DataBloomThis week In the NVIDIA Studio, we’re launching the April NVIDIA Studio Driver with optimizations for the most popular 3D apps, including Unreal Engine 5, Cinema4D and Chaos Vantage. The driver also supports new NVIDIA Omniverse Connectors from Blender and Redshift.
The post In the NVIDIA Studio: April Driver Launches Alongside New NVIDIA Studio Laptops and Featured 3D Artist appeared first on NVIDIA Blog.
Take a deep dive into the integrated cloud-ready infrastructure solution from Red Hat and NVIDIA
The IT world is moving to cloud, and cloud is built on containers managed with Kubernetes. We believe the next logical step is to accelerate this infrastructure with data processing units (DPUs) for greater performance, efficiency, and security.
Red Hat and NVIDIA are building an integrated cloud-ready infrastructure solution with the management and automation of Red Hat OpenShift combined with the acceleration, workload isolation, and security capabilities of NVIDIA BlueField DPUs.
Many popular cloud infrastructure projects use containers managed by Kubernetes. However, implementing Kubernetes can be a heavy lift, especially for organizations that cannot devote dedicated staff to becoming Kubernetes experts.
Red Hat OpenShift provides a powerful set of capabilities for managing Kubernetes containers as well as application deployment, updates, and lifecycle management. OpenShift includes automation and security tools, as well as a supported open-source model to make cloud infrastructure more affordable, reliable, and scalable.
According to a 2021 Red Hat survey, Kubernetes is used for over 85% of container orchestration projects, and Red Hat OpenShift is the most popular choice for hybrid and multicloud Kubernetes deployments. OpenShift is the industry’s leading enterprise Kubernetes platform, used by more than 50% of commercial banks, telecommunications companies, and airlines on the Fortune 500.
It is clear that most enterprises want a supported Kubernetes model, and Red Hat OpenShift is one of the most popular choices.
A DPU offloads, accelerates, and isolates infrastructure workloads from the server’s CPU. For example, the BlueField DPU can offload networking, network virtualization, data encryption, and time synchronization tasks from the CPU and run them on purpose-built silicon.
Other infrastructure software, such as remote management, firewall agents, network control plane, and storage virtualization, can run on BlueField’s Arm processor cores. Doing so frees up the server’s CPU cores that can instead run applications and tenant workloads.
This functionality also isolates infrastructure and security workloads in a separate domain. The result is a set of servers that run more applications with faster networking, increasing the efficiency and security of the data center.
In a typical cloud infrastructure, the network traffic traverses both physical servers and containers running on these servers. This requires a packet switching solution within each server, and to gain maximum efficiency, the application containers need a way to talk to the accelerated networking offloads of the DPU.
The traditional way is to go through Kubernetes and Open Virtual Network (OVN) to access the Open Virtual Switch (Open vSwitch or OVS). OVN provides network abstraction and the default deployment strategy is to run both OVN and OVS on the host server’s CPU.
However, this method consumes a significant number of CPU cores as the network speeds increase beyond 10 Gbps. A solution is needed for Kubernetes to run the OVN and OVS functionality on the DPU so that all the packet switching, header rewrites, encapsulation/decapsulation, and packet filtering can be done on networking hardware instead of in software on the CPU.
Red Hat and NVIDIA have collaborated to integrate the management power of OpenShift with the acceleration capabilities of the DPU.
The first stage of integration started in 2018 with Red Hat Enterprise Linux offloading network traffic to the NVIDIA ConnectX SmartNIC. The networking data plane–using OVS or DPDK–was running on the SmartNIC ASIC but the networking control plane was still running entirely in software on the X86 CPU.
In 2021, the companies took the next step and deployed Red Hat OpenShift with the NVIDIA BlueField DPU and ran performance benchmark tests. At NVIDIA GTC 2021, we demonstrated the advantages of shifting networking to the DPU and published a post, Optimizing server utilization in data centers by offloading network functions to NVIDIA BlueField-2 DPUs.
In this solution, the networking data plane with overlay offload (OVS and Geneve Offload) and the networking control plane (in the OVN Kubernetes pod) were running on the DPU with Red Hat Enterprise Linux. The major OpenShift components, including Red Hat Enterprise Linux CoreOS remained on the x86 CPU.
In the deployment scenario in Figure 2, the BlueField-2 does the heavy lifting in the following areas:
The host CPU and container see only simple unencapsulated, unencrypted packets and the CPU does not need to perform any of these tasks because they are offloaded to the DPU. This level of offload reduced CPU utilization by 70%, freeing up substantial CPU power on each server to run additional business/tenant workloads.
As presented at GTC 2022, Red Hat and NVIDIA have taken the next step, moving OpenShift, including Red Hat Enterprise Linux CoreOS, to run on the Arm cores of the BlueField DPU for the Red Hat OpenShift two cluster design that includes separate tenant and infrastructure clusters.
