Zobrazeno 1 - 10
of 26 739
pro vyhledávání: '"machine learning systems"'
Autor:
Zhao, Dongfang
Machine learning (ML) systems that guarantee security and privacy often rely on Fully Homomorphic Encryption (FHE) as a cornerstone technique, enabling computations on encrypted data without exposing sensitive information. However, a critical limitat
Externí odkaz:
http://arxiv.org/abs/2412.14392
Many development decisions affect the results obtained from ML experiments: training data, features, model architecture, hyperparameters, test data, etc. Among these aspects, arguably the most important design decisions are those that involve the eva
Externí odkaz:
http://arxiv.org/abs/2412.03700
Autor:
Huckelberry, Jacob, Zhang, Yuke, Sansone, Allison, Mickens, James, Beerel, Peter A., Reddi, Vijay Janapa
Tiny Machine Learning (TinyML) systems, which enable machine learning inference on highly resource-constrained devices, are transforming edge computing but encounter unique security challenges. These devices, restricted by RAM and CPU capabilities tw
Externí odkaz:
http://arxiv.org/abs/2411.07114
Research in Responsible AI has developed a range of principles and practices to ensure that machine learning systems are used in a manner that is ethical and aligned with human values. However, a critical yet often neglected aspect of ethical ML is t
Externí odkaz:
http://arxiv.org/abs/2408.10239
Autor:
Du, Linkang, Zhou, Xuanru, Chen, Min, Zhang, Chusong, Su, Zhou, Cheng, Peng, Chen, Jiming, Zhang, Zhikun
As the implementation of machine learning (ML) systems becomes more widespread, especially with the introduction of larger ML models, we perceive a spring demand for massive data. However, it inevitably causes infringement and misuse problems with th
Externí odkaz:
http://arxiv.org/abs/2410.16618
Autor:
Tschand, Arya, Rajan, Arun Tejusve Raghunath, Idgunji, Sachin, Ghosh, Anirban, Holleman, Jeremy, Kiraly, Csaba, Ambalkar, Pawan, Borkar, Ritika, Chukka, Ramesh, Cockrell, Trevor, Curtis, Oliver, Fursin, Grigori, Hodak, Miro, Kassa, Hiwot, Lokhmotov, Anton, Miskovic, Dejan, Pan, Yuechao, Manmathan, Manu Prasad, Raymond, Liz, John, Tom St., Suresh, Arjun, Taubitz, Rowan, Zhan, Sean, Wasson, Scott, Kanter, David, Reddi, Vijay Janapa
Rapid adoption of machine learning (ML) technologies has led to a surge in power consumption across diverse systems, from tiny IoT devices to massive datacenter clusters. Benchmarking the energy efficiency of these systems is crucial for optimization
Externí odkaz:
http://arxiv.org/abs/2410.12032
With the boom of machine learning (ML) techniques, software practitioners build ML systems to process the massive volume of streaming data for diverse software engineering tasks such as failure prediction in AIOps. Trained using historical data, such
Externí odkaz:
http://arxiv.org/abs/2410.09190
Widespread deployment of societal-scale machine learning systems necessitates a thorough understanding of the resulting long-term effects these systems have on their environment, including loss of trustworthiness, bias amplification, and violation of
Externí odkaz:
http://arxiv.org/abs/2405.02726