Deep Learning
Data-Efficient Operator Learning via Unsupervised Pretraining and In-Context Learning Wuyang Chen Simon Fraser University Jialin Song
Extensive empirical evaluations on a diverse set of PDEs demonstrate that our method is highly data-efficient, more gener-alizable, and even outperforms conventional vision-pretrained models.
Linking In-context Learning in Transformers to Human Episodic Memory Li Ji-An
Understanding connections between artificial and biological intelligent systems can reveal fundamental principles of general intelligence.
Eye-gaze Guided Multi-modal Alignment for Medical Representation Learning Chong Ma
This data-reliance may lead to low generalization of the learned alignment relationships.
KnowGPT: Knowledge Graph based PrompT ing for Large Language Models
Large Language Models (LLMs) have demonstrated remarkable capabilities in many real-world applications.
End-to-end Learnable Clustering for Intent Learning in Recommendation
Intent learning, which aims to learn users' intents for user understanding and
Trade-Offs of Diagonal Fisher Information Matrix Estimators
The Fisher information matrix can be used to characterize the local geometry of the parameter space of neural networks.
