Randomized Signature or random feature selection are two instances of machine learning, where randomly chosen structures appear to be highly expressive. We analyze several aspects of the theory behind it, show that these structures have several theoretically attractive properties and introduce two classes of examples from finance (joint works with Christa Cuchiero, Lukas Gonon, Lyudmila Grigoryeva, Martin Larsson, and Juan-Pablo Ortega).
Professor at ETH Zurich since 2009, Research Interests include Mathematical Finance, Machine Learning in Finance and Stochastic Analysis, Executive Secretary of the Bachelier Finance Society.
Posted inTEWI-Kolloquium|Kommentare deaktiviert für Machine Learning in Finance via Randomization
Abstract: In the latest years, we have witnessed a growing number of media transmitted and stored on computers and mobile devices. For this reason, there is an actual need to employ smart compression algorithms to reduce the size of our media files. However, such techniques are often responsible for severe reduction of user perceived quality. In this talk we present several approaches we have developed to restore degraded images and videos to match their original quality, making use of Generative Adversarial Networks. The aim of the talk is to highlight the main features of our research work, including the advantages of our solution, the current challenges and the possible directions for future improvements.
Bio: Leonardo Galteri is a Postdoctoral Researcher and Adjunct Professor at the University of Florence.
His research activity is focused on computer vision and pattern recognition techniques. Most of his work involves image and video reconstruction, compression artifact removal and noise removal.
In 2018 he obtained the title of PhD, presenting a thesis on the detection of objects in compressed images and videos using techniques based on deep learning. Throughout his research activity, he has participated in various European, national and technology transfer projects with different responsibilities. He is co-founder and Head of Engineering at Small Pixels s.r.l., a startup company that offers technological solutions for real-time video restoration and enhancement.
Posted inTEWI-Kolloquium|Kommentare deaktiviert für Advances in Visual Quality Restoration with Generative Adversarial Networks
Unterstützung bei Projekten in der Automatisierung
Wir sind auf der Suche nach motivierten und engagierten Studierenden (w/m/div)* zur Unterstützung unseres internationalen Teams in Villach. Ein spannendes Arbeitsumfeld zeichnet dieses Praktikum ebenso aus wie eine attraktive Entlohnung. Verstärken Sie unser Team!
Zu ihren neuen Aufgaben behören u. a.:
First point of contact als Schnittstelle zwischen Testwafer Bereitstellung und Produktion
Identifikation und Analyse des Verbesserungspotentials bei Testscheiben in der Produktion
Aufbau und Durchführung begleitender Tests in der Produktion
Support bei Reports in Tableau (SQL)
Erstellen von Online-Trainingsunterlagen in englischer Sprache
Aktive Mitarbeit bei der Wiki Wartung
Beschäftigungsart: Befristet / Teilzeit (Flexible Arbeitszeit von Montag bis Freitag zwischen 06:00 und 19:00 Uhr)
Recommendation systems today are widely used across many applications such as in multimedia content platforms, social networks, and ecommerce, to provide suggestions to users that are most likely to fulfill their needs, thereby improving the user experience. Academic research, to date, largely focuses on the performance of recommendation models in terms of ranking quality or accuracy measures, which often don’t directly translate into improvements in the real-world. In this talk, we present some of the most interesting challenges that we face in the personalization efforts at Netflix. The goal of this talk is to sunshine challenging research problems in industrial recommendation systems and start a conversation about exciting areas of future research.
Anuj Shah is a Senior Machine Learning Research Practitioner at Netflix. For the past 10+ years, he’s been working on an applied research team focused on developing the next generation of algorithms used to generate the Netflix homepage through machine learning, ranking, recommendation, and large-scale software engineering. He is extremely passionate about algorithms and technologies that help improve the Netflix customer experience with highly personalized consumer-facing products like the Continue Watching row, the Top 10 rows amongst many others. Prior to Netflix, he worked on machine learning in the Computational Sciences Division at the Pacific Northwest National Laboratory focusing on technologies at the intersection of proteomics, bioinformatics and Computer Science for 8 years. He has a Ph.D. from the Computer Science department at Washington State University and a Masters in C.S. from Virginia Tech.
Posted inTEWI-Kolloquium|Kommentare deaktiviert für Trends in Recommendations Systems – A Netflix Perspective
5G is the fifth generation of cellular networks. Up to 100 times faster than 4G, 5G is creating never-before-seen opportunities for people and businesses. Faster connectivity speeds, ultra-low latency and greater bandwidth is advancing societies, transforming industries and dramatically enhancing day-to-day experiences. Services that we used to see as futuristic, such as e-health, connected vehicles and traffic systems and advanced mobile cloud gaming have arrived. With 5G technology, we can help create a smarter, safer and more sustainable future.
