6 citations · 8 across the 4 of their papers we have counts for
7 papers
Example-based Explanations with Adversarial Attacks for Respiratory Sound Analysis
Yi Chang, Zhao Ren, Thanh Tam Nguyen +2
Respiratory sound classification is an important tool for remote screening of respiratory-related diseases such as pneumonia, asthma, and COVID-19. To facilitate the interpretabili…
Benefits of Jointly Training Autoencoders: An Improved Neural Tangent Kernel Analysis
Thanh V. Nguyen, Raymond K. W. Wong, Chinmay Hegde
A remarkable recent discovery in machine learning has been that deep neural networks can achieve impressive performance (in terms of both lower training error and higher generaliza…
BUZz: BUffer Zones for defending adversarial examples in image classification
Kaleel Mahmood, Phuong Ha Nguyen, Lam M. Nguyen +2
We propose a novel defense against all existing gradient based adversarial attacks on deep neural networks for image classification problems. Our defense is based on a combination…
Autoencoders Learn Generative Linear Models
Thanh V. Nguyen, Raymond K. W. Wong, Chinmay Hegde
We provide a series of results for unsupervised learning with autoencoders. Specifically, we study shallow two-layer autoencoder architectures with shared weights. We focus on thre…
On Learning Sparsely Used Dictionaries from Incomplete Samples
Thanh V. Nguyen, Akshay Soni, Chinmay Hegde
Most existing algorithms for dictionary learning assume that all entries of the (high-dimensional) input data are fully observed. However, in several practical applications (such a…
Provably Accurate Double-Sparse Coding
Thanh V. Nguyen, Raymond K. W. Wong, Chinmay Hegde
Sparse coding is a crucial subroutine in algorithms for various signal processing, deep learning, and other machine learning applications. The central goal is to learn an overcompl…