Schedule for Fall 2018
Official course name: Perspectives in Informatics 5, Graduate School of Informatics
Unless noted otherwise, all talks Thursday 14:45 16:15, Joho 2, Research Bldg. No.7 (総合７), Main Campus

 Francois Le Gall(Kyoto University), Random Walks and Space Complexity

 Francois Le Gall(Kyoto University), Quantum Algorithms for Algebraic and GraphTheoretic Problems

 Speaker(Affiliation), Title hosted by host

 Oliver Friedmann(Ziggeo), tbd hosted by David Avis

 Yihong Zhang(Kyoto University), Statistical Machine Learning and Its Application on Social Media hosted by Adam Jatowt

 Speaker(Affiliation), Title hosted by host

 Raj Dabre(NICT), Generative Adversarial Network (tentative) hosted by Fabien Cromieres

 Fabien Cromieres(Kyoto University), Neural Machine Translation

 Fabien Cromieres(Kyoto University), Memory Network and their application to Question Answering (tentative)

 Fabien Cromieres(Kyoto University), Automatic Image Captioning (tentative)

 Speaker(Affiliation), Title hosted by host

 Adam Jatowt(Kyoto University), Acrosstime Term Similarity Computation and Explanation

 Adam Jatowt(Kyoto University), Estimating Document Comprehensibility

 Speaker(Affiliation), Title hosted by host

 Speaker(Affiliation), Title hosted by host
Detailed Information and Abstracts
October 4
14:45 16:15, Joho 2, Research Bldg. No.7 (総合７), Main Campus
Francois Le Gall, Kyoto University
Title: Random Walks and Space Complexity
Abstract: The concept of random walk is an extremely versatile and powerful paradigm in the field of randomized algorithms. In the first part of this talk I will review the concept of random walks and describe several wellknown applications concerning the design of spaceefficient algorithms (e.g., how to test if an undirected graph is connected in logarithmic space). In the second part of this talk I will present new results about spaceefficient algorithms for solving linear systems of equations based on random walks.
Bio: Francois Le Gall is an Associate Professor in the Department of Communications and Computer Engineering, Graduate School of Informatics, Kyoto University. He received his PhD in 2006 from the University of Tokyo. His research interests include algorithms, computational complexity and quantum computation.
October 11
14:45 16:15, Joho 2, Research Bldg. No.7 (総合７), Main Campus
Francois Le Gall, Kyoto University
Title: Quantum Algorithms for Algebraic and GraphTheoretic Problems
Abstract: In this talk I will present recent developments on quantum algorithms for problems from linear algebra and graphtheoretic problems. I will first introduce the concept of quantum algorithms, and in particular the powerful technique known as “quantum search”, and describe several fundamental applications. Most examples will deal with problems from linear algebra (computing the product of two matrices, inverting a matrix,…), and their natural applications to graphtheoretic problems.
November 1
14:45 16:15, Joho 2, Research Bldg. No.7 (総合７), Main Campus
Yihong Zhang, Kyoto University
Title: Statistical Machine Learning and Its Application on Social Media
Abstract: Recent years have seen rapid growth in largescale social data. A social media platform such as Twitter generates hundreds of millions of short messages every day, which contain important information about various social phenomenon. Due to its large volume, it is almost impossible for human operators to manually digest this data. Statistical machine learning consists techniques that aim to extract important information from large volumes of data. In this lecture, I will show how statistical machine learning can be applied for mining social media data. I will talk about supervised learning, unsupervised learning, and optimization algorithms. Particularly, I will introduce techniques such as Naive Bayes Classifier, Neural Network, Kmeans clustering, and Particle Swarm Optimization. I will show how these techniques can be used in a particular application, namely, event extraction from social media.
November 15
14:45 16:15, Joho 2, Research Bldg. No.7 (総合７), Main Campus
Raj Dabre, NICT
Title: Generative Adversarial Network
Abstract:
Deep neural networks (DNN) are well known for their ability to excel at text or image classification aka discriminative tasks. Although, DNNs are also good at generative tasks (sequence or image), it is very difficult to say whether the generated content is similar to what humans would produce. Enter Generative Adversarial Networks (GANs)! GANs extend a classic generative DNN to include an additional component known as the discriminator which learns to distinguish between generated (fake) and natural (real) data. This enables the generator to learn to generate content that is nearly indistinguishable from real life content. In this talk we will start out with a primer of DNN and its classic applications and then dive into GANs. We will cover an indepth working of GANs and its variations and then explore several exciting applications of adversarial approaches. We will also focus on how GANs are good at leveraging unlabelled data to boost the quality of generative models.