3 citations · 7 across the 4 of their papers we have counts for
6 papers
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…