Bryan Lim

Orcid: 0000-0002-2324-1400

According to our database1, Bryan Lim authored at least 34 papers between 2018 and 2024.

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

Timeline

Legend:

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PhD thesis 
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Bibliography

2024
Beyond Expected Return: Accounting for Policy Reproducibility When Evaluating Reinforcement Learning Algorithms.
Proceedings of the Thirty-Eighth AAAI Conference on Artificial Intelligence, 2024

2023
Online Damage Recovery for Physical Robots with Hierarchical Quality-Diversity.
ACM Trans. Evol. Learn. Optim., June, 2023

Accelerated Quality-Diversity through Massive Parallelism.
Trans. Mach. Learn. Res., 2023

Mix-ME: Quality-Diversity for Multi-Agent Learning.
CoRR, 2023

QDax: A Library for Quality-Diversity and Population-based Algorithms with Hardware Acceleration.
CoRR, 2023

Multiple Hands Make Light Work: Enhancing Quality and Diversity using MAP-Elites with Multiple Parallel Evolution Strategies.
CoRR, 2023

Efficient Learning of Locomotion Skills through the Discovery of Diverse Environmental Trajectory Generator Priors.
Proceedings of the IEEE International Conference on Robotics and Automation, 2023

Neuroevolution is a Competitive Alternative to Reinforcement Learning for Skill Discovery.
Proceedings of the Eleventh International Conference on Learning Representations, 2023

Quality-Diversity Optimisation on a Physical Robot Through Dynamics-Aware and Reset-Free Learning.
Proceedings of the Companion Proceedings of the Conference on Genetic and Evolutionary Computation, 2023

Understanding the Synergies between Quality-Diversity and Deep Reinforcement Learning.
Proceedings of the Genetic and Evolutionary Computation Conference, 2023

Don't Bet on Luck Alone: Enhancing Behavioral Reproducibility of Quality-Diversity Solutions in Uncertain Domains.
Proceedings of the Genetic and Evolutionary Computation Conference, 2023

2022
Efficient Exploration using Model-Based Quality-Diversity with Gradients.
CoRR, 2022

Benchmarking Quality-Diversity Algorithms on Neuroevolution for Reinforcement Learning.
CoRR, 2022

Accelerated Quality-Diversity for Robotics through Massive Parallelism.
CoRR, 2022

Dynamics-Aware Quality-Diversity for Efficient Learning of Skill Repertoires.
Proceedings of the 2022 International Conference on Robotics and Automation, 2022

Learning to walk autonomously via reset-free quality-diversity.
Proceedings of the GECCO '22: Genetic and Evolutionary Computation Conference, Boston, Massachusetts, USA, July 9, 2022

QDax: on the benefits of massive parallelization for quality-diversity.
Proceedings of the GECCO '22: Genetic and Evolutionary Computation Conference, Companion Volume, Boston, Massachusetts, USA, July 9, 2022

2021
Enhancing Cross-Sectional Currency Strategies by Ranking Refinement with Transformer-based Architectures.
CoRR, 2021

Deep Learning for Market by Order Data.
CoRR, 2021

2020
Deep learning for time series prediction and decision making over time.
PhD thesis, 2020

Building Cross-Sectional Systematic Strategies By Learning to Rank.
CoRR, 2020

Time Series Forecasting With Deep Learning: A Survey.
CoRR, 2020

Detecting Changes in Asset Co-Movement Using the Autoencoder Reconstruction Ratio.
CoRR, 2020

Robust Autonomous Navigation of a Small-Scale Quadruped Robot in Real-World Environments.
Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems, 2020

Recurrent Neural Filters: Learning Independent Bayesian Filtering Steps for Time Series Prediction.
Proceedings of the 2020 International Joint Conference on Neural Networks, 2020

Vision Aided Dynamic Exploration of Unstructured Terrain with a Small-Scale Quadruped Robot.
Proceedings of the 2020 IEEE International Conference on Robotics and Automation, 2020

Tactile Object Pose Estimation from the First Touch with Geometric Contact Rendering.
Proceedings of the 4th Conference on Robot Learning, 2020

2019
Temporal Fusion Transformers for Interpretable Multi-horizon Time Series Forecasting.
CoRR, 2019

Population-based Global Optimisation Methods for Learning Long-term Dependencies with RNNs.
CoRR, 2019

Enhancing Time Series Momentum Strategies Using Deep Neural Networks.
CoRR, 2019

2018
Forecasting Disease Trajectories in Alzheimer's Disease Using Deep Learning.
CoRR, 2018

Disease-Atlas: Navigating Disease Trajectories with Deep Learning.
CoRR, 2018

Forecasting Treatment Responses Over Time Using Recurrent Marginal Structural Networks.
Proceedings of the Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, 2018

Disease-Atlas: Navigating Disease Trajectories using Deep Learning.
Proceedings of the Machine Learning for Healthcare Conference, 2018


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