Google and Facebook Introduce ‘LazyTensor’ That Enables Expressive Domain-Specific Compilers
Researchers at Facebook and Google introduce a new technique called ‘LazyTensor’ that combines eager execution and domain-specific compilers (DSCs) to employ both...
A New Study by NVIDIA, University of Toronto, McGill, and Vector Institute Proposes Neural...
Researchers from NVIDIA, the University of Toronto, McGill University, and the Vector Institute led a study that proposed an efficient neural representation...
Exploring Self-Supervised Policy Adaptation To Continue Training After Deployment Without Using Any Rewards
Humans possess a remarkable ability to adapt, generalize their knowledge and use their experiences in new situations. Simultaneously, building an intelligent system...
Researchers From Stanford, UCI and UC Santa Barbara Conducted a Study to Understand How...
Over the past few decades, Deep neural network-based models have been developed to complete a broad range of tasks. Some of them...
University of Wisconsin-Madison, UC Berkeley, and Google Brain Introduce Nystromformer: A Nystrom-based Algorithm for...
Early days of research in Natural language processing established long-term dependencies. It also brought the vanishing gradient problem in front of us...
Researchers From UC Berkeley, University of Maryland, and UC Irvine Introduce A new Contextual...
A team of researchers at UC Berkeley, University of Maryland, and UC Irvine conducted a study to identify that can cause instability...
Facebook AI And Sorbonne University Propose A New Benchmark, CTrL, To Evaluate How Efficiently...
In collaboration with Sorbonne University, Facebook AI introduced a new benchmark for continual learning (CL). This presents excellent means to improve traditional...
Google AI, DeepMind And The University of Toronto Introduce DreamerV2, The First Reinforcement Learning...
Google AI, in collaboration with DeepMind and the University of Toronto, has recently introduced DreamerV2. It is the first Reinforcement Learning (RL)...
IBM Develops AI Chip With Remarkable Energy Efficiency
As the demand for energy-efficient, sustainable and smart technology is rapidly increasing, IBM has developed a new technology, considered the world’s first energy-efficient...
DeepMind Researchers Propose Normalizer-Free ResNets (NFNets) To Achieve Large-Scale Image-Recognition Without Batch Normalization
A team of researchers at DeepMind introduces Normalizer-Free ResNets (NFNets) and demonstrates that the image recognition model can be trained without batch...
