Dibya Ghosh

Artificial Intelligence · 2017–2025

A researcher in the Science corpus: 7 papers; trusts 289 researchers and is trusted by 10 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 167 citations into it; #12,489 of 35,345 by trust here, #10,319 by citations
• Reinforcement Learning — 153 citations into it; #2,073 of 2,242 by trust here, #1,705 by citations
• Computer Vision and Pattern Recognition — 100 citations into it
• Control and Systems Engineering — 79 citations into it; #4,918 of 8,257 by trust here, #1,333 by citations
• Robotics & Control — 26 citations into it
• Computational Theory and Mathematics — 21 citations into it
• Optimization & Training — 16 citations into it
• Representation & Self-Supervised Learning — 16 citations into it

Whom Dibya Ghosh trusts overall

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

1. Sergey Levine
2. Pieter Abbeel
3. Diederik P. Kingma
4. Geoffrey E. Hinton
5. John Schulman
6. Alex Krizhevsky
7. Yoshua Bengio
8. Ronald J. Williams
9. Yann LeCun
10. Richard S. Sutton

Who trusts Dibya Ghosh overall

1. Sergey Levine
2. Florian Schmidt
3. Tianhe Yu
4. Benjamin Eysenbach
5. Michael Janner
6. Corey Lynch
7. Aviral Kumar
8. Abhishek Gupta
9. Youngwoon Lee
10. Chelsea Finn

Papers from Dibya Ghosh's perspective

Ordered by how strongly the researchers Dibya Ghosh 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. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
10. Learning Multiple Layers of Features from Tiny Images

Their papers

• Divide-and-Conquer Reinforcement Learning (2017) — 135 citations
• Variational Inverse Control with Events: A General Framework for Data-Driven Reward Definition (2018) — 133 citations
• Learning To Reach Goals Without Reinforcement Learning (2019) — 38 citations
• Learning Actionable Representations with Goal-Conditioned Policies (2018) — 119 citations
• Octo: An Open-Source Generalist Robot Policy (2024) — 1,572 citations
• π0.5: a Vision-Language-Action Model with Open-World Generalization (2025) — 1,400 citations
• Learning to Reach Goals via Iterated Supervised Learning (2019) — 245 citations