Adam D. Cobb | DeepAI

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∙ 06/08/2023

AircraftVerse: A Large-Scale Multimodal Dataset of Aerial Vehicle Designs

We present AircraftVerse, a publicly available aerial vehicle design dat...

0 Adam D. Cobb, et al. ∙

∙ 11/11/2022

Design of Unmanned Air Vehicles Using Transformer Surrogate Models

Computer-aided design (CAD) is a promising new area for the application ...

0 Adam D. Cobb, et al. ∙

∙ 02/08/2022

Impact of Parameter Sparsity on Stochastic Gradient MCMC Methods for Bayesian Deep Learning

Bayesian methods hold significant promise for improving the uncertainty ...

0 Meet P. Vadera, et al. ∙

∙ 10/14/2021

HumBugDB: A Large-scale Acoustic Mosquito Dataset

This paper presents the first large-scale multi-species dataset of acous...

0 Ivan Kiskin, et al. ∙

∙ 12/11/2020

Better call Surrogates: A hybrid Evolutionary Algorithm for Hyperparameter optimization

In this paper, we propose a surrogate-assisted evolutionary algorithm (E...

24 Subhodip Biswas, et al. ∙

∙ 10/14/2020

Scaling Hamiltonian Monte Carlo Inference for Bayesian Neural Networks with Symmetric Splitting

Hamiltonian Monte Carlo (HMC) is a Markov chain Monte Carlo (MCMC) appro...

0 Adam D. Cobb, et al. ∙

∙ 07/08/2020

URSABench: Comprehensive Benchmarking of Approximate Bayesian Inference Methods for Deep Neural Networks

While deep learning methods continue to improve in predictive accuracy o...

0 Meet P. Vadera, et al. ∙

∙ 01/14/2020

HumBug Zooniverse: a crowd-sourced acoustic mosquito dataset

Mosquitoes are the only known vector of malaria, which leads to hundreds...

0 Ivan Kiskin, et al. ∙

∙ 10/14/2019

Introducing an Explicit Symplectic Integration Scheme for Riemannian Manifold Hamiltonian Monte Carlo

We introduce a recent symplectic integration scheme derived for solving ...

0 Adam D. Cobb, et al. ∙

∙ 05/25/2019

An Ensemble of Bayesian Neural Networks for Exoplanetary Atmospheric Retrieval

Machine learning is now used in many areas of astrophysics, from detecti...

0 Adam D. Cobb, et al. ∙

∙ 12/12/2018

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 dise...

0 Wolfgang Fruehwirt, et al. ∙

∙ 11/08/2018

Bayesian Deep Learning for Exoplanet Atmospheric Retrieval

Over the past decade, the study of exoplanets has shifted from their det...

0 Frank Soboczenski, et al. ∙

∙ 05/10/2018

Loss-Calibrated Approximate Inference in Bayesian Neural Networks

Current approaches in approximate inference for Bayesian neural networks...

0 Adam D. Cobb, et al. ∙

∙ 02/22/2018

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 d...

0 Adam D. Cobb, et al. ∙

∙ 09/07/2017

Learning from lions: inferring the utility of agents from their trajectories

We build a model using Gaussian processes to infer a spatio-temporal vec...

0 Adam D. Cobb, et al. ∙

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