Zobrazeno 1 - 10
of 4 631
pro vyhledávání: '"Bilel, A."'
Autor:
Tihanyi, Norbert, Bisztray, Tamas, Dubniczky, Richard A., Toth, Rebeka, Borsos, Bertalan, Cherif, Bilel, Ferrag, Mohamed Amine, Muzsai, Lajos, Jain, Ridhi, Marinelli, Ryan, Cordeiro, Lucas C., Debbah, Merouane
As machine intelligence evolves, the need to test and compare the problem-solving abilities of different AI models grows. However, current benchmarks are often overly simplistic, allowing models to perform uniformly well, making it difficult to disti
Externí odkaz:
http://arxiv.org/abs/2410.15490
Autor:
Thapaliya, Bishal, Nguyen, Anh, Lu, Yao, Xie, Tian, Grudetskyi, Igor, Lin, Fudong, Valkanas, Antonios, Liu, Jingyu, Chakraborty, Deepayan, Fehri, Bilel
Classifying nodes in a graph is a common problem. The ideal classifier must adapt to any imbalances in the class distribution. It must also use information in the clustering structure of real-world graphs. Existing Graph Neural Networks (GNNs) have n
Externí odkaz:
http://arxiv.org/abs/2410.11765
Autor:
Hamil, Bilel, Lütfüoğlu, Bekir Can
This manuscript investigates a Schwarzschild black hole surrounded by perfect fluid dark matter embedded in a cloud of strings. The effects of its surroundings on thermodynamics, timelike and null geodesics, shadows, and quasinormal modes are analyze
Externí odkaz:
http://arxiv.org/abs/2410.09551
Denoising is one of the fundamental steps of the processing pipeline that converts data captured by a camera sensor into a display-ready image or video. It is generally performed early in the pipeline, usually before demosaicking, although studies sw
Externí odkaz:
http://arxiv.org/abs/2410.02572
The Increasing Population Covariance Matrix Adaptation Evolution Strategy (IPOP-CMA-ES) algorithm is a reference stochastic optimizer dedicated to blackbox optimization, where no prior knowledge about the underlying problem structure is available. Th
Externí odkaz:
http://arxiv.org/abs/2409.11765
Retrieval-Augmented Generation (RAG) has emerged as a common paradigm to use Large Language Models (LLMs) alongside private and up-to-date knowledge bases. In this work, we address the challenges of using LLM-as-a-Judge when evaluating grounded answe
Externí odkaz:
http://arxiv.org/abs/2409.06595
Autor:
Guetarni, Bilel, Windal, Feryal, Benhabiles, Halim, Chaibi, Mahfoud, Dubois, Romain, Leteurtre, Emmanuelle, Collard, Dominique
Predicting the response of a patient to a cancer treatment is of high interest. Nonetheless, this task is still challenging from a medical point of view due to the complexity of the interaction between the patient organism and the considered treatmen
Externí odkaz:
http://arxiv.org/abs/2408.03954
Both accelerated and adaptive gradient methods are among state of the art algorithms to train neural networks. The tuning of hyperparameters is needed to make them work efficiently. For classical gradient descent, a general and efficient way to adapt
Externí odkaz:
http://arxiv.org/abs/2407.15471
In this paper, we introduce the mean $\Psi$-intermediate dimension which has a value between the mean Hausdorff dimension and the metric mean dimension, and prove the equivalent definition of the mean Hausdorff dimension and the metric mean dimension
Externí odkaz:
http://arxiv.org/abs/2407.09843
Recently, many machine learning optimizers have been analysed considering them as the asymptotic limit of some differential equations when the step size goes to zero. In other words, the optimizers can be seen as a finite difference scheme applied to
Externí odkaz:
http://arxiv.org/abs/2407.01019