Tootfinder

Uncertainty Prioritized Experience Replay
Rodrigo Carrasco-Davis, Sebastian Lee, Claudia Clopath, Will Dabney
https://arxiv.org/abs/2506.09270 https://

Uncertainty Prioritized Experience Replay
Prioritized experience replay, which improves sample efficiency by selecting relevant transitions to update parameter estimates, is a crucial component of contemporary value-based deep reinforcement learning models. Typically, transitions are prioritized based on their temporal difference error. However, this approach is prone to favoring noisy transitions, even when the value estimation closely approximates the target mean. This phenomenon resembles the noisy TV problem postulated in the explo…

New Insights into Automatic Treatment Planning for Cancer Radiotherapy Using Explainable Artificial Intelligence
Md Mainul Abrar, Xun Jia, Yujie Chi
https://arxiv.org/abs/2508.14229

New Insights into Automatic Treatment Planning for Cancer Radiotherapy Using Explainable Artificial Intelligence
Objective: This study aims to uncover the opaque decision-making process of an artificial intelligence (AI) agent for automatic treatment planning. Approach: We examined a previously developed AI agent based on the Actor-Critic with Experience Replay (ACER) network, which automatically tunes treatment planning parameters (TPPs) for inverse planning in prostate cancer intensity modulated radiotherapy. We selected multiple checkpoint ACER agents from different stages of training and applied an …

Catastrophic Forgetting Mitigation via Discrepancy-Weighted Experience Replay
Xinrun Xu, Jianwen Yang, Qiuhong Zhang, Zhanbiao Lian, Zhiming Ding, Shan Jiang
https://arxiv.org/abs/2507.00042

Catastrophic Forgetting Mitigation via Discrepancy-Weighted Experience Replay
Continually adapting edge models in cloud-edge collaborative object detection for traffic monitoring suffers from catastrophic forgetting, where models lose previously learned knowledge when adapting to new data distributions. This is especially problematic in dynamic traffic environments characterised by periodic variations (e.g., day/night, peak hours), where past knowledge remains valuable. Existing approaches like experience replay and visual prompts offer some mitigation, but struggle to e…

This https://arxiv.org/abs/2504.00420 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csRO_…

Think Small, Act Big: Primitive Prompt Learning for Lifelong Robot Manipulation
Building a lifelong robot that can effectively leverage prior knowledge for continuous skill acquisition remains significantly challenging. Despite the success of experience replay and parameter-efficient methods in alleviating catastrophic forgetting problem, naively applying these methods causes a failure to leverage the shared primitives between skills. To tackle these issues, we propose Primitive Prompt Learning (PPL), to achieve lifelong robot manipulation via reusable and extensible primi…

SAGE: Spliced-Audio Generated Data for Enhancing Foundational Models in Low-Resource Arabic-English Code-Switched Speech Recognition
Muhammad Umar Farooq, Oscar Saz
https://arxiv.org/abs/2506.22143

SAGE: Spliced-Audio Generated Data for Enhancing Foundational Models in Low-Resource Arabic-English Code-Switched Speech Recognition
This paper investigates the performance of various speech SSL models on dialectal Arabic (DA) and Arabic-English code-switched (CS) speech. To address data scarcity, a modified audio-splicing approach is introduced to generate artificial CS speech data. Fine-tuning an already fine-tuned SSL model with the proposed Spliced-Audio Generated (SAGE) data results in an absolute improvement on Word Error Rate (WER) of 7.8% on Arabic and English CS benchmarks. Additionally, an Experience Replay (ER) in…