From irdta at irdta.eu Tue Jun 4 07:27:32 2019 From: irdta at irdta.eu (IRDTA) Date: Tue, 04 Jun 2019 07:27:32 +0200 Subject: [Opt-Net] DeepLearn 2019: early registration June 22 Message-ID: <545102060a010b0a08535903040a5a575a03010706040e5557030053535305545d560055050051500350040b070005@grlmc_ip-zone_com-6> DeepLearn 2019: early registration June 22*To be removed from our mailing list, please respond to this message with UNSUBSCRIBE in the subject line* ? *************************************************************** ? 3rd INTERNATIONAL SUMMER SCHOOL ON DEEP LEARNING ? DeepLearn 2019 ? Warsaw, Poland ? July 22-26, 2019 ? Co-organized by: ? Institute of Computer Science, Polish Academy of Sciences ? IRDTA ? Brussels/London ? http://deeplearn2019.irdta.eu/ ? *************************************************************** ? --- Early registration deadline: June 22, 2019 --- ? *************************************************************** ? SCOPE: ? DeepLearn 2019 will be a research training event with a global scope aiming at updating participants about the most recent advances in the critical and fast developing area of deep learning. This is a branch of artificial intelligence covering a spectrum of current exciting machine learning research and industrial innovation that provides more efficient algorithms to deal with large-scale data in neurosciences, computer vision, speech recognition, language processing, human-computer interaction, drug discovery, biomedical informatics, healthcare, recommender systems, learning theory, robotics, games, etc. Renowned academics and industry pioneers will lecture and share their views with the audience. ? Most deep learning subareas will be displayed, and main challenges identified through 3 keynote lectures and 23 four-hour and a half courses, which will tackle the most active and promising topics. The organizers are convinced that outstanding speakers will attract the brightest and most motivated students. Interaction will be a main component of the event. ? An open session will give participants the opportunity to present their own work in progress in 5 minutes. Moreover, there will be two special sessions with industrial and recruitment profiles. ? ADDRESSED TO: ? Master's students, PhD students, postdocs, and industry practitioners will be typical profiles of participants. However, there are no formal pre-requisites for attendance in terms of academic degrees. Since there will be a variety of levels, specific knowledge background may be assumed for some of the courses. Overall, DeepLearn 2019 is addressed to students, researchers and practitioners who want to keep themselves updated about recent developments and future trends. All will surely find it fruitful to listen and discuss with major researchers, industry leaders and innovators. ? STRUCTURE: ? 3 courses will run in parallel during the whole event. Participants will be able to freely choose the courses they wish to attend as well as to move from one to another. ? VENUE: ? DeepLearn 2019 will take place in Warsaw, whose historical Old Town was designated a UNESCO World Heritage Site. The venue will be: ? Global Expo Modlinska 6D 03-216 Warsaw ? KEYNOTE SPEAKERS: ? Maria-Florina Balcan (Carnegie Mellon University), Data Driven Clustering ? Mark Gales (University of Cambridge), Use of Deep Learning in Non-native Spoken English Assessment ? Mihaela van der Schaar (University of Cambridge), Learning Engines for Healthcare: Using Machine Learning to Transform Clinical Practice and Discovery ? PROFESSORS AND COURSES: ? Christopher Bishop (Microsoft Research Cambridge), [introductory] Introduction to the Key Concepts and Techniques of Machine Learning ? Aaron Courville (University of Montr?al), [introductory/intermediate] Deep Generative Models ? Issam El Naqa (University of Michigan), [introductory/intermediate] Deep Learning for Biomedicine ? Sergei V. Gleyzer (University of Florida), [introductory/intermediate] Feature Extraction, End-end Deep Learning and Applications to Very Large Scientific Data: Rare Signal Extraction, Uncertainty Estimation and Realtime Machine Learning Applications in Software and Hardware ? Vasant Honavar (Pennsylvania State University), [introductory/intermediate] Causal Models for Making Sense of Data ? Qiang Ji (Rensselaer Polytechnic Institute), [introductory/intermediate] Probabilistic Deep Learning for Computer Vision ? James Kwok (Hong Kong University of Science and Technology), [introductory/intermediate] Compressing Neural Networks ? Tomas Mikolov (Facebook), [introductory] Using Neural Networks for Modeling and Representing Natural Languages (with Piotr Bojanowski and Armand Joulin) ? Hermann Ney (RWTH Aachen University), [intermediate/advanced] Speech Recognition and Machine Translation: From Statistical Decision Theory to Machine Learning and Deep Neural Networks ? Jose C. Principe (University of Florida), [intermediate/advanced] Cognitive Architectures for Object Recognition in Video ? Fabio Roli (University of Cagliari), [introductory/intermediate] Adversarial Machine Learning ? Bj?rn Schuller (Imperial College London), [introductory/intermediate] Deep Learning for Intelligent Signal Processing ? Alex Smola (Amazon), [introductory] Dive into Deep Learning ? Sargur Srihari (University at Buffalo), [intermediate/advanced] Explainable Artificial Intelligence ? Ponnuthurai N Suganthan (Nanyang Technological University), [introductory/intermediate] Learning Algorithms for Classification, Forecasting and Visual Tracking ? Johan Suykens (KU Leuven), [introductory/intermediate] Deep Learning, Neural Networks and Kernel Machines ? Bertrand Thirion (INRIA), [introductory] Understanding the Brain with Machine Learning ? Ga?l Varoquaux (INRIA), [intermediate] Representation Learning in Limited Data Settings ? Ren? Vidal (Johns Hopkins University), [intermediate/advanced] Mathematics of Deep Learning ? Haixun Wang (WeWork), [intermediate] Abstractions, Concepts, and Machine Learning ? Xiaowei Xu (University of Arkansas, Little Rock), [introductory/advanced] Multi-resolution Models for Learning Multilevel Abstract Representations of Text ? Ming-Hsuan Yang (University of California, Merced), [intermediate/advanced] Learning to Track Objects ? Zhongfei Zhang (Binghamton University), [introductory/advanced] Knowledge Discovery from Complex Data with Deep Learning ? OPEN SESSION: ? An open session will collect 5-minute voluntary presentations of work in progress by participants. They should submit a half-page abstract containing title, authors, and summary of the research to david at irdta.eu by July 14, 2019. ? INDUSTRIAL SESSION: ? A session will be devoted to 10-minute demonstrations of practical applications of deep learning in industry. Companies interested in contributing are welcome to submit a 1-page abstract containing the program of the demonstration and the logistics needed. At least one of the people participating in the demonstration must register for the event. Expressions of interest have to be submitted to david at irdta.eu by July 14, 2019. ? EMPLOYER SESSION: ? Firms searching for personnel well skilled in deep learning will have a space reserved for one-to-one contacts. It is recommended to produce a 1-page .pdf leaflet with a brief description of the company and the profiles looked for, to be circulated among the participants prior to the event. At least one of the people in charge of the search must register for the event. Expressions of interest have to be submitted to david at irdta.eu by July 14, 2019. ? ORGANIZING COMMITTEE: ? ?ukasz Kobyli?ski (Warsaw, co-chair) Sara Morales (Brussels) Manuel J. Parra-Roy?n (Granada) David Silva (London, co-chair) ? REGISTRATION: ? It has to be done at ? http://deeplearn2019.irdta.eu/registration/ ? The selection of up to 8 courses requested in the registration template is only tentative and non-binding. For the sake of organization, it will be helpful to have an estimation of the respective demand for each course. During the event, participants will be free to attend the courses they wish. ? Since the capacity of the venue is limited, registration requests will be processed on a first come first served basis. The registration period will be closed and the on-line registration facility disabled when the capacity of the venue is exhausted. It is highly recommended to register prior to the event. ? FEES: ? Fees comprise access to all courses and lunches. There are several early registration deadlines. Fees depend on the registration deadline. ? ACCOMMODATION: ? Accommodation can be booked at ? http://www.deeplearn2019.promoest.com/hp.aspx?s=0 ? CERTIFICATE: ? A certificate of successful participation in the event will be delivered indicating the number of hours of lectures. ? QUESTIONS AND FURTHER