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Found 7 papers, 5 papers with code
Embracing Unknown Step by Step: Towards Reliable Sparse Training in Real World
Sparse training has emerged as a promising method for resource-efficient deep neural networks (DNNs) in real-world applications.
InVA: Integrative Variational Autoencoder for Harmonization of Multi-modal Neuroimaging Data
While there is a growing literature on image-on-image regression to delineate predictive inference of an image based on multiple images, existing approaches have limitations in efficiently borrowing information between multiple imaging modalities in the prediction of an image.
Adaptive Conditional Quantile Neural Processes
Neural processes are a family of probabilistic models that inherit the flexibility of neural networks to parameterize stochastic processes.
Calibrating the Rigged Lottery: Making All Tickets Reliable
Previous research has shown that deep neural networks tend to be over-confident, and we find that sparse training exacerbates this problem.
Ordinal Causal Discovery
Causal discovery for purely observational, categorical data is a long-standing challenging problem.
Off-Policy Evaluation Using Information Borrowing and Context-Based Switching
We consider the off-policy evaluation (OPE) problem in contextual bandits, where the goal is to estimate the value of a target policy using the data collected by a logging policy.
BAST: Bayesian Additive Regression Spanning Trees for Complex Constrained Domain
Nonparametric regression on complex domains has been a challenging task as most existing methods, such as ensemble models based on binary decision trees, are not designed to account for intrinsic geometries and domain boundaries.
