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Found 8 papers, 1 papers with code
Variational Sampling of Temporal Trajectories
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.
Using Intermediate Forward Iterates for Intermediate Generator Optimization
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.
Radial Spike and Slab Bayesian Neural Networks for Sparse Data in Ransomware Attacks
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.
Image2Gif: Generating Continuous Realistic Animations with Warping NODEs
Generating smooth animations from a limited number of sequential observations has a number of applications in vision.
Graph Reparameterizations for Enabling 1000+ Monte Carlo Iterations in Bayesian Deep Neural Networks
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.
Mixed Effects Neural ODE: A Variational Approximation for Analyzing the Dynamics of Panel Data
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.
Ordinal-Quadruplet: Retrieval of Missing Classes in Ordinal Time Series
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.
Understanding Uncertainty Maps in Vision With Statistical Testing
Quantitative descriptions of confidence intervals and uncertainties of the predictions of a model are needed in many applications in vision and machine learning.
