4 papers
Feedback-Induced Performance Decline in LLM-Based Decision-Making
Xiao Yang, Juxi Leitner, Michael Burke
The ability of Large Language Models (LLMs) to extract context from natural language problem descriptions naturally raises questions about their suitability in autonomous decision-…
Advancements and Challenges in Continual Reinforcement Learning: A Comprehensive Review
Amara Zuffer, Michael Burke, Mehrtash Harandi
The diversity of tasks and dynamic nature of reinforcement learning (RL) require RL agents to be able to learn sequentially and continuously, a learning paradigm known as continuou…
Efficiently Scanning and Resampling Spatio-Temporal Tasks with Irregular Observations
Bryce Ferenczi, Michael Burke, Tom Drummond
Various works have aimed at combining the inference efficiency of recurrent models and training parallelism of multi-head attention for sequence modeling. However, most of these wo…
Carefully Structured Compression: Efficiently Managing StarCraft II Data
Bryce Ferenczi, Rhys Newbury, Michael Burke +1
Creation and storage of datasets are often overlooked input costs in machine learning, as many datasets are simple image label pairs or plain text. However, datasets with more comp…