報告時間：2019年12月15日（周日）14:30；12月16日（周一）14:30
報告地點：北辰校區(qū)電信學(xué)院樓102會議室
報告嘉賓：Saman K. Halgamuge 教授
Lecture 1：Deep Neural Networks in an increasingly Networked World of Transducers
An inclusive framework for learning algorithms for Deep Neural Networks will be presented discussing the “known unknowns” and speculating about “unknown unknowns” in learning algorithm development. A paradigm shift can be observed in wide-ranging application domains such as energy management, image processing, neural engineering, bioinformatics, mechatronics etc, which are empowered by rapidly-advancing technologies ,e.g.,Internet of Things (IoT), that can generate large quantities of “imperfect” data for analysis of processes, compounds and organisms. These applications are increasingly demanding transparency thus the need for moving away from completely backbox approaches for learning. These technologies have been spurred by the improvements in processor technology (e.g. GPU), that have allowed practitioners and researchers to overcome the computational limitations of Deep Neural Networks that depend on fully human curated or labelled data (i.e. Supervised Learning). The following fundamental question then naturally arises: What happens when curated information or labels capture only a subset of critical classes, or the curation process itself is not fault- or error-free? Undoubtedly, the algorithm’s perceived reality will distort any subsequent analysis of these data, which may have detrimental downstream effects when new discoveries and critical decisions are made on a basis of these analyses. In such scenarios, learning algorithms that can find models –underlying structures or distinct patterns within data – without relying on labels (i.e. using Unsupervised Learning), have made great progress toward answering these sorts of questions; however, these algorithms only address part of the problem. Unsupervised Learning algorithms used in Unsupervised Deep Neural Networks do not consider any available and potentially reliable information or domain knowledge, which could prove useful in developing a robust model of the data. It can be advantageous to consider such information as well as any other available domain knowledge, not as ground truth but as a starting point to build a more complete picture of the problem under investigation. Application of Deep Neural Networks in Internet of Things (IoT) enabled world opens up the need for extensive new research. Some of the landmark contributions by my research groups at University of Melbourne and Australian National University are also highlighted. The recent work on Generative Adversarial Networks (GANs) and Self Organizing Nebulous Growths (SONG) are such research contributions.
Lecture 2：Optimization Algorithms for Energy
Optimization algorithms in AI are increasingly been applied in many areas of Engineering, including energy, communication networks, transport planning and construction. Since we do not know much about the fitness landscape of a real world optimization problem we try to solve, it can be challenging to pick the right method for agiven complex real problem. Comparisons of algorithms using numerous benchmarks may reveal the better algorithms suitable for the benchmarks. However, we do not know whether the set of benchmarks includes a problem similar to the one we try to solve. Selecting the correct optimization algorithm to a given problem can be achieved through the characterization of the fitness landscape. The solution to this problem becomes even more challenging when the fitness landscape changes dynamically. We report on some exciting new insights on the algorithm selection problem and its applications based on three PhD projects in my lab and some work of others. These PhD projects include Operational Optimization of Smart Grids with storage, road network planning and the integration of multiple renewable energy technologies including shallow geothermal energy generation in to the grids.
Saman K. Halgamuge教授簡介：
Prof Saman K. Halgamuge, FIEEE is a Fellow of Institute of Electrical and Electronics Engineering (IEEE), USA, and aDistinguished Lecturer/Speaker appointed by IEEE in the area of Computational Intelligence.Heiscurrently a Professor in the Department of Mechanical Engineering, School of Electrical, Mechanical and Infrastructure Engineering at the University of Melbourne, an honorary Professor of School of Electrical, Energy and Materials Engineering at Australian National University (ANU). He was previously the Director of the Research School of Engineering at the Australian National University (2016-18) and held Professor, Associate Dean International, Associate Professor and Reader and Senior Lecturer positions at University of Melbourne (1997-2016). He graduated with Dipl.-Ing and PhD degrees in Data Engineering from Technical University of Darmstadt.
His fundamental research contributions are in Unsupervised and Near Unsupervised type learning as well as in transparent Deep Learning and Bioinspired Optimization. His h-index is 43 (9200 citations) in Google Scholar and he graduated 45 PhD students as the primary supervisor. He has also been a keynote speaker for 40 research conferences.
Saman K. Halgamuge教授簡介（中文）：
Saman K. Halgamuge教授，IEEE Fellow，IEEE在計算智能領(lǐng)域任命的杰出講師演講者?，F(xiàn)任墨爾本大學(xué)電、機和基礎(chǔ)設(shè)施工程學(xué)院機械工程系教授，澳洲國立大學(xué)（ANU）電子電氣、能源和材料工程學(xué)院名譽教授。曾任澳洲國立大學(xué)工程研究學(xué)院院長（2016-2018年），并在墨爾本大學(xué)擔(dān)任教授、國際學(xué)院副院長、副教授和高級講師等職位（1997-2016年）。他畢業(yè)于德國達姆施塔特工業(yè)大學(xué)，獲數(shù)據(jù)工程專業(yè)碩士學(xué)位和博士學(xué)位。
他的基礎(chǔ)研究貢獻在于無監(jiān)督和近無監(jiān)督類型學(xué)習(xí)以及透明深度學(xué)習(xí)和生物啟發(fā)式優(yōu)化。在Google Scholar中他的H指數(shù)為43（9200次引用），并指導(dǎo)45名博士研究生畢業(yè)。另外，他還是40個研究會議的主講人之一。
