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Joint Client-and-Sample Selection for Federated Learning via Bi-Level Optimization
Federated Learning (FL) enables massive local data owners to collaboratively train a deep learning model without disclosing their …
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Efficient and Privacy-Preserving Feature Importance-Based Vertical Federated Learning
Vertical Federated Learning (VFL) enables multiple data owners, each holding a different subset of features about a largely overlapping …
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From Static to Dynamic: Knowledge Metabolism for Large Language Models
The immense parameter space of Large Language Models (LLMs) endows them with superior knowledge retention capabilities, allowing them …
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LAMPAT: Low-Rank Adaption for Multilingual Paraphrasing Using Adversarial Training
Paraphrases are texts that convey the same meaning while using different words or sentence structures. It can be used as an automatic …
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On the Affinity, Rationality, and Diversity of Hierarchical Topic Modeling
Hierarchical topic modeling aims to discover latent topics from a corpus and organize them into a hierarchy to understand documents …
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PoetryDiffusion: Towards Joint Semantic and Metrical Manipulation in Poetry Generation
Controllable text generation is a challenging and meaningful field in natural language generation (NLG). Especially, poetry generation …
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Incorporating Template-based Contrastive Learning into cognitively inspired, low resource relation extraction
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Learning facial expression and body gesture visual information for video emotion recognition
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A survey on neural topic models: methods, applications, and challenges
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Exploring Clean Label Backdoor Attacks and Defense in Language Models
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