Ishita Dasgupta

Artificial Intelligence · 2018–2023

A researcher in the Science corpus: 7 papers; trusts 56 researchers and is trusted by 4 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 214 citations into it; #20,828 of 35,345 by trust here, #5,970 by citations
• Natural Language Processing — 84 citations into it
• Reinforcement Learning — 42 citations into it
• Cognitive Science & Psychology — 29 citations into it
• Philosophy — 28 citations into it
• Developmental and Educational Psychology — 25 citations into it
• Evaluation & Benchmarks — 15 citations into it
• Experimental and Cognitive Psychology — 13 citations into it

Whom Ishita Dasgupta trusts overall

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

1. Geoffrey E. Hinton
2. Yoshua Bengio
3. Diederik P. Kingma
4. Richard Socher
5. Alex Krizhevsky
6. Ilya Sutskever
7. Richard S. Sutton
8. Yann LeCun
9. Kaiming He
10. Radford M. Neal

Who trusts Ishita Dasgupta overall

1. Yonatan Belinkov
2. Ellie Pavlick
3. Bernhard Schölkopf
4. R. Thomas McCoy
5. Marcel Binz
6. Nan Rosemary Ke
7. Yixin Nie
8. J'er'emy Scheurer
9. Charles Lovering
10. Damien Sileo

Papers from Ishita Dasgupta's perspective

Ordered by how strongly the researchers Ishita Dasgupta 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. Attention is All you Need (Neural Information Processing Systems)
4. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
5. Long Short-Term Memory
6. ImageNet: A large-scale hierarchical image database
7. Gradient-based learning applied to document recognition
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

• Can language models learn from explanations in context? (2022) — 383 citations
• Causal Reasoning from Meta-reinforcement Learning (2019) — 138 citations
• Evaluating Compositionality in Sentence Embeddings (2018) — 129 citations
• Language models show human-like content effects on reasoning (2022) — 230 citations
• Collaborating with language models for embodied reasoning (2023) — 91 citations
• Tell me why! Explanations support learning relational and causal structure (2021) — 51 citations
• Tell me why! - Explanations support learning of relational and causal structure (2021) — 6 citations