Vivek Natarajan
Artificial Intelligence · 2018–2025
A researcher in the Science corpus: 15 papers; trusts 297 researchers and is trusted by 6 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 407 citations into it; #34,875 of 35,345 by trust here, #2,929 by citations
- Computer Vision and Pattern Recognition — 202 citations into it; #21,290 of 21,575 by trust here, #4,212 by citations
- Natural Language Processing — 112 citations into it
- Representation & Self-Supervised Learning — 49 citations into it
- Multimodal Learning — 42 citations into it; #690 of 1,211 by trust here, #685 by citations
- Model Architectures — 33 citations into it
- Optimization & Training — 28 citations into it
- Management Science and Operations Research — 8 citations into it
Whom Vivek Natarajan trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Kaiming He
- Diederik P. Kingma
- Geoffrey E. Hinton
- Yoshua Bengio
- Alex Krizhevsky
- Ilya Sutskever
- Ross B. Girshick
- Yann LeCun
- Richard Socher
- Jimmy Ba
Who trusts Vivek Natarajan overall
- Amanpreet Singh
- Ronghang Hu
- Zhengyuan Yang
- Da Ju
- Vedanuj Goswami
- Ali Furkan Biten
- Marcus Rohrbach
- Oleksii Sidorov
- Nitesh Methani
- Dustin Schwenk
Papers from Vivek Natarajan's perspective
Ordered by how strongly the researchers Vivek Natarajan trusts endorse each paper. Same corpus, a different order for every researcher.
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- ImageNet classification with deep convolutional neural networks
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- Long Short-Term Memory
- Attention is All you Need (Neural Information Processing Systems)
- ImageNet: A large-scale hierarchical image database
- Gradient-based learning applied to document recognition
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
- Learning Multiple Layers of Features from Tiny Images
- ImageNet Large Scale Visual Recognition Challenge
Their papers
- Towards VQA Models That Can Read (2019) — 2,180 citations
- Large language models encode clinical knowledge (2022) — 4,703 citations
- Towards Expert-Level Medical Question Answering with Large Language Models (2023) — 799 citations
- Underspecification Presents Challenges for Credibility in Modern Machine Learning (2020) — 897 citations
- Big Self-Supervised Models Advance Medical Image Classification (2021) — 741 citations
- A deep learning system for differential diagnosis of skin diseases (2019) — 760 citations
- Contrastive Training for Improved Out-of-Distribution Detection (2020) — 271 citations
- Accelerating scientific discovery with Co-Scientist (2025) — 360 citations
- Toward expert-level medical question answering with large language models (2025) — 1,038 citations
- Towards conversational diagnostic artificial intelligence (2025) — 310 citations
- Capabilities of Gemini Models in Medicine (2024) — 407 citations
- Towards Generalist Biomedical AI (2023) — 549 citations
- Towards accurate differential diagnosis with large language models (2023) — 345 citations
- Pythia v0.1: the Winning Entry to the VQA Challenge 2018 (2018) — 210 citations
- Pythia-A platform for vision & language research (2018)