A theory of non-linear feature learning with one gradient step in two-layer neural networks
Feature learning is thought to be one of the fundamental reasons for the success of deep
neural networks. It is rigorously known that in two-layer fully-connected neural networks …
neural networks. It is rigorously known that in two-layer fully-connected neural networks …
Provable multi-task representation learning by two-layer relu neural networks
Feature learning, ie extracting meaningful representations of data, is quintessential to the
practical success of neural networks trained with gradient descent, yet it is notoriously …
practical success of neural networks trained with gradient descent, yet it is notoriously …
Learning sum of diverse features: computational hardness and efficient gradient-based training for ridge combinations
We study the computational and sample complexity of learning a target function $
f_*:\mathbb {R}^ d\to\mathbb {R} $ with additive structure, that is, $ f_*(x)=\frac {1}{\sqrt …
f_*:\mathbb {R}^ d\to\mathbb {R} $ with additive structure, that is, $ f_*(x)=\frac {1}{\sqrt …
How Does Gradient Descent Learn Features--A Local Analysis for Regularized Two-Layer Neural Networks
The ability of learning useful features is one of the major advantages of neural networks.
Although recent works show that neural network can operate in a neural tangent kernel …
Although recent works show that neural network can operate in a neural tangent kernel …
STATISTICAL AND HIGH-DIMENSIONAL PERSPECTIVES ON MACHINE LEARNING
D Lee - 2024 - repository.upenn.edu
In the first chapter, we consider the problem of calibration. While the accuracy of modern
machine learning techniques continues to improve, many models exhibit mis-calibration …
machine learning techniques continues to improve, many models exhibit mis-calibration …