2 citations · 5 across the 7 of their papers we have counts for
7 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:…
Using Intermediate Forward Iterates for Intermediate Generator Optimization
Harsh Mishra, Jurijs Nazarovs, Manmohan Dogra +1
Score-based models have recently been introduced as a richer framework to model distributions in high dimensions and are generally more suitable for generative tasks. In score-base…
Radial Spike and Slab Bayesian Neural Networks for Sparse Data in Ransomware Attacks
Jurijs Nazarovs, Jack W. Stokes, Melissa Turcotte +2
Ransomware attacks are increasing at an alarming rate, leading to large financial losses, unrecoverable encrypted data, data leakage, and privacy concerns. The prompt detection of…
Image2Gif: Generating Continuous Realistic Animations with Warping NODEs
Jurijs Nazarovs, Zhichun Huang
Generating smooth animations from a limited number of sequential observations has a number of applications in vision. For example, it can be used to increase number of frames per s…
Graph Reparameterizations for Enabling 1000+ Monte Carlo Iterations in Bayesian Deep Neural Networks
Jurijs Nazarovs, Ronak R. Mehta, Vishnu Suresh Lokhande +1
Uncertainty estimation in deep models is essential in many real-world applications and has benefited from developments over the last several years. Recent evidence suggests that ex…
Mixed Effects Neural ODE: A Variational Approximation for Analyzing the Dynamics of Panel Data
Jurijs Nazarovs, Rudrasis Chakraborty, Songwong Tasneeyapant +2
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…
Ordinal-Quadruplet: Retrieval of Missing Classes in Ordinal Time Series
Jurijs Nazarovs, Cristian Lumezanu, Qianying Ren +4
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…