Zobrazeno 1 - 10
of 37 521
pro vyhledávání: '"LI, NING"'
Crafting effective features is a crucial yet labor-intensive and domain-specific task within machine learning pipelines. Fortunately, recent advancements in Large Language Models (LLMs) have shown promise in automating various data science tasks, inc
Externí odkaz:
http://arxiv.org/abs/2410.12865
What will information entry look like in the next generation of digital products? Since the 1970s, user access to relevant information has relied on domain-specific architectures of information retrieval (IR). Over the past two decades, the advent of
Externí odkaz:
http://arxiv.org/abs/2410.09713
Even the AI has been widely used and significantly changed our life, deploying the large AI models on resource limited edge devices directly is not appropriate. Thus, the model split inference is proposed to improve the performance of edge intelligen
Externí odkaz:
http://arxiv.org/abs/2409.16537
In this paper, an orthogonal mode decomposition method is proposed to decompose ffnite length real signals on both the real and imaginary axes of the complex plane. The interpolation function space of ffnite length discrete signal is constructed, and
Externí odkaz:
http://arxiv.org/abs/2409.07242
Artificial Intelligence (AI) is increasingly being integrated into scientific research, particularly in the social sciences, where understanding human behavior is critical. Large Language Models (LLMs) like GPT-4 have shown promise in replicating hum
Externí odkaz:
http://arxiv.org/abs/2409.00128
GuessWhich is an engaging visual dialogue game that involves interaction between a Questioner Bot (QBot) and an Answer Bot (ABot) in the context of image-guessing. In this game, QBot's objective is to locate a concealed image solely through a series
Externí odkaz:
http://arxiv.org/abs/2408.08431
Visual Dialog (VD) is a task where an agent answers a series of image-related questions based on a multi-round dialog history. However, previous VD methods often treat the entire dialog history as a simple text input, disregarding the inherent conver
Externí odkaz:
http://arxiv.org/abs/2408.06725
This study explores the potential of Large Language Models (LLMs), specifically GPT-4, to enhance objectivity in organizational task performance evaluations. Through comparative analyses across two studies, including various task performance outputs,
Externí odkaz:
http://arxiv.org/abs/2408.05328
Machine learning and deep learning (ML/DL) have been extensively applied in malware detection, and some existing methods demonstrate robust performance. However, several issues persist in the field of malware detection: (1) Existing work often overem
Externí odkaz:
http://arxiv.org/abs/2408.02066
In dynamic Windows malware detection, deep learning models are extensively deployed to analyze API sequences. Methods based on API sequences play a crucial role in malware prevention. However, due to the continuous updates of APIs and the changes in
Externí odkaz:
http://arxiv.org/abs/2408.01661