Red Hat Enterprise Linux CoreOS is the supported operating system for the OpenShift control plane, or master and worker nodes. This is the portion of OpenShift that performs scheduling, maintenance, upgrades, and cluster automation. It includes container management tools and security hardening to make it more resistant to hackers, and it now runs on both the host x86 CPU and on the DPU Arm cores.
BlueField DPUs running OpenShift OVS and OVN containers and Red Hat Enterprise Linux CoreOS on the various host servers form an infrastructure worker cluster. Meanwhile, OpenShift running on the x86 CPUs manages the tenant pods and clusters.
Offloading the OpenShift infrastructure cluster software to run on the BlueField Arm cores instead of on the host x86 cores provides additional x86 CPU savings, higher performance, and stronger security isolation.
The cloud-native, software-defined networking is a good example of a BlueField DPU use case where OVN and OVS are running on and offloaded by the BlueField DPU in an OpenShift environment. Many other infrastructure services, such as network encryption, firewall agents, virtual routers, telemetry agents, and so on, can also be run on the DPU for an even greater benefit.
To understand the impact of the DPU offloads on reducing the data center costs, NVIDIA and Red Hat put together a TCO model for a mid-sized data center with 51K servers. We considered this data center to be supporting 1M applications, each application needing 10K packets per second (PPS) of switching performance.
We considered two server deployment scenarios: with and without a DPU:
Offloading virtual switching to the DPU also saved eight CPU cores per server. Based on this testing, the TCO model yielded amazing savings of $68.5M of CapEx. These savings are recognized by requiring 10K fewer DPU-enhanced servers due to much higher networking performance and CPU core savings per server.
We see power savings due to the smaller server footprint, which ultimately results in a better TCO model with the DPU-based servers. These TCO savings will get even better as we offload additional functions such as load balancers, firewalls, encryption, web servers, and so on to the DPUs, ultimately achieving amazing efficiency for cloud-ready data centers.
The two-cluster OpenShift architecture running OpenShift on BlueField is now available as a developer preview or early trial in OpenShift 4.10, and is expected to become generally available in 2022.
But the NVIDIA and Red Hat teams aren’t stopping here. We are planning to test the offloading of network traffic encryption/decryption as that is a CPU-intensive task.
Implementation of line-rate encryption offload from OpenShift to the DPU will improve data security for tenants and help you move closer to a zero-trust security stance.
Other potential integrations with the DPU include more sophisticated software-defined networking offloads, running a firewall agent on BlueField, precision time synchronization, video streaming with packet pacing, and using the DPU to collect telemetry data.
BlueField-2 DPUs are available now from NVIDIA and the BlueField-3 DPU will start sampling later in 2022. In addition, BlueField DPUs will soon be available for testing in the NVIDIA LaunchPad cloud service.
If you would like to test or develop on Red Hat OpenShift running with the NVIDIA BlueField DPU, please indicate your interest.
If your organization seeks to embrace cloud-native computing in data centers, the combination of NVIDIA BlueField DPUs, Red Hat Enterprise Linux, and Red Hat OpenShift provides an efficient and innovative open, hybrid-cloud platform with new security features. This powerful platform delivers hardware acceleration capabilities to run critical software-defined networking, storage, and security functions.
Now more server resources can be allocated to run cloud-native workloads, as well as traditional business applications.
For more information, see the following resources:
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I’m a newbee in the machine learning frameworks. When I’m learning machine learning frameworks such as TensorFlow, PyTorch, and MindSpore, I’m confused between accuracy and precision. What is the difference when we say to improve model accuracy and to improve model precision? Can you help me to figure it out? Thanks
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I built a custom generator that outputs X data with shape (100,2,2048) belonging to Y 16 (16) classes to be passed to a GRU model for video classification.
100 is the sequence length, 2 is for 2 simultaneous camera views, each with 2048 features, extracted earlier with a feature extractor.
I need to pass this to GRU model, but it throws an error (Input 0 of layer “gru” incompatible with the layer: expected ndim=3, found ndim=4. Full shape received: (None,100,2,2048)
) when I set the input shape in the input layer to (100,2,2048).
Using just one camera view and setting the it to (100,2048) works.
What input shape do I need to set to accommodate the two cameras?
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This is a deep dive into the Shader Profiler feature of NVIDIA Nsight Graphics. The Shader Profiler allows you to find hotspots in your shaders and why they’re hot.
A less well-known but cool feature of NVIDIA Nsight Graphics is the Shader Profiler. This enables you to find hot spots in your shaders that can help you to direct optimization effort. It can give you insights into why performance is sometimes not what you might like.
In this post, we use the NVIDIA Nsight Graphics Trace Analysis tool to identify a potential limiter and then use the Shader Profiler to dig deeper to find and fix an issue.
We always recommend starting with the Nsight Graphics GPU Trace tool rather than diving straight into the shader profiler. That way, you can understand what the performance limiters of any given DX12 or VK workload are. For example, there’s no point trying to fine-tune your shader if the real problem is that you have low GPU utilization because you have lots of tiny dispatches with barriers between them all.
