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pro vyhledávání: '"Vazé P"'
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
Detecting test-time distribution shift has emerged as a key capability for safely deployed machine learning models, with the question being tackled under various guises in recent years. In this paper, we aim to provide a consolidated view of the two
Externí odkaz:
http://arxiv.org/abs/2408.16757
Generalized Category Discovery (GCD) is a challenging task in which, given a partially labelled dataset, models must categorize all unlabelled instances, regardless of whether they come from labelled categories or from new ones. In this paper, we cha
Externí odkaz:
http://arxiv.org/abs/2408.04591
Autor:
Sinha, Abhishek, Vaze, Rahul
A well-studied generalization of the standard online convex optimization (OCO) is constrained online convex optimization (COCO). In COCO, on every round, a convex cost function and a convex constraint function are revealed to the learner after the ac
Externí odkaz:
http://arxiv.org/abs/2405.09296
Autor:
Vaze, Rahul
The problem of online scheduling of multi-server jobs is considered, where there are a total of $K$ servers, and each job requires concurrent service from multiple servers for it to be processed. Each job on its arrival reveals its processing time, t
Externí odkaz:
http://arxiv.org/abs/2404.05271
Autor:
Vaze, Rahul, Nair, Jayakrishnan
An online non-convex optimization problem is considered where the goal is to minimize the flow time (total delay) of a set of jobs by modulating the number of active servers, but with a switching cost associated with changing the number of active ser
Externí odkaz:
http://arxiv.org/abs/2403.17480
Generalized Category Discovery (GCD) aims to classify unlabelled images from both `seen' and `unseen' classes by transferring knowledge from a set of labelled `seen' class images. A key theme in existing GCD approaches is adapting large-scale pre-tra
Externí odkaz:
http://arxiv.org/abs/2403.13684
Autor:
Sinha, Abhishek, Vaze, Rahul
A well-studied generalization of the standard online convex optimization (OCO) framework is constrained online convex optimization (COCO). In COCO, on every round, a convex cost function and a convex constraint function are revealed to the learner af
Externí odkaz:
http://arxiv.org/abs/2310.18955
Autor:
Senapati, Spandan, Vaze, Rahul
We consider the online convex optimization (OCO) problem with quadratic and linear switching cost in the limited information setting, where an online algorithm can choose its action using only gradient information about the previous objective functio
Externí odkaz:
http://arxiv.org/abs/2310.11880
We argue that there are many notions of 'similarity' and that models, like humans, should be able to adapt to these dynamically. This contrasts with most representation learning methods, supervised or self-supervised, which learn a fixed embedding fu
Externí odkaz:
http://arxiv.org/abs/2306.07969