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Found 6 papers, 2 papers with code
EpilepsyLLM: Domain-Specific Large Language Model Fine-tuned with Epilepsy Medical Knowledge
Based on those powerful LLMs, the model fine-tuned with domain-specific datasets posseses more specialized knowledge and thus is more practical like medical LLMs.
ArCL: Enhancing Contrastive Learning with Augmentation-Robust Representations
Moreover, we show that contrastive learning fails to learn domain-invariant features, which limits its transferability.
When Noisy Labels Meet Long Tail Dilemmas: A Representation Calibration Method
In this paper, to handle the problem and address the limitations of prior works, we propose a representation calibration method RCAL.
Heterogeneous Federated Learning on a Graph
In this work, we consider parameter estimation in federated learning with data distribution and communication heterogeneity, as well as limited computational capacity of local devices.
Towards the Generalization of Contrastive Self-Supervised Learning
It reveals that the generalization ability of contrastive self-supervised learning is related to three key factors: alignment of positive samples, divergence of class centers, and concentration of augmented data.
Tensorizing Generative Adversarial Nets
Generative Adversarial Network (GAN) and its variants exhibit state-of-the-art performance in the class of generative models.
