Payel Das

Artificial Intelligence · 2006–2023

A researcher in the Science corpus: 9 papers; trusts 102 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 154 citations into it
• Molecular Biology — 116 citations into it
• Materials Chemistry — 92 citations into it
• Computational Theory and Mathematics — 76 citations into it
• Computer Vision and Pattern Recognition — 68 citations into it
• Model Architectures — 48 citations into it
• Natural Language Processing — 45 citations into it
• Graph Learning — 43 citations into it

Whom Payel Das trusts overall

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

1. Geoffrey E. Hinton
2. Diederik P. Kingma
3. Yoshua Bengio
4. Ilya Sutskever
5. Alex Krizhevsky
6. Yann LeCun
7. Kaiming He
8. Stu Daultrey
9. Oriol Vinyals
10. Ronald J. Williams

Who trusts Payel Das overall

1. Ronald R. Coifman
2. Yu Li
3. Wenhao Gao
4. Ioannis G. Kevrekidis

Papers from Payel Das's perspective

Ordered by how strongly the researchers Payel Das 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. 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. ImageNet Large Scale Visual Recognition Challenge
10. Learning Multiple Layers of Features from Tiny Images

Their papers

• Explanations based on the Missing: Towards Contrastive Explanations with Pertinent Negatives (2018) — 687 citations
• The Impact of Positional Encoding on Length Generalization in Transformers (2023) — 396 citations
• ReGen: Reinforcement Learning for Text and Knowledge Base Generation using Pretrained Language Models (2021) — 30 citations
• Low-dimensional, free-energy landscapes of protein-folding reactions by nonlinear dimensionality reduction (2006) — 317 citations
• Protein Representation Learning by Geometric Structure Pretraining (2022) — 328 citations
• Accelerated antimicrobial discovery via deep generative models and molecular dynamics simulations (2021) — 398 citations
• Do Large Scale Molecular Language Representations Capture Important Structural Information? (2021) — 13 citations
• Large-scale chemical language representations capture molecular structure and properties (2021) — 591 citations
• PepCVAE: Semi-Supervised Targeted Design of Antimicrobial Peptide Sequences (2018) — 69 citations