First, set up a connection to the app to be profiled. Choose Connect and fill in the required parameters for launching your game (Figure 1).
Select GPU Trace as the activity, with Metric Set configured to Advanced Mode Metrics. Using Advanced Mode Metrics requires a stable and consistent frame, because the analysis runs over several passes over several frames. If your application doesn’t meet these requirements, you can use the Nsight Graphics built-in C++ Capture tool to capture a frame of your application and create a new EXE that replays the same frame repeatedly.
Choose Launch GPU Trace to launch your application. When you reach a frame that you’d like to capture, choose Generate GPU Trace Capture or press F11.
When the capture is complete, stop the application and open the trace. Choose Trace Analysis. In the Analysis panel of GPU Trace (Figure 2), double-click or hover over the marker for the range to analyze, in this case, DispatchRays[0]:
The tooltip presents a compact view of all performance gain opportunities that the tool has detected in this GPU workload, sorted by their projected GPU frame-time gain. The workload has the following limiters:
Choose SM Warp Latency and Warp Stalled by Local-Memory Throttle.
The Explanation window gives a more meaningful description of the problem, with some helpful suggestions. It suggests launching the Shader Profiler to locate the specific HLSL instructions that have lg_throttle stalls.
Before you use the Shader Profiler, it’s important to make sure that Nsight Graphics can get access to symbols for your shaders. The easiest way to achieve this is to make sure that the shaders are compiled with the /Zi option, and embed the symbols in the shader binary.
Sometimes it’s preferable to configure the compilation so that the symbols go into an external PDB file. In that case, be sure to specify the correct path under Tools, Options.
When Nsight Graphics can see the shader symbols, it can map locations in the shader back to the source code, which makes it far easier for you to tell what’s going on. If Nsight Graphics doesn’t have access to symbols, then you can only see the shader disassembly (for example, DXIL).
The Shader Profiler is part of the Frame Profiler. Connect to the application again but this time, choose Frame Profiler under Activity. When you choose Launch Frame Profiler, the application should launch with this HUD (Figure 4) on top of it.
Navigate to the part of the application to profile and press F11 to capture a frame for analysis. From here, choose Profile Shaders in Nsight Graphics. This runs a short sampling session, and then presents you with a summary view (Figure 5).
Here’s a breakdown.
The Function Summary shows a list of the top shaders, in order of the number of samples that hit in those shaders. This is a good proxy for the shader latency and lets you concentrate on the shaders that can yield the biggest benefit from optimizing.
In the Correlation column, there are multiple green ticks, which are always good. In this case, it means that Nsight Graphics has been able to correlate the samples back to the source code.
To open up the shader view, select the first file name. On the left is the source code, and on the right is DXIL. For the purposes of this post, you don’t have to care about the DXIL, so change the view to just HLSL
It’s quite subtle, but there’s an important heat map of instruction samples on the far right, just to the right of the scroll bar. Remember, GPU Trace Analysis suggested that you should look for lg_throttle stalls. It said:
LSU is the unit that performs access to Local and Global memory.
Run the Shader Profiler and locate which HLSL instructions have most lg_thottle stalls.
Are dynamically indexed arrays declared in local scope?
Does the shader have register pressure causing spills?
If L1 and L2 hit rates are poor, then try to reduce misses.
In the Shader Profiler, the samples that show as LGTHR are stalled due to lg_throttle reasons.
Dynamically indexed arrays are indexed by a variable, where the value of the index is not known at compile time.
When this happens, the compiler often puts the array in local memory instead of it living in registers. Memory is slower than registers.
The following code example shows a dynamically indexed array.
vertUvs[vertexOrder[0]] = cornerUv + du; vertUvs[vertexOrder[1]] = cornerUv + dv; vertUvs[vertexOrder[2]] = cornerUv;
What’s going on? It looks like the code fills in the array in a different order, depending on whether the triangle is flipped.
int3 vertexOrder = isFlipped ? int3(2, 1, 0) : int3(0, 1, 2);
The act of dynamically indexing this array makes the compiler move this array into memory. It affects this bit of code and all the bits of code that reference that array. That’s why convertTriangleBaryUvsToBaryVws is showing up as hot, too.
Can you do this without dynamic indexing? Yes, you can. Changing how the flip is done results in Figure 7.
Those particular stalls are eliminated. It reduced the time for this dispatch from 8.67 ms down to 7.1 ms. Not only did it improve the efficiency of shader code, but it also massively reduced the limiter in L2 because of the reduced memory traffic.
NVIDIA Nsight Graphics is a powerful tool for analyzing your rendering workloads. This has been a quick walkthrough, just touching on some capabilities. We highly recommend using it.
The tests and results in this post were true as of driver version 467.07. Driver and compiler development continues all the time. That means that optimization opportunities can change over time too.
