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pro vyhledávání: '"Mejri, Mohamed"'
Modern transformer-based encoder-decoder architectures struggle with reasoning tasks due to their inability to effectively extract relational information between input objects (data/tokens). Recent work introduced the Abstractor module, embedded betw
Externí odkaz:
http://arxiv.org/abs/2411.08290
Human cognition excels at symbolic reasoning, deducing abstract rules from limited samples. This has been explained using symbolic and connectionist approaches, inspiring the development of a neuro-symbolic architecture that combines both paradigms.
Externí odkaz:
http://arxiv.org/abs/2405.14436
In recent years, both online and offline deep learning models have been developed for time series forecasting. However, offline deep forecasting models fail to adapt effectively to changes in time-series data, while online deep forecasting models are
Externí odkaz:
http://arxiv.org/abs/2402.01999
This paper investigates the impact of feature encoding techniques on the explainability of XAI (Explainable Artificial Intelligence) algorithms. Using a malware classification dataset, we trained an XGBoost model and compared the performance of two f
Externí odkaz:
http://arxiv.org/abs/2307.05614
Breast cancer is one of the factors that cause the increase of mortality of women. The most widely used method for diagnosing this geological disease i.e. breast cancer is the ultrasound scan. Several key features such as the smoothness and the textu
Externí odkaz:
http://arxiv.org/abs/2108.03287
Autor:
Fattahi, Jaouhar, Mejri, Mohamed
Fingerprint recognition is often a game-changing step in establishing evidence against criminals. However, we are increasingly finding that criminals deliberately alter their fingerprints in a variety of ways to make it difficult for technicians and
Externí odkaz:
http://arxiv.org/abs/2012.15041
Autor:
Mejri, Mohamed, Mejri, Aymen
We present a novel and practical deep learning pipeline termed RandomForestMLP. This core trainable classification engine consists of a convolutional neural network backbone followed by an ensemble-based multi-layer perceptrons core for the classific
Externí odkaz:
http://arxiv.org/abs/2011.01188
Autor:
Fattahi, Jaouhar, Mejri, Mohamed
In this paper, we put forward a new tool, called SpaML, for spam detection using a set of supervised and unsupervised classifiers, and two techniques imbued with Natural Language Processing (NLP), namely Bag of Words (BoW) and Term Frequency-Inverse
Externí odkaz:
http://arxiv.org/abs/2010.07444
The analysis of the internal structure of trees is highly important for both forest experts, biological scientists, and the wood industry. Traditionally, CT-scanners are considered as the most efficient way to get an accurate inner representation of
Externí odkaz:
http://arxiv.org/abs/2002.04571
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