Deep Learning Meetup #9

This is a past event

122 people went

Location image of event venue


Dear DeepLearners,

Our next Meetup is scheduled on Wednesday April 19 at 7:00 pm, with 4 (amazing) speakers :

• Alexis Conneau (Facebook AI Research): Transfer Learning for Sentence Embeddings

We present a new approach for learning high-quality sentence embeddings. In Computer Vision, image embeddings are usually obtained using ConvNets trained on large supervised datasets. For natural language processing, most successful approaches for word or sentence embeddings are based on architectures trained on large unsupervised corpus. Inspired by the analogy with computer vision, we describe an approach that leverages high-quality supervision from natural language inference dataset and demonstrates superior performance to previous supervised and unsupervised approaches

• Rahma Chaabouni (ENS): Multimodal Siamese Networks

Recent works have explored deep architectures for learning multimodal speech representation (e.g. audio and images, articulation and audio) in a supervised way. Here we investigate the role of combining different speech modalities, i.e. audio and visual information representing the lips’ movements, in a weakly supervised way using Siamese networks and lexical same-different side information. In particular, we ask whether one modality can benefit from the other to provide a richer representation for phone recognition in a weakly supervised setting. Furthermore, we present a qualitative analysis of the obtained phone embeddings, and show that cross-modal visual input can improve the discriminability of phonological features which are visually discernable (rounding, open/close, labial place of articulation), resulting in representations that are closer to abstract linguistic features than those based on audio only

• Loris Felardos (Heuritech): Generative Adversarial Networks and Variational Autoencoders

GANs and VAEs produce state of the art results in a number of tasks like image generation and semi-supervised learning. The purpose of this talk is to demonstrate how this is achieved from an engineering point of view, what is the mathematical framework behind those models, and how they can be merged together. More information available on this blog post:

Update: Lightning Talk Microsoft Cognitive Toolkit (aka CNTK) & Project Malmo

• Morgan Funtowicz (Software Development Engineer, Microsoft Research Cambridge)

In this 5’ lightning talk, Morgan Funtowicz will cover the most recent enhancements to the “Project Malmo” (*) platform through an integration with Cognitive Toolkit (, the deep learning library of Microsoft.

(*) Project Malmo ( is an open source platform for Artificial Intelligence experimentation and research built on top of Minecraft.

Food and drinks will be provided.