Bit of a newbie to TF/the machine learning world. I’ve built/trained image classification & segmentation models before and have tinkered with TF recommenders before. My knowledge probably doesn’t go beyond the first few layers of the documentation/tutorials, though.
I’m wondering as to how I might accomplish something along these lines: I have approximately 20,000 images and I have manually placed a simple text watermark over the image to partially cover the subject of the image (the subject takes up 80% of the image and the watermark takes up approx 5% of the image), the watermark is small and subtle, only really noticeable if you zoom in. I have saved the coordinates for each watermark for each image file. I’m now looking to automate the placing of watermarks in a subtle position on the subject.
Could someone please link to some documentation/guides which would be appropriate for training a model to achieve this goal? I am assuming I need something along the lines of image classification but a lot of what I’m seeing is that it’s just for classifying what’s in an image/segmenting (drawing a box) around an object in the image rather than saying “given this image, this particular point on the subject in the image is relevant”.
MTIA
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The 10th International Conference on Learning Representations (ICLR 2022) kicks off this week, bringing together researchers, entrepreneurs, engineers and students alike to discuss and explore the rapidly advancing field of deep learning. Entirely virtual this year, ICLR 2022 offers conference and workshop tracks that present some of the latest research in deep learning and its applications to areas ranging from computer vision, speech recognition and text understanding to robotics, computational biology, and more.
As a Platinum Sponsor of ICLR 2022 and Champion DEI Action Fund contributor, Google will have a robust presence with nearly 100 accepted publications and extensive participation on organizing committees and in workshops. If you have registered for ICLR 2022, we hope you’ll watch our talks and learn about the work done at Google to address complex problems that affect billions of people. Here you can learn more about the research we will be presenting as well as our general involvement at ICLR 2022 (those with Google affiliations in bold).
Senior Area Chairs:
Includes: Been Kim, Dale Schuurmans, Sergey Levine
Area Chairs:
Includes: Adam White, Aditya Menon, Aleksandra Faust, Amin Karbasi, Amir Globerson, Andrew Dai, Balaji Lakshminarayanan, Behnam Neyshabur, Ben Poole, Bhuwan Dhingra, Bo Dai, Boqing Gong, Cristian Sminchisescu, David Ha, David Woodruff, Denny Zhou, Dipanjan Das, Dumitru Erhan, Dustin Tran, Emma Strubell, Eunsol Choi, George Dahl, George Tucker, Hanie Sedghi, Heinrich Jiang, Hossein Mobahi, Hugo Larochelle, Izhak Shafran, Jasper Snoek, Jean-Philippe Vert, Jeffrey Pennington, Justin Gilmer, Karol Hausman, Kevin Swersky, Krzysztof Choromanski, Mathieu Blondel, Matt Kusner, Michael Ryoo, Ming-Hsuan Yang, Minmin Chen, Mirella Lapata, Mohammad Ghavamzadeh, Mohammad Norouzi, Naman Agarwal, Nicholas Carlini, Olivier Bachem, Piyush Rai, Prateek Jain, Quentin Berthet, Richard Nock, Rose Yu, Sewoong Oh, Silvio Lattanzi, Slav Petrov, Srinadh Bhojanapalli, Tim Salimans, Ting Chen, Tong Zhang, Vikas Sindhwani, Weiran Wang, William Cohen, Xiaoming Liu
Workflow Chairs:
Includes: Yaguang Li
Diversity Equity & Inclusion Chairs:
Includes: Rosanne Liu
Invited Talks
Beyond Interpretability: Developing a Language to Shape Our Relationships with AI
Google Speaker: Been Kim
Do You See What I See? Large-Scale Learning from Multimodal Videos
Google Speaker: Cordelia Schmid
Publications
Hyperparameter Tuning with Renyi Differential Privacy – 2022 Outstanding Paper Award
Nicolas Papernot, Thomas Steinke
MIDI-DDSP: Detailed Control of Musical Performance via Hierarchical Modeling
Yusong Wu, Ethan Manilow, Yi Deng, Rigel Swavely, Kyle Kastner, Tim Cooijmans, Aaron Courville, Cheng-Zhi Anna Huang, Jesse Engel
The Information Geometry of Unsupervised Reinforcement Learning
Benjamin Eysenbach, Ruslan Salakhutdinov, Sergey Levine
Learning Strides in Convolutional Neural Networks – 2022 Outstanding Paper Award
Rachid Riad*, Olivier Teboul, David Grangier, Neil Zeghidour
Poisoning and Backdooring Contrastive Learning
Nicholas Carlini, Andreas Terzis
Coordination Among Neural Modules Through a Shared Global Workspace
Anirudh Goyal, Aniket Didolkar, Alex Lamb, Kartikeya Badola, Nan Rosemary Ke, Nasim Rahaman, Jonathan Binas, Charles Blundell, Michael Mozer, Yoshua Bengio
Fine-Tuned Language Models Are Zero-Shot Learners (see the blog post)
