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pro vyhledávání: '"Kim, Yejin A."'
Autor:
Choi, Chanyeol, Kim, Junseong, Lee, Seolhwa, Kwon, Jihoon, Gu, Sangmo, Kim, Yejin, Cho, Minkyung, Sohn, Jy-yong
This report explores the enhancement of text retrieval performance using advanced data refinement techniques. We develop Linq-Embed-Mistral\footnote{\url{https://huggingface.co/Linq-AI-Research/Linq-Embed-Mistral}} by building on the E5-mistral and M
Externí odkaz:
http://arxiv.org/abs/2412.03223
Large language models (LLMs) typically improve performance by either retrieving semantically similar information, or enhancing reasoning abilities through structured prompts like chain-of-thought. While both strategies are considered crucial, it rema
Externí odkaz:
http://arxiv.org/abs/2410.11588
Achieving fairness across diverse clients in Federated Learning (FL) remains a significant challenge due to the heterogeneity of the data and the inaccessibility of sensitive attributes from clients' private datasets. This study addresses this issue
Externí odkaz:
http://arxiv.org/abs/2406.17102
In the era of big data, access to abundant data is crucial for driving research forward. However, such data is often inaccessible due to privacy concerns or high costs, particularly in healthcare domain. Generating synthetic (tabular) data can addres
Externí odkaz:
http://arxiv.org/abs/2406.10521
Recommender systems can be helpful for individuals to make well-informed decisions in complex financial markets. While many studies have focused on predicting stock prices, even advanced models fall short of accurately forecasting them. Additionally,
Externí odkaz:
http://arxiv.org/abs/2404.07223
This paper explores the utilization of Temporal Graph Networks (TGN) for financial anomaly detection, a pressing need in the era of fintech and digitized financial transactions. We present a comprehensive framework that leverages TGN, capable of capt
Externí odkaz:
http://arxiv.org/abs/2404.00060
Recommender systems have been actively studied and applied in various domains to deal with information overload. Although there are numerous studies on recommender systems for movies, music, and e-commerce, comparatively less attention has been paid
Externí odkaz:
http://arxiv.org/abs/2403.18305
Autor:
Kim, Yejin, Rome, Scott, Foley, Kevin, Nankani, Mayur, Melamed, Rimon, Morales, Javier, Yadav, Abhay, Peifer, Maria, Hamidian, Sardar, Huang, H. Howie
Addressing the challenges related to data sparsity, cold-start problems, and diversity in recommendation systems is both crucial and demanding. Many current solutions leverage knowledge graphs to tackle these issues by combining both item-based and u
Externí odkaz:
http://arxiv.org/abs/2403.18667
Recommender systems, crucial for user engagement on platforms like e-commerce and streaming services, often lag behind users' evolving preferences due to static data reliance. After Temporal Graph Networks (TGNs) were proposed, various studies have s
Externí odkaz:
http://arxiv.org/abs/2403.16066
Improving the accessibility of psychotherapy with the aid of Large Language Models (LLMs) is garnering a significant attention in recent years. Recognizing cognitive distortions from the interviewee's utterances can be an essential part of psychother
Externí odkaz:
http://arxiv.org/abs/2403.14255