Yanzhi Wang

Artificial Intelligence · European Conference on Computer Vision · 2017–2022

A researcher in the Science corpus: 17 papers; trusts 656 researchers and is trusted by 8 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 313 citations into it; #28,433 of 35,345 by trust here, #15,004 by citations
• Computer Vision and Pattern Recognition — 278 citations into it; #17,477 of 21,575 by trust here, #9,872 by citations
• Efficiency & Compression — 89 citations into it
• Model Architectures — 78 citations into it
• Safety, Robustness & Interpretability — 67 citations into it
• Optimization & Training — 48 citations into it; #2,261 of 2,601 by trust here, #2,313 by citations
• Reinforcement Learning — 48 citations into it
• Hardware and Architecture — 26 citations into it; #1,190 of 1,551 by trust here, #792 by citations

Whom Yanzhi Wang trusts overall

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

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

Who trusts Yanzhi Wang overall

1. Chaoyun Zhang
2. Liang Yu
3. Qian Mao
4. Paul Patras

Papers from Yanzhi Wang's perspective

Ordered by how strongly the researchers Yanzhi Wang 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. Gradient-based learning applied to document recognition
5. Attention is All you Need (Neural Information Processing Systems)
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

• Adversarial Meta-Learning (2018) — 46 citations
• Learn-Prune-Share for Lifelong Learning (2020) — 50 citations
• Adversarial T-Shirt! Evading Person Detectors in a Physical World (2019) — 428 citations
• Feature Distillation: DNN-Oriented JPEG Compression Against Adversarial Examples (2018) — 336 citations
• A deep reinforcement learning based framework for power-efficient resource allocation in cloud RANs (2017) — 243 citations
• Deep reinforcement learning for building HVAC control (2017) — 516 citations
• Deep Reinforcement Learning for Dynamic Treatment Regimes on Medical Registry Data (2017) — 117 citations
• EfficientFormer: Vision Transformers at MobileNet Speed (2022) — 661 citations
• F8Net: Fixed-Point 8-bit Only Multiplication for Network Quantization (2022) — 59 citations
• Rethinking Vision Transformers for MobileNet Size and Speed (2022) — 327 citations
• Adversarial Robustness vs. Model Compression, or Both? (2019) — 181 citations
• Interpreting and Evaluating Neural Network Robustness (2019) — 59 citations
• Interpreting Adversarial Robustness: A View from Decision Surface in Input Space (2018) — 30 citations
• A Systematic DNN Weight Pruning Framework using Alternating Direction Method of Multipliers (2018) — 471 citations
• Experience-driven Networking: A Deep Reinforcement Learning based Approach (2018) — 414 citations
• Spatiotemporal modeling and prediction in cellular networks: A big data enabled deep learning approach (2017) — 353 citations
• A Hierarchical Framework of Cloud Resource Allocation and Power Management Using Deep Reinforcement Learning (2017) — 279 citations