Jason Wei, Maarten Bosma, Vincent Y. Zhao, Kelvin Guu, Adams Wei Yu, Brian Lester, Nan Du, Andrew M. Dai, Quoc V. Le
Large Language Models Can Be Strong Differentially Private Learners
Xuechen Li, Florian Tramèr, Percy Liang, Tatsunori Hashimoto
Progressive Distillation for Fast Sampling of Diffusion Models
Tim Salimans, Jonathan Ho
Exploring the Limits of Large Scale Pre-training
Samira Abnar, Mostafa Dehghani, Behnam Neyshabur, Hanie Sedghi
Scarf: Self-Supervised Contrastive Learning Using Random Feature Corruption
Dara Bahri, Heinrich Jiang, Yi Tay, Donald Metzler
Scalable Sampling for Nonsymmetric Determinantal Point Processes
Insu Han, Mike Gartrell, Jennifer Gillenwater, Elvis Dohmatob, Amin Karbasi
When Vision Transformers Outperform ResNets without Pre-training or Strong Data Augmentations
Xiangning Chen, Cho-Jui Hsieh, Boqing Gong
ViTGAN: Training GANs with Vision Transformers
Kwonjoon Lee, Huiwen Chang, Lu Jiang, Han Zhang, Zhuowen Tu, Ce Liu
Generalized Decision Transformer for Offline Hindsight Information Matching
Hiroki Furuta, Yutaka Matsuo, Shixiang Shane Gu
The MultiBERTs: BERT Reproductions for Robustness Analysis
Thibault Sellam, Steve Yadlowsky, Ian Tenney, Jason Wei, Naomi Saphra, Alexander D’Amour, Tal Linzen, Jasmijn Bastings, Iulia Turc, Jacob Eisenstein, Dipanjan Das, Ellie Pavlick
Scaling Laws for Neural Machine Translation
Behrooz Ghorbani, Orhan Firat, Markus Freitag, Ankur Bapna, Maxim Krikun, Xavier Garcia, Ciprian Chelba, Colin Cherry
Interpretable Unsupervised Diversity Denoising and Artefact Removal
Mangal Prakash, Mauricio Delbracio, Peyman Milanfar, Florian Jug
Understanding Latent Correlation-Based Multiview Learning and Self-Supervision: An Identifiability Perspective
Qi Lyu, Xiao Fu, Weiran Wang, Songtao Lu
Memorizing Transformers
Yuhuai Wu, Markus N. Rabe, DeLesley Hutchins, Christian Szegedy
Churn Reduction via Distillation
Heinrich Jiang, Harikrishna Narasimhan, Dara Bahri, Andrew Cotter, Afshin Rostamizadeh
DR3: Value-Based Deep Reinforcement Learning Requires Explicit Regularization
Aviral Kumar, Rishabh Agarwal, Tengyu Ma, Aaron Courville, George Tucker, Sergey Levine
Path Auxiliary Proposal for MCMC in Discrete Space
Haoran Sun, Hanjun Dai, Wei Xia, Arun Ramamurthy
On the Relation Between Statistical Learning and Perceptual Distances
Alexander Hepburn, Valero Laparra, Raul Santos-Rodriguez, Johannes Ballé, Jesús Malo
Possibility Before Utility: Learning And Using Hierarchical Affordances
Robby Costales, Shariq Iqbal, Fei Sha
MT3: Multi-Task Multitrack Music Transcription
Josh Gardner*, Ian Simon, Ethan Manilow*, Curtis Hawthorne, Jesse Engel
Bayesian Neural Network Priors Revisited
Vincent Fortuin, Adrià Garriga-Alonso, Sebastian W. Ober, Florian Wenzel, Gunnar Rätsch, Richard E. Turner, Mark van der Wilk, Laurence Aitchison
GradMax: Growing Neural Networks using Gradient Information
Utku Evci, Bart van Merrienboer, Thomas Unterthiner, Fabian Pedregosa, Max Vladymyrov
Scene Transformer: A Unified Architecture for Predicting Future Trajectories of Multiple Agents
Jiquan Ngiam, Benjamin Caine, Vijay Vasudevan, Zhengdong Zhang, Hao-Tien Lewis Chiang, Jeffrey Ling, Rebecca Roelofs, Alex Bewley, Chenxi Liu, Ashish Venugopal, David Weiss, Ben Sapp, Zhifeng Chen, Jonathon Shlens
The Role of Pretrained Representations for the OOD Generalization of RL Agents
Frederik Träuble, Andrea Dittadi, Manuel Wüthrich, Felix Widmaier, Peter Gehler, Ole Winther, Francesco Locatello, Olivier Bachem, Bernhard Schölkopf, Stefan Bauer
Autoregressive Diffusion Models
Emiel Hoogeboom, Alexey A. Gritsenko, Jasmijn Bastings, Ben Poole, Rianne van den Berg, Tim Salimans
The Role of Permutation Invariance in Linear Mode Connectivity of Neural Networks
Rahim Entezari, Hanie Seghi, Olga Saukh, Behnam Neyshabur
DISSECT: Disentangled Simultaneous Explanations via Concept Traversals
Asma Ghandeharioun, Been Kim, Chun-Liang Li, Brendan Jou, Brian Eoff, Rosalind W. Picard
Anisotropic Random Feature Regression in High Dimensions
Gabriel C. Mel, Jeffrey Pennington
Open-Vocabulary Object Detection via Vision and Language Knowledge Distillation
Xiuye Gu, Tsung-Yi Lin*, Weicheng Kuo, Yin Cui
MCMC Should Mix: Learning Energy-Based Model with Flow-Based Backbone
Erik Nijkamp*, Ruiqi Gao, Pavel Sountsov, Srinivas Vasudevan, Bo Pang, Song-Chun Zhu, Ying Nian Wu
Effect of Scale on Catastrophic Forgetting in Neural Networks
Vinay Ramasesh, Aitor Lewkowycz, Ethan Dyer
Incremental False Negative Detection for Contrastive Learning
Tsai-Shien Chen, Wei-Chih Hung, Hung-Yu Tseng, Shao-Yi Chien, Ming-Hsuan Yang
Towards Evaluating the Robustness of Neural Networks Learned by Transduction
Jiefeng Chen, Xi Wu, Yang Guo, Yingyu Liang, Somesh Jha
What Do We Mean by Generalization in Federated Learning?
