Sachin Kumar

Artificial Intelligence · with Tsvetkov · 2019–2024

A researcher in the Science corpus: 7 papers; trusts 379 researchers and is trusted by 6 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 382 citations into it; #17,519 of 35,345 by trust here, #13,962 by citations
• Natural Language Processing — 279 citations into it; #2,766 of 5,688 by trust here, #3,478 by citations
• Safety, Robustness & Interpretability — 113 citations into it
• Computer Vision and Pattern Recognition — 95 citations into it; #20,771 of 21,575 by trust here, #18,782 by citations
• Large Language Models — 75 citations into it
• Evaluation & Benchmarks — 54 citations into it
• Prompting & Reasoning — 29 citations into it
• Generative Models — 17 citations into it

Whom Sachin Kumar trusts overall

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

1. Yoshua Bengio
2. Geoffrey E. Hinton
3. Diederik P. Kingma
4. Ilya Sutskever
5. Alex Krizhevsky
6. Alex Graves
7. Yann LeCun
8. Kaiming He
9. Richard Socher
10. Sepp Hochreiter

Who trusts Sachin Kumar overall

1. Yulia Tsvetkov
2. Anjalie Field
3. Vidhisha Balachandran
4. Mengzhou Xia
5. Lucille Njoo
6. Tanmay Garg
7. Yuxin Wen
8. Shangbin Feng
9. Antonios Anastasopoulos
10. Xiaochuang Han

Papers from Sachin Kumar's perspective

Ordered by how strongly the researchers Sachin Kumar trusts endorse each paper. Same corpus, a different order for every researcher.

1. Attention is All you Need (Neural Information Processing Systems)
2. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
3. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
4. ImageNet classification with deep convolutional neural networks
5. Long Short-Term Memory
6. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)
7. ImageNet: A large-scale hierarchical image database
8. Gradient-based learning applied to document recognition
9. Neural Machine Translation by Jointly Learning to Align and Translate (International Conference on Learning Representations)
10. Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)

Their papers

• Dolma: an Open Corpus of Three Trillion Tokens for Language Model Pretraining Research (2024) — 519 citations
• RewardBench: Evaluating Reward Models for Language Modeling (2024) — 450 citations
• Language Generation Models Can Cause Harm: So What Can We Do About It? An Actionable Survey (2022) — 119 citations
• Gradient-based Constrained Sampling from Language Models (2022) — 77 citations
• Controlled Text Generation as Continuous Optimization with Multiple Constraints (2021) — 90 citations
• Machine Translation into Low-resource Language Varieties (2021) — 34 citations
• Topics to Avoid: Demoting Latent Confounds in Text Classification (2019) — 35 citations