Siddharth Karamcheti
Artificial Intelligence · 2017–2024
A researcher in the Science corpus: 14 papers; trusts 569 researchers and is trusted by 8 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 355 citations into it; #23,586 of 35,345 by trust here, #1,946 by citations
- Computer Vision and Pattern Recognition — 269 citations into it; #12,914 of 21,575 by trust here, #6,606 by citations
- Natural Language Processing — 180 citations into it; #3,099 of 5,688 by trust here, #1,813 by citations
- Reinforcement Learning — 119 citations into it
- Large Language Models — 72 citations into it
- Control and Systems Engineering — 50 citations into it
- Evaluation & Benchmarks — 46 citations into it
- Robotics & Control — 32 citations into it
Whom Siddharth Karamcheti trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Diederik P. Kingma
- Geoffrey E. Hinton
- Yoshua Bengio
- Alex Krizhevsky
- Ilya Sutskever
- Kaiming He
- Yann LeCun
- Ross B. Girshick
- J. Urgen Schmidhuber
- Jimmy Ba
Who trusts Siddharth Karamcheti overall
- Dorsa Sadigh
- Percy Liang
- Kurt Shuster
- Cynthia Matuszek
- Moo Jin Kim
- Ethan Perez
- Megha Srivastava
- Prithviraj Ammanabrolu
- Suvir Mirchandani
- Joyce Yue Chai
Papers from Siddharth Karamcheti's perspective
Ordered by how strongly the researchers Siddharth Karamcheti trusts endorse each paper. Same corpus, a different order for every researcher.
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- ImageNet classification with deep convolutional neural networks
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- Attention is All you Need (Neural Information Processing Systems)
- Long Short-Term Memory
- ImageNet: A large-scale hierarchical image database
- Gradient-based learning applied to document recognition
- BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
- ImageNet Large Scale Visual Recognition Challenge
Their papers
- On the Opportunities and Risks of Foundation Models (2021) — 6,923 citations
- OBELISC: An Open Web-Scale Filtered Dataset of Interleaved Image-Text Documents (2023) — 370 citations
- Accurately and Efficiently Interpreting Human-Robot Instructions of Varying Granularities (2017) — 64 citations
- Finding Generalizable Evidence by Learning to Convince Q&A Models (2019) — 40 citations
- ELLA: Exploration through Learned Language Abstraction (2021) — 64 citations
- LILA: Language-Informed Latent Actions (2021) — 42 citations
- DROID: A Large-Scale In-The-Wild Robot Manipulation Dataset (2024) — 905 citations
- OpenVLA: An Open-Source Vision-Language-Action Model (2024) — 2,794 citations
- Learning Adaptive Language Interfaces through Decomposition (2020) — 32 citations
- A Tale of Two DRAGGNs: A Hybrid Approach for Interpreting Action-Oriented and Goal-Oriented Instructions (2017) — 17 citations
- Generating Interactive Worlds with Text (2019) — 30 citations
- Prismatic VLMs: Investigating the Design Space of Visually-Conditioned Language Models (2024) — 367 citations
- Learning Visually Guided Latent Actions for Assistive Teleoperation (2021) — 28 citations
- Learning to Speak and Act in a Fantasy Text Adventure Game (2019) — 226 citations