The article presents a new framework for Interactive Machine Learning (IML), aiming to incorporate human expertise into machine learning algorithms. The framework is grounded in Bayesian Optimization (BO), thus called…
Continue readingThe study presents a method of dynamically selecting layers in deep transformer networks to reduce the number of trainable parameters. This is achieved by employing Reinforcement Learning to decide whether…
Continue readingThe research addresses the challenge of enforcing safety in Reinforcement Learning (RL), crucial for its application in real-world scenarios. The researchers propose a method of overcoming the non-contractiveness of safety…
Continue readingThe article presents a study on the use of Reinforcement Learning from Human Feedback (RLHF) to improve the performance of the GPT Neo 125M in the Community Question Answering (CQA)…
Continue readingThis paper addresses the challenge of controlling a drone in unknown dynamic environments using a new model predictive control (MPC) approach called hyMPC. The hyMPC leverages high-level decision variables to…
Continue readingThe paper presents a combined task-level reinforcement learning and motion planning framework to solve a multi-class in-rack test tube rearrangement problem. The framework uses reinforcement learning at the task level…
Continue readingThis academic article introduces DeLF (Designing Learning Environments with Foundation Models), a method for designing components of the Reinforcement Learning (RL) environment for a specific, user-intended application. The authors focus…
Continue readingThe research introduces the Continuous Time Continuous Space Homeostatic Regulated Reinforcement Learning (CTCS-HRRL) framework. This framework extends the Homeostatic Regulated Reinforcement Learning (HRRL) framework to a continuous time-space environment. The…
Continue readingThis study introduces a new DQN-based Cross-Stream Crisis event Summarizer that addresses the challenges in summarizing multiple disaster-relevant data streams. It uses Deep Reinforcement Learning (DRL) to retrieve relevant texts…
Continue readingThe authors propose a generic model-based re-ranking framework, MultiSlot ReRanker, that optimizes relevance, diversity, and freshness. It uses the Sequential Greedy Algorithm, which is efficient for large-scale recommendation engines. The…
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