Ben Athiwaratkun
Artificial Intelligence · 2016–2022
A researcher in the Science corpus: 7 papers; trusts 183 researchers and is trusted by 24 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 181 citations into it; #10,374 of 35,345 by trust here, #7,310 by citations
- Natural Language Processing — 92 citations into it
- Computer Vision and Pattern Recognition — 62 citations into it; #11,944 of 21,575 by trust here, #8,502 by citations
- Optimization & Training — 49 citations into it
- Representation & Self-Supervised Learning — 48 citations into it; #1,468 of 3,145 by trust here, #1,692 by citations
- Evaluation & Benchmarks — 15 citations into it
- Scaling & Learning Theory — 15 citations into it
- Model Architectures — 12 citations into it
Whom Ben Athiwaratkun trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Geoffrey E. Hinton
- Diederik P. Kingma
- Yoshua Bengio
- Kaiming He
- Alex Krizhevsky
- Ilya Sutskever
- Tomas Mikolov
- Yann LeCun
- Richard Socher
- Jimmy Ba
Who trusts Ben Athiwaratkun overall
- Sudhanshu Mittal
- Ugur Kursuncu
- Alexander Kolesnikov
- Pavel Izmailov
- Xiaohua Zhai
- Garrett Wilson
- Xilun Chen
- Jacob Jackson
- Chongxuan Li
- Jean Kaddour
Papers from Ben Athiwaratkun's perspective
Ordered by how strongly the researchers Ben Athiwaratkun 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
- Learning Multiple Layers of Features from Tiny Images
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
- Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
Their papers
- There Are Many Consistent Explanations of Unlabeled Data: Why You Should Average (2018) — 262 citations
- Adversarial Deep Averaging Networks for Cross-Lingual Sentiment Classification (2016) — 330 citations
- Multi-lingual Evaluation of Code Generation Models (2022) — 247 citations
- Probabilistic FastText for Multi-Sense Word Embeddings (2018) — 158 citations
- Structured Prediction as Translation between Augmented Natural Languages (2021) — 364 citations
- Improving Stability in Deep Reinforcement Learning with Weight Averaging (2018)
- Improving Consistency-Based Semi-Supervised Learning with Weight Averaging (2018) — 36 citations