Songze Li
Artificial Intelligence · with Avestimehr · 2015–2020
A researcher in the Science corpus: 8 papers; trusts 55 researchers and is trusted by 21 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 194 citations into it; #15,696 of 35,345 by trust here, #7,945 by citations
- Computer Networks and Communications — 74 citations into it; #1,438 of 9,886 by trust here, #1,475 by citations
- Computer Vision and Pattern Recognition — 60 citations into it
- Optimization & Training — 55 citations into it
- ML Systems & Hardware — 45 citations into it
- Electrical and Electronic Engineering — 29 citations into it
- Information Systems — 27 citations into it; #7,686 of 8,771 by trust here, #2,667 by citations
- Computational Mechanics — 23 citations into it
Whom Songze Li trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Mohammad Ali Maddah-Ali
- Muthu Dayalan
- Alex Krizhevsky
- Amir Salman Avestimehr
- Rashish Tandon
- Geoffrey E. Hinton
- Yann LeCun
- Urs Niesen
- Kaiming He
- Yoshua Bengio
Who trusts Songze Li overall
- Min Ye
- Qian Yu
- Amir Salman Avestimehr
- Amirhossein Reisizadeh
- Mohammad Ali Maddah-Ali
- Yuanming Shi
- Sanghamitra Dutta
- Zachary B. Charles
- Emmanuel Abbe
- Seyed Mohammadreza Mousavi Kalan
Papers from Songze Li's perspective
Ordered by how strongly the researchers Songze Li trusts endorse each paper. Same corpus, a different order for every researcher.
- ImageNet classification with deep convolutional neural networks
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- Gradient-based learning applied to document recognition
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- Learning Multiple Layers of Features from Tiny Images
- ImageNet: A large-scale hierarchical image database
- Long Short-Term Memory
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
- Attention is All you Need (Neural Information Processing Systems)
- ImageNet Large Scale Visual Recognition Challenge
Their papers
- FedML: A Research Library and Benchmark for Federated Machine Learning (2020) — 716 citations
- Polynomially Coded Regression: Optimal Straggler Mitigation via Data Encoding (2018) — 49 citations
- GradiVeQ: Vector Quantization for Bandwidth-Efficient Gradient Aggregation in Distributed CNN Training (2018) — 70 citations
- Pipe-SGD: A Decentralized Pipelined SGD Framework for Distributed Deep Net Training (2018) — 112 citations
- Coded TeraSort (2017) — 54 citations
- Near-Optimal Straggler Mitigation for Distributed Gradient Methods (2017) — 103 citations
- A Fundamental Tradeoff Between Computation and Communication in Distributed Computing (2016) — 504 citations
- Coded MapReduce (2015) — 173 citations