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Waymo Releases Block-NeRF 3D View Synthesis Deep-Learning Model
Waymo released a ground-breaking deep-learning model called Block-NeRF for large-scale 3D world-view synthesis reconstructed from images collected by its self-driving cars. NeRF has the ability to encode surface and volume representation in neural networks.
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Meta Open-Sources Multi-Modal AI Algorithm Data2vec
Meta AI recently open-sourced data2vec, a unified framework for self-supervised deep learning on images, text, and speech audio data. When evaluated on common benchmarks, models trained using data2vec perform as well as or better than state-of-the-art models trained with modality-specific objectives.
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How GitHub Uses Machine Learning to Extend Vulnerability Code Scanning
Applying machine learning techniques to its rule-based security code scanning capabilities, GitHub hopes to be able to extend them to less common vulnerability patterns by automatically inferring new rules from the existing ones.
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DeepMind Open-Sources Quantum Chemistry AI Model DM21
Researchers at Google subsidiary DeepMind have open-sourced DM21, a neural network model for mapping electron density to chemical interaction energy, a key component of quantum mechanical simulation. DM21 outperforms traditional models on several benchmarks and is available as an extension to the PySCF simulation framework.
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Alibaba Open-Sources AutoML Algorithm KNAS
Researchers from Alibaba Group and Peking University have open-sourced Kernel Neural Architecture Search (KNAS), an efficient automated machine learning (AutoML) algorithm that can evaluate proposed architectures without training. KNAS uses a gradient kernel as a proxy for model quality, and uses an order of magnitude less compute power than baseline methods.
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LambdaML: Pros and Cons of Serverless for Deep Network Training
A new study entitled "Towards Demystifying Serverless Machine Learning Training" aims to provide an experimental analysis of training deep networks by leveraging serverless platforms. FaaS for training has challenges due to its distributed nature and aggregation step in the learning algorithms. Results indicate FaaS can be a faster (for lightweight models) but not cheaper alternative than IaaS.
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Meta AI’s Convolution Networks Upgrade Improves Image Classification
Meta AI released a new generation of improved Convolution Networks, achieving state-of-the-art performance of 87.8% accuracy on Image-Net top-1 dataset and outperforming Swin Transformers on COCO dataset where object detection performance is evaluated. The new design and training approach is inspired by the Swin Transformers model.
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Evaluating Continual Deep Learning: a New Benchmark for Image Classification
Continual learning aims to preserve knowledge across deep network training iterations. A new dataset entitled "The CLEAR Benchmark: Continual LEArning on Real-World Imagery" has recently been published. The goal of the study is to establish a consistent image classification benchmark with the natural time evolution of objects for a more realistic comparison of continual learning models.
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OpenAI Announces Question-Answering AI WebGPT
OpenAI has developed WebGPT, an AI model for long-form question-answering based on GPT-3. WebGPT can use web search queries to collect supporting references for its response, and on Reddit questions its answers were preferred by human judges over the highest-voted answer 69% of the time.
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DeepMind Releases Weather Forecasting AI Deep Generative Models of Rainfall
DeepMind open-sourced a dataset and trained model snapshot for Deep Generative Models of Rainfall (DGMR), an AI system for short-term precipitation forecasts. In evaluations conducted by 58 expert meteorologists comparing it to other existing methods, DGMR was ranked first in accuracy and usefulness in 89% of test cases.
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MIT Researchers Investigate Deep Learning's Computational Burden
A team of researchers from MIT, Yonsei University, and University of Brasilia have launched a new website, Computer Progress, which analyzes the computational burden from over 1,000 deep learning research papers. Data from the site show that computational burden is growing faster than the expected rate, suggesting that algorithms still have room for improvement.
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AMD Introduces Its Deep-Learning Accelerator Instinct MI200 Series GPUs
In its recent Accelerated Data Center Premiere Keynote, AMD unveiled its MI200 accelerator series Instinct MI250x and slightly lower-end Instinct MI250 GPUs. Designed with CDNA-2 architecture and TSMC’s 6nm FinFET lithography, the high-end MI250X provides 47.9 TFLOPs peak double precision performance and memory that will allow training larger deep networks by minimizing model sharding.
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Facebook Open-Sources GHN-2 AI for Fast Initialization of Deep-Learning Models
A team from Facebook AI Research (FAIR) and the University of Guelph have open-sourced an improved Graph HyperNetworks (GHN-2) meta-model that predicts initial parameters for deep-learning neural networks. GHN-2 executes in less than a second on a CPU and predicts values for computer vision (CV) networks that achieve up to 77% top-1 accuracy on CIFAR-10 with no additional training.
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PyTorch 1.10 Release Includes CUDA Graphs APIs, Compiler Improvements, and Android NNAPI Support
PyTorch, Facebook's open-source deep-learning framework, announced the release of version 1.10 which includes an integration with CUDA Graphs APIs and JIT compiler updates to increase CPU performance, as well as beta support for the Android Neural Networks API (NNAPI). New versions of domain-specific libraries TorchVision and TorchAudio were also released.
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Facebook Develops New AI Model That Can Anticipate Future Actions
Facebook unveiled its latest machine-learning process called Anticipative Video Transformer (AVT), which is able to predict future actions by using visual interpretation. AVT works as an end-to-end attention-based model for action anticipation in videos.