Zobrazeno 1 - 10
of 31 190
pro vyhledávání: '"A Bou"'
Ballistic Convergence in Hit-and-Run Monte Carlo and a Coordinate-free Randomized Kaczmarz Algorithm
Hit-and-Run is a coordinate-free Gibbs sampler, yet the quantitative advantages of its coordinate-free property remain largely unexplored beyond empirical studies. In this paper, we prove sharp estimates for the Wasserstein contraction of Hit-and-Run
Externí odkaz:
http://arxiv.org/abs/2412.07643
Autor:
Bou, Xavier, Facciolo, Gabriele, von Gioi, Rafael Grompone, Morel, Jean-Michel, Ehret, Thibaud
Oriented object detection predicts orientation in addition to object location and bounding box. Precisely predicting orientation remains challenging due to angular periodicity, which introduces boundary discontinuity issues and symmetry ambiguities.
Externí odkaz:
http://arxiv.org/abs/2411.10497
Autor:
Liu, Puze, Günster, Jonas, Funk, Niklas, Gröger, Simon, Chen, Dong, Bou-Ammar, Haitham, Jankowski, Julius, Marić, Ante, Calinon, Sylvain, Orsula, Andrej, Olivares-Mendez, Miguel, Zhou, Hongyi, Lioutikov, Rudolf, Neumann, Gerhard, Zhalehmehrabi, Amarildo Likmeta Amirhossein, Bonenfant, Thomas, Restelli, Marcello, Tateo, Davide, Liu, Ziyuan, Peters, Jan
Machine learning methods have a groundbreaking impact in many application domains, but their application on real robotic platforms is still limited. Despite the many challenges associated with combining machine learning technology with robotics, robo
Externí odkaz:
http://arxiv.org/abs/2411.05718
Autor:
Manuel, Dylan, Islam, Nafis Tanveer, Khoury, Joseph, Nunez, Ana, Bou-Harb, Elias, Najafirad, Peyman
Security experts reverse engineer (decompile) binary code to identify critical security vulnerabilities. The limited access to source code in vital systems - such as firmware, drivers, and proprietary software used in Critical Infrastructures (CI) -
Externí odkaz:
http://arxiv.org/abs/2411.04981
Autor:
Grosnit, Antoine, Maraval, Alexandre, Doran, James, Paolo, Giuseppe, Thomas, Albert, Beevi, Refinath Shahul Hameed Nabeezath, Gonzalez, Jonas, Khandelwal, Khyati, Iacobacci, Ignacio, Benechehab, Abdelhakim, Cherkaoui, Hamza, El-Hili, Youssef Attia, Shao, Kun, Hao, Jianye, Yao, Jun, Kegl, Balazs, Bou-Ammar, Haitham, Wang, Jun
We introduce Agent K v1.0, an end-to-end autonomous data science agent designed to automate, optimise, and generalise across diverse data science tasks. Fully automated, Agent K v1.0 manages the entire data science life cycle by learning from experie
Externí odkaz:
http://arxiv.org/abs/2411.03562
Autor:
Yan, Xue, Song, Yan, Feng, Xidong, Yang, Mengyue, Zhang, Haifeng, Ammar, Haitham Bou, Wang, Jun
In sequential decision-making (SDM) tasks, methods like reinforcement learning (RL) and heuristic search have made notable advances in specific cases. However, they often require extensive exploration and face challenges in generalizing across divers
Externí odkaz:
http://arxiv.org/abs/2410.07927
Autor:
Agrawal, Pravesh, Antoniak, Szymon, Hanna, Emma Bou, Bout, Baptiste, Chaplot, Devendra, Chudnovsky, Jessica, Costa, Diogo, De Monicault, Baudouin, Garg, Saurabh, Gervet, Theophile, Ghosh, Soham, Héliou, Amélie, Jacob, Paul, Jiang, Albert Q., Khandelwal, Kartik, Lacroix, Timothée, Lample, Guillaume, Casas, Diego Las, Lavril, Thibaut, Scao, Teven Le, Lo, Andy, Marshall, William, Martin, Louis, Mensch, Arthur, Muddireddy, Pavankumar, Nemychnikova, Valera, Pellat, Marie, Von Platen, Patrick, Raghuraman, Nikhil, Rozière, Baptiste, Sablayrolles, Alexandre, Saulnier, Lucile, Sauvestre, Romain, Shang, Wendy, Soletskyi, Roman, Stewart, Lawrence, Stock, Pierre, Studnia, Joachim, Subramanian, Sandeep, Vaze, Sagar, Wang, Thomas, Yang, Sophia
We introduce Pixtral-12B, a 12--billion-parameter multimodal language model. Pixtral-12B is trained to understand both natural images and documents, achieving leading performance on various multimodal benchmarks, surpassing a number of larger models.
Externí odkaz:
http://arxiv.org/abs/2410.07073
Autor:
Bou-Rabee, Nawaf, Oberdörster, Stefan
We prove that the mixing time of the No-U-Turn Sampler (NUTS), when initialized in the concentration region of the canonical Gaussian measure, scales as $d^{1/4}$, up to logarithmic factors, where $d$ is the dimension. This scaling is expected to be
Externí odkaz:
http://arxiv.org/abs/2410.06978
Preference Optimization (PO) has proven an effective step for aligning language models to human-desired behaviors. Current variants, following the offline Direct Preference Optimization objective, have focused on a strict setting where all tokens are
Externí odkaz:
http://arxiv.org/abs/2410.05102
The growth in the number of parameters of Large Language Models (LLMs) has led to a significant surge in computational requirements, making them challenging and costly to deploy. Speculative decoding (SD) leverages smaller models to efficiently propo
Externí odkaz:
http://arxiv.org/abs/2410.03804