Hila Chefer

Computer Vision and Pattern Recognition · 2020–2024

A researcher in the Science corpus: 4 papers; trusts 647 researchers and is trusted by 22 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 135 citations into it; #6,867 of 21,575 by trust here, #7,584 by citations
• Artificial Intelligence — 85 citations into it; #9,884 of 35,345 by trust here, #15,542 by citations
• Generative Models — 47 citations into it
• Multimodal Learning — 37 citations into it
• Safety, Robustness & Interpretability — 28 citations into it
• Model Architectures — 22 citations into it
• Biophysics — 18 citations into it
• Natural Language Processing — 14 citations into it

Whom Hila Chefer trusts overall

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

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

Who trusts Hila Chefer overall

1. S. Gadre
2. Sachit Menon
3. Xuhong Li
4. Zizhao Zhang
5. Patrick Schramowski
6. Siyuan Li
7. Fanglei Xue
8. Omer Bar-Tal
9. Sen Yang
10. Mitchell Wortsman

Papers from Hila Chefer's perspective

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

1. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
2. ImageNet classification with deep convolutional neural networks
3. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
4. Attention is All you Need (Neural Information Processing Systems)
5. Long Short-Term Memory
6. ImageNet: A large-scale hierarchical image database
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. Going deeper with convolutions

Their papers

• Generic Attention-model Explainability for Interpreting Bi-Modal and Encoder-Decoder Transformers (2021) — 484 citations
• Transformer Interpretability Beyond Attention Visualization (2020) — 1,103 citations
• Attend-and-Excite: Attention-Based Semantic Guidance for Text-to-Image Diffusion Models (2023) — 864 citations
• Lumiere: A Space-Time Diffusion Model for Video Generation (2024) — 526 citations