Nikhil Mehta

Artificial Intelligence · with Carin · 2020–2023

A researcher in the Science corpus: 4 papers; trusts 303 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 124 citations into it
• Computer Vision and Pattern Recognition — 95 citations into it
• Representation & Self-Supervised Learning — 37 citations into it
• Meta & Continual Learning — 35 citations into it
• Generative Models — 23 citations into it
• Model Architectures — 18 citations into it
• Management Science and Operations Research — 16 citations into it
• Optimization & Training — 14 citations into it

Whom Nikhil Mehta trusts overall

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

1. Alex Krizhevsky
2. Geoffrey E. Hinton
3. Diederik P. Kingma
4. Kaiming He
5. Yann LeCun
6. Yoshua Bengio
7. Ilya Sutskever
8. Jimmy Ba
9. Radford M. Neal
10. Oriol Vinyals

Who trusts Nikhil Mehta overall

1. Liyuan Wang
2. Vinay Kumar Verma
3. Rakib Hyder
4. Xingxing Zhang
5. Kevin J Liang

Papers from Nikhil Mehta's perspective

Ordered by how strongly the researchers Nikhil 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. Gradient-based learning applied to document recognition
5. Attention is All you Need (Neural Information Processing Systems)
6. Long Short-Term Memory
7. ImageNet: A large-scale hierarchical image database
8. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. Learning Multiple Layers of Features from Tiny Images
10. ImageNet Large Scale Visual Recognition Challenge

Their papers

• Recommender Systems with Generative Retrieval (2023) — 342 citations
• Efficient Feature Transformations for Discriminative and Generative Continual Learning (2021) — 93 citations
• Continual Learning using a Bayesian Nonparametric Dictionary of Weight Factors (2021)
• Bayesian Nonparametric Weight Factorization for Continual Learning (2020) — 5 citations