[HTML][HTML] A review of uncertainty quantification in deep learning: Techniques, applications and challenges
Uncertainty quantification (UQ) methods play a pivotal role in reducing the impact of
uncertainties during both optimization and decision making processes. They have been …
uncertainties during both optimization and decision making processes. They have been …
Human-in-the-loop reinforcement learning: A survey and position on requirements, challenges, and opportunities
Artificial intelligence (AI) and especially reinforcement learning (RL) have the potential to
enable agents to learn and perform tasks autonomously with superhuman performance …
enable agents to learn and perform tasks autonomously with superhuman performance …
Deep reinforcement learning-based energy-efficient edge computing for internet of vehicles
Mobile network operators (MNOs) allocate computing and caching resources for mobile
users by deploying a central control system. Existing studies mainly use programming and …
users by deploying a central control system. Existing studies mainly use programming and …
More than privacy: Applying differential privacy in key areas of artificial intelligence
Artificial Intelligence (AI) has attracted a great deal of attention in recent years. However,
alongside all its advancements, problems have also emerged, such as privacy violations …
alongside all its advancements, problems have also emerged, such as privacy violations …
Human-in-the-loop reinforcement learning in continuous-action space
Human-in-the-loop for reinforcement learning (RL) is usually employed to overcome the
challenge of sample inefficiency, in which the human expert provides advice for the agent …
challenge of sample inefficiency, in which the human expert provides advice for the agent …
Cost-based or learning-based? A hybrid query optimizer for query plan selection
Traditional cost-based optimizers are efficient and stable to generate optimal plans for
simple SQL queries, but they may not generate high-quality plans for complicated queries …
simple SQL queries, but they may not generate high-quality plans for complicated queries …
Ask4help: Learning to leverage an expert for embodied tasks
Embodied AI agents continue to become more capable every year with the advent of new
models, environments, and benchmarks, but are still far away from being performant and …
models, environments, and benchmarks, but are still far away from being performant and …
Credit assignment: Challenges and opportunities in developing human-like ai agents
Temporal credit assignment is crucial for learning and skill development in natural and
artificial intelligence. While computational methods like the TD approach in reinforcement …
artificial intelligence. While computational methods like the TD approach in reinforcement …
Uncertainty-aware reinforcement learning for risk-sensitive player evaluation in sports game
A major task of sports analytics is player evaluation. Previous methods commonly measured
the impact of players' actions on desirable outcomes (eg, goals or winning) without …
the impact of players' actions on desirable outcomes (eg, goals or winning) without …
Explainable action advising for multi-agent reinforcement learning
Action advising is a knowledge transfer technique for reinforcement learning based on the
teacher-student paradigm. An expert teacher provides advice to a student during training in …
teacher-student paradigm. An expert teacher provides advice to a student during training in …