no code implementations • 18 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.
1 code implementation • 20 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.
1 code implementation • NAACL (maiworkshop) 2021 • Varun Nagaraj Rao, Xingjian Zhen, Karen Hovsepian, Mingwei Shen
Explainable deep learning models are advantageous in many situations.
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.
no code implementations • 1 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.
1 code implementation • 18 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.
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.
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).
1 code implementation • NeurIPS 2018 • Rudrasis Chakraborty, Chun-Hao Yang, Xingjian Zhen, Monami Banerjee, Derek Archer, David Vaillancourt, Vikas Singh, Baba C. Vemuri
We show how recurrent statistical recurrent network models can be defined in such spaces.