Udit Gupta
Computer Vision and Pattern Recognition · 2017–2022
A researcher in the Science corpus: 14 papers; trusts 60 researchers and is trusted by 3 researchers here, as inferred from the citation graph.
Subfields
- Computer Vision and Pattern Recognition — 203 citations into it; #18,611 of 21,575 by trust here, #7,421 by citations
- Artificial Intelligence — 196 citations into it; #27,039 of 35,345 by trust here, #6,157 by citations
- Electrical and Electronic Engineering — 138 citations into it; #15,639 of 16,539 by trust here, #5,458 by citations
- Efficiency & Compression — 108 citations into it; #1,195 of 1,264 by trust here, #1,092 by citations
- Computer Networks and Communications — 98 citations into it; #9,882 of 9,886 by trust here, #2,099 by citations
- ML Systems & Hardware — 97 citations into it; #497 of 575 by trust here, #265 by citations
- Optimization & Training — 50 citations into it
- Model Architectures — 30 citations into it
Whom Udit Gupta trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Geoffrey E. Hinton
- Kaiming He
- Yann LeCun
- Alex Krizhevsky
- Yoshua Bengio
- Alex Graves
- Karen Simonyan
- Song Han
- Diederik P. Kingma
- Sepp Hochreiter
Who trusts Udit Gupta overall
- Bilge Acun
- Carole-Jean Wu
- Chunxing Yin
- Sheng-Chun Kao
- Geet Sethi
- Georgios Georgiadis
- Michael Lui
- Md Maruf Hossain Shuvo
- Xing Liu
- Samuel Hsia
Papers from Udit Gupta's perspective
Ordered by how strongly the researchers Udit 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)
- Long Short-Term Memory
- Attention is All you Need (Neural Information Processing Systems)
- Gradient-based learning applied to document recognition
- ImageNet: A large-scale hierarchical image database
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
- Going deeper with convolutions
- ImageNet Large Scale Visual Recognition Challenge
Their papers
- MLPerf Training Benchmark (2019) — 388 citations
- Deep Learning Recommendation Model for Personalization and Recommendation Systems (2019) — 944 citations
- Chasing Carbon: The Elusive Environmental Footprint of Computing (2020) — 376 citations
- DeepRecSys: A System for Optimizing End-To-End At-Scale Neural Recommendation Inference (2020) — 211 citations
- Sustainable AI: Environmental Implications, Challenges and Opportunities (2021) — 662 citations
- Ares: A framework for quantifying the resilience of deep neural networks (2018) — 352 citations
- MaxNVM: Maximizing DNN Storage Density and Inference Efficiency with Sparse Encoding and Error Mitigation (2019) — 35 citations
- MASR: A Modular Accelerator for Sparse RNNs (2019) — 48 citations
- ACT: designing sustainable computer systems with an architectural carbon modeling tool (2022) — 280 citations
- A Holistic Approach for Designing Carbon Aware Datacenters (2022) — 31 citations
- RecSSD: near data processing for solid state drive based recommendation inference (2021) — 135 citations
- RecNMP: Accelerating Personalized Recommendation with Near-Memory Processing (2019) — 273 citations
- The Architectural Implications of Facebook's DNN-Based Personalized Recommendation (2019) — 330 citations
- Weightless: Lossy Weight Encoding For Deep Neural Network Compression (2017) — 42 citations