Zobrazeno 1 - 10
of 77
pro vyhledávání: '"RUSSINOVICH, MARK"'
Autor:
Russinovich, Mark, Salem, Ahmed
Amid growing concerns over the ease of theft and misuse of Large Language Models (LLMs), the need for fingerprinting models has increased. Fingerprinting, in this context, means that the model owner can link a given model to their original version, t
Externí odkaz:
http://arxiv.org/abs/2407.10887
Large Language Models (LLMs) have risen significantly in popularity and are increasingly being adopted across multiple applications. These LLMs are heavily aligned to resist engaging in illegal or unethical topics as a means to avoid contributing to
Externí odkaz:
http://arxiv.org/abs/2404.01833
Autor:
Howard, Heidi, Alder, Fritz, Ashton, Edward, Chamayou, Amaury, Clebsch, Sylvan, Costa, Manuel, Delignat-Lavaud, Antoine, Fournet, Cedric, Jeffery, Andrew, Kerner, Matthew, Kounelis, Fotios, Kuppe, Markus A., Maffre, Julien, Russinovich, Mark, Wintersteiger, Christoph M.
Confidentiality, integrity protection, and high availability, abbreviated to CIA, are essential properties for trustworthy data systems. The rise of cloud computing and the growing demand for multiparty applications however means that building modern
Externí odkaz:
http://arxiv.org/abs/2310.11559
Autor:
Eldan, Ronen, Russinovich, Mark
Large language models (LLMs) are trained on massive internet corpora that often contain copyrighted content. This poses legal and ethical challenges for the developers and users of these models, as well as the original authors and publishers. In this
Externí odkaz:
http://arxiv.org/abs/2310.02238
Autor:
Shukla, Dharma, Sivathanu, Muthian, Viswanatha, Srinidhi, Gulavani, Bhargav, Nehme, Rimma, Agrawal, Amey, Chen, Chen, Kwatra, Nipun, Ramjee, Ramachandran, Sharma, Pankaj, Katiyar, Atul, Modi, Vipul, Sharma, Vaibhav, Singh, Abhishek, Singhal, Shreshth, Welankar, Kaustubh, Xun, Lu, Anupindi, Ravi, Elangovan, Karthik, Rahman, Hasibur, Lin, Zhou, Seetharaman, Rahul, Xu, Cheng, Ailijiang, Eddie, Krishnappa, Suresh, Russinovich, Mark
Lowering costs by driving high utilization across deep learning workloads is a crucial lever for cloud providers. We present Singularity, Microsoft's globally distributed scheduling service for highly-efficient and reliable execution of deep learning
Externí odkaz:
http://arxiv.org/abs/2202.07848
Autor:
Shamis, Alex, Pietzuch, Peter, Castro, Miguel, Fournet, Cédric, Ashton, Edward, Chamayou, Amaury, Clebsch, Sylvan, Delignat-Lavaud, Antoine, Kerner, Matthew, Maffre, Julien, Costa, Manuel, Russinovich, Mark
Permissioned ledger systems allow a consortium of members that do not trust one another to execute transactions safely on a set of replicas. Such systems typically use Byzantine fault tolerance (BFT) protocols to distribute trust, which only ensures
Externí odkaz:
http://arxiv.org/abs/2105.13116
Autor:
Shahrad, Mohammad, Fonseca, Rodrigo, Goiri, Íñigo, Chaudhry, Gohar, Batum, Paul, Cooke, Jason, Laureano, Eduardo, Tresness, Colby, Russinovich, Mark, Bianchini, Ricardo
Function as a Service (FaaS) has been gaining popularity as a way to deploy computations to serverless backends in the cloud. This paradigm shifts the complexity of allocating and provisioning resources to the cloud provider, which has to provide the
Externí odkaz:
http://arxiv.org/abs/2003.03423
Autor:
DELIGNAT-LAVAUD, ANTOINE, FOURNET, CÉDRIC, VASWANI, KAPIL, CLEBSCH, SYLVAN, RIECHERT, MAIK, COSTA, MANUEL, RUSSINOVICH, MARK
Publikováno v:
Communications of the ACM; Jan2024, Vol. 67 Issue 1, p68-78, 9p
Autor:
RUSSINOVICH, MARK1
Publikováno v:
Communications of the ACM. Jan2024, Vol. 67 Issue 1, p52-53. 2p.
Autor:
RUSSINOVICH, MARK, COSTA, MANUEL, FOURNET, CÉDRIC, CHISNALL, DAVID, DELIGNAT-LAVAUD, ANTOINE, CLEBSCH, SYLVAN, VASWANI, KAPIL, BHATIA, VIKAS
Publikováno v:
Communications of the ACM. Jun2021, Vol. 64 Issue 6, p54-61. 8p. 1 Color Photograph.