Hamed Hassani

Artificial Intelligence · 2018–2024

A researcher in the Science corpus: 10 papers; trusts 161 researchers and is trusted by 4 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 327 citations into it; #31,490 of 35,345 by trust here, #6,720 by citations
• Safety, Robustness & Interpretability — 125 citations into it
• Computer Networks and Communications — 93 citations into it; #7,518 of 9,886 by trust here, #5,518 by citations
• Optimization & Training — 82 citations into it
• Computer Vision and Pattern Recognition — 68 citations into it
• Computational Mechanics — 46 citations into it
• Federated & Distributed Learning — 39 citations into it; #294 of 319 by trust here, #190 by citations
• Electrical and Electronic Engineering — 28 citations into it

Whom Hamed Hassani trusts overall

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

1. Geoffrey E. Hinton
2. Yoshua Bengio
3. Alex Krizhevsky
4. Yann LeCun
5. Ian J. Goodfellow
6. Diederik P. Kingma
7. Kaiming He
8. Christian Szegedy
9. Ilya Sutskever
10. Radford M. Neal

Who trusts Hamed Hassani overall

1. Amirhossein Reisizadeh
2. Jaehoon Oh
3. Aryan Mokhtari
4. Yue Tan
5. Jianqing Zhang

Papers from Hamed Hassani's perspective

Ordered by how strongly the researchers Hamed Hassani 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. Long Short-Term Memory
6. Attention is All you Need (Neural Information Processing Systems)
7. ImageNet: A large-scale hierarchical image database
8. Learning Multiple Layers of Features from Tiny Images
9. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
10. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• Exploiting Shared Representations for Personalized Federated Learning (2021) — 1,173 citations
• Jailbreaking Black Box Large Language Models in Twenty Queries (2023) — 1,568 citations
• Efficient and Accurate Estimation of Lipschitz Constants for Deep Neural Networks (2019) — 570 citations
• FedPAQ: A Communication-Efficient Federated Learning Method with Periodic Averaging and Quantization (2019) — 974 citations
• Robust and Communication-Efficient Collaborative Learning (2019) — 98 citations
• JailbreakBench: An Open Robustness Benchmark for Jailbreaking Large Language Models (2024) — 556 citations
• SmoothLLM: Defending Large Language Models Against Jailbreaking Attacks (2023) — 521 citations
• Adversarial Robustness with Semi-Infinite Constrained Learning (2021) — 50 citations
• Precise Tradeoffs in Adversarial Training for Linear Regression (2020) — 124 citations
• An Exact Quantized Decentralized Gradient Descent Algorithm (2018) — 139 citations