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Found 10 papers, 3 papers with code
Evidential Uncertainty Quantification: A Variance-Based Perspective
Uncertainty quantification of deep neural networks has become an active field of research and plays a crucial role in various downstream tasks such as active learning.
Applications of Sequential Learning for Medical Image Classification
We begin by comparing two methods for a sequentially trained CNN with and without base pre-training.
Learning sources of variability from high-dimensional observational studies
Causal inference studies whether the presence of a variable influences an observed outcome.
The Multiple Subnetwork Hypothesis: Enabling Multidomain Learning by Isolating Task-Specific Subnetworks in Feedforward Neural Networks
Neural networks have seen an explosion of usage and research in the past decade, particularly within the domains of computer vision and natural language processing.
Prospective Learning: Principled Extrapolation to the Future
We conjecture that certain sequences of tasks are not retrospectively learnable (in which the data distribution is fixed), but are prospectively learnable (in which distributions may be dynamic), suggesting that prospective learning is more difficult in kind than retrospective learning.
Alpha-1 adrenergic receptor antagonists to prevent hyperinflammation and death from lower respiratory tract infection
Here, we conducted retrospective analyses in two cohorts of patients with acute respiratory distress (ARD, n=18, 547) and three cohorts with pneumonia (n=400, 907).
Robust Portfolio Optimization
We propose a robust portfolio optimization approach based on quantile statistics.
MONEYBaRL: Exploiting pitcher decision-making using Reinforcement Learning
This manuscript uses machine learning techniques to exploit baseball pitchers' decision making, so-called "Baseball IQ," by modeling the at-bat information, pitch selection and counts, as a Markov Decision Process (MDP).
Fast, Exact Bootstrap Principal Component Analysis for p>1 million
As a result, all bootstrap principal components are limited to the same $n$-dimensional subspace and can be efficiently represented by their low dimensional coordinates in that subspace.
Methodology Applications Computation
Joint Estimation of Multiple Graphical Models from High Dimensional Time Series
In this manuscript we consider the problem of jointly estimating multiple graphical models in high dimensions.
