research
∙
09/19/2023
Minimum width for universal approximation using ReLU networks on compact domain
The universal approximation property of width-bounded networks has been ...
share
research
∙
03/06/2023
Guiding Energy-based Models via Contrastive Latent Variables
An energy-based model (EBM) is a popular generative framework that offer...
research
∙
01/31/2023
On the Correctness of Automatic Differentiation for Neural Networks with Machine-Representable Parameters
Recent work has shown that automatic differentiation over the reals is a...
research
∙
09/29/2022
Neural Networks Efficiently Learn Low-Dimensional Representations with SGD
We study the problem of training a two-layer neural network (NN) of arbi...
research
∙
09/19/2022
Generalization Bounds for Stochastic Gradient Descent via Localized ε-Covers
In this paper, we propose a new covering technique localized for the tra...
research
∙
11/17/2021
SmoothMix: Training Confidence-calibrated Smoothed Classifiers for Certified Robustness
Randomized smoothing is currently a state-of-the-art method to construct...
research
∙
10/26/2020
Provable Memorization via Deep Neural Networks using Sub-linear Parameters
It is known that Θ(N) parameters are sufficient for neural networks to m...
research
∙
10/15/2020
A Deeper Look at the Layerwise Sparsity of Magnitude-based Pruning
Recent discoveries on neural network pruning reveal that, with a careful...
research
∙
07/17/2020
Distribution Aligning Refinery of Pseudo-label for Imbalanced Semi-supervised Learning
While semi-supervised learning (SSL) has proven to be a promising way fo...
research
∙
06/16/2020
Minimum Width for Universal Approximation
The universal approximation property of width-bounded networks has been ...
research
∙
06/15/2020
Learning Bounds for Risk-sensitive Learning
In risk-sensitive learning, one aims to find a hypothesis that minimizes...
research
∙
02/12/2020
Lookahead: A Far-Sighted Alternative of Magnitude-based Pruning
Magnitude-based pruning is one of the simplest methods for pruning neura...
research
∙
05/14/2019
Spectral Approximate Inference
Given a graphical model (GM), computing its partition function is the mo...
research
∙
04/06/2017
Rapid Mixing Swendsen-Wang Sampler for Stochastic Partitioned Attractive Models
The Gibbs sampler is a particularly popular Markov chain used for learni...
research
∙
03/12/2017
Sequential Local Learning for Latent Graphical Models
Learning parameters of latent graphical models (GM) is inherently much h...
research
∙
09/23/2015
Minimum Weight Perfect Matching via Blossom Belief Propagation
Max-product Belief Propagation (BP) is a popular message-passing algorit...
research
∙
12/16/2014