Honglin Yuan*, Warren Morningstar, Lin Ning, Karan Singhal
ViDT: An Efficient and Effective Fully Transformer-Based Object Detector
Hwanjun Song, Deqing Sun, Sanghyuk Chun, Varun Jampani, Dongyoon Han, Byeongho Heo, Wonjae Kim, Ming-Hsuan Yang
Measuring CLEVRness: Black-Box Testing of Visual Reasoning Models
Spyridon Mouselinos, Henryk Michalewski, Mateusz Malinowski
Wisdom of Committees: An Overlooked Approach To Faster and More Accurate Models (see the blog post)
Xiaofang Wang, Dan Kondratyuk, Eric Christiansen, Kris M. Kitani, Yair Alon (prev. Movshovitz-Attias), Elad Eban
Leveraging Unlabeled Data to Predict Out-of-Distribution Performance
Saurabh Garg*, Sivaraman Balakrishnan, Zachary C. Lipton, Behnam Neyshabur, Hanie Sedghi
Data-Driven Offline Optimization for Architecting Hardware Accelerators (see the blog post)
Aviral Kumar, Amir Yazdanbakhsh, Milad Hashemi, Kevin Swersky, Sergey Levine
Diurnal or Nocturnal? Federated Learning of Multi-branch Networks from Periodically Shifting Distributions
Chen Zhu*, Zheng Xu, Mingqing Chen, Jakub Konecny, Andrew Hard, Tom Goldstein
Policy Gradients Incorporating the Future
David Venuto, Elaine Lau, Doina Precup, Ofir Nachum
Discrete Representations Strengthen Vision Transformer Robustness
Chengzhi Mao*, Lu Jiang, Mostafa Dehghani, Carl Vondrick, Rahul Sukthankar, Irfan Essa
SimVLM: Simple Visual Language Model Pretraining with Weak Supervision (see the blog post)
Zirui Wang, Jiahui Yu, Adams Wei Yu, Zihang Dai, Yulia Tsvetkov, Yuan Cao
Neural Stochastic Dual Dynamic Programming
Hanjun Dai, Yuan Xue, Zia Syed, Dale Schuurmans, Bo Dai
PolyLoss: A Polynomial Expansion Perspective of Classification Loss Functions
Zhaoqi Leng, Mingxing Tan, Chenxi Liu, Ekin Dogus Cubuk, Xiaojie Shi, Shuyang Cheng, Dragomir Anguelov
Information Prioritization Through Empowerment in Visual Model-Based RL
Homanga Bharadhwaj*, Mohammad Babaeizadeh, Dumitru Erhan, Sergey Levine
Value Function Spaces: Skill-Centric State Abstractions for Long-Horizon Reasoning
Dhruv Shah, Peng Xu, Yao Lu, Ted Xiao, Alexander Toshev, Sergey Levine, Brian Ichter
Understanding and Leveraging Overparameterization in Recursive Value Estimation
Chenjun Xiao, Bo Dai, Jincheng Mei, Oscar Ramirez, Ramki Gummadi, Chris Harris, Dale Schuurmans
The Efficiency Misnomer
Mostafa Dehghani, Anurag Arnab, Lucas Beyer, Ashish Vaswani, Yi Tay
On the Role of Population Heterogeneity in Emergent Communication
Mathieu Rita, Florian Strub, Jean-Bastien Grill, Olivier Pietquin, Emmanuel Dupoux
No One Representation to Rule Them All: Overlapping Features of Training Methods
Raphael Gontijo-Lopes, Yann Dauphin, Ekin D. Cubuk
Data Poisoning Won’t Save You From Facial Recognition
Evani Radiya-Dixit, Sanghyun Hong, Nicholas Carlini, Florian Tramèr
AdaMatch: A Unified Approach to Semi-Supervised Learning and Domain Adaptation
David Berthelot, Rebecca Roelofs, Kihyuk Sohn, Nicholas Carlini, Alex Kurakin
Maximum Entropy RL (Provably) Solves Some Robust RL Problems
Benjamin Eysenbach, Sergey Levine
Auto-scaling Vision Transformers Without Training
Wuyang Chen, Wei Huang, Xianzhi Du, Xiaodan Song, Zhangyang Wang, Denny Zhou
Optimizing Few-Step Diffusion Samplers by Gradient Descent
Daniel Watson, William Chan, Jonathan Ho, Mohammad Norouzi
ExT5: Towards Extreme Multi-Task Scaling for Transfer Learning
Vamsi Aribandi, Yi Tay, Tal Schuster, Jinfeng Rao, Huaixiu Steven Zheng, Sanket Vaibhav Mehta, Honglei Zhuang, Vinh Q. Tran, Dara Bahri, Jianmo Ni, Jai Gupta, Kai Hui, Sebastian Ruder, Donald Metzler
