Jeff M. Phillips
Artificial Intelligence · 2012–2021
A researcher in the Science corpus: 6 papers; trusts 81 researchers and is trusted by 27 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 87 citations into it; #6,118 of 35,345 by trust here, #7,606 by citations
- Natural Language Processing — 62 citations into it; #3,830 of 5,688 by trust here, #2,605 by citations
- Safety, Robustness & Interpretability — 48 citations into it; #1,497 of 2,251 by trust here, #1,063 by citations
- Representation & Self-Supervised Learning — 22 citations into it; #916 of 3,145 by trust here, #2,148 by citations
- Computer Vision and Pattern Recognition — 14 citations into it
- Evaluation & Benchmarks — 10 citations into it
- Safety Research — 8 citations into it
- Information Systems — 6 citations into it
Whom Jeff M. Phillips trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Tomas Mikolov
- Michael Collins
- Omer Levy
- Ilya Sutskever
- Dekang Lin
- Aylin Caliskan
- Geoffrey E. Hinton
- Yoshua Bengio
- Jeffrey Pennington
- Richard Socher
Who trusts Jeff M. Phillips overall
- Sunipa Dev
- Anne Lauscher
- Tao Li
- Yusuke Hirota
- Gauri Gupta
- Radovan Popović
- Enoch Opanin Gyamfi
- Oskar van der Wal
- Goran Glavas
- Ilias Diakonikolas
Papers from Jeff M. Phillips's perspective
Ordered by how strongly the researchers Jeff M. Phillips 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)
- ImageNet classification with deep convolutional neural networks
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- Long Short-Term Memory
- BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)
- Gradient-based learning applied to document recognition
- ImageNet: A large-scale hierarchical image database
- Neural Machine Translation by Jointly Learning to Align and Translate (International Conference on Learning Representations)
- Distributed Representations of Words and Phrases and their Compositionality (Neural Information Processing Systems)
Their papers
- Harms of Gender Exclusivity and Challenges in Non-Binary Representation in Language Technologies (2021) — 214 citations
- On Measuring and Mitigating Biased Inferences of Word Embeddings (2019) — 198 citations
- Attenuating Bias in Word Vectors (2019) — 161 citations
- OSCaR: Orthogonal Subspace Correction and Rectification of Biases in Word Embeddings (2020) — 60 citations
- Protocols for Learning Classifiers on Distributed Data (2012) — 29 citations
- Efficient Protocols for Distributed Classification and Optimization (2012) — 41 citations