* Network performance and evolution lead for all Europe and Latin America, Ericsson Company (Poland office)
* Professional consultant for network performance and 5G evolution lead with more than 15 years of experience in different Telco topics
* Responsibilities covering all Europe and Latin America:
Spectrum & Regulatory Advisory (spectrum and bandwidth acquisition advisory to operators, also spectrum interference topics)
NSA to SA Evolutions (5G spectrum architecture and deployment strategy
5G Evolution Proof Points (NSA/SA coverage extension NR mid band link budget, ESS spectrum sharing system simulator and SA strategy)
Performance Benchmarking (OOKLA speed test and crowdsourced data analytics)
Abstract: AI has infected the world. Today, there is a huge hype around Data Science activities all over the world, where one of the biggest challenges for the industry is to deliver financial value quickly but also sustainably. In her talk, she will show some examples on latest Use Cases in the area of Data Science within the semiconductor industry, including technical approaches and practical challenges. Further, she will give some personal insights on important enabling factors that make a Data Science project successful.
Bio: Anja Zernig coordinates Data Science projects at KAI Kompetenzzentrum Automobil- und Industrieelektronik GmbH in Villach, which is a 100% subsidiary of Infineon Technologies Austria AG. Dr. Zernig studied Technical Mathematics at the University of Klagenfurt and received her PhD in 2016. Afterwards, she has been applied as a researcher at KAI, focusing on topics like outlier and anomaly detection, pattern recognition, applied statistical methods and Machine Learning techniques. Since 2019 she is coordinating a team of Data Scientists, involved in various national and international funding projects and acts as a link between the industry and academic collaboration partners. She is supervising researchers and students, dealing with innovative data-analytical concepts within the semiconductor production, testing and optimization and publishes latest scientific insights in different conference and Journal papers. Beside this, Dr. Zernig participates in and supports local Data Science activities, e.g. she is part of the organizing team of the Women in Data Science Villach. In recent times, she is focusing on deployment strategies to guarantee sustainable Machine Learning lifecycles.
Sergey Gorinsky | IMDEA Networks Institute, Madrid | Friday, November 12, 2021 | 14:00 (CET, 13:00 UTC)| S.0.05
Abstract: Content delivery networks (CDNs) distribute much of the Internet content by caching and serving the objects requested by users. A major goal of a CDN is to maximize the hit rates of its caches, thereby enabling faster content downloads to the users. Content caching involves two components: an admission algorithm to decide whether to cache an object and an eviction algorithm to decide which object to evict from the cache when it is full. In this paper, we focus on cache admission and propose an algorithm called RL-Cache that uses model-free reinforcement learning (RL) to decide whether or not to admit a requested object into the CDN’s cache. Unlike prior approaches that use a small set of criteria for decision making, RL-Cache weights a large set of features that include the object size, recency, and frequency of access. We develop a publicly available implementation of RL-Cache and perform an evaluation using production traces for the image, video, and web traffic classes from Akamai’s CDN. The evaluation shows that RL-Cache improves the hit rate in comparison with the state of the art and imposes only a modest resource overhead on the CDN servers. Further, RL-Cache is robust enough that it can be trained in one location and executed on request traces of the same or different traffic classes in other locations of the same geographic region.
Bio: Sergey Gorinsky is a tenured Research Associate Professor at IMDEA Networks Institute in Madrid, Spain. He joined the institute in 2009 and leads the NetEcon (Network Economics) research group there. Dr. Gorinsky received his Ph.D. and M.S. degrees from the University of Texas at Austin, USA in 2003 and 1999 respectively and Engineer degree from Moscow Institute of Electronic Technology, Zelenograd, Russia in 1994. From 2003 to 2009, he served on the tenure-track faculty at Washington University in St. Louis, USA. In 2010-2014, Dr. Gorinsky was a Ramón y Cajal Fellow funded by the Spanish Government. Sergey Gorinsky graduated four Ph.D. students. The areas of his primary research interests are computer networking, distributed systems, and network economics. His work appeared at top conferences and journals such as SIGCOMM, CoNEXT, INFOCOM, Transactions on Networking, and Journal on Selected Areas in Communications. He served as a TPC chair of ICNP 2017 and other conferences, as well as a TPC member for a much broader conference population. Sergey Gorinsky contributed to conference organization in many roles, such as a general chair of SIGCOMM 2018 and ICNP 2020. He also served as an evaluator of research proposals and projects for the European Research Council (ERC StG), European Commission (Horizon 2020, FP7), COST Association, and numerous other funding agencies.
Posted inTEWI-Kolloquium|Kommentare deaktiviert für RL-Cache: Learning-Based Cache Admission for Content Delivery
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