In the LTS-GAT, a divide-and-conquer scheme was used to examine local information on temporal and spatial dimensions of EEG patterns based on the graph attention mechanism.
In this paper, we propose a homotopy-based soft actor-critic method (HSAC) which focuses on addressing these problems via following the homotopy path between the original task with sparse reward and the auxiliary task with artificial prior experience reward.
To address these issues, we propose Phoebe, an efficient learning-based checkpoint optimizer.
no code implementations • 27 Sep 2020 • Olga Poppe, Tayo Amuneke, Dalitso Banda, Aritra De, Ari Green, Manon Knoertzer, Ehi Nosakhare, Karthik Rajendran, Deepak Shankargouda, Meina Wang, Alan Au, Carlo Curino, Qun Guo, Alekh Jindal, Ajay Kalhan, Morgan Oslake, Sonia Parchani, Vijay Ramani, Raj Sellappan, Saikat Sen, Sheetal Shrotri, Soundararajan Srinivasan, Ping Xia, Shize Xu, Alicia Yang, Yiwen Zhu
Microsoft Azure is dedicated to guarantee high quality of service to its customers, in particular, during periods of high customer activity, while controlling cost.
In this paper we present: MLOS, an ML-powered infrastructure and methodology to democratize and automate Software Performance Engineering.
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.
no code implementations • 19 Dec 2019 • Fotis Psallidas, Yiwen Zhu, Bojan Karlas, Matteo Interlandi, Avrilia Floratou, Konstantinos Karanasos, Wentao Wu, Ce Zhang, Subru Krishnan, Carlo Curino, Markus Weimer
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.
no code implementations • 30 Aug 2019 • Ashvin Agrawal, Rony Chatterjee, Carlo Curino, Avrilia Floratou, Neha Gowdal, Matteo Interlandi, Alekh Jindal, Kostantinos Karanasos, Subru Krishnan, Brian Kroth, Jyoti Leeka, Kwanghyun Park, Hiren Patel, Olga Poppe, Fotis Psallidas, Raghu Ramakrishnan, Abhishek Roy, Karla Saur, Rathijit Sen, Markus Weimer, Travis Wright, Yiwen Zhu
Consequently, rigorous data management has emerged as a key requirement in enterprise settings.
no code implementations • 23 Aug 2019 • Liqun Shao, Yiwen Zhu, Abhiram Eswaran, Kristin Lieber, Janhavi Mahajan, Minsoo Thigpen, Sudhir Darbha, SiQi Liu, Subru Krishnan, Soundar Srinivasan, Carlo Curino, Konstantinos Karanasos
In contrast, in Griffin we cast the problem to a corresponding regression one that predicts the runtime of a job, and show how the relative contributions of the features used to train our interpretable model can be exploited to rank the potential causes of job slowdowns.
no code implementations • 14 May 2019 • Zeeshan Ahmed, Saeed Amizadeh, Mikhail Bilenko, Rogan Carr, Wei-Sheng Chin, Yael Dekel, Xavier Dupre, Vadim Eksarevskiy, Eric Erhardt, Costin Eseanu, Senja Filipi, Tom Finley, Abhishek Goswami, Monte Hoover, Scott Inglis, Matteo Interlandi, Shon Katzenberger, Najeeb Kazmi, Gleb Krivosheev, Pete Luferenko, Ivan Matantsev, Sergiy Matusevych, Shahab Moradi, Gani Nazirov, Justin Ormont, Gal Oshri, Artidoro Pagnoni, Jignesh Parmar, Prabhat Roy, Sarthak Shah, Mohammad Zeeshan Siddiqui, Markus Weimer, Shauheen Zahirazami, Yiwen Zhu
Machine Learning is transitioning from an art and science into a technology available to every developer.