Hanie Sedghi

Artificial Intelligence · 2014–2025

A researcher in the Science corpus: 16 papers; trusts 504 researchers and is trusted by 54 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 221 citations into it; #14,313 of 35,345 by trust here, #9,170 by citations
• Computer Vision and Pattern Recognition — 110 citations into it; #16,541 of 21,575 by trust here, #19,310 by citations
• Scaling & Learning Theory — 64 citations into it; #174 of 428 by trust here, #300 by citations
• Optimization & Training — 50 citations into it; #902 of 2,601 by trust here, #988 by citations
• Statistical Learning Theory — 37 citations into it; #507 of 1,038 by trust here, #424 by citations
• Representation & Self-Supervised Learning — 34 citations into it; #1,813 of 3,145 by trust here, #1,982 by citations
• Model Architectures — 30 citations into it; #673 of 2,278 by trust here, #1,433 by citations
• Computational Mechanics — 25 citations into it; #3,179 of 10,001 by trust here, #4,749 by citations

Whom Hanie Sedghi trusts overall

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

1. Kaiming He
2. Geoffrey E. Hinton
3. Yoshua Bengio
4. Yann LeCun
5. Alex Krizhevsky
6. Karen Simonyan
7. Diederik P. Kingma
8. Aapo Hyvärinen
9. Behnam Neyshabur
10. Peter L. Bartlett

Who trusts Hanie Sedghi overall

1. Behnam Neyshabur
2. Philip M. Long
3. Niladri S. Chatterji
4. Yuchen Zhang
5. Hossein Mobahi
6. Alberto Bietti
7. Florian Graf
8. Jean Kossaifi
9. Jens Behrmann
10. Majid Janzamin

Papers from Hanie Sedghi's perspective

Ordered by how strongly the researchers Hanie Sedghi 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. 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. Learning Multiple Layers of Features from Tiny Images
10. ImageNet Large Scale Visual Recognition Challenge

Their papers

• What is being transferred in transfer learning? (2020) — 631 citations
• The Role of Permutation Invariance in Linear Mode Connectivity of Neural Networks (2021) — 334 citations
• Teaching Algorithmic Reasoning via In-context Learning (2022) — 135 citations
• Generalization bounds for deep convolutional neural networks (2019) — 102 citations
• The Singular Values of Convolutional Layers (2018) — 225 citations
• Gemini 2.5: Pushing the Frontier with Advanced Reasoning, Multimodality, Long Context, and Next Generation Agentic Capabilities (2025) — 3,609 citations
• Beyond Human Data: Scaling Self-Training for Problem-Solving with Language Models (2023) — 337 citations
• The intriguing role of module criticality in the generalization of deep networks (2019) — 60 citations
• SysML: The New Frontier of Machine Learning Systems (2019) — 23 citations
• MLSys: The New Frontier of Machine Learning Systems (2019) — 25 citations
• Size-free generalization bounds for convolutional neural networks (2019) — 27 citations
• Beating the Perils of Non-Convexity: Guaranteed Training of Neural Networks using Tensor Methods (2017) — 245 citations
• Supplementary Material for the AISTATS 2016 Paper: Provable Tensor Methods for Learning Mixtures of Generalized Linear Models (2016) — 55 citations
• Learning Mixed Membership Community Models in Social Tagging Networks through Tensor Methods (2015) — 11 citations
• Generalization Bounds for Neural Networks through Tensor Factorization (2015) — 26 citations
• Score Function Features for Discriminative Learning: Matrix and Tensor Framework (2014) — 45 citations