INFORMATION: ? david at irdta.eu ? ACKNOWLEDGMENTS: ? Institute of Computer Science, Polish Academy of Sciences ? Institute for Research Development, Training and Advice (IRDTA) ? Brussels/London ? -------------- next part -------------- An HTML attachment was scrubbed... URL: From jussi.hakanen at jyu.fi Tue Jun 18 09:28:06 2019 From: jussi.hakanen at jyu.fi (Hakanen, Jussi) Date: Tue, 18 Jun 2019 07:28:06 +0000 Subject: [Opt-Net] Special issue in Optimization and Engineering journal: Multiobjective Optimization and Decision Making in Engineering Sciences Message-ID: < We apologize for multiple posting. Please kindly disseminate this Call for Abstracts to your colleagues and contacts> *********************************************************** CALL FOR PAPERS *********************************************************** Special Session on Multiobjective Optimization and Decision Making in Engineering Sciences Optimization and Engineering journal Paper submission deadline: 30 November, 2019 *********************************************************** Guest Editors: Jussi Hakanen (Faculty of Information Technology, University of Jyv?skyl?) Richard Allmendinger (Alliance Manchester Business School, University of Manchester, UK) Aim and scope The emergence of advanced technologies and digitalization has been changing our world and leading to a situation where physical assets are being augmented heavily with non-physical assets. In addition to widely used simulation-based optimization, data-driven optimization approaches have become more popular in engineering sciences due to availability of large amounts of data collected in every field (e.g. from IoT, sensors, experimental measurements etc.). Therefore, the effective combination of data and advanced engineering and management technologies and skills is becoming a key asset to a company urging the need to rethink how to tackle modern decision making problems. The consideration of various competing factors related to business, technical, workforce, safety and environmental aspects further increases the complexity of decision making and leads to multiple criteria decision making (MCDM) problems. This special issue focuses on the intersection between Engineering, Data Science, Multiple Criteria Decision Making (MCDM) and Multiobjective Optimization (MO). The development of new models and algorithmic methods to solve such problems is in the focus as much as the application of these concepts to real problems. The aim of the issue is to bring together academics and practitioners with different expertise, in particular, in engineering, computer science, mathematics, data science and business. This special issue is connected to but not restricted to papers presented at the 25th International Conference on Multiple Criteria Decision Making, MCDM2019 (to be held in Istanbul, Turkey, in June 2019). Major topics of interest All submissions related to the development/application of multi/many-objective optimization in engineering are welcome. Especially, submissions considering novel approaches combining engineering and data science are highly encouraged. Topics of interest (but not limited to) include: ? Preference-based approaches actively incorporating human decision makers ? Data-driven methods and their combination with simulation-based approaches ? Applications in engineering sciences where multi/many-objective optimization has been used to make more informed decisions (e.g. advanced manufacturing, material sciences and digital technology) ? MCDM system development aimed at practical use in engineering enabling interactive participation of the decision maker (visualization, decision support, graphical user interfaces, automatic configuration and tuning of optimization and decision making algorithms) ? Hybrid methodologies combining mathematical programming, evolutionary computation and/or machine learning ? Approaches for computationally expensive (black-box) multi/many-objective problems and/or challenges in MCDM ? Methods to deal with problem challenges arising in engineering sciences such as uncertainty, dynamic landscapes/constraints, noisy functions/data, robustness requirements etc. ? Test/benchmark problems/simulators and performance measures to validate optimization and decision making approaches in engineering sciences, large-scale/mixed type/highly constrained problems Submission instructions Papers