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Monthly Archives: April 2019

ML seminar, Tue 28 May, 3:30pm

News, Seminar.

Machine Learning seminar

When: Tue, 28 May 2019, 3:30pm
Where: AG07b, College Building

Who: Marco Gori, University of Siena, Italy.

Title: The Principle of Least Cognitive Action

Abstract: In this talk we introduce the principle of Least Cognitive Action with the purpose of understanding perceptual learning processes. The principle closely parallels related approaches in physics, and suggests to regard neural networks as systems whose weights are Lagrangian variables, namely functions depending on time. Interestingly, neural networks “conquer their own life” and there is no neat distinction between learning and test; their behavior is characterized by the stationarity of the cognitive action, an appropriate functional which contains a potential and a kinetic term. While the potential term is somewhat related to the loss function used in supervised and unsupervised learning, the kinetic term represents the energy connected with the velocity of weight change. Unlike traditional gradient descent, the stationarity of the cognitive action yields differential equations in the connection weights, and gives rise to a dissipative process which is needed to yield ordered configurations. We give conditions under which this learning process reduces to stochastic gradient descent and to Backpropagation. We give examples on supervised and unsupervised learning, and briefly discuss the application to deep convolutional neural networks, where an appropriate Lagrangian term is used to enforce motion invariance in the visual feature extraction.

Bio: Marco Gori received the Ph.D. degree in 1990 from Università di Bologna, Italy, working partly at the School of Computer Science (McGill University, Montreal). In 1992, he became an Associate Professor of Computer Science at Università di Firenze and, in November 1995, he joint the Università di Siena, where he is currently full professor of computer science.
His main interests are in machine learning with applications to pattern recognition, Web mining, and game playing. He is especially interested in bridging logic and learning and in the connections between symbolic and sub-symbolic representation of information. He was the leader of the WebCrow project for automatic solving of crosswords, that outperformed human competitors in an official competition which took place during the ECAI-06 conference. As a follow up of this grand challenge he founded QuestIt, a spin-off company of the University of Siena, working in the field of question-answering. He is co-author of “Web Dragons: Inside the myths of search engines technologies,” Morgan Kauffman (Elsevier), 2006, and “Machine Learning: A Constrained-Based Approach,” Morgan Kauffman (Elsevier), 2018.
Dr. Gori serves (has served) as an Associate Editor of a number of technical journals related to his areas of expertise, he has been the recipient of best paper awards, and keynote speakers in a number of international conferences. He was the Chairman of the Italian Chapter of the IEEE Computational Intelligence Society, and the President of the Italian Association for Artificial Intelligence.
He is a fellow of the IEEE, ECCAI, IAPR. He is in the list of top Italian scientists kept by the VIA-Academy (http://www.topitalianscientists.org/top_italian_scientists.aspx)

All welcome!

ML seminar, Fri 17 May, 2pm

News, Seminar.

Machine Learning seminar

When: Fri, 17 May 2019, 2pm
Where: AG03, College Building

Who: Wang-Zhou Dai, Imperial College London.

Title: Bridging Machine Learning and Logical Reasoning by Abductive Learning

Abstract: Perception and reasoning are two representative abilities of intelligence that are integrated seamlessly during problem-solving processes. In the area of artificial intelligence (AI), perception is usually realised by machine learning and reasoning is often formalised by logic programming. However, the two categories of techniques were developed separately throughout most of the history of AI. This talk will introduce the abductive learning framework targeted at unifying the two AI paradigms in a mutually beneficial way. In this framework, machine learning models learn to perceive primitive logical facts from the raw data, while logical reasoning is able to correct the wrongly perceived facts for improving the machine learning models. We demonstrate that by using the abductive learning framework, computers can learn to recognise numbers and resolve equations with unknown arithmetic operations simultaneously from images of simple hand-written equations. Moreover, the learned models can be generalized to complex equations and adapted to different tasks, which is beyond the capability of state-of-the-art deep learning models.

Bio: Wang-Zhou Dai is a research associate in the Department of Computing, Imperial College London. He completed his PhD at Nanjing University in machine learning and his undergraduate studies at Northwestern Polytechnical University in applied maths at 2019 and 2010, respectively. His research interests lie in the area of Artificial Intelligence and machine learning, especially in applying first-order logical background knowledge in general machine learning techniques. He has published multiple research papers on major conferences and journals in AI and machine learning including AAAI, ILP, ICDM, ACML and Machine Learning, etc. He has been awarded the IBM PhD Fellowship and Google Excellence Scholarship during his PhD study, and now he is serving as a PC member and reviewer in many top AI & machine learning conferences including IJCAI, AAAI, NeurIPS, ICML, ACML, PRICAI, PAKDD and so on.

All welcome!

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