Ohad Fried
Computer Vision and Pattern Recognition · 2019–2022
A researcher in the Science corpus: 4 papers; trusts 519 researchers and is trusted by 22 researchers here, as inferred from the citation graph.
Subfields
- Computer Vision and Pattern Recognition — 276 citations into it; #6,395 of 21,575 by trust here, #4,344 by citations
- Generative Models — 159 citations into it; #1,102 of 1,956 by trust here, #730 by citations
- Computer Graphics and Computer-Aided Design — 55 citations into it; #939 of 1,584 by trust here, #294 by citations
- Artificial Intelligence — 50 citations into it; #25,696 of 35,345 by trust here, #19,873 by citations
- Multimodal Learning — 35 citations into it
- Signal Processing — 21 citations into it
- Probabilistic & Bayesian Methods — 15 citations into it
- Statistical and Nonlinear Physics — 10 citations into it
Whom Ohad Fried trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Geoffrey E. Hinton
- Diederik P. Kingma
- Alex Krizhevsky
- Kaiming He
- Yoshua Bengio
- Yann LeCun
- Ilya Sutskever
- Karen Simonyan
- Tero Karras
- Ross B. Girshick
Who trusts Ohad Fried overall
- Hany Farid
- Keunhong Park
- Omer Bar-Tal
- Yisroel Mirsky
- Amir Hertz
- Zhimeng Zhang
- Chenyang Qi
- Can Wang
- Justus Thies
- Utkarsh Sinha
Papers from Ohad Fried's perspective
Ordered by how strongly the researchers Ohad Fried 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)
- Attention is All you Need (Neural Information Processing Systems)
- ImageNet: A large-scale hierarchical image database
- Long Short-Term Memory
- Gradient-based learning applied to document recognition
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
- ImageNet Large Scale Visual Recognition Challenge
- GENERATIVE ADVERSARIAL NETS
Their papers
- Blended Diffusion for Text-driven Editing of Natural Images (2021) — 1,278 citations
- State of the Art on Neural Rendering (2020) — 534 citations
- Blended Latent Diffusion (2022) — 568 citations
- Text-based editing of talking-head video (2019) — 291 citations