Experimental design for MRI by greedy policy search
Timsey/pg_mri
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NeurIPS 2020
In today's clinical practice, magnetic resonance imaging (MRI) is routinely accelerated through subsampling of the associated Fourier domain.
hls4ml: An Open-Source Codesign Workflow to Empower Scientific Low-Power Machine Learning Devices
fastmachinelearning/hls4ml
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Accessible machine learning algorithms, software, and diagnostic tools for energy-efficient devices and systems are extremely valuable across a broad range of application domains.
Edge Proposal Sets for Link Prediction
Here, we demonstrate how simply adding a set of edges, which we call a \emph{proposal set}, to the graph as a pre-processing step can improve the performance of several link prediction algorithms.
A-Optimal Active Learning
tueboesen/a-optimal-active-learning
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The first is based on a Bayesian interpretation of the semi-supervised learning problem with the graph Laplacian that is used for the prior distribution and the second is based on a frequentist approach, that updates the estimation of the bias term based on the recovery of the labels.
GeneDisco: A Benchmark for Experimental Design in Drug Discovery
GeneDisco contains a curated set of multiple publicly available experimental data sets as well as open-source implementations of state-of-the-art active learning policies for experimental design and exploration.
Emulation of physical processes with Emukit
Decision making in uncertain scenarios is an ubiquitous challenge in real world systems.
Learning High-Dimensional Parametric Maps via Reduced Basis Adaptive Residual Networks
We propose a scalable framework for the learning of high-dimensional parametric maps via adaptively constructed residual network (ResNet) maps between reduced bases of the inputs and outputs.
ODBO: Bayesian Optimization with Search Space Prescreening for Directed Protein Evolution
Directed evolution is a versatile technique in protein engineering that mimics the process of natural selection by iteratively alternating between mutagenesis and screening in order to search for sequences that optimize a given property of interest, such as catalytic activity and binding affinity to a specified target.
Investigating Emergent Goal-Like Behaviour in Large Language Models Using Experimental Economics
In this study, we investigate the capacity of large language models (LLMs), specifically GPT-3. 5, to operationalise natural language descriptions of cooperative, competitive, altruistic, and self-interested behavior in social dilemmas.
Empirical evaluation of scoring functions for Bayesian network model selection
In this work, we empirically evaluate the capability of various scoring functions of Bayesian networks for recovering true underlying structures.