INSIGHT: End-to-End Neuro-Symbolic Visual Reinforcement Learning with Language Explanations
Neuro-symbolic reinforcement learning (NS-RL) has emerged as a promising paradigm for
explainable decision-making, characterized by the interpretability of symbolic policies. For …
explainable decision-making, characterized by the interpretability of symbolic policies. For …
Embedding-Aligned Language Models
We propose a novel approach for training large language models (LLMs) to adhere to
objectives defined within a latent embedding space. Our method leverages reinforcement …
objectives defined within a latent embedding space. Our method leverages reinforcement …
Creative Problem Solving in Large Language and Vision Models--What Would it Take?
In this paper, we discuss approaches for integrating Computational Creativity (CC) with
research in large language and vision models (LLVMs) to address a key limitation of these …
research in large language and vision models (LLVMs) to address a key limitation of these …
[HTML][HTML] Filling the gaps: leveraging large language models for temporal harmonization of clinical text across multiple medical visits for clinical prediction
Electronic health records offer great promise for early disease detection, treatment
evaluation, information discovery, and other important facets of precision health. Clinical …
evaluation, information discovery, and other important facets of precision health. Clinical …
End-to-End Neuro-Symbolic Reinforcement Learning with Textual Explanations
Neuro-symbolic reinforcement learning (NS-RL) has emerged as a promising paradigm for
explainable decision-making, characterized by the interpretability of symbolic policies. NS …
explainable decision-making, characterized by the interpretability of symbolic policies. NS …