Fortuitous Forgetting in Connectionist Networks
Hattie Zhou, Ankit Vani, Hugo Larochelle, Aaron Courville
Evading Adversarial Example Detection Defenses with Orthogonal Projected Gradient Descent
Oliver Bryniarski, Nabeel Hingun, Pedro Pachuca, Vincent Wang, Nicholas Carlini
Benchmarking the Spectrum of Agent Capabilities
Danijar Hafner
Charformer: Fast Character Transformers via Gradient-Based Subword Tokenization
Yi Tay, Vinh Q. Tran, Sebastian Ruder, Jai Gupta, Hyung Won Chung, Dara Bahri, Zhen Qin, Simon Baumgartner, Cong Yu, Donald Metzler
Mention Memory: Incorporating Textual Knowledge into Transformers Through Entity Mention Attention
Michiel de Jong, Yury Zemlyanskiy, Nicholas FitzGerald, Fei Sha, William Cohen
Eigencurve: Optimal Learning Rate Schedule for SGD on Quadratic Objectives with Skewed Hessian Spectrums
Rui Pan, Haishan Ye, Tong Zhang
Scale Efficiently: Insights from Pre-training and Fine-Tuning Transformers
Yi Tay, Mostafa Dehghani, Jinfeng Rao, William Fedus, Samira Abnar, Hyung Won Chung, Sharan Narang, Dani Yogatama, Ashish Vaswani, Donald Metzler
Omni-Scale CNNs: A Simple and Effective Kernel Size Configuration for Time Series Classification
Wensi Tang, Guodong Long, Lu Liu,Tianyi Zhou, Michael Blumenstein, Jing Jiang
Embedded-Model Flows: Combining the Inductive Biases of Model-Free Deep Learning and Explicit Probabilistic Modeling
Gianluigi Silvestri, Emily Fertig, Dave Moore, Luca Ambrogioni
Post Hoc Explanations May be Ineffective for Detecting Unknown Spurious Correlation
Julius Adebayo, Michael Muelly, Hal Abelson, Been Kim
Axiomatic Explanations for Visual Search, Retrieval, and Similarity Learning
Mark Hamilton, Scott Lundberg, Stephanie Fu, Lei Zhang, William T. Freeman
Pix2seq: A Language Modeling Framework for Object Detection (see the blog post)
Ting Chen, Saurabh Saxena, Lala Li, David J. Fleet, Geoffrey Hinton
Mirror Descent Policy Optimization
Manan Tomar, Lior Shani, Yonathan Efroni, Mohammad Ghavamzadeh
CodeTrek: Flexible Modeling of Code Using an Extensible Relational Representation
Pardis Pashakhanloo, Aaditya Naik, Yuepeng Wang, Hanjun Dai, Petros Maniatis, Mayur Naik
Conditional Object-Centric Learning From Video
Thomas Kipf, Gamaleldin F. Elsayed, Aravindh Mahendran, Austin Stone, Sara Sabour, Georg Heigold, Rico Jonschkowski, Alexey Dosovitskiy, Klaus Greff
A Loss Curvature Perspective on Training Instabilities of Deep Learning Models
Justin Gilmer, Behrooz Ghorbani, Ankush Garg, Sneha Kudugunta, Behnam Neyshabur, David Cardoze, George E. Dahl, Zack Nado, Orhan Firat
Autonomous Reinforcement Learning: Formalism and Benchmarking
Archit Sharma, Kelvin Xu, Nikhil Sardana, Abhishek Gupta, Karol Hausman, Sergey Levine, Chelsea Finn
TRAIL: Near-Optimal Imitation Learning with Suboptimal Data
Mengjiao Yang, Sergey Levine, Ofir Nachum
Minimax Optimization With Smooth Algorithmic Adversaries
Tanner Fiez, Lillian J. Ratliff, Chi Jin, Praneeth Netrapalli
Unsupervised Semantic Segmentation by Distilling Feature Correspondences
Mark Hamilton, Zhoutong Zhang, Bharath Hariharan, Noah Snavely, William T. Freeman
InfinityGAN: Towards Infinite-Pixel Image Synthesis
Chieh Hubert Lin, Hsin-Ying Lee, Yen-Chi Cheng, Sergey Tulyakov, Ming-Hsuan Yang
Shuffle Private Stochastic Convex Optimization
Albert Cheu, Matthew Joseph, Jieming Mao, Binghui Peng
Hybrid Random Features
Krzysztof Choromanski, Haoxian Chen, Han Lin, Yuanzhe Ma, Arijit Sehanobish, Deepali Jain, Michael S Ryoo, Jake Varley, Andy Zeng, Valerii Likhosherstov, Dmitry Kalashnikov, Vikas Sindhwani, Adrian Weller
