Statistical Learning
Sharpness-Aware Minimization Leads to Low-Rank Features
Sharpness-aware minimization (SAM) is a recently proposed method that minimizes the sharpness of the training loss of a neural network.
Adversarial Examples Might be A voidable: The Role of Data Concentration in Adversarial Robustness
However, these results can be too general to be applicable to natural data distributions. Indeed, humans are quite robust for tasks involving vision.
The Sample Complexity of Gradient Descent in Stochastic Convex Optimization Roi Livni School of Electrical Engineering Tel Aviv University rlivni@tauex.tau.ac.il
But, also, it has become focus of study because it is one of few theoretical settings that exhibit overparameterized learning .
BMRS: Bayesian Model Reduction for Structured Pruning
In this work, we address this challenge by proposing BMRS: B ayesian M odel R eduction for S tructured pruning.
Learning to Tokenize for Generative Retrieval Weiwei Sun 1, Lingyong Y an
Document retrieval plays an essential role in web search applications and various downstream knowledge-intensive tasks by identifying relevant documents to satisfy users' queries.