8th CODH Seminar
Exploring Deep Learning for Classical Japanese Literature, Machine Creativity, and Recurrent World Models!

We are pleased to announce that David Ha, a research scientist at Google Brain, will give a talk about deep learning, especially about deep generative models and machine creativity. You are all invited. Registration in advance is required. #codh8

Basic Information

Title Exploring Deep Learning for Classical Japanese Literature, Machine Creativity, and Recurrent World Models!
Speaker David Ha
Date 14:30-15:30, November 22 (Thu), 2018
Venue 1208/1210 Meeting Room (12F), National Institute of Informatics. Access to NII.
Abstract Deep generative models are proving to become powerful methods to generate realistic media, such as images, speech, and even video. However, they are also seen to be black boxes that are without much interpretability. My recent research interest has been to investigate the abstract representations created using deep generative models. Our group has shown that understanding the latent space of these models not only allows deep neural networks to become more interpretable, but also opens up vast applications. In this talk, I will highlight some recent applications of generative models to the domain of classical Japanese literature. I will also be talking about potential use case of machine learning algorithms in creative applications, and discuss whether we believe the algorithms are just a tool for an artist, or if there is something inherently creative about an algorithm. Finally, we discuss some interesting applications of using generative models for generating reinforcement learning game environments.
Bio David Ha is a staff research scientist at Google Brain. His research interests include Recurrent Neural Networks, Creative AI, and Evolutionary Computing. Prior to joining Google, he worked at Goldman Sachs as a Managing Director, where he ran the fixed-income trading business in Japan. He obtained undergraduate and graduate degrees in Engineering Science and Applied Math from the University of Toronto.

Registration

You are all invited, free of charge.

The seminar has fininshed. Thank you for your participation.

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Past CODH Seminars

2019-03-11

10th CODH Seminar - Document Analysis and Character Recognition

2019-01-08

9th CODH Seminar - Computer Vision with Limited Labeled Data

2018-11-22

8th CODH Seminar - Exploring Deep Learning for Classical Japanese Literature, Machine Creativity, and Recurrent World Models!

2018-07-31

7th CODH Seminar - Manifold Mixup: Encouraging Meaningful On-Manifold Interpolation as a Regularizer

2018-03-12

6th CODH Seminar: Historical Big Data - Challenges in Transforming Historical Documents to Structured Data for the Integrated Analysis of Records in the Past -

2017-12-04

5th CODH Seminar: Trustworthy Data Repositories - Forum for Sharing Practical Information about CoreTrustSeal Certification -

2017-07-27

4th CODH Seminar: A New Trend on Image Delivery in Digital Archives - IIIF's Potential for Standardization and Sophistication of Image Access -

2017-05-30

3rd CODH Seminar: Usage of DOI for Humanities - Assignment of DOI for Scholarly Resources such as Research Data and Museum Collections -

2017-02-10

2nd CODH Seminar: Old Japanese Character Challenge - Future of Machine Recognition and Human Transcription -

2017-01-23

1st CODH Seminar: Big Data and Digital Humanities