Nimit Sharad Sohoni
Artificial Intelligence · 2019–2022
A researcher in the Science corpus: 4 papers; trusts 222 researchers and is trusted by 3 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 132 citations into it; #27,314 of 35,345 by trust here, #17,412 by citations
- Computer Vision and Pattern Recognition — 106 citations into it; #16,803 of 21,575 by trust here, #14,151 by citations
- Efficiency & Compression — 62 citations into it
- Model Architectures — 35 citations into it; #1,781 of 2,278 by trust here, #2,009 by citations
- Optimization & Training — 33 citations into it
- Signal Processing — 23 citations into it
- Computational Mechanics — 17 citations into it
- Representation & Self-Supervised Learning — 17 citations into it
Whom Nimit Sharad Sohoni trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Alex Krizhevsky
- Geoffrey E. Hinton
- Diederik P. Kingma
- Kaiming He
- Yoshua Bengio
- Sergey Ioffe
- Yann LeCun
- Ilya Sutskever
- Christian Szegedy
- Ross B. Girshick
Who trusts Nimit Sharad Sohoni overall
- Tri Dao
- Elliot Creager
- Mitchell A. Gordon
- Nikita Kitaev
- Jiezhong Qiu
- Beidi Chen
- Jörn-Henrik Jacobsen
- Lukasz Kaiser
- Kevin Duh
Papers from Nimit Sharad Sohoni's perspective
Ordered by how strongly the researchers Nimit Sharad Sohoni 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
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
- Learning Multiple Layers of Features from Tiny Images
- ImageNet Large Scale Visual Recognition Challenge
Their papers
- No Subclass Left Behind: Fine-Grained Robustness in Coarse-Grained Classification Problems (2020) — 306 citations
- Low-Memory Neural Network Training: A Technical Report (2019) — 114 citations
- Monarch: Expressive Structured Matrices for Efficient and Accurate Training (2022) — 133 citations
- Kaleidoscope: An Efficient, Learnable Representation For All Structured Linear Maps (2020) — 56 citations