| Abstract |
In this talk, I introduce the notion of developmental agents, that are based on the
theory of “learning from constraints” (see e.g. http://videolectures.net/marco_gori/). It is
claimed that in most interesting tasks, learning from constraints naturally leads to “deep
architectures”, that emerge when following the developmental principle of focusing
attention on “easy constraints”, at each stage. Interestingly, this suggests that stage-based
learning, as discussed in developmental psychology, might not be primarily the outcome
of biology, but it could be instead the consequence of optimization principles and
complexity issues that hold regardless of the “body.”
In the second part of the talk, I give insights on the adoption of the proposed
framework in computer vision. The proposed functional approach leads naturally to
develop different notions of features, the lower level of which are somehow related to
classical SIFT. It is pointed out that the adoption of information-theoretic principles are at
the basis of the feature generation either at low or high level of the vision computer
hierarchy. The functions that are developed are inherently independent of roto-translations
and do acquire scale invariance by the minimization of an appropriate entropy-based
measure, that is also at the basis of the focus of attention. Finally, I give an overview of
different constraints emerging at different layers of the hierarchy, and claim that the
overall system is expected to work in any visual environment by acting continuously,
with no separation between learning and scene interpretation.
Marco Gori
University of Siena, Italy
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 Universitá 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 is the leader of the WebCrow project for automatic solving of
crosswords, that outperformed human competitors in an official competition which
took place within 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 the book “Web Dragons: Inside the myths
of search engines technologies,” Morgan Kauffman (Elsevier), 2006.
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 in the list of top Italian
scientists kept by VIA-Academy (http://www.topitalianscientists.org/
top_italian_scientists.aspx) based on the h-index and he is a fellow of the ECCAI and
of the IEEE. |
