Sejun Park | DeepAI

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∙ 09/19/2023

Minimum width for universal approximation using ReLU networks on compact domain

The universal approximation property of width-bounded networks has been ...

0 Namjun Kim, et al. ∙

∙ 03/06/2023

Guiding Energy-based Models via Contrastive Latent Variables

An energy-based model (EBM) is a popular generative framework that offer...

0 Hankook Lee, et al. ∙

∙ 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...

0 Wonyeol Lee, et al. ∙

∙ 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...

0 Alireza Mousavi Hosseini, et al. ∙

∙ 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...

0 Sejun Park, et al. ∙

∙ 11/17/2021

SmoothMix: Training Confidence-calibrated Smoothed Classifiers for Certified Robustness

Randomized smoothing is currently a state-of-the-art method to construct...

0 Jongheon Jeong, et al. ∙

∙ 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...

0 Sejun Park, et al. ∙

∙ 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...

0 Jaeho Lee, et al. ∙

∙ 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...

15 Jaehyung Kim, et al. ∙

∙ 06/16/2020

Minimum Width for Universal Approximation

The universal approximation property of width-bounded networks has been ...

0 Sejun Park, et al. ∙

∙ 06/15/2020

Learning Bounds for Risk-sensitive Learning

In risk-sensitive learning, one aims to find a hypothesis that minimizes...

0 Jaeho Lee, et al. ∙

∙ 02/12/2020

Lookahead: A Far-Sighted Alternative of Magnitude-based Pruning

Magnitude-based pruning is one of the simplest methods for pruning neura...

12 Sejun Park, et al. ∙

∙ 05/14/2019

Spectral Approximate Inference

Given a graphical model (GM), computing its partition function is the mo...

3 Sejun Park, et al. ∙

∙ 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...

0 Sejun Park, et al. ∙

∙ 03/12/2017

Sequential Local Learning for Latent Graphical Models

Learning parameters of latent graphical models (GM) is inherently much h...

0 Sejun Park, et al. ∙

∙ 09/23/2015

Minimum Weight Perfect Matching via Blossom Belief Propagation

Max-product Belief Propagation (BP) is a popular message-passing algorit...

0 Sungsoo Ahn, et al. ∙

∙ 12/16/2014

Max-Product Belief Propagation for Linear Programming: Applications to Combinatorial Optimization

The max-product belief propagation (BP) is a popular message-passing heu...

0 Sejun Park, et al. ∙

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