This workshop is associated with the ERC Consolidator Grant Project FlexNets “Quantifying Flexibility in Communication Networks” that has received funding from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation program grant agreement No 647158 – FlexNets (2015 – 2020). http://www.networkflexibility.org
Topic: | Machine learning in the context of communication networks |
Begins: | February 20th, 2020, 11:45 |
Ends: | February 21st, 2020, 13:00 |
Venue: | Arcisstr. 21, 80333 Munich, Germany, Room 1977 |
How to find the room? | https://portal.mytum.de/campus/roomfinder/roomfinder_viewmap?mapid=12&roomid=1977@0509 |
Fee: | Free of charge (social dinner is self paid) |
Organizers: | TUM, UPB, KIT |
Contact: | fgmln2020@easychair.org |
Machine learning and artificial intelligence (ML/AI), in particular deep learning, has led to breakthroughs in various domains such as image recognition or natural language processing.This workshop focuses on the topic of ML/AI in the context of communication networks. It aims to discuss research visions and results as well as opportunities and challenges in the intersection of these two areas. The workshop looks for contributions and ideas that provide useful combinations of ML/AI approaches to address networking challenges on all layers from MAC to Application.
Registration (until 2020-01-31) | List for Registration (please add yourself) |
How to find the venue | https://www.ei.tum.de/lkn/adresse-und-anfahrt |
Link to this page | https://mlkuvs.lkn.ei.tum.de |
Location Self Paid Dinner | Park Café, Sophienstraße 7, 80333 München |
Start | End | Event |
11:45 | 12:00 | Small snacks |
12:00 | 12:15 | Welcome by Andreas Blenk and Prof. Kellerer |
12:15 | 13:15 | Session 1: Can ML finally solve congestion control problems? Session chair: Andreas Blenk (TUM) Talk 1: Using Deep Learning in Network Measurements for Passive Congestion Control Identification; C. Sander, J. Rüth (RWTH Aachen), O. Hohlfeld, K. Wehrle (BTU) Talk 2: TCP Congestion Control Using Imitation Learning; B. Jaeger, J. Schmeißer (TUM) |
13:15 | 13:45 | Teaser Session for Posters and Demos * NOracle: Who is communicating with whom in my network? (TUM-LKN) * The Softwarised Data Zoo (UPB) * Learning from Hierarchical Heavy Hitters (KIT) * Artificial Intelligence and Machine Learning at LKN (TUM-LKN) * FlexNets: Quantifiying Flexibility in Communication Networks (TUM-LKN) * Veni Vidi Dixi: reliable wireless communication with depth images (TUM-LKN) * NCSbench: Reproducible Benchmarking Platform for NCS (TUM-LKN) |
13:45 | 14:15 | Short Break with Posters and Demos |
14:15 | 15:45 | Session 2: ML for Network Modeling Session chair: Stefan Schneider (UPB) Talk 1: DeepMPLS: Fast Analysis of MPLS Configurations Using Deep Learning; F. Geyer (TUM) Talk 2: Runtime Verification of P4 Switches with Reinforcement Learning; A. Shukla, K. N. Hudemann (TU Berlin), A. Hecker (Huawei), S. Schmid (University of Vienna) Talk 3: Optimising the Performance of Deep Transfer Learning for Communication Networking Applications; T. V. Phan, T. Bauschert (TU Chemnitz) |
15:45 | 16:30 | Group photo & Longer Break & 5G Demo |
16:30 | 18:00 | Session 3: Reinforcement Learning for Wireless Session chair: Murat Gürsu (TUM) Talk 1: Reinforcement Learning Scheduler for V2V Communications under Intermittent Coverage; T. Sahin, M. Boban, R. Khalili (Huawei), A. Wolisz (TU Berlin) Talk 2: CBMoS: Combinatorial Bandit Learning for Mode Selection and Resource Allocation in D2D Systems; A. Ortiz, A. Asadi, M. Engelhardt, A. Klein, M. Hollick (TU Darmstadt) Talk 3: Towards Delay-Minimal Scheduling through Reinforcement Learning in IEEE 802.15.4 DSME; F. Meyer, V. Turau (TU Hamburg) |
19:30 | open | Social dinner (self-paid) at Park Café München |
Start | Stop | Event |
08:30 | 09:00 | Small snacks |
09:00 | 10:30 | Session 4: ML against Privacy Session chair: Hauke Heseding (KIT) Talk 1: Machine Learning-based Real-time Estimation of Quality of Experience from Encrypted Video Streaming Traffic; N. Wehner, M. Seufert (University of Würzburg), S. Wassermann, P. Casas (AIT), T. Hoßfeld (University of Würzburg) Talk 2: Reducing Consumed Data Volume in Bandwidth Measurements via a Machine Learning Approach; C. Maier, P. Dorfinger, J. L. Du (Salzburg Research Forschungsgesellschaft), S. Gschweitl, J. Lusak (alladin-IT) Talk 3: A Concept for Crowd-sensed Prediction of Mobile Network Connectivity; S. Herrnleben, B. Zeidler, M. Züfle, C. Krupitzer, S. Kounev (University of Würzburg) |
10:30 | 11:00 | Short break with Posters and Demos |
11:00 | 12:00 | Session 5: Prediction and Incremental Learning Session chair: Robert Bauer (KIT) Talk 1: Prediction for Location-Aware Network Automation in Radio; M. Kajó (TUM), J. Ali-Toppa (Nokia Bell Labs) Talk 2: Deployment of Incremental Learning Methods for Neural Networks in Resource Allocation Problems; L. Fisser, S. Linder, A. Timm-Giel (TU Hamburg) |
12:00 | 13:00 | Closing & Lunch & Chill-out |
Submission | January 17th, 2020 | https://easychair.org/conferences/?conf=fgmln2020 |
Notification | January 24th, 2020 | Notifications will be sent per mail |
Final submission & Registration | January 31st, 2020 | Details for registration will follow |
Topics of interest include but are not limited to: