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 • 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 • 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.
no code implementations • 1 Dec 2021 • Zhichun Huang, Rudrasis Chakraborty, Vikas Singh
Generative models which use explicit density modeling (e. g., variational autoencoders, flow-based generative models) involve finding a mapping from a known distribution, e. g. Gaussian, to the unknown input distribution.
no code implementations • NeurIPS 2021 • Zhichun Huang, Shaojie Bai, J. Zico Kolter
Recent research in deep learning has investigated two very different forms of ''implicitness'': implicit representations model high-frequency data such as images or 3D shapes directly via a low-dimensional neural network (often using e. g., sinusoidal bases or nonlinearities); implicit layers, in contrast, refer to techniques where the forward pass of a network is computed via non-linear dynamical systems, such as fixed-point or differential equation solutions, with the backward pass computed via the implicit function theorem.
no code implementations • 29 Sep 2021 • Zhichun Huang, Rudrasis Chakraborty, Vikas Singh
Generative models which use explicit density modeling (e. g., variational autoencoders, flow-based generative models) involve finding a mapping from a known distribution, e. g. Gaussian, to the unknown input distribution.
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 • ICCV 2019 • Haoliang Sun, Ronak Mehta, Hao H. Zhou, Zhichun Huang, Sterling C. Johnson, Vivek Prabhakaran, Vikas Singh
Motivated by developments in modality transfer in vision, we study the generation of certain types of PET images from MRI data.