a number of
disadvantages that are common among text-based data formats.

In this section, we will briefly outline why sparklyr should
support at least one serialization format other than arrow,
deep-dive into issues with CSV-based serialization, and then show
how the new RDS-based serialization is free from those issues.

Why arrow is not for everyone?

To transfer data between Spark and R correctly and efficiently,
sparklyr must rely on some data serialization format that is
well-supported by both Spark and R. Unfortunately, not many
serialization formats satisfy this requirement, and among the ones
that do are text-based formats such as CSV and JSON, and binary
formats such as Apache Arrow, Protobuf, and as of recent, a small
subset of RDS version 2. Further complicating the matter is the
additional consideration that sparklyr should support at least one
serialization format whose implementation can be fully
self-contained within the sparklyr code base, i.e., such
serialization should not depend on any external R package or system
library, so that it can accommodate users who want to use sparklyr
but who do not necessarily have the required C++ compiler tool
chain and other system dependencies for setting up R packages such
as arrow
or protolite.
Prior to sparklyr 1.5, CSV-based serialization was the default
alternative to fallback to when users do not have the arrow package
installed or when the type of data being transported from R to
Spark is unsupported by the version of arrow available.

Why is the CSV format not ideal?

There are at least three reasons to believe CSV format is not
the best choice when it comes to exporting data from R to
Spark.

One reason is efficiency. For example, a double-precision
floating point number such as .Machine$double.eps needs to be
expressed as “2.22044604925031e-16” in CSV format in order to not
incur any loss of precision, thus taking up 20 bytes rather than 8
bytes.

But more important than efficiency are correctness concerns. In
a R dataframe, one can store both NA_real_ and NaN in a column of
floating point numbers. NA_real_ should ideally translate to null
within a Spark dataframe, whereas NaN should continue to be NaN
when transported from R to Spark. Unfortunately, NA_real_ in R
becomes indistinguishable from NaN once serialized in CSV format,
as evident from a quick demo shown below:

original_df <- data.frame(x = c(NA_real_, NaN)) original_df %>% dplyr::mutate(is_nan = is.nan(x)) %>% print()

## x is_nan ## 1 NA FALSE ## 2 NaN TRUE

csv_file <- "/tmp/data.csv" write.csv(original_df, file = csv_file, row.names = FALSE) deserialized_df <- read.csv(csv_file) deserialized_df %>% dplyr::mutate(is_nan = is.nan(x)) %>% print()

## x is_nan ## 1 NA FALSE ## 2 NA FALSE

Another correctness issue very much similar to the one above was
the fact that “NA” and NA within a string column of an R dataframe
become indistinguishable once serialized in CSV format, as
correctly pointed out in this Github
issue by @caewok and
others.

RDS to the rescue!

RDS format is one of the most widely used binary formats for
serializing R objects. It is described in some detail in chapter 1,
section 8 of this
document. Among advantages of the RDS format are efficiency and
accuracy: it has a reasonably efficient implementation in base R,
and supports all R data types.

Also worth noticing is the fact that when an R dataframe
containing only data types with sensible equivalents in Apache
Spark (e.g., RAWSXP, LGLSXP, CHARSXP, REALSXP, etc) is saved using
RDS version 2, (e.g., serialize(mtcars, connection = NULL, version
= 2L, xdr = TRUE)), only a tiny subset of the RDS format will be
involved in the serialization process, and implementing
deserialization routines in Scala capable of decoding such a
restricted subset of RDS constructs is in fact a reasonably simple
and straightforward task (as shown in
here ).

Last but not least, because RDS is a binary format, it allows
NA_character_, “NA”, NA_real_, and NaN to all be encoded in an
unambiguous manner, hence allowing sparklyr 1.5 to avoid all
correctness issues detailed above in non-arrow serialization use
cases.

Other benefits of RDS serialization

In addition to correctness guarantees, RDS format also offers
quite a few other advantages.

One advantage is of course performance: for example, importing a
non-trivially-sized dataset such as nycflights13::flights from R to
Spark using the RDS format in sparklyr 1.5 is roughly 40%-50%
faster compared to CSV-based serialization in sparklyr 1.4. The
current RDS-based implementation is still nowhere as fast as
arrow-based serialization though (arrow is about 3-4x faster), so
for performance-sensitive tasks involving heavy serialization,
arrow should still be the top choice.

Another advantage is that with RDS serialization, sparklyr can
import R dataframes containing raw columns directly into binary
columns in Spark. Thus, use cases such as the one below will work
in sparklyr 1.5

library(sparklyr) sc <- spark_connect(master = "local") tbl <- tibble::tibble( x = list(serialize("sparklyr", NULL), serialize(c(123456, 789), NULL)) ) sdf <- copy_to(sc, tbl)

While most sparklyr users probably won’t find this capability
of importing binary columns to Spark immediately useful in their
typical sparklyr::copy_to() or sparklyr::collect() usages, it does
play a crucial role in reducing serialization overheads in the
Spark-based foreach
parallel backend that was first introduced in sparklyr 1.2. This is
because Spark workers can directly fetch the serialized R closures
to be computed from a binary Spark column instead of extracting
those serialized bytes from intermediate representations such as
base64-encoded strings. Similarly, the R results from executing
worker closures will be directly available in RDS format which can
be efficiently deserialized in R, rather than being delivered in
other less efficient formats.

Acknowledgement

In chronological order, we would like to thank the following
contributors for making their pull requests part of sparklyr
1.5:

• @wkdavis
• @yitao-li
• @falaki
• @nathaneastwood
• @pgramme

We would also like to express our gratitude towards numerous bug
reports and feature requests for sparklyr from a fantastic
open-source community.

Finally, the author of this blog post is indebted to @javierluraschi, @batpigandme, and @skeydan for their valuable
editorial inputs.

If you wish to learn more about sparklyr, check out sparklyr.ai, spark.rstudio.com, and some of the
previous release posts such as
sparklyr 1.4 and sparklyr
1.3.

Thanks for reading!