Contextual Self-attentive Temporal Point Process for Physical Decommissioning Prediction of Cloud Assets-Reference-Cited by-同舟云学术

Contextual Self-attentive Temporal Point Process for Physical Decommissioning Prediction of Cloud Assets

Published:2023-08-04 Issue: Volume: Page:
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Container-title:Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining
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Author:

Yang Fangkai¹^ORCID,Zhang Jue¹^ORCID,Wang Lu¹^ORCID,Qiao Bo¹^ORCID,Weng Di¹^ORCID,Qin Xiaoting¹^ORCID,Weber Gregory²^ORCID,Das Durgesh Nandini³^ORCID,Rakhunathan Srinivasan³^ORCID,Srikanth Ranganathan²^ORCID,Lin Qingwei¹^ORCID,Zhang Dongmei¹^ORCID

Affiliation:

1. Microsoft, Beijing, China

2. Microsoft, Redmond, USA

3. Microsoft, Telangana, India

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3580305.3599794

Reference29 articles.

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2. Designing a New Location-Allocation and Routing Model with Simultaneous Pick-Up and Delivery in a Closed-Loop Supply Chain Network under Uncertainty

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4. Daryl J Daley and David Vere-Jones. 2007. An introduction to the theory of point processes: volume II: general theory and structure. Springer Science & Business Media. Daryl J Daley and David Vere-Jones. 2007. An introduction to the theory of point processes: volume II: general theory and structure. Springer Science & Business Media.

5. Recurrent Marked Temporal Point Processes

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