NTUA Seminar: "Towards a Syntactic/Probabilistic Framework for Vision/Speech Recognition and Biological Processes" by Vasilis Gidas

Πρώτη ομιλία για το εαρινό εξάμηνο 2020 στο Colloquium του Τομέα Μαθηματικών ΣΕΜΦΕ ΕΜΠ.

Προσκεκλημένος μας είναι ο καθηγητής κ. Βασίλης Γίδας (Brown University, https://www.brown.edu/academics/applied-mathematics/basilis-gidas)

Η διάλεξη θα δοθεί την Παρασκευή 21/2 στις 13:05, στην αίθουσα Σεμιναρίων του Τομέα Μαθηματικών ΣΕΜΦΕ (κτ. Ε Γενικών Εδρών, β’ όροφος)
και έχει τίτλο "Towards a Syntactic/Probabilistic Framework for Vision/Speech Recognition and Biological Processes”.

TITLE: Towards a Syntactic/Probabilistic Framework for Vision/Speech Recognition and Biological Processes

ABSTRACT
The goal of computer vision is to build “intelligent” machines that interpret scenes, in the sense of recognizing objects or other structures in a scene, and provide succinct descriptions of context, actions, and intentions. In computer speech the goal is similar – “interpret” or recognize speech. In the past few decades, important advances have been made in concrete applications such as industrial inspection, medical imagery, remote sensing, animation, robotics, and airline reservation. Such applications have motivated the development of powerful statistical/mathematical tools such as statistical learning theory and Monte Carlo optimization and simulation algorithms. But a coherent conceptual and mathematical framework for high-level vision and speech recognition tasks, is missing. The same is the case for complex biological processes such as protein folding or the collective behavior of genes, proteins and cells. In this talk we will present a probabilistic syntactic/grammatical framework, reminiscent to Chomsky’s grammatical systems in linguistics, for studying vision/speech recognition tasks as well as biological processes. The framework will be motivated by isolating some key sources of difficulty in vision/speech systems which exhibit regularities from local to global – regularities which can be modeled only by syntactic/grammatical rules. The framework will be used to define a finite automata representation of genes (analogous to the grammatical representation of languages) and a statistical algorithmic procedure for finding genes in the human genome.