5 papers
DOT-VAE: Disentangling One Factor at a Time
Vaishnavi Patil, Matthew Evanusa, Joseph JaJa
As we enter the era of machine learning characterized by an overabundance of data, discovery, organization, and interpretation of the data in an unsupervised manner becomes a criti…
Feature Prioritization and Regularization Improve Standard Accuracy and Adversarial Robustness
Chihuang Liu, Joseph JaJa
Adversarial training has been successfully applied to build robust models at a certain cost. While the robustness of a model increases, the standard classification accuracy decline…
Learning Graph-Level Representations with Recurrent Neural Networks
Yu Jin, Joseph F. JaJa
Recently a variety of methods have been developed to encode graphs into low-dimensional vectors that can be easily exploited by machine learning algorithms. The majority of these m…
A High Performance Implementation of Spectral Clustering on CPU-GPU Platforms
Yu Jin, Joseph F. JaJa
Spectral clustering is one of the most popular graph clustering algorithms, which achieves the best performance for many scientific and engineering applications. However, existing…
A Data-Driven Approach to Extract Connectivity Structures from Diffusion Tensor Imaging Data
Yu Jin, Joseph F. JaJa, Rong Chen +1
Diffusion Tensor Imaging (DTI) is an effective tool for the analysis of structural brain connectivity in normal development and in a broad range of brain disorders. However efforts…