Hi all,
This is a kind reminder about tomorrow's presentation.
Best, Dima
On Mon, 23 Jan 2017 at 12:14 Dzmitry Bahdanau dimabgv@gmail.com wrote:
Hi all,
Next Friday (27.01) at 1:30pm, room 6214, we will have not just one, but two tea-talk presentations by Çağlar and Vincent, both MILA students. Please come in great numbers!
Speaker 1: Çağlar Gülçehre Title: TARDIS: an RNN with Wormhole Connections Abstract: In this talk, I will discuss a new memory augmented model which we have been working on with Sarath and Yoshua since last summer. Basically we revisit the idea of memory augmented models and investigate some of their aspects theoretically and propose a new model. The presentation will be about an ongoing work(although the arXiv paper will be out soon). Basically, we revisit the idea of memory augmented models and investigate some of their aspects theoretically and propose a new model. In this paper, we propose a novel memory augmented neural network model called TARDIS (Temporal Automatic Relation Discovery in Sequences). Our model (TARDIS) has an RNN controller which modifies an external memory component where it can store a selective set of embeddings of its own previous hidden states and revisit them as and when needed. In terms of the memory structure, our proposed model is similar to Dynamic Neural Turing Machines ~(D-NTM), but both read and write operations are simpler and more efficient. TARDIS uses discrete addressing for read/write operations which helps to substantially reduce the vanishing gradient problem with very long sequences. We evaluate our models on different long-term dependency tasks and report competitive results in all of them.
Speaker 2: Vincent Dumoulin Title: A Learned Representation for Artistic Style Abstract: In this work (done while interning at Google Brain and submitted to ICLR2017) we investigate the construction of a single, scalable deep network that can parsimoniously capture the artistic style of a diversity of paintings. We demonstrate that such a network generalizes across a diversity of artistic styles by reducing a painting to a point in an embedding space. Importantly, this model permits a user to explore new painting styles by arbitrarily combining the styles learned from individual paintings.
Best, Dima