should be submitted online at https://www.opte-journal.com and the submission deadline for full-length papers is November 30, 2019. Upon manuscript submission, please select the special issue "MCDM 2019". Submissions will be peer-reviewed according to the standards of the journal. Manuscripts will be processed as they arrive and will be published online as soon as they are accepted. For planning purposes, interested authors are encouraged to email a tentative title to the guest editors. Contact Please feel free to contact us in case you have any questions Jussi Hakanen: jussi.hakanen at jyu.fi Richard Allmendinger: richard.allmendinger at manchester.ac.uk -------------- next part -------------- An HTML attachment was scrubbed... URL: From mulbrich at ma.tum.de Wed Jun 5 12:55:05 2019 From: mulbrich at ma.tum.de (Michael Ulbrich) Date: Wed, 05 Jun 2019 10:55:05 -0000 Subject: [Opt-Net] OPTE Special Issue: PDE-Constrained Optimization Message-ID: (apologies for cross-posting) Dear Colleagues, we are inviting submissions for a special issue on ``PDE-Constrained Optimization'' of the journal Optimization and Engineering (OPTE). Please find the details below. Best Regards, Michael Ulbrich and Boris Vexler OPTE Special Issue: PDE-Constrained Optimization Submission Deadline: October 31, 2019 Journal: Optimization and Engineering (http://www.springer.com/ mathematics/journal/11081 ) Guest Editors: Michael Ulbrich, Professor, Chair of Mathematical Optimization, Department of Mathematics, Technical University of Munich ?mulbrich at ma.tum.de ?. Boris Vexler, Professor, Chair of Optimal Control, Department of Mathematics, Technical University of Munich ?vexler at ma.tum.de ?. Aim: This call aims at publishing research work on PDE-constrained optimization connected to applications in engineering. The call is open to all types of papers, including theoretical, applied, and algorithmic articles, or combinations of them. Theme: The accurate modeling of complex physical and technical systems heavily relies on PDEs. The resulting systems live in infinite-dimensional function spaces and can involve nonlinearity, nonsmoothness, or uncertainty. Optimization with PDE constraints is the enabling discipline for analyzing and solving highly important problem classes connected to these systems, such as: shape and topology optimization, optimal control, inverse problems, parameter identification, etc. Beyond more traditional applications, the field is increasingly interacting with other timely and important areas, such as uncertainty quantification, data science, or mathematical imaging. Studying the theoretical and numerical aspects of PDE-constrained optimization problems in their original function space setting and tying the developments closely to the latest theoretical and computational advances for PDEs are key elements for a strong theory and for robust, mesh-independent solvers. Mathematically, PDE-constrained optimization is as rich as its numerous applications: It combines theoretical and practical methodology from optimization, PDEs, functional analysis, nonsmooth and variational analysis, numerical analysis, and scientific computing; it also can involve probability and measure theory. This special issues targets at showcasing the latest advances in PDE-constrained optimization at the intersection of mathematics and engineering applications. Submission Procedure: Please submit to the Optimization and Engineering (OPTE) journal at https://www.springer.com/mathematics/journal/11081 and select special issue ?SI: PDE 2019?. All sub- missions must be original and may not be under review by another publication. Interested authors should consult the journal?s ?Instructions for Authors?, at http://www.springer.com/ mathematics/journal/11081 . All submitted papers will be reviewed on a peer review basis as soon as they are received. Accepted papers will be available at Online First until the complete Special Issue appears. All inquiries should be directed to the attention of: Michael Ulbrich, Subject Editor and Guest Editor (mulbrich at ma.tum.de ) Boris Vexler, Guest Editor (vexler at ma.tum.de ) Optimization and Engineering (OPTE) journal -------------- next part -------------- An HTML attachment was scrubbed... URL: From irdta at irdta.eu Wed Jun 26 06:09:26 2019 From: irdta at irdta.eu (IRDTA) Date: Wed, 26 Jun 2019 06:09:26 +0200 Subject: [Opt-Net] DeepLearn 2019: regular registration July 19 Message-ID: <545102060a010b02005654030600545e565e54070157050407050100540e5b0054035754000e06500057530554015955@grlmc_ip-zone_com-6> DeepLearn 2019: regular registration July 19*To be removed from our mailing list, please respond to this message with UNSUBSCRIBE in the subject line* ? *************************************************************** ? 