Giannis Daras

Computer Vision and Pattern Recognition · 2019–2024

A researcher in the Science corpus: 6 papers; trusts 335 researchers and is trusted by 8 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 169 citations into it; #11,885 of 21,575 by trust here, #8,122 by citations
• Artificial Intelligence — 132 citations into it; #29,231 of 35,345 by trust here, #17,110 by citations
• Generative Models — 58 citations into it
• Evaluation & Benchmarks — 47 citations into it
• Natural Language Processing — 39 citations into it
• Computational Mechanics — 36 citations into it; #9,085 of 10,001 by trust here, #5,157 by citations
• Radiology, Nuclear Medicine and Imaging — 28 citations into it
• Model Architectures — 25 citations into it; #2,040 of 2,278 by trust here, #2,162 by citations

Whom Giannis Daras trusts overall

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

1. Diederik P. Kingma
2. Geoffrey E. Hinton
3. Karen Simonyan
4. Alex Krizhevsky
5. Yoshua Bengio
6. Kaiming He
7. Ilya Sutskever
8. Yann LeCun
9. Jimmy Ba
10. Jia Deng

Who trusts Giannis Daras overall

1. Tri Dao
2. Shiyu Chang
3. Beidi Chen
4. Hyungjin Chung
5. Zhangyang Wang
6. Daniel Y. Fu
7. Stefano Ermon
8. Atri Rudra
9. Ajil Jalal

Papers from Giannis Daras's perspective

Ordered by how strongly the researchers Giannis Daras 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. Learning Multiple Layers of Features from Tiny Images

Their papers

• DataComp: In search of the next generation of multimodal datasets (2023) — 734 citations
• Intermediate Layer Optimization for Inverse Problems using Deep Generative Models (2021) — 105 citations
• Robust Compressed Sensing MRI with Deep Generative Priors (2021) — 470 citations
• Your Local GAN: Designing Two Dimensional Local Attention Mechanisms for Generative Models (2019) — 62 citations
• SMYRF: Efficient Attention using Asymmetric Clustering (2020) — 52 citations
• DataComp-LM: In search of the next generation of training sets for language models (2024) — 374 citations