Search Results for author: Xingjian Zhen

Found 9 papers, 6 papers with code

Variational Sampling of Temporal Trajectories

no code implementations18 Mar 2024 Jurijs Nazarovs, Zhichun Huang, Xingjian Zhen, Sourav Pal, Rudrasis Chakraborty, Vikas Singh

In this work, we introduce a mechanism to learn the distribution of trajectories by parameterizing the transition function $f$ explicitly as an element in a function space.

Out-of-Distribution Detection

On the Versatile Uses of Partial Distance Correlation in Deep Learning

1 code implementation20 Jul 2022 Xingjian Zhen, Zihang Meng, Rudrasis Chakraborty, Vikas Singh

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 understanding what they are learning (and what they are not), and for identifying strategies for regularization or efficiency improvements.

Simpler Certified Radius Maximization by Propagating Covariances

1 code implementation CVPR 2021 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 remains unchanged.

Can Kernel Transfer Operators Help Flow based Generative Models?

no code implementations1 Jan 2021 Zhichun Huang, Rudrasis Chakraborty, Xingjian Zhen, Vikas Singh

Flow-based generative models refer to deep generative models with tractable likelihoods, and offer several attractive properties including efficient density estimation and sampling.

Density Estimation

Flow-based Generative Models for Learning Manifold to Manifold Mappings

1 code implementation18 Dec 2020 Xingjian Zhen, Rudrasis Chakraborty, Liu Yang, Vikas Singh

Partly due to this gap, there are also no modality transfer/translation models for manifold-valued data whereas numerous such methods based on generative models are available for natural images.

CPR-GCN: Conditional Partial-Residual Graph Convolutional Network in Automated Anatomical Labeling of Coronary Arteries

no code implementations CVPR 2020 Han Yang, Xingjian Zhen, Ying Chi, Lei Zhang, Xian-Sheng Hua

On the technical side, the Partial-Residual GCN takes the position features of the branches, with the 3D spatial image features as conditions, to predict the label for each branches.

Anatomy Position

Dilated Convolutional Neural Networks for Sequential Manifold-valued Data

1 code implementation ICCV 2019 Xingjian Zhen, Rudrasis Chakraborty, Nicholas Vogt, Barbara B. Bendlin, Vikas Singh

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 data (e. g., unit vectors or special matrices).

Cannot find the paper you are looking for? You can Submit a new open access paper.