Archit Sharma
Artificial Intelligence · Neural Information Processing Systems · 2019–2024
A researcher in the Science corpus: 5 papers; trusts 80 researchers and is trusted by 2 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 151 citations into it; #29,726 of 35,345 by trust here, #13,962 by citations
- Reinforcement Learning — 115 citations into it; #1,961 of 2,242 by trust here, #1,705 by citations
- Control and Systems Engineering — 59 citations into it
- Computer Vision and Pattern Recognition — 55 citations into it
- Biomedical Engineering — 22 citations into it
- Robotics & Control — 19 citations into it
- Management Science and Operations Research — 18 citations into it
- Alignment & RLHF — 15 citations into it
Whom Archit Sharma trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Sergey Levine
- Diederik P. Kingma
- J. Urgen Schmidhuber
- Geoffrey E. Hinton
- Richard S. Sutton
- Pieter Abbeel
- Ronald J. Williams
- Yoshua Bengio
- John Schulman
- Marc Peter Deisenroth
Who trusts Archit Sharma overall
- Kimin Lee
- Kevin Lu
- Pieter Abbeel
- Kelvin Xu
- Víctor Campos
- Dhruv Shah
- Wei Xiong
- Sergey Levine
- Danijar Hafner
- Chelsea Finn
Papers from Archit Sharma's perspective
Ordered by how strongly the researchers Archit Sharma trusts endorse each paper. Same corpus, a different order for every researcher.
- ImageNet classification with deep convolutional neural networks
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- Attention is All you Need (Neural Information Processing Systems)
- Long Short-Term Memory
- Gradient-based learning applied to document recognition
- ImageNet: A large-scale hierarchical image database
- Learning Multiple Layers of Features from Tiny Images
- Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
Their papers
- Dynamics-Aware Unsupervised Discovery of Skills (2019) — 496 citations
- DROID: A Large-Scale In-The-Wild Robot Manipulation Dataset (2024) — 905 citations
- Autonomous Reinforcement Learning via Subgoal Curricula (2021) — 38 citations
- Direct Preference Optimization: Your Language Model is Secretly a Reward Model (2023) — 9,702 citations
- Just Ask for Calibration: Strategies for Eliciting Calibrated Confidence Scores from Language Models Fine-Tuned with Human Feedback (2023) — 810 citations