Peter West
Artificial Intelligence · 2020–2023
A researcher in the Science corpus: 13 papers; trusts 349 researchers and is trusted by 34 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 423 citations into it; #6,399 of 35,345 by trust here, #4,602 by citations
- Natural Language Processing — 328 citations into it; #1,327 of 5,688 by trust here, #937 by citations
- Computer Vision and Pattern Recognition — 153 citations into it; #9,185 of 21,575 by trust here, #6,542 by citations
- Large Language Models — 102 citations into it
- Evaluation & Benchmarks — 87 citations into it
- Prompting & Reasoning — 57 citations into it; #93 of 359 by trust here, #188 by citations
- Safety, Robustness & Interpretability — 27 citations into it
- Knowledge Graphs & Representation — 17 citations into it
Whom Peter West trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Geoffrey E. Hinton
- Diederik P. Kingma
- Yoshua Bengio
- Alec Radford
- Ilya Sutskever
- Alex Krizhevsky
- Yann LeCun
- Kaiming He
- Jimmy Ba
- Alex Graves
Who trusts Peter West overall
- Sachin Kumar
- Sewon Min
- Yejin Choi
- Sean Welleck
- Ximing Lu
- Boshi Wang
- Jaehun Jung
- Kumar Shridhar
- Sarah Wiegreffe
- Nora Kassner
Papers from Peter West's perspective
Ordered by how strongly the researchers Peter West trusts endorse each paper. Same corpus, a different order for every researcher.
- Attention is All you Need (Neural Information Processing Systems)
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- ImageNet classification with deep convolutional neural networks
- Long Short-Term Memory
- BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)
- ImageNet: A large-scale hierarchical image database
- Gradient-based learning applied to document recognition
- Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)
- Neural Machine Translation by Jointly Learning to Align and Translate (International Conference on Learning Representations)
Their papers
- Surface Form Competition: Why the Highest Probability Answer Isn’t Always Right (2021) — 301 citations
- Unsupervised Commonsense Question Answering with Self-Talk (2020) — 287 citations
- Symbolic Knowledge Distillation: from General Language Models to Commonsense Models (2021) — 392 citations
- Quark: Controllable Text Generation with Reinforced Unlearning (2022) — 277 citations
- Back to the Future: Unsupervised Backprop-based Decoding for Counterfactual and Abductive Commonsense Reasoning (2020) — 96 citations
- NeuroLogic Decoding: (Un)supervised Neural Text Generation with Predicate Logic Constraints (2020) — 177 citations
- Reflective Decoding: Beyond Unidirectional Generation with Off-the-Shelf Language Models (2020) — 14 citations
- NeuroLogic A*esque Decoding: Constrained Text Generation with Lookahead Heuristics (2021) — 200 citations
- Faith and Fate: Limits of Transformers on Compositionality (2023) — 650 citations
- Impossible Distillation for Paraphrasing and Summarization: How to Make High-quality Lemonade out of Small, Low-quality Model (2023) — 39 citations
- Generating Sequences by Learning to Self-Correct (2022) — 352 citations
- Generated Knowledge Prompting for Commonsense Reasoning (2021) — 407 citations
- PLink: Discovering and Exploiting Locality for Accelerated Distributed Training on the public Cloud (2020) — 57 citations