Devendra Singh Sachan

Artificial Intelligence · 2018–2022

A researcher in the Science corpus: 8 papers; trusts 386 researchers and is trusted by 45 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 206 citations into it; #6,936 of 35,345 by trust here, #6,848 by citations
• Natural Language Processing — 142 citations into it; #1,256 of 5,688 by trust here, #1,636 by citations
• Computer Vision and Pattern Recognition — 82 citations into it
• Information Retrieval & Search — 49 citations into it; #764 of 1,022 by trust here, #350 by citations
• Optimization & Training — 37 citations into it; #2,374 of 2,601 by trust here, #1,448 by citations
• Representation & Self-Supervised Learning — 34 citations into it
• Model Architectures — 33 citations into it
• Large Language Models — 28 citations into it

Whom Devendra Singh Sachan trusts overall

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

1. Diederik P. Kingma
2. Geoffrey E. Hinton
3. Ilya Sutskever
4. Yoshua Bengio
5. Jimmy Ba
6. Tomas Mikolov
7. Yann LeCun
8. Alex Krizhevsky
9. Kaiming He
10. Quoc V. Le

Who trusts Devendra Singh Sachan overall

1. Sercan Ö. Arik
2. Graham Neubig
3. Gautier Izacard
4. Qizhe Xie
5. Hong-Liu Cao
6. Ori Ram
7. Raj Dabre
8. Patrick Lewis
9. Zirui Wang
10. Tomas Pfister

Papers from Devendra Singh Sachan's perspective

Ordered by how strongly the researchers Devendra Singh Sachan 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. Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)

Their papers

• End-to-End Training of Multi-Document Reader and Retriever for Open-Domain Question Answering (2021) — 192 citations
• When and Why Are Pre-Trained Word Embeddings Useful for Neural Machine Translation? (2018) — 371 citations
• Revisiting LSTM Networks for Semi-Supervised Text Classification via Mixed Objective Function (2019) — 143 citations
• Investigating the Working of Text Classifiers (2018) — 9 citations
• Improving Passage Retrieval with Zero-Shot Question Generation (2022) — 249 citations
• End-to-End Training of Neural Retrievers for Open-Domain Question Answering (2021) — 110 citations
• Parameter Sharing Methods for Multilingual Self-Attentional Translation Models (2018) — 118 citations
• Adaptive Methods for Nonconvex Optimization (2018) — 496 citations