Image de couverture de livre

A Theoretical Analysis of Self-Supervised Learning for Vision Transformers

Fuente: arXiv

Enregistré dans:

Détails bibliographiques
Auteurs principaux:	Huang, Yu, Wen, Zixin, Chi, Yuejie, Liang, Yingbin
Format:	Preprint
Publié:	2024
Sujets:	Machine Learning Optimization and Control
Accès en ligne:	Acceder al recurso
Tags:	Ajouter un tag Pas de tags, Soyez le premier à ajouter un tag!

Documents similaires

Agentic Transformers Provably Learn to Search via Reinforcement Learning
par: Yang, Tong, et autres
Publié: (2026)

In-Context Learning with Representations: Contextual Generalization of Trained Transformers
par: Yang, Tong, et autres
Publié: (2024)

The Implicit Curriculum: Learning Dynamics in RL with Verifiable Rewards
par: Huang, Yu, et autres
Publié: (2026)

Multi-head Transformers Provably Learn Symbolic Multi-step Reasoning via Gradient Descent
par: Yang, Tong, et autres
Publié: (2025)

Transformers Provably Learn Chain-of-Thought Reasoning with Length Generalization
par: Huang, Yu, et autres
Publié: (2025)

The Sample-Communication Complexity Trade-off in Federated Q-Learning
par: Salgia, Sudeep, et autres
Publié: (2024)

Preconditioning Benefits of Spectral Orthogonalization in Muon
par: Ma, Jianhao, et autres
Publié: (2026)

Exploration from a Primal-Dual Lens: Value-Incentivized Actor-Critic Methods for Sample-Efficient Online RL
par: Yang, Tong, et autres
Publié: (2025)

Statistical and Algorithmic Foundations of Reinforcement Learning
par: Chi, Yuejie, et autres
Publié: (2025)

Communication-Efficient Federated Optimization over Semi-Decentralized Networks
par: Wang, He, et autres
Publié: (2023)

Is Q-Learning Minimax Optimal? A Tight Sample Complexity Analysis
par: Li, Gen, et autres
Publié: (2021)

The Power of Preconditioning in Overparameterized Low-Rank Matrix Sensing
par: Xu, Xingyu, et autres
Publié: (2023)

Self-Supervised Learning of Iterative Solvers for Constrained Optimization
par: Lüken, Lukas, et autres
Publié: (2024)

Beyond Expectations: Learning with Stochastic Dominance Made Practical
par: Cen, Shicong, et autres
Publié: (2024)

A Retention-Centric Framework for Continual Learning with Guaranteed Model Developmental Safety
par: Li, Gang, et autres
Publié: (2024)

Self-Supervised Penalty-Based Learning for Robust Constrained Optimization
par: Benslimane, Wyame, et autres
Publié: (2025)

Reevaluating Theoretical Analysis Methods for Optimization in Deep Learning
par: Tran, Hoang, et autres
Publié: (2024)

Breaking the Sample Size Barrier in Model-Based Reinforcement Learning with a Generative Model
par: Li, Gen, et autres
Publié: (2020)

Theoretical Analysis on how Learning Rate Warmup Accelerates Convergence
par: Liu, Yuxing, et autres
Publié: (2025)

Accelerating Convergence of Score-Based Diffusion Models, Provably
par: Li, Gen, et autres
Publié: (2024)

Gradient Methods with Online Scaling Part I. Theoretical Foundations
par: Gao, Wenzhi, et autres
Publié: (2025)

Vertical Federated Learning with Missing Features During Training and Inference
par: Valdeira, Pedro, et autres
Publié: (2024)

Self-Supervised Learning for Large-Scale Preventive Security Constrained DC Optimal Power Flow
par: Park, Seonho, et autres
Publié: (2023)

Theoretical Analysis of Heteroscedastic Gaussian Processes with Posterior Distributions
par: Ito, Yuji
Publié: (2024)

Supervised Feature Compression based on Counterfactual Analysis
par: Piccialli, Veronica, et autres
Publié: (2022)

How Transformers Get Rich: Approximation and Dynamics Analysis
par: Wang, Mingze, et autres
Publié: (2024)

Towards Robust Learning to Optimize with Theoretical Guarantees
par: Song, Qingyu, et autres
Publié: (2025)

A Theoretical and Experimental Study of a Novel Adaptive Learning Algorithm
par: Kumari, Sakshi, et autres
Publié: (2026)

A Multi-Token Coordinate Descent Method for Semi-Decentralized Vertical Federated Learning
par: Valdeira, Pedro, et autres
Publié: (2023)

Control Theoretic Approach to Fine-Tuning and Transfer Learning
par: Bayram, Erkan, et autres
Publié: (2024)

A Control Theoretic Framework for Adaptive Gradient Optimizers in Machine Learning
par: Chakrabarti, Kushal, et autres
Publié: (2022)

Fast Computation of Optimal Transport via Entropy-Regularized Extragradient Methods
par: Li, Gen, et autres
Publié: (2023)

Convergence rates of stochastic gradient method with independent sequences of step-size and momentum weight
par: Hwang, Wen-Liang
Publié: (2024)

Provably Robust Score-Based Diffusion Posterior Sampling for Plug-and-Play Image Reconstruction
par: Xu, Xingyu, et autres
Publié: (2024)

Communication-efficient Vertical Federated Learning via Compressed Error Feedback
par: Valdeira, Pedro, et autres
Publié: (2024)

Achieving Logarithmic Regret in KL-Regularized Zero-Sum Markov Games
par: Nayak, Anupam, et autres
Publié: (2025)

Learning-to-Optimize with PAC-Bayesian Guarantees: Theoretical Considerations and Practical Implementation
par: Sucker, Michael, et autres
Publié: (2024)

On Building Myopic MPC Policies using Supervised Learning
par: Orrico, Christopher A., et autres
Publié: (2024)

Sporadic Gradient Tracking over Directed Graphs: A Theoretical Perspective on Decentralized Federated Learning
par: Zehtabi, Shahryar, et autres
Publié: (2026)

Control and optimization for Neural Partial Differential Equations in Supervised Learning
par: Bensoussan, Alain, et autres
Publié: (2025)