4 citations · 10 across the 10 of their papers we have counts for
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Contrastive Network Representation Learning
Zihan Dong, Xin Zhou, Ryumei Nakada +2
Network representation learning seeks to embed networks into a low-dimensional space while preserving the structural and semantic properties, thereby facilitating downstream tasks…
Residual Feature Integration is Sufficient to Prevent Negative Transfer
Yichen Xu, Ryumei Nakada, Linjun Zhang +1
Transfer learning has become a central paradigm in modern machine learning, yet it suffers from the long-standing problem of negative transfer, where leveraging source representati…
A Theoretical Framework for Prompt Engineering: Approximating Smooth Functions with Transformer Prompts
Ryumei Nakada, Wenlong Ji, Tianxi Cai +2
Prompt engineering has emerged as a powerful technique for guiding large language models (LLMs) toward desired responses, significantly enhancing their performance across diverse t…
A Statistical Hypothesis Testing Framework for Data Misappropriation Detection in Large Language Models
Yinpeng Cai, Lexin Li, Linjun Zhang
Large Language Models (LLMs) are rapidly gaining enormous popularity in recent years. However, the training of LLMs has raised significant privacy and legal concerns, particularly…