Abhimanyu Dubey

Computer Vision and Pattern Recognition · 2016–2024

A researcher in the Science corpus: 8 papers; trusts 709 researchers and is trusted by 14 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 180 citations into it; #7,888 of 21,575 by trust here, #8,612 by citations
• Artificial Intelligence — 163 citations into it
• Optimization & Training — 28 citations into it
• Privacy & Security — 28 citations into it; #621 of 709 by trust here, #615 by citations
• Efficiency & Compression — 24 citations into it
• Model Architectures — 24 citations into it
• Representation & Self-Supervised Learning — 20 citations into it
• Evaluation & Benchmarks — 17 citations into it

Whom Abhimanyu Dubey trusts overall

Their strongest outbound trust, strongest first, inferred from their citations.

1. Alex Krizhevsky
2. Geoffrey E. Hinton
3. Kaiming He
4. Karen Simonyan
5. Yann LeCun
6. Andrew Zisserman
7. Ross B. Girshick
8. Yoshua Bengio
9. Ilya Sutskever
10. Christian Szegedy

Who trusts Abhimanyu Dubey overall

1. Yang He
2. Ben Mussay
3. Ping Liu
4. Frederick Tung
5. Mingjie Sun
6. Deniz Oktay
7. Nam N. Vo
8. Nimit Sharad Sohoni
9. Dongliang Chang
10. Vivek Sharma

Papers from Abhimanyu Dubey's perspective

Ordered by how strongly the researchers Abhimanyu Dubey trusts endorse each paper. Same corpus, a different order for every researcher.

1. ImageNet classification with deep convolutional neural networks
2. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
3. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
4. ImageNet: A large-scale hierarchical image database
5. Gradient-based learning applied to document recognition
6. Long Short-Term Memory
7. Attention is All you Need (Neural Information Processing Systems)
8. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. Going deeper with convolutions
10. ImageNet Large Scale Visual Recognition Challenge

Their papers

• Coreset-Based Neural Network Compression (2018) — 87 citations
• Emu: Enhancing Image Generation Models Using Photogenic Needles in a Haystack (2023) — 312 citations
• Improving Fine-Grained Visual Classification using Pairwise Confusion (2017) — 93 citations
• Deep Learning the City: Quantifying Urban Perception at a Global Scale (2016) — 540 citations
• The Llama 3 Herd of Models (2024) — 17,085 citations
• R EDUCING LEAKAGE IN DISTRIBUTED DEEP LEARNING FOR SENSITIVE HEALTH DATA (2019)
• No Peek: A Survey of private distributed deep learning (2018) — 113 citations
• Training with Confusion for Fine-Grained Visual Classification (2017) — 10 citations