Zobrazeno 1 - 10
of 25 289
pro vyhledávání: '"MATEI, A."'
The increasing availability and diversity of multimodal data in recommender systems offer new avenues for enhancing recommendation accuracy and user satisfaction. However, these systems must contend with high-dimensional, sparse user-item rating matr
Externí odkaz:
http://arxiv.org/abs/2412.02295
Recently, deep matrix factorization has been established as a powerful model for unsupervised tasks, achieving promising results, especially for multi-view clustering. However, existing methods often lack effective feature selection mechanisms and re
Externí odkaz:
http://arxiv.org/abs/2412.02292
Autor:
Benkedadra, Mohamed, Rimez, Dany, Godelaine, Tiffanie, Chidambaram, Natarajan, Khosroshahi, Hamed Razavi, Tellez, Horacio, Mancas, Matei, Macq, Benoit, Mahmoudi, Sidi Ahmed
Publikováno v:
2024 IEEE 7th International Conference on Multimedia Information Processing and Retrieval (MIPR) 2024 IEEE 7th International Conference on Multimedia Information Processing and Retrieval (MIPR) 10.1109/MIPR62202.2024
Computer vision tasks such as object detection and segmentation rely on the availability of extensive, accurately annotated datasets. In this work, We present CIA, a modular pipeline, for (1) generating synthetic images for dataset augmentation using
Externí odkaz:
http://arxiv.org/abs/2411.16128
Autor:
Coiculescu, Matei P.
We prove that the stationary power-law vortex $\overline{\omega}(x) = \beta |x|^{-\alpha}$, which explicitly solves the incompressible Euler equations in $\mathbb{R}^2$, is linearly stable in self-similar coordinates with the natural scaling.
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Externí odkaz:
http://arxiv.org/abs/2411.13397
Autor:
Jacob, Mathew, Lindgren, Erik, Zaharia, Matei, Carbin, Michael, Khattab, Omar, Drozdov, Andrew
Rerankers, typically cross-encoders, are often used to re-score the documents retrieved by cheaper initial IR systems. This is because, though expensive, rerankers are assumed to be more effective. We challenge this assumption by measuring reranker p
Externí odkaz:
http://arxiv.org/abs/2411.11767
Autor:
Cao, Shiyi, Liu, Shu, Griggs, Tyler, Schafhalter, Peter, Liu, Xiaoxuan, Sheng, Ying, Gonzalez, Joseph E., Zaharia, Matei, Stoica, Ion
Efficient deployment of large language models, particularly Mixture of Experts (MoE), on resource-constrained platforms presents significant challenges, especially in terms of computational efficiency and memory utilization. The MoE architecture, ren
Externí odkaz:
http://arxiv.org/abs/2411.11217
Retrieval Augmented Generation (RAG) has emerged as a crucial technique for enhancing the accuracy of Large Language Models (LLMs) by incorporating external information. With the advent of LLMs that support increasingly longer context lengths, there
Externí odkaz:
http://arxiv.org/abs/2411.03538
Autor:
Zangene, Farzane, Radulescu, Matei I.
This study investigates the role of two inert mono-atomic diluents, argon and helium, on the detonation structure in order to assess the importance of vibrational non-equilibrium and wall losses. When relaxation effects and wall losses are neglected,
Externí odkaz:
http://arxiv.org/abs/2410.17561
Efficient video tokenization remains a key bottleneck in learning general purpose vision models that are capable of processing long video sequences. Prevailing approaches are restricted to encoding videos to a fixed number of tokens, where too few to
Externí odkaz:
http://arxiv.org/abs/2410.08368
Force/torque sensing is an important modality for robotic manipulation, but commodity solutions, generally developed with other applications in mind, do not generally fit the needs of robot hands. This paper introduces a novel method for six-axis for
Externí odkaz:
http://arxiv.org/abs/2410.03481