Search Results for author:
Found 17 papers, 10 papers with code
Direct Amortized Likelihood Ratio Estimation
Our estimator is simple to train and estimates the likelihood ratio using a single forward pass of the neural estimator.
Design of Unmanned Air Vehicles Using Transformer Surrogate Models
In this paper, we develop an AI Designer that synthesizes novel UAV designs.
Impact of Parameter Sparsity on Stochastic Gradient MCMC Methods for Bayesian Deep Learning
In this paper, we investigate the potential of sparse network structures to flexibly trade-off model storage costs and inference run time against predictive performance and uncertainty quantification ability.
Can Sequential Bayesian Inference Solve Continual Learning?
Previous work in Continual Learning (CL) has used sequential Bayesian inference to prevent forgetting and accumulate knowledge from previous tasks.
HumBugDB: A Large-scale Acoustic Mosquito Dataset
Our extensive dataset is both challenging to machine learning researchers focusing on acoustic identification, and critical to entomologists, geo-spatial modellers and other domain experts to understand mosquito behaviour, model their distribution, and manage the threat they pose to humans.
Physical System Design Using Hamiltonian Monte Carlo over Learned Manifolds
We use deep generative models to learn a manifold of the valid design space, followed by Hamiltonian Monte Carlo (HMC) with simulated annealing to explore and optimize design over the learned manifold, producing a diverse set of optimal designs.
Scaling Hamiltonian Monte Carlo Inference for Bayesian Neural Networks with Symmetric Splitting
Hamiltonian Monte Carlo (HMC) is a Markov chain Monte Carlo (MCMC) approach that exhibits favourable exploration properties in high-dimensional models such as neural networks.
URSABench: Comprehensive Benchmarking of Approximate Bayesian Inference Methods for Deep Neural Networks
In this paper, we describe initial work on the development ofURSABench(the Uncertainty, Robustness, Scalability, and Accu-racy Benchmark), an open-source suite of bench-marking tools for comprehensive assessment of approximate Bayesian inference methods with a focus on deep learning-based classification tasks
Accurate Machine Learning Atmospheric Retrieval via a Neural Network Surrogate Model for Radiative Transfer
Machine learning (ML) has recently been shown to provide a significant reduction in runtime for retrievals, mainly by training inverse ML models that predict parameter distributions, given observed spectra, albeit with reduced posterior accuracy.
Instrumentation and Methods for Astrophysics Earth and Planetary Astrophysics
HumBug Zooniverse: a crowd-sourced acoustic mosquito dataset
Mosquitoes are the only known vector of malaria, which leads to hundreds of thousands of deaths each year.
Introducing an Explicit Symplectic Integration Scheme for Riemannian Manifold Hamiltonian Monte Carlo
We introduce a recent symplectic integration scheme derived for solving physically motivated systems with non-separable Hamiltonians.
An Ensemble of Bayesian Neural Networks for Exoplanetary Atmospheric Retrieval
We expand upon their approach by presenting a new machine learning model, \texttt{plan-net}, based on an ensemble of Bayesian neural networks that yields more accurate inferences than the random forest for the same data set of synthetic transmission spectra.
Bayesian deep neural networks for low-cost neurophysiological markers of Alzheimer's disease severity
As societies around the world are ageing, the number of Alzheimer's disease (AD) patients is rapidly increasing.
Bayesian Deep Learning for Exoplanet Atmospheric Retrieval
Here we present an ML-based retrieval framework called Intelligent exoplaNet Atmospheric RetrievAl (INARA) that consists of a Bayesian deep learning model for retrieval and a data set of 3, 000, 000 synthetic rocky exoplanetary spectra generated using the NASA Planetary Spectrum Generator.
Loss-Calibrated Approximate Inference in Bayesian Neural Networks
Current approaches in approximate inference for Bayesian neural networks minimise the Kullback-Leibler divergence to approximate the true posterior over the weights.
Identifying Sources and Sinks in the Presence of Multiple Agents with Gaussian Process Vector Calculus
In systems of multiple agents, identifying the cause of observed agent dynamics is challenging.
Learning from lions: inferring the utility of agents from their trajectories
We build a model using Gaussian processes to infer a spatio-temporal vector field from observed agent trajectories.
