Tongliang Liu
Artificial Intelligence · 2014–2023
A researcher in the Science corpus: 29 papers; trusts 1,502 researchers and is trusted by 57 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 543 citations into it; #14,467 of 35,345 by trust here, #4,772 by citations
- Computer Vision and Pattern Recognition — 492 citations into it; #8,124 of 21,575 by trust here, #3,680 by citations
- Representation & Self-Supervised Learning — 200 citations into it; #1,808 of 3,145 by trust here, #1,460 by citations
- Optimization & Training — 117 citations into it
- Statistical Learning Theory — 69 citations into it; #253 of 1,038 by trust here, #424 by citations
- Evaluation & Benchmarks — 58 citations into it
- Signal Processing — 56 citations into it
- Model Architectures — 53 citations into it
Whom Tongliang Liu trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Alex Krizhevsky
- Geoffrey E. Hinton
- Kaiming He
- Yoshua Bengio
- Yann LeCun
- Karen Simonyan
- Ilya Sutskever
- Diederik P. Kingma
- David G. Lowe
- Ross B. Girshick
Who trusts Tongliang Liu overall
- Dacheng Tao
- Xiaobo Xia
- Bo Han
- Mingming Gong
- Changxing Ding
- Curtis G. Northcutt
- Hwanjun Song
- Jessa Bekker
- Jiaxian Guo
- Hongxin Wei
Papers from Tongliang Liu's perspective
Ordered by how strongly the researchers Tongliang Liu 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)
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- Gradient-based learning applied to document recognition
- Attention is All you Need (Neural Information Processing Systems)
- 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)
- Learning Multiple Layers of Features from Tiny Images
- ImageNet Large Scale Visual Recognition Challenge
Their papers
- Deep Domain Generalization via Conditional Invariant Adversarial Networks (2018) — 831 citations
- Domain Generalization via Conditional Invariant Representations (2018) — 305 citations
- CRIS: CLIP-Driven Referring Image Segmentation (2021) — 540 citations
- On Compressing Deep Models by Low Rank and Sparse Decomposition (2017) — 482 citations
- Sub-center ArcFace: Boosting Face Recognition by Large-Scale Noisy Web Faces (2020) — 294 citations
- Transfer Learning with Label Noise (2017) — 32 citations
- Are Anchor Points Really Indispensable in Label-Noise Learning? (2019) — 461 citations
- Expressive power of parametrized quantum circuits (2020) — 326 citations
- DeepInception: Hypnotize Large Language Model to Be Jailbreaker (2023) — 398 citations
- Understanding and Improving Early Stopping for Learning with Noisy Labels (2021) — 298 citations
- Learning with Noisy Labels Revisited: A Study Using Real-World Human Annotations (2021) — 357 citations
- Robust early-learning: Hindering the memorization of noisy labels (2021) — 338 citations
- Two-Stream Deep Hashing With Class-Specific Centers for Supervised Image Search (2020) — 83 citations
- Domain Generalization via Entropy Regularization (2020) — 317 citations
- Heterogeneous Graph Attention Network for Unsupervised Multiple-Target Domain Adaptation (2020) — 153 citations
- LTF: A Label Transformation Framework for Correcting Label Shift (2020) — 43 citations
- Part-dependent Label Noise: Towards Instance-dependent Label Noise (2020) — 313 citations
- Orthogonal Deep Neural Networks (2019) — 164 citations
- Unsupervised Semantic-Preserving Adversarial Hashing for Image Search (2019) — 150 citations
- Why ResNet Works? Residuals Generalize (2019) — 373 citations