Tim Pearce

According to our database1, Tim Pearce authored at least 26 papers between 2014 and 2024.

Collaborative distances:
  • Dijkstra number2 of four.
  • Erdős number3 of four.

Timeline

Legend:

Book 
In proceedings 
Article 
PhD thesis 
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Other 

Links

On csauthors.net:

Bibliography

2024
C-GAIL: Stabilizing Generative Adversarial Imitation Learning with Control Theory.
CoRR, 2024

DGPO: Discovering Multiple Strategies with Diversity-Guided Policy Optimization.
Proceedings of the Thirty-Eighth AAAI Conference on Artificial Intelligence, 2024

2023
Fair collaborative vehicle routing: A deep multi-agent reinforcement learning approach.
CoRR, 2023

Coalitional Bargaining via Reinforcement Learning: An Application to Collaborative Vehicle Routing.
CoRR, 2023

Imitating Human Behaviour with Diffusion Models.
Proceedings of the Eleventh International Conference on Learning Representations, 2023

TiZero: Mastering Multi-Agent Football with Curriculum Learning and Self-Play.
Proceedings of the 2023 International Conference on Autonomous Agents and Multiagent Systems, 2023

2022
Censored Quantile Regression Neural Networks.
CoRR, 2022

Fuzzy Inference for Well Log Lithology Classification.
Proceedings of the Advances in Computational Intelligence Systems, 2022

Censored Quantile Regression Neural Networks for Distribution-Free Survival Analysis.
Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Counter-Strike Deathmatch with Large-Scale Behavioural Cloning.
Proceedings of the IEEE Conference on Games, CoG 2022, Beijing, 2022

2021
Bayesian Autoencoders: Analysing and Fixing the Bernoulli likelihood for Out-of-Distribution Detection.
CoRR, 2021

Understanding Softmax Confidence and Uncertainty.
CoRR, 2021

Avoiding Kernel Fixed Points: Computing with ELU and GELU Infinite Networks.
Proceedings of the Thirty-Fifth AAAI Conference on Artificial Intelligence, 2021

2020
Uncertainty in neural networks: Bayesian ensembles, priors & prediction intervals.
PhD thesis, 2020

Supply chain data analytics for predicting supplier disruptions: a case study in complex asset manufacturing.
Int. J. Prod. Res., 2020

Structured Weight Priors for Convolutional Neural Networks.
CoRR, 2020

Universal access for object-based media experiences.
Proceedings of the 11th ACM Multimedia Systems Conference, 2020

Uncertainty in Neural Networks: Approximately Bayesian Ensembling.
Proceedings of the 23rd International Conference on Artificial Intelligence and Statistics, 2020

2019
Expressive Priors in Bayesian Neural Networks: Kernel Combinations and Periodic Functions.
CoRR, 2019

Expressive Priors in Bayesian Neural Networks: Kernel Combinations and Periodic Functions.
Proceedings of the Thirty-Fifth Conference on Uncertainty in Artificial Intelligence, 2019

2018
Bayesian Neural Network Ensembles.
CoRR, 2018

Uncertainty in Neural Networks: Bayesian Ensembling.
CoRR, 2018

Bayesian Inference with Anchored Ensembles of Neural Networks, and Application to Reinforcement Learning.
CoRR, 2018

Recurrent Neural Networks for real-time distributed collaborative prognostics.
Proceedings of the 2018 IEEE International Conference on Prognostics and Health Management, 2018

High-Quality Prediction Intervals for Deep Learning: A Distribution-Free, Ensembled Approach.
Proceedings of the 35th International Conference on Machine Learning, 2018

2014
Musculoskeletal Symptoms Amongst Clinical Radiologists and the Implications of Reporting Environment Ergonomics - A Multicentre Questionnaire Study.
J. Digit. Imaging, 2014


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