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Found 8 papers, 4 papers with code
Minstrel: Structural Prompt Generation with Multi-Agents Coordination for Non-AI Experts
LLMs have demonstrated commendable performance across diverse domains.
Affective Computing in the Era of Large Language Models: A Survey from the NLP Perspective
The emergence of Large Language Models (LLMs), such as the ChatGPT series and LLaMA models, brings new opportunities and challenges, catalyzing a paradigm shift in AC.
Continuous-time q-Learning for Jump-Diffusion Models under Tsallis Entropy
As a consequence, the characterization of the optimal policy using the q-function also involves a Lagrange multiplier.
LangGPT: Rethinking Structured Reusable Prompt Design Framework for LLMs from the Programming Language
Experiments illustrate that LangGPT significantly enhances the performance of LLMs.
On optimal tracking portfolio in incomplete markets: The reinforcement learning approach
This paper studies an infinite horizon optimal tracking portfolio problem using capital injection in incomplete market models.
CLIP Brings Better Features to Visual Aesthetics Learners
Specifically, we first integrate and leverage a multi-source unlabeled dataset to align rich features between a given visual encoder and an off-the-shelf CLIP image encoder via feature alignment loss.
Box-Level Active Detection
Active learning selects informative samples for annotation within budget, which has proven efficient recently on object detection.
Unsupervised Learning of Local Discriminative Representation for Medical Images
However, the commonly applied supervised representation learning methods require a large amount of annotated data, and unsupervised discriminative representation learning distinguishes different images by learning a global feature, both of which are not suitable for localized medical image analysis tasks.
