9 citations · 18 across the 8 of their papers we have counts for
8 papers
Variational Sampling of Temporal Trajectories
Jurijs Nazarovs, Zhichun Huang, Xingjian Zhen +3
A deterministic temporal process can be determined by its trajectory, an element in the product space of (a) initial condition and (b) transition function $f:…
On the Versatile Uses of Partial Distance Correlation in Deep Learning
Xingjian Zhen, Zihang Meng, Rudrasis Chakraborty +1
Comparing the functional behavior of neural network models, whether it is a single network over time or two (or more networks) during or post-training, is an essential step in unde…
A First Look: Towards Explainable TextVQA Models via Visual and Textual Explanations
Varun Nagaraj Rao, Xingjian Zhen, Karen Hovsepian +1
Explainable deep learning models are advantageous in many situations. Prior work mostly provide unimodal explanations through post-hoc approaches not part of the original system de…
Simpler Certified Radius Maximization by Propagating Covariances
Xingjian Zhen, Rudrasis Chakraborty, Vikas Singh
One strategy for adversarially training a robust model is to maximize its certified radius -- the neighborhood around a given training sample for which the model's prediction remai…
Flow-based Generative Models for Learning Manifold to Manifold Mappings
Xingjian Zhen, Rudrasis Chakraborty, Liu Yang +1
Many measurements or observations in computer vision and machine learning manifest as non-Euclidean data. While recent proposals (like spherical CNN) have extended a number of deep…
CPR-GCN: Conditional Partial-Residual Graph Convolutional Network in Automated Anatomical Labeling of Coronary Arteries
Han Yang, Xingjian Zhen, Ying Chi +2
Automated anatomical labeling plays a vital role in coronary artery disease diagnosing procedure. The main challenge in this problem is the large individual variability inherited i…
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…
A Statistical Recurrent Model on the Manifold of Symmetric Positive Definite Matrices
Rudrasis Chakraborty, Chun-Hao Yang, Xingjian Zhen +5
In a number of disciplines, the data (e.g., graphs, manifolds) to be analyzed are non-Euclidean in nature. Geometric deep learning corresponds to techniques that generalize deep ne…