Wencong Xiao

Artificial Intelligence · 2018–2021

A researcher in the Science corpus: 7 papers; trusts 193 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 118 citations into it
• Computer Vision and Pattern Recognition — 117 citations into it
• Efficiency & Compression — 52 citations into it
• Model Architectures — 42 citations into it
• Computer Networks and Communications — 38 citations into it
• Optimization & Training — 33 citations into it
• Large Language Models — 10 citations into it
• Media Technology — 8 citations into it

Whom Wencong Xiao trusts overall

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

1. Alex Krizhevsky
2. Kaiming He
3. Geoffrey E. Hinton
4. Ilya Sutskever
5. Diederik P. Kingma
6. Yoshua Bengio
7. Karen Simonyan
8. Yann LeCun
9. Christian Szegedy
10. X. Zhang

Who trusts Wencong Xiao overall

1. Myeongjae Jeon
2. Peifeng Yu
3. Zhihao Bai
4. D. Narayanan
5. Mosharaf Chowdhury
6. Guanhua Wang
7. Shubham Chaudhary
8. Liam Li
9. Juncheng Gu
10. Amar Phanishayee

Papers from Wencong Xiao's perspective

Ordered by how strongly the researchers Wencong Xiao trusts endorse each paper. Same corpus, a different order for every researcher.

1. ImageNet classification with deep convolutional neural networks
2. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
3. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
4. Attention is All you Need (Neural Information Processing Systems)
5. Long Short-Term Memory
6. ImageNet: A large-scale hierarchical image database
7. Gradient-based learning applied to document recognition
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. Going deeper with convolutions

Their papers

• SeerNet: Predicting Convolutional Neural Network Feature-Map Sparsity Through Low-Bit Quantization (2019) — 80 citations
• Analysis of Large-Scale Multi-Tenant GPU Clusters for DNN Training Workloads (2019) — 471 citations
• Zico: Efficient GPU Memory Sharing for Concurrent DNN Training (2021) — 63 citations
• AntMan: Dynamic Scaling on GPU Clusters for Deep Learning (2020) — 274 citations
• Gandiva: Introspective Cluster Scheduling for Deep Learning (2018) — 605 citations
• Multi-tenant GPU Clusters for Deep Learning Workloads: Analysis and Implications (2018) — 71 citations
• Balanced Sparsity for Efficient DNN Inference on GPU (2018) — 99 citations