Maxwell Nye

Artificial Intelligence · 2018–2021

A researcher in the Science corpus: 10 papers; trusts 250 researchers and is trusted by 24 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 244 citations into it; #8,160 of 35,345 by trust here, #1,870 by citations
• Natural Language Processing — 159 citations into it; #2,436 of 5,688 by trust here, #981 by citations
• Computer Vision and Pattern Recognition — 92 citations into it
• Information Systems — 85 citations into it; #6,141 of 8,771 by trust here, #196 by citations
• Software — 50 citations into it
• Code Generation & Program Synthesis — 46 citations into it; #144 of 220 by trust here, #80 by citations
• Model Architectures — 35 citations into it
• Large Language Models — 34 citations into it

Whom Maxwell Nye trusts overall

Their strongest outbound trust, strongest first, inferred from their citations.

1. Geoffrey E. Hinton
2. Yoshua Bengio
3. Brenden M. Lake
4. Alex Graves
5. Diederik P. Kingma
6. Ilya Sutskever
7. Alex Krizhevsky
8. J. Urgen Schmidhuber
9. Yann LeCun
10. Kaiming He

Who trusts Maxwell Nye overall

1. Xuezhi Wang
2. Xinyun Chen
3. Jason Wei
4. Luyu Gao
5. Denny Zhou
6. Freda Shi
7. Peter Shaw
8. Josh Tenenbaum
9. Eric Zelikman
10. Thilo Hagendorff

Papers from Maxwell Nye's perspective

Ordered by how strongly the researchers Maxwell Nye trusts endorse each paper. Same corpus, a different order for every researcher.

1. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
2. ImageNet classification with deep convolutional neural networks
3. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
4. Attention is All you Need (Neural Information Processing Systems)
5. Long Short-Term Memory
6. Gradient-based learning applied to document recognition
7. ImageNet: A large-scale hierarchical image database
8. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)
9. Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)
10. Neural Machine Translation by Jointly Learning to Align and Translate (International Conference on Learning Representations)

Their papers

• Program Synthesis with Large Language Models (2021) — 4,124 citations
• Show Your Work: Scratchpads for Intermediate Computation with Language Models (2021) — 1,075 citations
• Learning Compositional Rules via Neural Program Synthesis (2020) — 120 citations
• Implicit Representations of Meaning in Neural Language Models (2021) — 240 citations
• The Variational Homoencoder: Learning to learn high capacity generative models from few examples (2018) — 70 citations
• DreamCoder: growing generalizable, interpretable knowledge with wake–sleep Bayesian program learning (2020) — 266 citations
• Write, Execute, Assess: Program Synthesis with a REPL (2019) — 187 citations
• DreamCoder: bootstrapping inductive program synthesis with wake-sleep library learning (2021) — 278 citations
• Improving Coherence and Consistency in Neural Sequence Models with Dual-System, Neuro-Symbolic Reasoning (2021) — 149 citations
• A large-scale benchmark for few-shot program induction and synthesis (2021) — 20 citations