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Found 5 papers, 4 papers with code
Learning Objective-Specific Active Learning Strategies with Attentive Neural Processes
In this work, we propose a novel LAL method for classification that exploits symmetry and independence properties of the active learning problem with an Attentive Conditional Neural Process model.
E-Valuating Classifier Two-Sample Tests
Compared to $p$-values-based tests, tests with E-values have finite sample guarantees for the type I error.
On learning adaptive acquisition policies for undersampled multi-coil MRI reconstruction
Most current approaches to undersampled multi-coil MRI reconstruction focus on learning the reconstruction model for a fixed, equidistant acquisition trajectory.
Back to Basics: Deep Reinforcement Learning in Traffic Signal Control
In this paper we revisit some of the fundamental premises for a reinforcement learning (RL) approach to self-learning traffic lights.
Experimental design for MRI by greedy policy search
In today's clinical practice, magnetic resonance imaging (MRI) is routinely accelerated through subsampling of the associated Fourier domain.
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