Alexander Sax

Computer Vision and Pattern Recognition · 2018–2025

A researcher in the Science corpus: 9 papers; trusts 996 researchers and is trusted by 404 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 370 citations into it; #2,074 of 21,575 by trust here, #2,165 by citations
• Artificial Intelligence — 298 citations into it; #13,844 of 35,345 by trust here, #4,682 by citations
• Reinforcement Learning — 95 citations into it
• Representation & Self-Supervised Learning — 93 citations into it; #1,974 of 3,145 by trust here, #736 by citations
• Aerospace Engineering — 68 citations into it; #1,748 of 6,421 by trust here, #641 by citations
• Evaluation & Benchmarks — 59 citations into it
• Control and Systems Engineering — 40 citations into it; #2,866 of 8,257 by trust here, #464 by citations
• Optimization & Training — 33 citations into it

Whom Alexander Sax trusts overall

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

1. Geoffrey E. Hinton
2. Alex Krizhevsky
3. Kaiming He
4. Diederik P. Kingma
5. Yoshua Bengio
6. Ilya Sutskever
7. Ross B. Girshick
8. Yann LeCun
9. Karen Simonyan
10. David G. Lowe

Who trusts Alexander Sax overall

1. Amir Zamir
2. Jitendra Malik
3. Xingyu Lin
4. Roman Bachmann
5. Yi-Lin Sung
6. Dhruv Batra
7. Priya Goyal
8. Simon Vandenhende
9. Wei Yin
10. Weicheng Kuo

Papers from Alexander Sax's perspective

Ordered by how strongly the researchers Alexander Sax 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. Attention is All you Need (Neural Information Processing Systems)
5. ImageNet: A large-scale hierarchical image database
6. Long Short-Term Memory
7. Gradient-based learning applied to document recognition
8. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. ImageNet Large Scale Visual Recognition Challenge
10. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• Taskonomy: Disentangling Task Transfer Learning (2018) — 1,404 citations
• Gibson Env: Real-World Perception for Embodied Agents (2018) — 1,020 citations
• Side-Tuning: A Baseline for Network Adaptation via Additive Side Networks (2019) — 298 citations
• Omnidata: A Scalable Pipeline for Making Multi-Task Mid-Level Vision Datasets from 3D Scans (2021) — 448 citations
• Mid-Level Visual Representations Improve Generalization and Sample Efficiency for Learning Active Tasks (2018) — 16 citations
• OpenEQA: Embodied Question Answering in the Era of Foundation Models (2024) — 348 citations
• Fast3R: Towards 3D Reconstruction of 1000+ Images in One Forward Pass (2025) — 356 citations
• Side-Tuning: Network Adaptation via Additive Side Networks (2019) — 29 citations
• Mid-Level Visual Representations Improve Generalization and Sample Efficiency for Learning Visuomotor Policies (2018) — 74 citations