3rd INTERNATIONAL SUMMER SCHOOL ON DEEP LEARNING ? DeepLearn 2019 ? Warsaw, Poland ? July 22-26, 2019 ? Co-organized by: ? Institute of Computer Science, Polish Academy of Sciences ? IRDTA ? Brussels/London ? http://deeplearn2019.irdta.eu/ ? *************************************************************** ? --- Regular registration deadline: July 19, 2019 --- ? *************************************************************** ? SCOPE: ? DeepLearn 2019 will be a research training event with a global scope aiming at updating participants about the most recent advances in the critical and fast developing area of deep learning. This is a branch of artificial intelligence covering a spectrum of current exciting machine learning research and industrial innovation that provides more efficient algorithms to deal with large-scale data in neurosciences, computer vision, speech recognition, language processing, human-computer interaction, drug discovery, biomedical informatics, healthcare, recommender systems, learning theory, robotics, games, etc. Renowned academics and industry pioneers will lecture and share their views with the audience. ? Most deep learning subareas will be displayed, and main challenges identified through 3 keynote lectures and 23 four-hour and a half courses, which will tackle the most active and promising topics. The organizers are convinced that outstanding speakers will attract the brightest and most motivated students. Interaction will be a main component of the event. ? An open session will give participants the opportunity to present their own work in progress in 5 minutes. Moreover, there will be two special sessions with industrial and recruitment profiles. ? ADDRESSED TO: ? Master's students, PhD students, postdocs, and industry practitioners will be typical profiles of participants. However, there are no formal pre-requisites for attendance in terms of academic degrees. Since there will be a variety of levels, specific knowledge background may be assumed for some of the courses. Overall, DeepLearn 2019 is addressed to students, researchers and practitioners who want to keep themselves updated about recent developments and future trends. All will surely find it fruitful to listen and discuss with major researchers, industry leaders and innovators. ? STRUCTURE: ? 3 courses will run in parallel during the whole event. Participants will be able to freely choose the courses they wish to attend as well as to move from one to another. ? VENUE: ? DeepLearn 2019 will take place in Warsaw, whose historical Old Town was designated a UNESCO World Heritage Site. The venue will be: ? Global Expo Modlinska 6D 03-216 Warsaw ? KEYNOTE SPEAKERS: ? Maria-Florina Balcan (Carnegie Mellon University), Data Driven Clustering ? Mark Gales (University of Cambridge), Use of Deep Learning in Non-native Spoken English Assessment ? Mihaela van der Schaar (University of Cambridge), Learning Engines for Healthcare: Using Machine Learning to Transform Clinical Practice and Discovery ? PROFESSORS AND COURSES: ? Aaron Courville (University of Montr?al), [introductory/intermediate] Deep Generative Models ? Issam El Naqa (University of Michigan), [introductory/intermediate] Deep Learning for Biomedicine ? Sergei V. Gleyzer (University of Florida), [introductory/intermediate] Feature Extraction, End-end Deep Learning and Applications to Very Large Scientific Data: Rare Signal Extraction, Uncertainty Estimation and Realtime Machine Learning Applications in Software and Hardware ? Vasant Honavar (Pennsylvania State University), [introductory/intermediate] Causal Models for Making Sense of Data ? Qiang Ji (Rensselaer Polytechnic Institute), [introductory/intermediate] Probabilistic Deep Learning for Computer Vision ? James Kwok (Hong Kong University of Science and Technology), [introductory/intermediate] Compressing Neural Networks ? Tomas Mikolov (Facebook), [introductory] Using Neural Networks for Modeling and Representing Natural Languages (with Piotr Bojanowski and Armand