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Adam D Cobb
Adam D Cobb
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Title
Cited by
Cited by
Year
Bayesopt adversarial attack
B Ru, A Cobb, A Blaas, Y Gal
International Conference on Learning Representations, 2019
852019
Improving differential evolution through Bayesian hyperparameter optimization
S Biswas, D Saha, S De, AD Cobb, S Das, BA Jalaian
2021 IEEE congress on evolutionary computation (CEC), 832-840, 2021
802021
Scaling Hamiltonian Monte Carlo Inference for Bayesian Neural Networks with Symmetric Splitting
AD Cobb, B Jalaian
Uncertainty in Artificial Intelligence (UAI), 2021, 2020
702020
An ensemble of bayesian neural networks for exoplanetary atmospheric retrieval
AD Cobb, MD Himes, F Soboczenski, S Zorzan, MD O’Beirne, AG Baydin, ...
The astronomical journal 158 (1), 33, 2019
662019
Loss-calibrated approximate inference in Bayesian neural networks
AD Cobb, SJ Roberts, Y Gal
Theory of deep learning workshop, ICML, 2018, 2018
472018
Introducing an explicit symplectic integration scheme for Riemannian manifold Hamiltonian Monte Carlo
AD Cobb, AG Baydin, A Markham, SJ Roberts
arXiv preprint arXiv:1910.06243, 2019
322019
Accurate machine-learning atmospheric retrieval via a neural-network surrogate model for radiative transfer
MD Himes, J Harrington, AD Cobb, AG Baydin, F Soboczenski, ...
The Planetary Science Journal 3 (4), 91, 2022
272022
HumBug Zooniverse: a crowd-sourced acoustic mosquito dataset
I Kiskin, AD Cobb, L Wang, S Roberts
Machine Learning for the Developing World Workshop, NeurIPS, 2019, 2020
212020
HumBugDB: A Large-scale Acoustic Mosquito Dataset
I Kiskin, M Sinka, AD Cobb, W Rafique, L Wang, D Zilli, B Gutteridge, ...
NeurIPS 2021, 2021
202021
Principal component flows
E Cunningham, AD Cobb, S Jha
International Conference on Machine Learning, 4492-4519, 2022
17*2022
URSABench: Comprehensive Benchmarking of Approximate Bayesian Inference Methods for Deep Neural Networks
MP Vadera, AD Cobb, B Jalaian, BM Marlin
Workshop on Uncertainty & Robustness in Deep Learning, ICML 2020, 2020
162020
Bayesian deep learning for exoplanet atmospheric retrieval
F Soboczenski, MD Himes, MD O'Beirne, S Zorzan, AG Baydin, AD Cobb, ...
Third workshop on Bayesian Deep Learning, NeurIPS, 2018, 2018
15*2018
Bayesian deep neural networks for low-cost neurophysiological markers of Alzheimer's disease severity
W Fruehwirt, AD Cobb, M Mairhofer, L Weydemann, H Garn, R Schmidt, ...
Machine Learning for Health (ML4H) Workshop, NeurIPS, 2018, 2018
142018
Runtime monitoring of deep neural networks using top-down context models inspired by predictive processing and dual process theory
A Roy, A Cobb, N Bastian, B Jalaian, S Jha
AAAI Spring Symposium 2022, 2022
102022
Robust decision-making in the internet of battlefield things using bayesian neural networks
AD Cobb, BA Jalaian, ND Bastian, S Russell
2021 Winter Simulation Conference (WSC), 1-12, 2021
102021
Automatic acoustic mosquito tagging with bayesian neural networks
I Kiskin, AD Cobb, M Sinka, K Willis, SJ Roberts
Joint European Conference on Machine Learning and Knowledge Discovery in …, 2021
102021
On uncertainty and robustness in large-scale intelligent data fusion systems
BM Marlin, T Abdelzaher, G Ciocarlie, AD Cobb, M Dennison, B Jalaian, ...
2020 IEEE Second International Conference on Cognitive Machine Intelligence …, 2020
102020
Better call surrogates: A hybrid evolutionary algorithm for hyperparameter optimization
S Biswas, AD Cobb, A Sistrunk, N Ramakrishnan, B Jalaian
arXiv preprint arXiv:2012.06453, 2020
82020
The practicalities of scaling Bayesian neural networks to real-world applications
AD Cobb
University of Oxford, 2020
82020
Optimising Worlds to Evaluate and Influence Reinforcement Learning Agents.
R Everett, AD Cobb, A Markham, SJ Roberts
AAMAS, 1943-1945, 2019
72019
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