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Found 8 papers, 1 papers with code
Enhancing Wind Speed and Wind Power Forecasting Using Shape-Wise Feature Engineering: A Novel Approach for Improved Accuracy and Robustness
This noise has the potential to diminish the performance of these algorithms, leading to inaccurate predictions.
Learning to Write with Coherence From Negative Examples
We compare our approach with Unlikelihood (UL) training in a text continuation task on commonsense natural language inference (NLI) corpora to show which method better models the coherence by avoiding unlikely continuations.
Recursive Constraints to Prevent Instability in Constrained Reinforcement Learning
In this work, after describing and motivating our problem with a simple example, we present a suitable constrained reinforcement learning algorithm that prevents learning instability, using recursive constraints.
Predictive PER: Balancing Priority and Diversity towards Stable Deep Reinforcement Learning
Prioritized experience replay (PER) samples important transitions, rather than uniformly, to improve the performance of a deep reinforcement learning agent.
Collaborative Method for Incremental Learning on Classification and Generation
In this paper, we introduce a novel algorithm, Incremental Class Learning with Attribute Sharing (ICLAS), for incremental class learning with deep neural networks.
Design Space of Behaviour Planning for Autonomous Driving
We explore the complex design space of behaviour planning for autonomous driving.
WiseMove: A Framework for Safe Deep Reinforcement Learning for Autonomous Driving
Machine learning can provide efficient solutions to the complex problems encountered in autonomous driving, but ensuring their safety remains a challenge.
Policy Iterations for Reinforcement Learning Problems in Continuous Time and Space -- Fundamental Theory and Methods
Policy iteration (PI) is a recursive process of policy evaluation and improvement for solving an optimal decision-making/control problem, or in other words, a reinforcement learning (RL) problem.
