Nikhil Vijay Naik

Artificial Intelligence · 2019–2023

A researcher in the Science corpus: 8 papers; trusts 159 researchers and is trusted by 3 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 133 citations into it
• Molecular Biology — 121 citations into it
• Computer Vision and Pattern Recognition — 91 citations into it; #17,273 of 21,575 by trust here, #18,782 by citations
• Computational Biology & Bioinformatics — 82 citations into it
• Natural Language Processing — 44 citations into it
• Generative Models — 33 citations into it
• Large Language Models — 31 citations into it
• Representation & Self-Supervised Learning — 28 citations into it

Whom Nikhil Vijay Naik trusts overall

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

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

Who trusts Nikhil Vijay Naik overall

1. Tomer Michaeli
2. Vladimir Kulikov
3. Inbar Huberman-Spiegelglas

Papers from Nikhil Vijay Naik's perspective

Ordered by how strongly the researchers Nikhil Vijay Naik 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. Gradient-based learning applied to document recognition
6. Long Short-Term Memory
7. ImageNet: A large-scale hierarchical image database
8. Learning Multiple Layers of Features from Tiny Images
9. GENERATIVE ADVERSARIAL NETS
10. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)

Their papers

• EDICT: Exact Diffusion Inversion via Coupled Transformations (2022) — 308 citations
• Deep learning-enabled medical computer vision (2021) — 1,179 citations
• ProGen: Language Modeling for Protein Generation (2020) — 353 citations
• Deep neural language modeling enables functional protein generation across families (2021) — 55 citations
• ProGen2: Exploring the Boundaries of Protein Language Models (2022) — 555 citations
• Large language models generate functional protein sequences across diverse families (2023) — 1,084 citations
• Designing Interpretable Approximations to Deep Reinforcement Learning with Soft Decision Trees (2020) — 10 citations
• DIME: An Information-Theoretic Difficulty Measure for AI Datasets (2019) — 13 citations