Krishnan Srinivasan
Artificial Intelligence · 2017–2023
A researcher in the Science corpus: 8 papers; trusts 41 researchers and is trusted by 6 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 152 citations into it; #19,228 of 35,345 by trust here, #2,263 by citations
- Computer Vision and Pattern Recognition — 95 citations into it
- Reinforcement Learning — 86 citations into it; #1,628 of 2,242 by trust here, #2,036 by citations
- Control and Systems Engineering — 82 citations into it; #5,149 of 8,257 by trust here, #1,475 by citations
- Robotics & Control — 39 citations into it
- Cognitive Neuroscience — 34 citations into it
- Biomedical Engineering — 28 citations into it
- Natural Language Processing — 28 citations into it; #4,203 of 5,688 by trust here, #4,250 by citations
Whom Krishnan Srinivasan trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Diederik P. Kingma
- Sergey Levine
- Geoffrey E. Hinton
- Yoshua Bengio
- Brenna D. Argall
- Alex Krizhevsky
- Pieter Abbeel
- Yann LeCun
- Karen Simonyan
- Ilya Sutskever
Who trusts Krishnan Srinivasan overall
Papers from Krishnan Srinivasan's perspective
Ordered by how strongly the researchers Krishnan Srinivasan 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
- Gradient-based learning applied to document recognition
- ImageNet: A large-scale hierarchical image database
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
- Learning Multiple Layers of Features from Tiny Images
- Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
Their papers
- On the Opportunities and Risks of Foundation Models (2021) — 6,923 citations
- Making Sense of Vision and Touch: Self-Supervised Learning of Multimodal Representations for Contact-Rich Tasks (2018) — 456 citations
- Open X-Embodiment: Robotic Learning Datasets and RT-X Models : Open X-Embodiment Collaboration0 (2023) — 1,105 citations
- Graph-based Neural Multi-Document Summarization (2017) — 280 citations
- Recovery RL: Safe Reinforcement Learning With Learned Recovery Zones (2020) — 307 citations
- Making Sense of Vision and Touch: Learning Multimodal Representations for Contact-Rich Tasks (2019) — 283 citations
- Controlling Assistive Robots with Learned Latent Actions (2019) — 79 citations
- Learning latent actions to control assistive robots (2021) — 79 citations