Moksh Jain

Artificial Intelligence · 2021

A researcher in the Science corpus: 1 paper; trusts 4 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 10 citations into it
• Computational Theory and Mathematics — 10 citations into it
• Materials Chemistry — 7 citations into it

Whom Moksh Jain trusts overall

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

1. Richard S. Sutton
2. Jan H. Jensen
3. Tuomas Haarnoja
4. Charlie Nash
5. Nicola De Cao
6. John Schulman
7. Andrew Barto
8. Geoffrey E. Hinton
9. Diederik P. Kingma
10. Emmanuel Bengio

Papers from Moksh Jain's perspective

Ordered by how strongly the researchers Moksh Jain 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. Gradient-based learning applied to document recognition
6. Long Short-Term Memory
7. Reinforcement Learning: An Introduction
8. Learning Multiple Layers of Features from Tiny Images
9. ImageNet: A large-scale hierarchical image database
10. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• Flow Network based Generative Models for Non-Iterative Diverse Candidate Generation (2021) — 514 citations