:: Library Catalog

Cover Image

Saved in:

Bibliographic Details
Main Authors:	Obi, Gakuto, Saito, Ayato, Sasaki, Yuto, Kato, Tsuyoshi
Format:	Preprint
Published:	2025
Subjects:	Machine Learning
Online Access:	https://arxiv.org/abs/2501.07794
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

On Mixup Regularization
by: Carratino, Luigi, et al.
Published: (2020)

Adversarial Mixup Unlearning
by: Peng, Zhuoyi, et al.
Published: (2025)

Mixup Regularization: A Probabilistic Perspective
by: El-Laham, Yousef, et al.
Published: (2025)

Augment on Manifold: Mixup Regularization with UMAP
by: El-Laham, Yousef, et al.
Published: (2023)

Learning Fair Robustness via Domain Mixup
by: Zhong, Meiyu, et al.
Published: (2024)

Tailoring Mixup to Data for Calibration
by: Bouniot, Quentin, et al.
Published: (2023)

On the Equivalence of Graph Convolution and Mixup
by: Han, Xiaotian, et al.
Published: (2023)

Exact Subgraph Isomorphism Network with Mixed $L_{0,2}$ Norm Constraint for Predictive Graph Mining
by: Kojima, Taiga, et al.
Published: (2025)

ID-MixGCL: Identity Mixup for Graph Contrastive Learning
by: Zhang, Gehang, et al.
Published: (2023)

Pushing Boundaries: Mixup's Influence on Neural Collapse
by: Fisher, Quinn, et al.
Published: (2024)

Learning Gradient-based Mixup with Extrapolation toward Flatter Minima for Domain Generalization
by: Peng, Danni, et al.
Published: (2022)

Mixup Augmentation with Multiple Interpolations
by: Shen, Lifeng, et al.
Published: (2024)

Region Mixup
by: Saha, Saptarshi, et al.
Published: (2024)

On a construction of stable maps from 3-manifolds into surfaces
by: Kato, Gakuto
Published: (2025)

On the Effectiveness of Hybrid Pooling in Mixup-Based Graph Learning for Language Processing
by: Dong, Zeming, et al.
Published: (2022)

Quantum-Informed Contrastive Learning with Dynamic Mixup Augmentation for Class-Imbalanced Expert Systems
by: Jahin, Md Abrar, et al.
Published: (2025)

SAMix: Calibrated and Accurate Continual Learning via Sphere-Adaptive Mixup and Neural Collapse
by: Dang, Trung-Anh, et al.
Published: (2025)

Dual-level Mixup for Graph Few-shot Learning with Fewer Tasks
by: Liu, Yonghao, et al.
Published: (2025)

CAARMA: Class Augmentation with Adversarial Mixup Regularization
by: Baali, Massa, et al.
Published: (2025)

RegMixMatch: Optimizing Mixup Utilization in Semi-Supervised Learning
by: Han, Haorong, et al.
Published: (2024)

LC-Tsallis-INF: Generalized Best-of-Both-Worlds Linear Contextual Bandits
by: Kato, Masahiro, et al.
Published: (2024)

Asymptotic Midpoint Mixup for Margin Balancing and Moderate Broadening
by: Kim, Hoyong, et al.
Published: (2024)

Single Domain Generalization with Model-aware Parametric Batch-wise Mixup
by: Heidari, Marzi, et al.
Published: (2025)

Communication Efficient Federated Learning with Linear Convergence on Heterogeneous Data
by: Liu, Jie, et al.
Published: (2025)

Convergence Analysis for Learning Orthonormal Deep Linear Neural Networks
by: Qin, Zhen, et al.
Published: (2023)

A Survey on Mixup Augmentations and Beyond
by: Jin, Xin, et al.
Published: (2024)

Comparative Study of Neural Network Methods for Solving Topological Solitons
by: Hashimoto, Koji, et al.
Published: (2024)

FedSM: Robust Semantics-Guided Feature Mixup for Bias Reduction in Federated Learning with Long-Tail Data
by: Zhang, Jingrui, et al.
Published: (2025)

A Generalized Theory of Mixup for Structure-Preserving Synthetic Data
by: Lee, Chungpa, et al.
Published: (2025)

AGMixup: Adaptive Graph Mixup for Semi-supervised Node Classification
by: Lu, Weigang, et al.
Published: (2024)

Mixup Domain Adaptations for Dynamic Remaining Useful Life Predictions
by: Furqon, Muhammad Tanzil, et al.
Published: (2024)

MathMixup: Boosting LLM Mathematical Reasoning with Difficulty-Controllable Data Synthesis and Curriculum Learning
by: Li, Xuchen, et al.
Published: (2026)

Towards Optimization and Model Selection for Domain Generalization: A Mixup-guided Solution
by: Lu, Wang, et al.
Published: (2022)

Transfer Learning and Mixup for Fine-Grained Few-Shot Fungi Classification
by: Tam, Jason Kahei, et al.
Published: (2025)

Convergence of Distributionally Robust Q-Learning with Linear Function Approximation
by: Mandal, Saptarshi, et al.
Published: (2025)

A Study on Unsupervised Anomaly Detection and Defect Localization using Generative Model in Ultrasonic Non-Destructive Testing
by: Ando, Yusaku, et al.
Published: (2024)

Almost Sure Convergence of Linear Temporal Difference Learning with Arbitrary Features
by: Wang, Jiuqi, et al.
Published: (2024)

Convergent Linear Representations of Emergent Misalignment
by: Soligo, Anna, et al.
Published: (2025)

Bridging the Physics-Data Gap with FNO-Guided Conditional Flow Matching: Designing Inductive Bias through Hierarchical Physical Constraints
by: Okita, Tsuyoshi
Published: (2025)

Machine-learning emergent spacetime from linear response in future tabletop quantum gravity experiments
by: Hashimoto, Koji, et al.
Published: (2024)