Chaowei Xiao

Artificial Intelligence · Neural Information Processing Systems · 2014–2023

A researcher in the Science corpus: 17 papers; trusts 698 researchers and is trusted by 7 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 477 citations into it; #19,502 of 35,345 by trust here, #3,673 by citations
• Computer Vision and Pattern Recognition — 316 citations into it; #20,583 of 21,575 by trust here, #3,659 by citations
• Safety, Robustness & Interpretability — 224 citations into it; #1,347 of 2,251 by trust here, #367 by citations
• Signal Processing — 69 citations into it
• Model Architectures — 57 citations into it; #2,136 of 2,278 by trust here, #2,037 by citations
• Reinforcement Learning — 56 citations into it
• Natural Language Processing — 54 citations into it
• Generative Models — 51 citations into it

Whom Chaowei Xiao trusts overall

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

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

Who trusts Chaowei Xiao overall

1. Avishek Joey Bose
2. Yulong Cao
3. Eric Wong
4. Anand Bhattad
5. Daniel Kang
6. Han Xu
7. Bo Li
8. Huan Zhang
9. Tianyuan Zhang
10. Lei Ma

Papers from Chaowei Xiao's perspective

Ordered by how strongly the researchers Chaowei Xiao 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. Attention is All you Need (Neural Information Processing Systems)
5. ImageNet: A large-scale hierarchical image database
6. Long Short-Term Memory
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. ImageNet Large Scale Visual Recognition Challenge

Their papers

• Robust Physical-World Attacks on Deep Learning Visual Classification (2018) — 2,198 citations
• Diffusion Models for Adversarial Purification (2022) — 722 citations
• Voyager: An Open-Ended Embodied Agent with Large Language Models (2023) — 1,998 citations
• Generating Adversarial Examples with Adversarial Networks (2018) — 1,047 citations
• Towards Stable and Efficient Training of Verifiably Robust Neural Networks (2019) — 393 citations
• Long-Short Transformer: Efficient Transformers for Language and Vision (2021) — 175 citations
• Adversarial Sensor Attack on LiDAR-based Perception in Autonomous Driving (2019) — 667 citations
• Test-Time Prompt Tuning for Zero-Shot Generalization in Vision-Language Models (2022) — 552 citations
• Tagoram: real-time tracking of mobile RFID tags to high precision using COTS devices (2014) — 754 citations
• GenSLMs: Genome-scale language models reveal SARS-CoV-2 evolutionary dynamics (2022) — 142 citations
• Invisible for both Camera and LiDAR: Security of Multi-Sensor Fusion based Perception in Autonomous Driving Under Physical-World Attacks (2021) — 332 citations
• VoxFormer: Sparse Voxel Transformer for Camera-Based 3D Semantic Scene Completion (2023) — 428 citations
• Exploring the Limits of Domain-Adaptive Training for Detoxifying Large-Scale Language Models (2022) — 90 citations
• Robust Deep Reinforcement Learning against Adversarial Perturbations on State Observations (2020) — 405 citations
• Data Poisoning Attack against Unsupervised Node Embedding Methods (2018) — 70 citations
• Spatially Transformed Adversarial Examples (2018) — 572 citations
• Hardening Classifiers against Evasion: the Good, the Bad, and the Ugly (2017) — 9 citations