Zobrazeno 1 - 10
of 855
pro vyhledávání: '"ROKACH, LIOR"'
Embedding news articles is a crucial tool for multiple fields, such as media bias detection, identifying fake news, and making news recommendations. However, existing news embedding methods are not optimized to capture the latent context of news even
Externí odkaz:
http://arxiv.org/abs/2405.13071
Multi-participant discussions tend to unfold in a tree structure rather than a chain structure. Branching may occur for multiple reasons -- from the asynchronous nature of online platforms to a conscious decision by an interlocutor to disengage with
Externí odkaz:
http://arxiv.org/abs/2404.13613
Autor:
Cohen, Seffi, Rokach, Lior
This paper introduces BagStacking, a novel ensemble learning method designed to enhance the detection of Freezing of Gait (FOG) in Parkinson's Disease (PD) by using a lower-back sensor to track acceleration. Building on the principles of bagging and
Externí odkaz:
http://arxiv.org/abs/2402.17783
Adverse drug interactions are largely preventable causes of medical accidents, which frequently result in physician and emergency room encounters. The detection of drug interactions in a lab, prior to a drug's use in medical practice, is essential, h
Externí odkaz:
http://arxiv.org/abs/2302.03355
Backdoor poisoning attacks pose a well-known risk to neural networks. However, most studies have focused on lenient threat models. We introduce Silent Killer, a novel attack that operates in clean-label, black-box settings, uses a stealthy poison and
Externí odkaz:
http://arxiv.org/abs/2301.02615
Software Defect Prediction aims at predicting which software modules are the most probable to contain defects. The idea behind this approach is to save time during the development process by helping find bugs early. Defect Prediction models are based
Externí odkaz:
http://arxiv.org/abs/2212.14404
In this paper, we propose an innovative Transfer learning for Time series classification method. Instead of using an existing dataset from the UCR archive as the source dataset, we generated a 15,000,000 synthetic univariate time series dataset that
Externí odkaz:
http://arxiv.org/abs/2207.07897