In contrast to prior studies that relied on deep learning models with low transparency, or balanced datasets that fail to ...

Researchers investigate reinforcement learning for climate adaptation, demonstrating its effectiveness in optimizing coastal ...

From flexible machine learning algorithms, the dynamic adaptation involves personalized content by assessing the student's performance, preferences, and analysis of their interaction. According to ...

Adaptive learning systems in online education customize learning experiences based on individual needs, allowing students to learn at their own pace. These systems provide personalized content ...

My personal prompt library for various LLMs + scripts & tools. Suitable for models from Deepseek, OpenAI, Claude, Meta, Mistral, Google, Grok, and others.

Researchers from UNC Chapel Hill have proposed SYMBOLIC-MOE, a symbolic, text-based, and gradient-free Mixture-of-Experts framework to enable adaptive instance-level mixing of pre-trained LLM experts.

Different from traditional approaches which generally require perfect knowledge of system models and state measurements, in this paper, a novel learning-based adaptive resilient control framework is ...

This project will develop and apply a domain-specific scientific machine learning (SciML) framework in which a physics-informed machine learning will assimilate infrared satellite data to provide ...

2) An adaptive learning framework is proposed for online-offline combined learning of models. Online learning and offline retraining are adaptive switched based on the real-time loss to achieve ...

The ALMT implementation has been added to MMSA; you can also refer to the implementation and make a fairer comparison with other methods in the same framework ... {zhang-etal-2023-learning-language, ...