Sanket Vaibhav Mehta

Artificial Intelligence · 2020–2025

A researcher in the Science corpus: 4 papers; trusts 158 researchers and is trusted by 6 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 225 citations into it; #18,955 of 35,345 by trust here, #9,275 by citations
• Natural Language Processing — 123 citations into it
• Computer Vision and Pattern Recognition — 105 citations into it; #11,921 of 21,575 by trust here, #9,563 by citations
• Representation & Self-Supervised Learning — 51 citations into it
• Meta & Continual Learning — 29 citations into it; #581 of 1,041 by trust here, #494 by citations
• Optimization & Training — 21 citations into it
• Model Architectures — 12 citations into it
• Reinforcement Learning — 10 citations into it

Whom Sanket Vaibhav Mehta trusts overall

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

1. Alex Krizhevsky
2. Kaiming He
3. Geoffrey E. Hinton
4. Yoshua Bengio
5. Diederik P. Kingma
6. Yann LeCun
7. Ilya Sutskever
8. Marc'Aurelio Ranzato
9. Tomas Mikolov
10. Richard Socher

Who trusts Sanket Vaibhav Mehta overall

1. Zixuan Ke
2. B. Ermiş
3. Liyuan Wang
4. Zifeng Wang
5. Giovanni Zappella
6. Francesco Pelosin
7. Xingxing Zhang
8. Thomas Scialom
9. Nithin Holla
10. Haowei Lin

Papers from Sanket Vaibhav Mehta's perspective

Ordered by how strongly the researchers Sanket Vaibhav Mehta 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. ImageNet: A large-scale hierarchical image database
7. Gradient-based learning applied to document recognition
8. Learning Multiple Layers of Features from Tiny Images
9. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
10. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)

Their papers

• ExT5: Towards Extreme Multi-Task Scaling for Transfer Learning (2021) — 240 citations
• An Empirical Investigation of the Role of Pre-training in Lifelong Learning (2021) — 180 citations
• Gemini 2.5: Pushing the Frontier with Advanced Reasoning, Multimodality, Long Context, and Next Generation Agentic Capabilities (2025) — 3,609 citations
• Efficient Meta Lifelong-Learning with Limited Memory (2020) — 83 citations