26 citations · 59 across the 12 of their papers we have counts for
6 papers · 1 filter
ProtoVAE: Prototypical Networks for Unsupervised Disentanglement
Vaishnavi Patil, Matthew Evanusa, Joseph JaJa
Generative modeling and self-supervised learning have in recent years made great strides towards learning from data in a completely unsupervised way. There is still however an open…
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…
FedNet2Net: Saving Communication and Computations in Federated Learning with Model Growing
Amit Kumar Kundu, Joseph Jaja
Federated learning (FL) is a recently developed area of machine learning, in which the private data of a large number of distributed clients is used to develop a global model under…
Class-Similarity Based Label Smoothing for Confidence Calibration
Chihuang Liu, Joseph JaJa
Generating confidence calibrated outputs is of utmost importance for the applications of deep neural networks in safety-critical decision-making systems. The output of a neural net…
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…