Ehsan Amid

Artificial Intelligence · 2016–2025

A researcher in the Science corpus: 7 papers; trusts 375 researchers and is trusted by 5 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 251 citations into it; #20,691 of 35,345 by trust here, #25,040 by citations
• Computer Vision and Pattern Recognition — 138 citations into it; #12,907 of 21,575 by trust here, #16,367 by citations
• Representation & Self-Supervised Learning — 77 citations into it; #2,084 of 3,145 by trust here, #3,009 by citations
• Optimization & Training — 48 citations into it
• Information Systems — 31 citations into it
• Computational Mechanics — 26 citations into it
• Signal Processing — 17 citations into it
• Statistics and Probability — 16 citations into it

Whom Ehsan Amid trusts overall

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

1. Geoffrey E. Hinton
2. L. V. D. van der Maaten
3. Alex Krizhevsky
4. Kaiming He
5. Yoshua Bengio
6. Yann LeCun
7. Ilya Sutskever
8. Diederik P. Kingma
9. Matthew D. Zeiler
10. Ross B. Girshick

Who trusts Ehsan Amid overall

1. J. Böhm
2. Yen-Chang Hsu
3. Yingfan Wang
4. Nicolas Papernot
5. Philipp Berens
6. Manfred K. Warmuth
7. Zhaoyang Lv
8. Cynthia Rudin
9. Anna C. Belkina
10. Ying-Fan Wang

Papers from Ehsan Amid's perspective

Ordered by how strongly the researchers Ehsan Amid 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. Gradient-based learning applied to document recognition
5. Attention is All you Need (Neural Information Processing Systems)
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. Learning Multiple Layers of Features from Tiny Images
10. ImageNet Large Scale Visual Recognition Challenge

Their papers

• Efficiently Identifying Task Groupings for Multi-Task Learning (2021) — 332 citations
• Gemini 2.5: Pushing the Frontier with Advanced Reasoning, Multimodality, Long Context, and Next Generation Agentic Capabilities (2025) — 3,609 citations
• Multi-task Learning (2020)
• TriMap: Large-scale Dimensionality Reduction Using Triplets (2019) — 171 citations
• Robust Bi-Tempered Logistic Loss Based on Bregman Divergences (2019) — 147 citations
• A more globally accurate dimensionality reduction method using triplets (2018) — 16 citations
• Semi-supervised Kernel Metric Learning Using Relative Comparisons (2016) — 10 citations