Farzin Haddadpour
Artificial Intelligence · 2017–2020
A researcher in the Science corpus: 6 papers; trusts 112 researchers and is trusted by 13 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 146 citations into it; #12,080 of 35,345 by trust here, #12,387 by citations
- Optimization & Training — 106 citations into it; #1,077 of 2,601 by trust here, #1,178 by citations
- Computer Networks and Communications — 45 citations into it
- Federated & Distributed Learning — 44 citations into it; #144 of 319 by trust here, #250 by citations
- Computational Mechanics — 42 citations into it; #3,912 of 10,001 by trust here, #2,942 by citations
- Computer Vision and Pattern Recognition — 36 citations into it
- ML Systems & Hardware — 33 citations into it; #440 of 575 by trust here, #485 by citations
- Efficiency & Compression — 25 citations into it
Whom Farzin Haddadpour trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Sebastian U. Stich
- Hao Yu
- L. Eon Bottou
- Alex Krizhevsky
- Jianyu Wang
- H. Brendan McMahan
- Geoffrey E. Hinton
- Kaiming He
- Fan Zhou
- Yoshua Bengio
Who trusts Farzin Haddadpour overall
- Jianyu Wang
- Yae Jee Cho
- Mohammad Mahdi Kamani
- Yuyang Deng
- Mehrdad Mahdavi
- John Nguyen
- Grigory Malinovsky
- Xinmeng Huang
- Gauri Joshi
- Dmitrii Avdiukhin
Papers from Farzin Haddadpour's perspective
Ordered by how strongly the researchers Farzin Haddadpour trusts endorse each paper. Same corpus, a different order for every researcher.
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- ImageNet classification with deep convolutional neural networks
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- Gradient-based learning applied to document recognition
- Long Short-Term Memory
- Learning Multiple Layers of Features from Tiny Images
- ImageNet: A large-scale hierarchical image database
- Attention is All you Need (Neural Information Processing Systems)
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
- Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
Their papers
- Local SGD with Periodic Averaging: Tighter Analysis and Adaptive Synchronization (2019) — 228 citations
- Federated Learning with Compression: Unified Analysis and Sharp Guarantees (2020) — 337 citations
- On the Convergence of Local Descent Methods in Federated Learning (2019) — 316 citations
- Trading Redundancy for Communication: Speeding up Distributed SGD for Non-convex Optimization (2019) — 72 citations
- Cross-Iteration Coded Computing (2018) — 15 citations
- On the optimal recovery threshold of coded matrix multiplication (2017) — 318 citations