60 citations · 124 across the 19 of their papers we have counts for
8 papers · 1 filter
Dilated Convolutional Neural Networks for Sequential Manifold-valued Data
Xingjian Zhen, Rudrasis Chakraborty, Nicholas Vogt +2
Efforts are underway to study ways via which the power of deep neural networks can be extended to non-standard data types such as structured data (e.g., graphs) or manifold-valued…
Optimizing Nondecomposable Data Dependent Regularizers via Lagrangian Reparameterization offers Significant Performance and Efficiency Gains
Sathya N. Ravi, Abhay Venkatesh, Glenn Moo Fung +1
Data dependent regularization is known to benefit a wide variety of problems in machine learning. Often, these regularizers cannot be easily decomposed into a sum over a finite num…
Quantum Graph Neural Networks
Guillaume Verdon, Trevor McCourt, Enxhell Luzhnica +3
We introduce Quantum Graph Neural Networks (QGNN), a new class of quantum neural network ansatze which are tailored to represent quantum processes which have a graph structure, and…
Generating Accurate Pseudo-labels in Semi-Supervised Learning and Avoiding Overconfident Predictions via Hermite Polynomial Activations
Vishnu Suresh Lokhande, Songwong Tasneeyapant, Abhay Venkatesh +2
Rectified Linear Units (ReLUs) are among the most widely used activation function in a broad variety of tasks in vision. Recent theoretical results suggest that despite their excel…
DUAL-GLOW: Conditional Flow-Based Generative Model for Modality Transfer
Haoliang Sun, Ronak Mehta, Hao H. Zhou +4
Positron emission tomography (PET) imaging is an imaging modality for diagnosing a number of neurological diseases. In contrast to Magnetic Resonance Imaging (MRI), PET is costly a…
Dimension constraints improve hypothesis testing for large-scale, graph-associated, brain-image data
TIen Vo, Vamsi Ithapu, Vikas Singh +1
For large-scale testing with graph-associated data, we present an empirical Bayes mixture technique to score local false discovery rates. Compared to empirical Bayes procedures tha…