Saved in:
| Main Authors: | Wang, Jialiang, Zhang, Ning, Di, Shimin, Wang, Ruidong, Chen, Lei |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2408.06743 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
Weakly-Supervised Contrastive Learning for Imprecise Class Labels
by: Zhou, Zi-Hao, et al.
Published: (2025)
by: Zhou, Zi-Hao, et al.
Published: (2025)
Proficient Graph Neural Network Design by Accumulating Knowledge on Large Language Models
by: Wang, Jialiang, et al.
Published: (2024)
by: Wang, Jialiang, et al.
Published: (2024)
Beyond Model Base Retrieval: Weaving Knowledge to Master Fine-grained Neural Network Design
by: Wang, Jialiang, et al.
Published: (2025)
by: Wang, Jialiang, et al.
Published: (2025)
Theoretical Proportion Label Perturbation for Learning from Label Proportions in Large Bags
by: Kubo, Shunsuke, et al.
Published: (2024)
by: Kubo, Shunsuke, et al.
Published: (2024)
Learning to Compose for Cross-domain Agentic Workflow Generation
by: Wang, Jialiang, et al.
Published: (2026)
by: Wang, Jialiang, et al.
Published: (2026)
Leveraging Label Proportion Prior for Class-Imbalanced Semi-Supervised Learning
by: Akiba, Kohki, et al.
Published: (2026)
by: Akiba, Kohki, et al.
Published: (2026)
Optimistic Rates for Learning from Label Proportions
by: Li, Gene, et al.
Published: (2024)
by: Li, Gene, et al.
Published: (2024)
Proportion Estimation by Masked Learning from Label Proportion
by: Okuo, Takumi, et al.
Published: (2024)
by: Okuo, Takumi, et al.
Published: (2024)
Can Class-Priors Help Single-Positive Multi-Label Learning?
by: Liu, Biao, et al.
Published: (2023)
by: Liu, Biao, et al.
Published: (2023)
Improving Multi-Label Contrastive Learning by Leveraging Label Distribution
by: Chen, Ning, et al.
Published: (2025)
by: Chen, Ning, et al.
Published: (2025)
Learning from Emergence: A Study on Proactively Inhibiting the Monosemantic Neurons of Artificial Neural Networks
by: Wang, Jiachuan, et al.
Published: (2023)
by: Wang, Jiachuan, et al.
Published: (2023)
Learning from Label Proportions and Covariate-shifted Instances
by: Singh, Sagalpreet, et al.
Published: (2024)
by: Singh, Sagalpreet, et al.
Published: (2024)
Learning from Label Proportions with Dual-proportion Constraints
by: Ma, Tianhao, et al.
Published: (2026)
by: Ma, Tianhao, et al.
Published: (2026)
Search to Fine-tune Pre-trained Graph Neural Networks for Graph-level Tasks
by: Wang, Zhili, et al.
Published: (2023)
by: Wang, Zhili, et al.
Published: (2023)
A Selective Learning Method for Temporal Graph Continual Learning
by: Liu, Hanmo, et al.
Published: (2025)
by: Liu, Hanmo, et al.
Published: (2025)
Optimal Learning from Label Proportions with General Loss Functions
by: Applebaum, Lorne, et al.
Published: (2025)
by: Applebaum, Lorne, et al.
Published: (2025)
Nearly Optimal Sample Complexity for Learning with Label Proportions
by: Busa-Fekete, Robert, et al.
Published: (2025)
by: Busa-Fekete, Robert, et al.
Published: (2025)
An Augmentation Overlap Theory of Contrastive Learning
by: Zhang, Qi, et al.
Published: (2025)
by: Zhang, Qi, et al.
Published: (2025)
Cardinality Estimation on Hyper-relational Knowledge Graphs
by: Teng, Fei, et al.
Published: (2024)
by: Teng, Fei, et al.
Published: (2024)
Neural Tractability via Structure: Learning-Augmented Algorithms for Graph Combinatorial Optimization
by: Li, Jialiang, et al.
Published: (2025)
by: Li, Jialiang, et al.
Published: (2025)
Sharpness-aware Second-order Latent Factor Model for High-dimensional and Incomplete Data
by: Wang, Jialiang, et al.
Published: (2025)
by: Wang, Jialiang, et al.
Published: (2025)
An Unbiased Risk Estimator for Partial Label Learning with Augmented Classes
by: Hu, Jiayu, et al.
Published: (2024)
by: Hu, Jiayu, et al.
