Matthew Evanusa — accepted AI-conference papers

2026

A Bayesian Nonparametric Framework For Learning Disentangled Representations

Vaishnavi S Patil, Siddhi Patil, Matthew Evanusa, Amit Kumar Kundu, Cornelia Fermuller, Joseph JaJa

ICLR 2026poster

Disentangled representation learning aims to identify and organize the underlying sources of variation in observed data. However, learning disentangled representations without any additional supervision necessitates inductive biases to solve the fundamental identifiability problem of uniquely recove…

Cited by 0 Source ↗Scholar ↗

2020

Network Deconvolution

Chengxi Ye, Matthew Evanusa, Hua He, Anton Mitrokhin, Tom Goldstein, James A. Yorke +2

ICLR 2020spotlight

Convolution is a central operation in Convolutional Neural Networks (CNNs), which applies a kernel to overlapping regions shifted across the image. However, because of the strong correlations in real-world image data, convolutional kernels are in effect re-learning redundant data. In this work, we…

Cited by 55 Source ↗Scholar ↗