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.
no code implementations • 5 Feb 2023 • Harsh Mishra, Jurijs Nazarovs, Manmohan Dogra, Sathya N. Ravi
In score-based models, a generative task is formulated using a parametric model (such as a neural network) to directly learn the gradient of such high dimensional distributions, instead of the density functions themselves, as is done traditionally.
no code implementations • 29 May 2022 • Jurijs Nazarovs, Jack W. Stokes, Melissa Turcotte, Justin Carroll, Itai Grady
While traditional deep learning models have been able to achieve state-of-the-art results in a wide variety of domains, Bayesian Neural Networks, which are a class of probabilistic models, are better suited to the issues of the ransomware data.
1 code implementation • 9 May 2022 • Jurijs Nazarovs, Zhichun Huang
Generating smooth animations from a limited number of sequential observations has a number of applications in vision.
no code implementations • 19 Feb 2022 • Jurijs Nazarovs, Ronak R. Mehta, Vishnu Suresh Lokhande, Vikas Singh
This is directly related to the structure of the computation graph, which can grow linearly as a function of the number of MC samples needed.
no code implementations • 18 Feb 2022 • Jurijs Nazarovs, Rudrasis Chakraborty, Songwong Tasneeyapant, Sathya N. Ravi, Vikas Singh
Panel data involving longitudinal measurements of the same set of participants taken over multiple time points is common in studies to understand childhood development and disease modeling.
no code implementations • 24 Jan 2022 • Jurijs Nazarovs, Cristian Lumezanu, Qianying Ren, Yuncong Chen, Takehiko Mizoguchi, Dongjin Song, Haifeng Chen
In this paper, we propose an ordered time series classification framework that is robust against missing classes in the training data, i. e., during testing we can prescribe classes that are missing during training.
no code implementations • CVPR 2022 • Jurijs Nazarovs, Zhichun Huang, Songwong Tasneeyapant, Rudrasis Chakraborty, Vikas Singh
Quantitative descriptions of confidence intervals and uncertainties of the predictions of a model are needed in many applications in vision and machine learning.