Yochai Blau
Computer Vision and Pattern Recognition · 2017–2025
A researcher in the Science corpus: 7 papers; trusts 998 researchers and is trusted by 47 researchers here, as inferred from the citation graph.
Subfields
- Computer Vision and Pattern Recognition — 196 citations into it; #3,630 of 21,575 by trust here, #5,333 by citations
- Media Technology — 98 citations into it
- Generative Models — 62 citations into it; #309 of 1,956 by trust here, #859 by citations
- Artificial Intelligence — 36 citations into it
- Evaluation & Benchmarks — 31 citations into it; #482 of 3,147 by trust here, #1,322 by citations
- Optimization & Training — 15 citations into it
- Representation & Self-Supervised Learning — 9 citations into it
- Statistical and Nonlinear Physics — 9 citations into it
Whom Yochai Blau trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Zhou Wang
- A. Bovik
- Hamid R. Sheikh
- Yann LeCun
- Kaiming He
- Geoffrey E. Hinton
- Alex Krizhevsky
- Diederik P. Kingma
- Yoshua Bengio
- Karen Simonyan
Who trusts Yochai Blau overall
- Roey Mechrez
- Tomer Michaeli
- Zhihao Wang
- Tamar Rott Shaham
- Chongyi Li
- Subeesh Vasu
- Chunle Guo
- Chao Dong
- Jinjin Gu
- Jianyi Wang
Papers from Yochai Blau's perspective
Ordered by how strongly the researchers Yochai Blau 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)
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- ImageNet: A large-scale hierarchical image database
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
- Gradient-based learning applied to document recognition
- Attention is All you Need (Neural Information Processing Systems)
- Long Short-Term Memory
- ImageNet Large Scale Visual Recognition Challenge
- Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
Their papers
- The Perception-Distortion Tradeoff (2017) — 1,151 citations
- Rethinking Lossy Compression: The Rate-Distortion-Perception Tradeoff (2019) — 444 citations
- 2018 PIRM Challenge on Perceptual Image Super-resolution (2018) — 545 citations
- Gemini 2.5: Pushing the Frontier with Advanced Reasoning, Multimodality, Long Context, and Next Generation Agentic Capabilities (2025) — 3,609 citations
- Direct Validation of the Information Bottleneck Principle for Deep Nets (2019) — 27 citations
- The 2018 PIRM Challenge on Perceptual Image Super-Resolution (2018) — 94 citations
- The effectiveness of layer-by-layer training using the information bottleneck principle (2018) — 10 citations