Amir Feder

Artificial Intelligence · 2019–2022

A researcher in the Science corpus: 8 papers; trusts 302 researchers and is trusted by 6 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 240 citations into it; #17,623 of 35,345 by trust here, #11,624 by citations
• Natural Language Processing — 166 citations into it; #3,880 of 5,688 by trust here, #3,529 by citations
• Computer Vision and Pattern Recognition — 70 citations into it; #12,399 of 21,575 by trust here, #10,905 by citations
• Safety, Robustness & Interpretability — 67 citations into it; #1,309 of 2,251 by trust here, #1,312 by citations
• Large Language Models — 41 citations into it
• Causal Inference & Discovery — 36 citations into it; #220 of 364 by trust here, #151 by citations
• Evaluation & Benchmarks — 32 citations into it
• Representation & Self-Supervised Learning — 32 citations into it

Whom Amir Feder trusts overall

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

1. Diederik P. Kingma
2. Geoffrey E. Hinton
3. Yoshua Bengio
4. Yann LeCun
5. Alex Krizhevsky
6. Ilya Sutskever
7. Kaiming He
8. Jimmy Ba
9. Ruslan Salakhutdinov
10. Alex Graves

Who trusts Amir Feder overall

1. Yonatan Belinkov
2. Jacob Eisenstein
3. Leonard Bereska
4. Yanai Elazar
5. Yaakov Ophir
6. Efstratios Gavves
7. Arun Das
8. Mert Yuksekgonul
9. Roi Reichart
10. Nora Kassner

Papers from Amir Feder's perspective

Ordered by how strongly the researchers Amir Feder 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. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
10. Neural Machine Translation by Jointly Learning to Align and Translate (International Conference on Learning Representations)

Their papers

• Explaining Classifiers with Causal Concept Effect (CaCE) (2019) — 213 citations
• CausaLM: Causal Model Explanation Through Counterfactual Language Models (2020) — 193 citations
• Measuring Causal Effects of Data Statistics on Language Model's 'Factual' Predictions (2022) — 67 citations
• Causal Inference in Natural Language Processing: Estimation, Prediction, Interpretation and Beyond (2021) — 330 citations
• Shared computational principles for language processing in humans and deep language models (2022) — 506 citations
• Predicting In-Game Actions from Interviews of NBA Players (2019) — 29 citations
• Active deep learning to detect demographic traits in free-form clinical notes (2020) — 25 citations
• Predicting In-game Actions From the Language of NBA Players (2019)