A type of artificial intelligence that focuses on designing systems that can learn from and make decisions based on data.
The article discusses the value-loading problem in artificial intelligence (AI), which is the challenge of ensuring AI systems align with human values and preferences. The authors propose a solution called…
Continue readingThe rapid progress of foundation models has led to the prosperity of autonomous agents, which leverage the universal capabilities of these models for reasoning, decision-making, and environmental interaction. However, these…
Continue readingThe study introduces MAIDCRL (Multi-Agent Dense-CNN Reinforcement Learning), a reinforcement learning algorithm that incorporates convolutional layers into the deep model architecture for better multi-agent control in StarCraft Multi-Agent Challenge scenarios….
Continue readingThe paper introduces a new method, Diffusion-ES, that combines gradient-free optimization with trajectory denoising to optimize non-differentiable objectives. This method is particularly useful in autonomous driving scenarios. Diffusion-ES samples trajectories…
Continue readingThis research paper discusses the issue of missing data in electronic patient records (EPRs) and its impact on clinical data analysis. The authors argue that missing data can lead to…
Continue readingThe article introduces a new approach, RQP-SGD, for privacy-preserving machine learning. The rise of IoT devices necessitates efficient and secure data processing. Traditional machine learning models can be impractical for…
Continue readingThe article presents a study on how feedback loops in language models can cause in-context reward hacking (ICRH). This occurs when a language model optimizes an objective but creates negative…
Continue readingThe paper discusses TWIG (Topologically-Weighted Intelligence Generation), an innovative paradigm for simulating the output of Knowledge Graph Embeddings (KGEs) using a significantly lesser number of parameters. TWIG learns from inputs…
Continue readingThe article discusses the challenges in Knowledge Graph (KG) learning due to its immense size. Four key deficiencies are identified: lack of expert knowledge integration, instability to node degree extremity…
Continue readingThe paper discusses challenges in Temporal Interaction Graphs (TIGs) used in systems like e-commerce and social networks. Existing models face issues with timely predictions and lack versatility for diverse tasks….
Continue readingLooks like you're using an ad blocker. Please support us by disabling these ad blocker.