Joulin) ? Hermann Ney (RWTH Aachen University), [intermediate/advanced] Speech Recognition and Machine Translation: From Statistical Decision Theory to Machine Learning and Deep Neural Networks ? Jose C. Principe (University of Florida), [intermediate/advanced] Cognitive Architectures for Object Recognition in Video ? Fabio Roli (University of Cagliari), [introductory/intermediate] Adversarial Machine Learning ? Bj?rn Schuller (Imperial College London), [introductory/intermediate] Deep Learning for Intelligent Signal Processing ? Alex Smola (Amazon), [introductory] Dive into Deep Learning ? Sargur Srihari (University at Buffalo), [intermediate/advanced] Explainable Artificial Intelligence ? Ponnuthurai N Suganthan (Nanyang Technological University), [introductory/intermediate] Learning Algorithms for Classification, Forecasting and Visual Tracking ? Johan Suykens (KU Leuven), [introductory/intermediate] Deep Learning, Neural Networks and Kernel Machines ? Bertrand Thirion (INRIA), [introductory] Understanding the Brain with Machine Learning ? Ga?l Varoquaux (INRIA), [intermediate] Representation Learning in Limited Data Settings ? Ren? Vidal (Johns Hopkins University), [intermediate/advanced] Mathematics of Deep Learning ? Haixun Wang (WeWork), [intermediate] Abstractions, Concepts, and Machine Learning ? Xiaowei Xu (University of Arkansas, Little Rock), [introductory/advanced] Multi-resolution Models for Learning Multilevel Abstract Representations of Text ? Ming-Hsuan Yang (University of California, Merced), [intermediate/advanced] Learning to Track Objects ? Zhongfei Zhang (Binghamton University), [introductory/advanced] Knowledge Discovery from Complex Data with Deep Learning ? OPEN SESSION: ? An open session will collect 5-minute voluntary presentations of work in progress by participants. They should submit a half-page abstract containing title, authors, and summary of the research to david at irdta.eu by July 14, 2019. ? INDUSTRIAL SESSION: ? A session will be devoted to 10-minute demonstrations of practical applications of deep learning in industry. Companies interested in contributing are welcome to submit a 1-page abstract containing the program of the demonstration and the logistics needed. At least one of the people participating in the demonstration must register for the event. Expressions of interest have to be submitted to david at irdta.eu by July 14, 2019. ? EMPLOYER SESSION: ? Firms searching for personnel well skilled in deep learning will have a space reserved for one-to-one contacts. It is recommended to produce a 1-page .pdf leaflet with a brief description of the company and the profiles looked for, to be circulated among the participants prior to the event. At least one of the people in charge of the search must register for the event. Expressions of interest have to be submitted to david at irdta.eu by July 14, 2019. ? ORGANIZING COMMITTEE: ? ?ukasz Kobyli?ski (Warsaw, co-chair) Sara Morales (Brussels) Manuel J. Parra-Roy?n (Granada) David Silva (London, co-chair) ? REGISTRATION: ? It has to be done at ? http://deeplearn2019.irdta.eu/registration/ ? The selection of up to 8 courses requested in the registration template is only tentative and non-binding. For the sake of organization, it will be helpful to have an estimation of the respective demand for each course. During the event, participants will be free to attend the courses they wish. ? Since the capacity of the venue is limited, registration requests will be processed on a first come first served basis. The registration period will be closed and the on-line registration facility disabled when the capacity of the venue is exhausted. It is highly recommended to register prior to the event. ? FEES: ? Fees comprise access to all courses and lunches. There are several early registration deadlines. Fees depend on the registration deadline. ? ACCOMMODATION: ? Accommodation can be booked at ? http://www.deeplearn2019.promoest.com/hp.aspx?s=0 ? CERTIFICATE: ? A certificate of successful participation in the event will be delivered indicating the number of hours of lectures. ? QUESTIONS AND FURTHER INFORMATION: ? david at irdta.eu ? ACKNOWLEDGMENTS: ? Institute of Computer Science, Polish Academy of Sciences ? Institute for Research Development, Training and Advice (IRDTA) ? Brussels/London ? -------------- next part -------------- An HTML attachment was scrubbed... URL: