Trevor Gale
Artificial Intelligence · with Elsen · 2019–2022
A researcher in the Science corpus: 6 papers; trusts 316 researchers and is trusted by 65 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 118 citations into it; #3,960 of 35,345 by trust here, #1,771 by citations
- Computer Vision and Pattern Recognition — 112 citations into it; #4,533 of 21,575 by trust here, #4,887 by citations
- Efficiency & Compression — 86 citations into it; #134 of 1,264 by trust here, #264 by citations
- Model Architectures — 39 citations into it
- Hardware and Architecture — 17 citations into it
- Optimization & Training — 13 citations into it
- Generative Models — 11 citations into it
- Natural Language Processing — 10 citations into it
Whom Trevor Gale trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Diederik P. Kingma
- Kaiming He
- Yann LeCun
- Alex Krizhevsky
- Geoffrey E. Hinton
- Max Welling
- Yoshua Bengio
- Ilya Sutskever
- Christian Szegedy
- Ashish Vaswani
Who trusts Trevor Gale overall
- Sara Hooker
- Dan Alistarh
- Elias Frantar
- Sidak Pal Singh
- Jonathan Frankle
- Orevaoghene Ahia
- Utku Evci
- Pedro H. P. Savarese
- Jaeho Lee
- Shiwei Liu
Papers from Trevor Gale's perspective
Ordered by how strongly the researchers Trevor Gale trusts endorse each paper. Same corpus, a different order for every researcher.
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- ImageNet classification with deep convolutional neural networks
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- Attention is All you Need (Neural Information Processing Systems)
- Long Short-Term Memory
- Gradient-based learning applied to document recognition
- ImageNet: A large-scale hierarchical image database
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
- Learning Multiple Layers of Features from Tiny Images
- ImageNet Large Scale Visual Recognition Challenge
Their papers
- On the Opportunities and Risks of Foundation Models (2021) — 6,923 citations
- The State of Sparsity in Deep Neural Networks (2019) — 891 citations
- Rigging the Lottery: Making All Tickets Winners (2019) — 758 citations
- Fast Sparse ConvNets (2019) — 161 citations
- Sparse GPU Kernels for Deep Learning (2020) — 296 citations
- MegaBlocks: Efficient Sparse Training with Mixture-of-Experts (2022) — 234 citations