Vector-Quantized Image Modeling With Improved VQGAN
Jiahui Yu, Xin Li, Jing Yu Koh, Han Zhang, Ruoming Pang, James Qin, Alexander Ku, Yuanzhong Xu, Jason Baldridge, Yonghui Wu
On the Benefits of Maximum Likelihood Estimation for Regression and Forecasting
Pranjal Awasthi, Abhimanyu Das, Rajat Sen, Ananda Theertha Suresh
Surrogate Gap Minimization Improves Sharpness-Aware Training
Juntang Zhuang*, Boqing Gong, Liangzhe Yuan, Yin Cui, Hartwig Adam, Nicha C. Dvornek, Sekhar Tatikonda, James S. Duncan, Ting Liu
Online Target Q-learning With Reverse Experience Replay: Efficiently Finding the Optimal Policy for Linear MDPs
Naman Agarwal, Prateek Jain, Dheeraj Nagaraj, Praneeth Netrapalli, Syomantak Chaudhuri
CrossBeam: Learning to Search in Bottom-Up Program Synthesis
Kensen Shi, Hanjun Dai, Kevin Ellis, Charles Sutton
Workshops
Workshop on the Elements of Reasoning: Objects, Structure, and Causality (OSC)
Organizers include: Klaus Greff, Thomas Kipf
Workshop on Agent Learning in Open-Endedness
Organizers include: Krishna Srinivasan
Speakers include: Natasha Jaques, Danijar Hafner
Wiki-M3L: Wikipedia and Multi-modal & Multi-lingual Research
Organizers include: Klaus Greff, Thomas Kipf
Speakers include: Jason Baldridge, Tom Duerig
Setting Up ML Evaluation Standards to Accelerate Progress
Organizers include: Rishabh Agarwal
Speakers and Panelists include: Katherine Heller, Sara Hooker, Corinna Cortes
From Cells to Societies: Collective Learning Across Scales
Organizers include: Mark Sandler, Max Vladymyrov
Speakers include: Blaise Aguera y Arcas, Alexander Mordvintsev, Michael Mozer
Emergent Communication: New Frontiers
Speakers include: Natasha Jaques
Deep Learning for Code
Organizers include: Jonathan Herzig
GroundedML: Anchoring Machine Learning in Classical Algorithmic Theory
Speakers include: Gintare Karolina Dziugaite
Generalizable Policy Learning in the Physical World
Speakers and Panelists include: Mrinal Kalakrishnan
CoSubmitting Summer (CSS) Workshop
Organizers include: Rosanne Liu
When brainstorming a scene to best showcase the groundbreaking capabilities of the Omniverse platform, some NVIDIA artists turned to a cherished memory: enjoying ramen together in a mom-and-pop shop down a side street in Tokyo. Simmering pots of noodles, steaming dumplings, buzzing kitchen appliances, warm ambient lighting and glistening black ledger stools. These were all Read article >
The post Let Me Shoyu How It’s Done: Creating the NVIDIA Omniverse Ramen Shop appeared first on NVIDIA Blog.
I am a beginner in the machine learning field and while learning the tensorflow introductory I understood that tensor is just nothing but a generalized name for quantities that require multiple variables (features) for it’s description.
Then there is this line -> Matrix and Vectors are the tensors with different rank. Matrix is 2d tensor and vector is 1 d tensor.
I then searched for the difference between Matrix and Vector and this text confused me
A vector is a matrix with just one row or column
And later in the tensor’s definition
A tensor is often thought of as a generalized matrix. That is, it could be a 1-D matrix (a vector is actually such a tensor)
I am coming from the CS background where I have learnt that 1D is array 2D is matrix and we used to take 1D array like std::vector<float> height_vector
So what is difference between arr[10] and arr[1][10] or arr[10][1] ?
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