Sina Honari

Computer Vision and Pattern Recognition · 2015–2021

A researcher in the Science corpus: 7 papers; trusts 418 researchers and is trusted by 15 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 126 citations into it; #12,393 of 21,575 by trust here, #11,286 by citations
• Artificial Intelligence — 79 citations into it
• Generative Models — 37 citations into it; #836 of 1,956 by trust here, #1,601 by citations
• Representation & Self-Supervised Learning — 31 citations into it
• Safety, Robustness & Interpretability — 31 citations into it
• Optimization & Training — 20 citations into it
• Model Architectures — 16 citations into it
• Evaluation & Benchmarks — 13 citations into it

Whom Sina Honari trusts overall

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

1. Yoshua Bengio
2. Alex Krizhevsky
3. Geoffrey E. Hinton
4. Yann LeCun
5. Diederik P. Kingma
6. Kaiming He
7. Ruslan Salakhutdinov
8. Ilya Sutskever
9. Ross B. Girshick
10. Shaoqing Ren

Who trusts Sina Honari overall

1. Philip Bachman
2. Saurabh Saxena
3. Alexander Kirillov
4. Vikram S. Voleti
5. Jiaming Song
6. Ross B. Girshick
7. Huiyu Wang
8. Yang Song
9. Stefano Ermon
10. Tsung-Yi Lin

Papers from Sina Honari's perspective

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

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

• Recombinator Networks: Learning Coarse-to-Fine Feature Aggregation (2015) — 127 citations
• Depth-Based 3D Hand Pose Estimation: From Current Achievements to Future Goals (2017) — 46 citations
• Benchmarking Bias Mitigation Algorithms in Representation Learning through Fairness Metrics (2021) — 38 citations
• On Adversarial Mixup Resynthesis (2019) — 75 citations
• Distribution Matching Losses Can Hallucinate Features in Medical Image Translation (2018) — 352 citations
• Learning to Generate Samples from Noise through Infusion Training (2017) — 44 citations
• Theano: A Python framework for fast computation of mathematical expressions (2016) — 2,380 citations