Zobrazeno 1 - 10
of 71
pro vyhledávání: '"Weimer, Markus"'
Autor:
Walsh, Brendan, Hamilton, Mark, Newby, Greg, Wang, Xi, Ruan, Serena, Zhao, Sheng, He, Lei, Zhang, Shaofei, Dettinger, Eric, Freeman, William T., Weimer, Markus
An audiobook can dramatically improve a work of literature's accessibility and improve reader engagement. However, audiobooks can take hundreds of hours of human effort to create, edit, and publish. In this work, we present a system that can automati
Externí odkaz:
http://arxiv.org/abs/2309.03926
Autor:
Weimer, Markus
Many applications of Technology Enhanced Learning are based on strong assumptions: Knowledge needs to be standardized, structured and most of all externalized into learning material that preferably is annotated with meta-data for efficient re-use. A
Autor:
Nakandala, Supun, Saur, Karla, Yu, Gyeong-In, Karanasos, Konstantinos, Curino, Carlo, Weimer, Markus, Interlandi, Matteo
Machine Learning (ML) adoption in the enterprise requires simpler and more efficient software infrastructure---the bespoke solutions typical in large web companies are simply untenable. Model scoring, the process of obtaining predictions from a train
Externí odkaz:
http://arxiv.org/abs/2010.04804
Autor:
Curino, Carlo, Godwal, Neha, Kroth, Brian, Kuryata, Sergiy, Lapinski, Greg, Liu, Siqi, Oks, Slava, Poppe, Olga, Smiechowski, Adam, Thayer, Ed, Weimer, Markus, Zhu, Yiwen
Developing modern systems software is a complex task that combines business logic programming and Software Performance Engineering (SPE). The later is an experimental and labor-intensive activity focused on optimizing the system for a given hardware,
Externí odkaz:
http://arxiv.org/abs/2006.02155
Autor:
Namaki, Mohammad Hossein, Floratou, Avrilia, Psallidas, Fotis, Krishnan, Subru, Agrawal, Ashvin, Wu, Yinghui, Zhu, Yiwen, Weimer, Markus
There has recently been a lot of ongoing research in the areas of fairness, bias and explainability of machine learning (ML) models due to the self-evident or regulatory requirements of various ML applications. We make the following observation: All
Externí odkaz:
http://arxiv.org/abs/2001.01861
Autor:
Psallidas, Fotis, Zhu, Yiwen, Karlas, Bojan, Interlandi, Matteo, Floratou, Avrilia, Karanasos, Konstantinos, Wu, Wentao, Zhang, Ce, Krishnan, Subru, Curino, Carlo, Weimer, Markus
The recent success of machine learning (ML) has led to an explosive growth both in terms of new systems and algorithms built in industry and academia, and new applications built by an ever-growing community of data science (DS) practitioners. This qu
Externí odkaz:
http://arxiv.org/abs/1912.09536
We study the problem of using low computational cost to automate the choices of learners and hyperparameters for an ad-hoc training dataset and error metric, by conducting trials of different configurations on the given training data. We investigate
Externí odkaz:
http://arxiv.org/abs/1911.04706
Autor:
Karanasos, Konstantinos, Interlandi, Matteo, Xin, Doris, Psallidas, Fotis, Sen, Rathijit, Park, Kwanghyun, Popivanov, Ivan, Nakandal, Supun, Krishnan, Subru, Weimer, Markus, Yu, Yuan, Ramakrishnan, Raghu, Curino, Carlo
The broadening adoption of machine learning in the enterprise is increasing the pressure for strict governance and cost-effective performance, in particular for the common and consequential steps of model storage and inference. The RDBMS provides a n
Externí odkaz:
http://arxiv.org/abs/1911.00231
Autor:
Agrawal, Ashvin, Chatterjee, Rony, Curino, Carlo, Floratou, Avrilia, Gowdal, Neha, Interlandi, Matteo, Jindal, Alekh, Karanasos, Kostantinos, Krishnan, Subru, Kroth, Brian, Leeka, Jyoti, Park, Kwanghyun, Patel, Hiren, Poppe, Olga, Psallidas, Fotis, Ramakrishnan, Raghu, Roy, Abhishek, Saur, Karla, Sen, Rathijit, Weimer, Markus, Wright, Travis, Zhu, Yiwen
Machine learning (ML) has proven itself in high-value web applications such as search ranking and is emerging as a powerful tool in a much broader range of enterprise scenarios including voice recognition and conversational understanding for customer
Externí odkaz:
http://arxiv.org/abs/1909.00084
Autor:
Yu, Gyeong-In, Amizadeh, Saeed, Kim, Sehoon, Pagnoni, Artidoro, Chun, Byung-Gon, Weimer, Markus, Interlandi, Matteo
Classical Machine Learning (ML) pipelines often comprise of multiple ML models where models, within a pipeline, are trained in isolation. Conversely, when training neural network models, layers composing the neural models are simultaneously trained u
Externí odkaz:
http://arxiv.org/abs/1906.03822