Raja Giryes

Computer Vision and Pattern Recognition · 2015–2023

A researcher in the Science corpus: 17 papers; trusts 1,229 researchers and is trusted by 25 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 436 citations into it; #9,973 of 21,575 by trust here, #3,765 by citations
• Artificial Intelligence — 342 citations into it; #8,287 of 35,345 by trust here, #4,829 by citations
• Computational Mechanics — 123 citations into it; #4,263 of 10,001 by trust here, #738 by citations
• Model Architectures — 85 citations into it
• Representation & Self-Supervised Learning — 84 citations into it
• Generative Models — 65 citations into it
• Optimization & Training — 65 citations into it
• Computer Graphics and Computer-Aided Design — 43 citations into it

Whom Raja Giryes trusts overall

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

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

Who trusts Raja Giryes overall

1. Edouard Oyallon
2. Dinghuai Zhang
3. Kenji Kawaguchi
4. Simon Robert Arridge
5. Vardan Papyan
6. René Vidal
7. David Stutz
8. Colin Wei
9. Gabriel Erion-Barner
10. Jure Sokolić

Papers from Raja Giryes's perspective

Ordered by how strongly the researchers Raja Giryes 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. Long Short-Term Memory
5. ImageNet: A large-scale hierarchical image database
6. Gradient-based learning applied to document recognition
7. Attention is All you Need (Neural Information Processing Systems)
8. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. Learning Multiple Layers of Features from Tiny Images
10. ImageNet Large Scale Visual Recognition Challenge

Their papers

• Robust Large Margin Deep Neural Networks (2016) — 349 citations
• Delta-encoder: an effective sample synthesis method for few-shot object recognition (2018) — 387 citations
• RepMet: Representative-Based Metric Learning for Classification and Few-Shot Object Detection (2018) — 355 citations
• Latent-NeRF for Shape-Guided Generation of 3D Shapes and Textures (2022) — 615 citations
• Image-Adaptive GAN based Reconstruction (2019) — 104 citations
• TEXTure: Text-Guided Texturing of 3D Shapes (2023) — 384 citations
• ASAP: Architecture Search, Anneal and Prune (2019) — 108 citations
• Deep Neural Networks with Random Gaussian Weights: A Universal Classification Strategy? (2015) — 191 citations
• UNIQ (2018) — 43 citations
• Mathematics of Deep Learning (2017) — 122 citations
• Improving DNN Robustness to Adversarial Attacks using Jacobian Regularization (2018) — 243 citations
• Tradeoffs Between Convergence Speed and Reconstruction Accuracy in Inverse Problems (2016) — 86 citations
• Baby steps towards few-shot learning with multiple semantics (2019) — 116 citations
• MeshCNN: a network with an edge (2019) — 375 citations
• NICE: Noise Injection and Clamping Estimation for Neural Network Quantization (2018) — 68 citations
• RepMet: Representative-based metric learning for classification and one-shot object detection (2018) — 39 citations
• Generalization Error of Invariant Classifiers (2016) — 82 citations