Adam D. Cobb

Orcid: 0000-0002-6638-1788

According to our database¹, Adam D. Cobb authored at least 31 papers between 2017 and 2024.

Collaborative distances:

Dijkstra number² of three.
Erdős number³ of four.

Timeline

Legend:

Book

In proceedings

Article

PhD thesis

Dataset

Other

Bibliography

2024

Direct Amortized Likelihood Ratio Estimation.

[BibT_eX]

[DOI]

Proceedings of the Thirty-Eighth AAAI Conference on Artificial Intelligence, 2024

2023

On Sequential Bayesian Inference for Continual Learning.

[BibT_eX]

[DOI]

Entropy, June, 2023

Decentralized Bayesian learning with Metropolis-adjusted Hamiltonian Monte Carlo.

[BibT_eX]

[DOI]

Vyacheslav Kungurtsev

Adam D. Cobb

Tara Javidi

Brian Jalaian

Mach. Learn., 2023

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

[BibT_eX]

[DOI]

CoRR, 2023

Challenges and Opportunities in Neuro-Symbolic Composition of Foundation Models.

[BibT_eX]

[DOI]

Susmit Jha

Anirban Roy

Adam D. Cobb

Alexander M. Berenbeim

Nathaniel D. Bastian

Proceedings of the IEEE Military Communications Conference, 2023

hamiltorch: A PyTorch-based library for Hamiltonian Monte Carlo.

[BibT_eX]

[DOI]

Adam D. Cobb

Proceedings of Cyber-Physical Systems and Internet of Things Week 2023, 2023

2022

Design of Unmanned Air Vehicles Using Transformer Surrogate Models.

[BibT_eX]

[DOI]

CoRR, 2022

Principal Manifold Flows.

[BibT_eX]

[DOI]

Edmond Cunningham

Adam D. Cobb

Susmit Jha

CoRR, 2022

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

[BibT_eX]

[DOI]

CoRR, 2022

URSABench: A System for Comprehensive Benchmarking of Bayesian Deep Neural Network Models and Inference methods.

[BibT_eX]

[DOI]

Proceedings of Machine Learning and Systems 2022, 2022

Principal Component Flows.

[BibT_eX]

[DOI]

Edmond Cunningham

Adam D. Cobb

Susmit Jha

Proceedings of the International Conference on Machine Learning, 2022

2021

Robust Decision-Making in the Internet of Battlefield Things Using Bayesian Neural Networks.

[BibT_eX]

[DOI]

Proceedings of the Winter Simulation Conference, 2021

Scaling Hamiltonian Monte Carlo inference for Bayesian neural networks with symmetric splitting.

[BibT_eX]

[DOI]

Adam D. Cobb

Brian Jalaian

Proceedings of the Thirty-Seventh Conference on Uncertainty in Artificial Intelligence, 2021

Automatic Acoustic Mosquito Tagging with Bayesian Neural Networks.

[BibT_eX]

[DOI]

Proceedings of the Machine Learning and Knowledge Discovery in Databases. Applied Data Science Track, 2021

HumBugDB: A Large-scale Acoustic Mosquito Dataset.

[BibT_eX]

[DOI]

Proceedings of the Neural Information Processing Systems Track on Datasets and Benchmarks 1, 2021

Improving Differential Evolution through Bayesian Hyperparameter Optimization.

[BibT_eX]

[DOI]

Proceedings of the IEEE Congress on Evolutionary Computation, 2021

2020

The practicalities of scaling Bayesian neural networks to real-world applications.

[BibT_eX]

[DOI]

Adam D. Cobb

PhD thesis, 2020

Better call Surrogates: A hybrid Evolutionary Algorithm for Hyperparameter optimization.

[BibT_eX]

[DOI]

CoRR, 2020

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

[BibT_eX]

[DOI]

CoRR, 2020

BayesOpt Adversarial Attack.

[BibT_eX]

[DOI]

Proceedings of the 8th International Conference on Learning Representations, 2020

Humbug Zooniverse: A Crowd-Sourced Acoustic Mosquito Dataset.

[BibT_eX]

[DOI]

Proceedings of the 2020 IEEE International Conference on Acoustics, 2020

On Uncertainty and Robustness in Large-Scale Intelligent Data Fusion Systems.

[BibT_eX]

[DOI]

Benjamin M. Marlin

Tarek F. Abdelzaher

Gabriela F. Ciocarlie

Proceedings of the 2nd IEEE International Conference on Cognitive Machine Intelligence, 2020

2019

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

[BibT_eX]

[DOI]

CoRR, 2019

An Ensemble of Bayesian Neural Networks for Exoplanetary Atmospheric Retrieval.

[BibT_eX]

[DOI]

Shawn D. Domagal-Goldman

Giada N. Arney

Daniel Angerhausen

CoRR, 2019

Optimising Worlds to Evaluate and Influence Reinforcement Learning Agents.

[BibT_eX]

[DOI]

Proceedings of the 18th International Conference on Autonomous Agents and MultiAgent Systems, 2019

2018

Bayesian deep neural networks for low-cost neurophysiological markers of Alzheimer's disease severity.

[BibT_eX]

[DOI]

CoRR, 2018

Bayesian Deep Learning for Exoplanet Atmospheric Retrieval.

[BibT_eX]

[DOI]

Shawn D. Domagal-Goldman

CoRR, 2018

Loss-Calibrated Approximate Inference in Bayesian Neural Networks.

[BibT_eX]

[DOI]

Adam D. Cobb

Stephen J. Roberts

Yarin Gal

CoRR, 2018

Scalable Bounding of Predictive Uncertainty in Regression Problems with SLAC.

[BibT_eX]

[DOI]

Proceedings of the Scalable Uncertainty Management - 12th International Conference, 2018

Identifying Sources and Sinks in the Presence of Multiple Agents with Gaussian Process Vector Calculus.

[BibT_eX]

[DOI]

Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, 2018

2017

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

[BibT_eX]

[DOI]

Adam D. Cobb

Andrew Markham

Stephen J. Roberts

CoRR, 2017

Adam D. Cobb

Timeline

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