Suyog Gupta
Artificial Intelligence · 2015–2019
A researcher in the Science corpus: 5 papers; trusts 243 researchers and is trusted by 187 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 55 citations into it; #1,608 of 35,345 by trust here, #2,023 by citations
- Computer Vision and Pattern Recognition — 51 citations into it; #3,143 of 21,575 by trust here, #4,030 by citations
- Optimization & Training — 30 citations into it
- Efficiency & Compression — 24 citations into it; #42 of 1,264 by trust here, #101 by citations
- Model Architectures — 13 citations into it
- ML Systems & Hardware — 11 citations into it; #206 of 575 by trust here, #431 by citations
- Computational Mechanics — 6 citations into it
- Electrical and Electronic Engineering — 6 citations into it
Whom Suyog Gupta trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Alex Krizhevsky
- Geoffrey E. Hinton
- Yann LeCun
- Yoshua Bengio
- Ilya Sutskever
- Kaiming He
- Christian Szegedy
- Diederik P. Kingma
- L. Eon Bottou
- John C. Duchi
Who trusts Suyog Gupta overall
- Markus Nagel
- Tim Dettmers
- Dan Alistarh
- Mohammad Mohammadi Amiri
- Sara Hooker
- Jianyu Wang
- Ahmed Khaled
- Yu Cheng
- Chia-Yu Chen
- Asit K. Mishra
Papers from Suyog Gupta's perspective
Ordered by how strongly the researchers Suyog Gupta 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)
- Gradient-based learning applied to document recognition
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- Long Short-Term Memory
- ImageNet: A large-scale hierarchical image database
- Attention is All you Need (Neural Information Processing Systems)
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
- Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
- Learning Multiple Layers of Features from Tiny Images
Their papers
- Deep Learning with Limited Numerical Precision (2015) — 2,190 citations
- To prune, or not to prune: exploring the efficacy of pruning for model compression (2017) — 1,473 citations
- Lingvo: a Modular and Scalable Framework for Sequence-to-Sequence Modeling (2019) — 215 citations
- Staleness-Aware Async-SGD for Distributed Deep Learning (2015) — 282 citations
- Model Accuracy and Runtime Tradeoff in Distributed Deep Learning: A Systematic Study (2015) — 179 citations