Cover Image

Learn To Learn More Precisely

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Cheng, Runxi, Wei, Yongxian, He, Xianglong, Zhu, Wanyun, Huang, Songsong, Yu, Fei Richard, Ma, Fei, Yuan, Chun
Format:	Preprint
Published:	2024
Subjects:	Machine Learning
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Whoever Started the Interference Should End It: Guiding Data-Free Model Merging via Task Vectors
by: Cheng, Runxi, et al.
Published: (2025)

Task Groupings Regularization: Data-Free Meta-Learning with Heterogeneous Pre-trained Models
by: Wei, Yongxian, et al.
Published: (2024)

FREE: Faster and Better Data-Free Meta-Learning
by: Wei, Yongxian, et al.
Published: (2024)

Task-Distributionally Robust Data-Free Meta-Learning
by: Hu, Zixuan, et al.
Published: (2023)

End-to-End Reaction Field Energy Modeling via Deep Learning based Voxel-to-voxel Transform
by: Wu, Yongxian, et al.
Published: (2024)

Less-to-More Generalization: Unlocking More Controllability by In-Context Generation
by: Wu, Shaojin, et al.
Published: (2025)

Lifelong Ensemble Learning based on Multiple Representations for Few-Shot Object Recognition
by: Kasaei, Hamidreza, et al.
Published: (2022)

Modeling Multi-Task Model Merging as Adaptive Projective Gradient Descent
by: Wei, Yongxian, et al.
Published: (2025)

ProtoGCD: Unified and Unbiased Prototype Learning for Generalized Category Discovery
by: Ma, Shijie, et al.
Published: (2025)

Unlocking Tuning-Free Few-Shot Adaptability in Visual Foundation Models by Recycling Pre-Tuned LoRAs
by: Hu, Zixuan, et al.
Published: (2024)

Analytical Discovery of Manifold with Machine Learning
by: Shen, Yafei, et al.
Published: (2025)

PP-TIL: Personalized Planning for Autonomous Driving with Instance-based Transfer Imitation Learning
by: Lin, Fangze, et al.
Published: (2024)

Mixed-Precision Federated Learning via Multi-Precision Over-The-Air Aggregation
by: Yuan, Jinsheng, et al.
Published: (2024)

Training Deep Learning Models with Norm-Constrained LMOs
by: Pethick, Thomas, et al.
Published: (2025)

Branch-Tuning: Balancing Stability and Plasticity for Continual Self-Supervised Learning
by: Liu, Wenzhuo, et al.
Published: (2024)

Constrained Reinforcement Learning Under Model Mismatch
by: Sun, Zhongchang, et al.
Published: (2024)

Operator-Guided Invariance Learning for Continuous Reinforcement Learning
by: Zhang, Zuyuan, et al.
Published: (2026)

MMEdge: Accelerating On-device Multimodal Inference via Pipelined Sensing and Encoding
by: Huang, Runxi, et al.
Published: (2025)

Global Convergence of Continual Learning on Non-IID Data
by: Zhu, Fei, et al.
Published: (2025)

Multi-Task Model Merging via Adaptive Weight Disentanglement
by: Xiong, Feng, et al.
Published: (2024)

SAMPa: Sharpness-aware Minimization Parallelized
by: Xie, Wanyun, et al.
Published: (2024)

MaD-Mix: Multi-Modal Data Mixtures via Latent Space Coupling for Vision-Language Model Training
by: Xie, Wanyun, et al.
Published: (2026)

Fewer is More: A Deep Graph Metric Learning Perspective Using Fewer Proxies
by: Zhu, Yuehua, et al.
Published: (2020)

Distribution Shift Is Key to Learning Invariant Prediction
by: Zheng, Hong, et al.
Published: (2026)

Exploiting Defenses against GAN-Based Feature Inference Attacks in Federated Learning
by: Luo, Xinjian, et al.
Published: (2020)

Beyond Retention: Orchestrating Structural Safety and Plasticity in Continual Learning for LLMs
by: Meng, Fei
Published: (2026)

Uncertainty Quantification via Hölder Divergence for Multi-View Representation Learning
by: Zhang, Yan, et al.
Published: (2024)

CFO: Learning Continuous-Time PDE Dynamics via Flow-Matched Neural Operators
by: Hou, Xianglong, et al.
Published: (2025)

PASS++: A Dual Bias Reduction Framework for Non-Exemplar Class-Incremental Learning
by: Zhu, Fei, et al.
Published: (2024)

VisualRWKV: Exploring Recurrent Neural Networks for Visual Language Models
by: Hou, Haowen, et al.
Published: (2024)

Chorus: Harmonizing Context and Sensing Signals for Data-Free Model Customization in IoT
by: Zhang, Liyu, et al.
Published: (2025)

GPG: A Simple and Strong Reinforcement Learning Baseline for Model Reasoning
by: Chu, Xiangxiang, et al.
Published: (2025)

OLMA: One Loss for More Accurate Time Series Forecasting
by: Shi, Tianyi, et al.
Published: (2025)

A Huber Loss Minimization Approach to Byzantine Robust Federated Learning
by: Zhao, Puning, et al.
Published: (2023)

On-Chip Hardware-Aware Quantization for Mixed Precision Neural Networks
by: Huang, Wei, et al.
Published: (2023)

Automated Design and Optimization of Distributed Filtering Circuits via Reinforcement Learning
by: Gao, Peng, et al.
Published: (2024)

Physics-Informed Extreme Learning Machine (PIELM): Opportunities and Challenges
by: Yang, He, et al.
Published: (2025)

Learning with Complementary Labels Revisited: The Selected-Completely-at-Random Setting Is More Practical
by: Wang, Wei, et al.
Published: (2023)

Less is More: Towards Simple Graph Contrastive Learning
by: Zhao, Yanan, et al.
Published: (2025)

Chameleon: A Flexible Data-mixing Framework for Language Model Pretraining and Finetuning
by: Xie, Wanyun, et al.
Published: (2025)