Hang Su
Artificial Intelligence · Computer Vision and Pattern Recognition · 2017–2023
A researcher in the Science corpus: 21 papers; trusts 1,929 researchers and is trusted by 58 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 949 citations into it; #4,861 of 35,345 by trust here, #2,568 by citations
- Computer Vision and Pattern Recognition — 810 citations into it; #4,504 of 21,575 by trust here, #1,850 by citations
- Meta & Continual Learning — 399 citations into it; #943 of 1,041 by trust here, #635 by citations
- Safety, Robustness & Interpretability — 235 citations into it; #456 of 2,251 by trust here, #445 by citations
- Representation & Self-Supervised Learning — 133 citations into it
- Model Architectures — 132 citations into it
- Optimization & Training — 103 citations into it
- Generative Models — 95 citations into it
Whom Hang Su trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Alex Krizhevsky
- Kaiming He
- Geoffrey E. Hinton
- Yoshua Bengio
- Diederik P. Kingma
- Christian Szegedy
- Yann LeCun
- Ross B. Girshick
- Ilya Sutskever
- Ian J. Goodfellow
Who trusts Hang Su overall
- Yinpeng Dong
- Tianyu Pang
- Vamsi Krishna Ithapu
- Naveed Akhtar
- Gabrielle Ras
- Shuyu Cheng
- Tianyuan Zhang
- Xiao Yang
- Micah Goldblum
- Sven Gowal
Papers from Hang Su's perspective
Ordered by how strongly the researchers Hang Su 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)
- Attention is All you Need (Neural Information Processing Systems)
- ImageNet: A large-scale hierarchical image database
- Gradient-based learning applied to document recognition
- Long Short-Term Memory
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
- Learning Multiple Layers of Features from Tiny Images
- ImageNet Large Scale Visual Recognition Challenge
Their papers
- Boosting Adversarial Attacks with Momentum (2017) — 3,379 citations
- Grounding DINO: Marrying DINO with Grounded Pre-Training for Open-Set Object Detection (2023) — 4,547 citations
- ProlificDreamer: High-Fidelity and Diverse Text-to-3D Generation with Variational Score Distillation (2023) — 1,425 citations
- DINO: DETR with Improved DeNoising Anchor Boxes for End-to-End Object Detection (2022) — 2,919 citations
- DAB-DETR: Dynamic Anchor Boxes are Better Queries for DETR (2022) — 1,335 citations
- Evading Defenses to Transferable Adversarial Examples by Translation-Invariant Attacks (2019) — 1,121 citations
- A Comprehensive Survey of Continual Learning: Theory, Method and Application (2023) — 1,490 citations
- All are Worth Words: A ViT Backbone for Diffusion Models (2022) — 631 citations
- Boosting Adversarial Training with Hypersphere Embedding (2020) — 170 citations
- Bag of Tricks for Adversarial Training (2020) — 306 citations
- Efficient Decision-Based Black-Box Adversarial Attacks on Face Recognition (2019) — 470 citations
- Reward Shaping via Meta-Learning (2019) — 80 citations
- Towards Interpretable Deep Neural Networks by Leveraging Adversarial Examples (2017) — 133 citations
- Incorporating neuro-inspired adaptability for continual learning in artificial intelligence (2023) — 101 citations
- GNOT: A General Neural Operator Transformer for Operator Learning (2023) — 432 citations
- DQ-DETR: Dual Query Detection Transformer for Phrase Extraction and Grounding (2022) — 45 citations
- Triple-Memory Networks: A Brain-Inspired Method for Continual Learning (2020) — 64 citations
- Benchmarking Adversarial Robustness on Image Classification (2020) — 304 citations
- Improving Black-box Adversarial Attacks with a Transfer-based Prior (2019) — 306 citations
- Geometric Universality of Adversarial Examples in Deep Learning (2018)