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Advanced Topics in AI for Networking (Winter 2022/2023): Difference between revisions
Advanced Topics in AI for Networking (Winter 2022/2023) (view source)
Revision as of 16:08, 12 January 2023
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{{CourseDetails | {{CourseDetails | ||
|credits=5 ECTS | |credits=5 ECTS | ||
|module=M.Inf. | |module=M.Inf.1123 | ||
|lecturer=[http://134.76.18.81/?q=people/prof-dr-xiaoming-fu Prof. Xiaoming Fu]; [http://www.net.informatik.uni-goettingen.de/?q=people/dr-tingting-yuan, Dr. Tingting Yuan] | |lecturer=[http://134.76.18.81/?q=people/prof-dr-xiaoming-fu Prof. Xiaoming Fu]; [http://www.net.informatik.uni-goettingen.de/?q=people/dr-tingting-yuan, Dr. Tingting Yuan] | ||
|ta=[NA] | |ta=[NA] | ||
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** Summary of the paper | ** Summary of the paper | ||
** Pros and cons of the paper (your conclusion) | ** Pros and cons of the paper (your conclusion) | ||
** '''NOTE!! Every participant should provide the paper review BEFORE the seminar (23:59 | ** '''NOTE!! Every participant should provide the paper review BEFORE the seminar (23:59 on Wedesday). => the review form is available at [[http://user.informatik.uni-goettingen.de/~fu/Paper_Review_Form_ATCN_WS201112.doc Paper_Review_Form_ATCN_WS201112.doc]] | ||
* During each weekly seminar, one participant is assigned for presenting the paper (each presentation lasts for ~20 minutes) and the list of pros and cons are discussed by all the participants. | * During each weekly seminar, one participant is assigned for presenting the paper (each presentation lasts for ~20 minutes) and the list of pros and cons are discussed by all the participants. | ||
* In the middle of the semester, everyone is requested to prepare: | * In the middle of the semester, everyone is requested to prepare: | ||
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==List of Papers == | ==List of Papers == | ||
1. Genet: automatic curriculum generation for learning adaptation in networking [https://dl.acm.org/doi/10.1145/3544216.3544243] | 1. Genet: automatic curriculum generation for learning adaptation in networking [https://dl.acm.org/doi/10.1145/3544216.3544243] [Celine] | ||
2. Multi-resource interleaving for deep learning training [https://dl.acm.org/doi/10.1145/3544216.3544224] | 2. Multi-resource interleaving for deep learning training [https://dl.acm.org/doi/10.1145/3544216.3544224] | ||
3. NeuroScaler: neural video enhancement at scale [https://dl.acm.org/doi/10.1145/3544216.3544218] | 3. NeuroScaler: neural video enhancement at scale [https://dl.acm.org/doi/10.1145/3544216.3544218] [Tim] | ||
4. Deep Interest Network for Click-Through Rate Prediction [https://arxiv.org/pdf/1706.06978.pdf] [ | 4. Deep Interest Network for Click-Through Rate Prediction [https://arxiv.org/pdf/1706.06978.pdf] [Tim] | ||
5. Reducing the Service Function Chain Backup Cost over the Edge and Cloud by a Self-Adapting Scheme [https://ieeexplore.ieee.org/document/9312434] | 5. Reducing the Service Function Chain Backup Cost over the Edge and Cloud by a Self-Adapting Scheme [https://ieeexplore.ieee.org/document/9312434] | ||
6. Routing on Multiple Optimality Criteria[https://dl.acm.org/doi/pdf/10.1145/3387514.3405864] | 6. Routing on Multiple Optimality Criteria[https://dl.acm.org/doi/pdf/10.1145/3387514.3405864] | ||
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7. Understanding, Detecting and Localizing Partial Failures in Large System Software [https://www.cs.jhu.edu/~huang/paper/omegagen-nsdi20-preprint.pdf] | 7. Understanding, Detecting and Localizing Partial Failures in Large System Software [https://www.cs.jhu.edu/~huang/paper/omegagen-nsdi20-preprint.pdf] | ||
8. Understanding Lifecycle Management Complexity of Datacenter Topologies [https://www. | 8. Understanding Lifecycle Management Complexity of Datacenter Topologies [https://www.usenix.org/system/files/nsdi19-zhang.pdf] [Celine] | ||
9. ACC: Automatic ECN Tuning for High-Speed Datacenter Networks [https://dl.acm.org/doi/pdf/10.1145/3452296.3472927] [Tim] | |||
10. Seven Years in the Life of Hypergiants’ Off-Nets [https://dl.acm.org/doi/pdf/10.1145/3452296.3472928] | 10. Seven Years in the Life of Hypergiants’ Off-Nets [https://dl.acm.org/doi/pdf/10.1145/3452296.3472928] [Celine] | ||
11. ATP: In-network Aggregation for Multi-tenant Learning [https://www.usenix.org/system/files/nsdi21-lao.pdf] | 11. ATP: In-network Aggregation for Multi-tenant Learning [https://www.usenix.org/system/files/nsdi21-lao.pdf] | ||
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W1: Open Talk (27.10) | W1: Open Talk (27.10) | ||
W2: Assignment | W2: Assignment papers (03.11) | ||
W4: Paper ID: (17.11) #4 #1 | |||
W6: Paper ID: (01.12 online) #9 #8 | |||
W8: Paper ID: (22.12 online) #3 #10 | |||
W10: Rehearsal: (12.01) #3 #8 | |||
'''!! 10.01 deadline for registration on Flexnow''' | |||
Final Presentation | Final Presentation (19.01) | ||
*'''Paper Title''': | *'''Paper Title''': | ||
*'''Paper Title''': | *'''Paper Title''': | ||
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*Final Report: | *Final Report: | ||
**Essay (~6 pages, double column, IEEE format: https://journals.ieeeauthorcenter.ieee.org/create-your-ieee-journal-article/authoring-tools-and-templates/ieee-article-templates/templates-for-transactions/) | **Essay (~6 pages, double column, IEEE format: https://journals.ieeeauthorcenter.ieee.org/create-your-ieee-journal-article/authoring-tools-and-templates/ieee-article-templates/templates-for-transactions/) | ||
**Due by 23:59pm | **Due by 23:59pm 15th Feb. | ||
[[Category:Courses]] | [[Category:Courses]] | ||