Neev Parikh

Artificial Intelligence · 2021

A researcher in the Science corpus: 1 paper; trusts 70 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 31 citations into it
• Reinforcement Learning — 24 citations into it
• Computer Vision and Pattern Recognition — 8 citations into it
• Representation & Self-Supervised Learning — 7 citations into it
• Control and Systems Engineering — 6 citations into it
• Computational Theory and Mathematics — 5 citations into it

Whom Neev Parikh trusts overall

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

1. Pieter Abbeel
2. Geoffrey E. Hinton
3. Richard S. Sutton
4. Sergey Levine
5. Diederik P. Kingma
6. Amy Zhang
7. Yoshua Bengio
8. Alex Krizhevsky
9. Pulkit Agrawal
10. J. Urgen Schmidhuber

Papers from Neev Parikh's perspective

Ordered by how strongly the researchers Neev Parikh 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. Learning Multiple Layers of Features from Tiny Images
9. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
10. Human-level control through deep reinforcement learning

Their papers

• Learning Markov State Abstractions for Deep Reinforcement Learning (2021) — 62 citations