Xiong Zhou
18 papers · 2019–2026 · 9 venues · across top CS/AI venues
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Achievements
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Research topics
Machine Learning
(15)
Core Methods
(10)
Deep Learning
(9)
Learning Types Β· Machine Learning
(8)
Optimization & Theory Β· Machine Learning
(7)
Artificial Intelligence
(4)
Computer Vision
(3)
Architectures
(3)
Learning Types Β· Deep Learning
(3)
Techniques
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Core AI
(2)
Learning Paradigms
(2)
Models
(2)
Optimization & Theory Β· Deep Learning
(2)
Keywords
deep neural network
(6)
label noise
(4)
noisy label
(3)
loss function
(3)
noisy label learning
(2)
semi-supervised learning
(2)
margin-based loss
(2)
gradient descent
(2)
asymmetric loss
(2)
transfer learning
(1)
self-supervised learning
(1)
neural network training
(1)
knowledge distillation
(1)
batch normalization
(1)
neural tangent kernel
(1)
prototype learning
(1)
feature transformation
(1)
label propagation
(1)
image reconstruction
(1)
test-time adaptation
(1)
Papers
Variation-Bounded Loss for Noise-Tolerant Learning
AAAI 2026
Joint Asymmetric Loss for Learning with Noisy Labels
ICCV 2025
Proposer-Agent-Evaluator (PAE): Autonomous Skill Discovery For Foundation Model Internet Agents
ICML 2025
Robust Test-Time Adaptation for Single Image Denoising Using Deep Gaussian Prior
ICCV 2025
Ξ΅-Softmax: Approximating One-Hot Vectors for Mitigating Label Noise
NeurIPS 2024
Neural Field Classifiers via Target Encoding and Classification Loss
ICLR 2024
Socratic Human Feedback (SoHF): Expert Steering Strategies for LLM Code Generation
EMNLP 2024
Variance-enlarged Poisson Learning for Graph-based Semi-Supervised Learning with Extremely Sparse Labeled Data
ICLR 2024
ViGoR: Improving Visual Grounding of Large Vision Language Models with Fine-Grained Reward Modeling
ECCV 2024
Zero-Mean Regularized Spectral Contrastive Learning: Implicitly Mitigating Wrong Connections in Positive-Pair Graphs
ICLR 2024
No One Idles: Efficient Heterogeneous Federated Learning with Parallel Edge and Server Computation
ICML 2023
On the Dynamics Under the Unhinged Loss and Beyond
JMLR 2023
Exploiting Invariance in Training Deep Neural Networks
AAAI 2022
Learning Towards The Largest Margins
ICLR 2022
Prototype-Anchored Learning for Learning with Imperfect Annotations
ICML 2022
Asymmetric Loss Functions for Learning with Noisy Labels
ICML 2021
Learning With Noisy Labels via Sparse Regularization
ICCV 2021
d-SNE: Domain Adaptation Using Stochastic Neighborhood Embedding
CVPR 2019