Published: (2024)
Time-Series Contrastive Learning against False Negatives and Class Imbalance
by: Jin, Xiyuan, et al.
Published: (2023)
by: Jin, Xiyuan, et al.
Published: (2023)
Hardness of Learning Boolean Functions from Label Proportions
by: Guruswami, Venkatesan, et al.
Published: (2024)
by: Guruswami, Venkatesan, et al.
Published: (2024)
When Noisy Labels Meet Class Imbalance on Graphs: A Graph Augmentation Method with LLM and Pseudo Label
by: Xia, Riting, et al.
Published: (2025)
by: Xia, Riting, et al.
Published: (2025)
Simple and Asymmetric Graph Contrastive Learning without Augmentations
by: Xiao, Teng, et al.
Published: (2023)
by: Xiao, Teng, et al.
Published: (2023)
Seismic Traveltime Tomography with Label-free Learning
by: Wang, Feng, et al.
Published: (2024)
by: Wang, Feng, et al.
Published: (2024)
Parametric Augmentation for Time Series Contrastive Learning
by: Zheng, Xu, et al.
Published: (2024)
by: Zheng, Xu, et al.
Published: (2024)
Enriching Knowledge Distillation with Intra-Class Contrastive Learning
by: Yuan, Hua, et al.
Published: (2025)
by: Yuan, Hua, et al.
Published: (2025)
Randomized Neural Network with Adaptive Forward Regularization for Online Task-free Class Incremental Learning
by: Wang, Junda, et al.
Published: (2025)
by: Wang, Junda, et al.
Published: (2025)
LLP-Bench: A Large Scale Tabular Benchmark for Learning from Label Proportions
by: Brahmbhatt, Anand, et al.
Published: (2023)
by: Brahmbhatt, Anand, et al.
Published: (2023)
REAL: Representation Enhanced Analytic Learning for Exemplar-free Class-incremental Learning
by: He, Run, et al.
Published: (2024)
by: He, Run, et al.
Published: (2024)
RxnNano:Training Compact LLMs for Chemical Reaction and Retrosynthesis Prediction via Hierarchical Curriculum Learning
by: Li, Ran, et al.
Published: (2026)
by: Li, Ran, et al.
Published: (2026)
Instances and Labels: Hierarchy-aware Joint Supervised Contrastive Learning for Hierarchical Multi-Label Text Classification
by: Yu, Simon, et al.
Published: (2023)
by: Yu, Simon, et al.
Published: (2023)
Forming Auxiliary High-confident Instance-level Loss to Promote Learning from Label Proportions
by: Ma, Tianhao, et al.
Published: (2024)
by: Ma, Tianhao, et al.
Published: (2024)
Quantum-Informed Contrastive Learning with Dynamic Mixup Augmentation for Class-Imbalanced Expert Systems
by: Jahin, Md Abrar, et al.
Published: (2025)
by: Jahin, Md Abrar, et al.
Published: (2025)
Learning from Label Proportions: Bootstrapping Supervised Learners via Belief Propagation
by: Havaldar, Shreyas, et al.
Published: (2023)
by: Havaldar, Shreyas, et al.
Published: (2023)
On the Learning with Augmented Class via Forests
by: Xu, Fan, et al.
Published: (2025)
by: Xu, Fan, et al.
Published: (2025)
Motif-aware Riemannian Graph Neural Network with Generative-Contrastive Learning
by: Sun, Li, et al.
Published: (2024)
by: Sun, Li, et al.
Published: (2024)
Edge Contrastive Learning: An Augmentation-Free Graph Contrastive Learning Model
by: Li, Yujun, et al.
Published: (2024)
by: Li, Yujun, et al.
Published: (2024)
Similar Items
-
Weakly-Supervised Contrastive Learning for Imprecise Class Labels
by: Zhou, Zi-Hao, et al.
Published: (2025) -
Proficient Graph Neural Network Design by Accumulating Knowledge on Large Language Models
by: Wang, Jialiang, et al.
Published: (2024) -
Beyond Model Base Retrieval: Weaving Knowledge to Master Fine-grained Neural Network Design
by: Wang, Jialiang, et al.
Published: (2025) -
Theoretical Proportion Label Perturbation for Learning from Label Proportions in Large Bags
by: Kubo, Shunsuke, et al.
Published: (2024) -
Learning to Compose for Cross-domain Agentic Workflow Generation
by: Wang, Jialiang, et al.
Published: (2026)