Agenda:
6:00 - Doors open. Networking. Wine, beer & snacks
6:45 - Opening remarks
7:00 - Multimodal Sentiment Analysis with TensorFlow by Anthony Hu, Ph.D. at the University of Cambridge, Computer Vision and Machine Learning
7:20 - Talk #2. To be announced shortly
7:40 - Q&A break
8:00 - Wrap-up
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DETAILED AGENDA:
Talk #1
Speaker: Anthony Hu, Ph.D. at the University of Cambridge, Computer Vision and Machine Learning
Title: Multimodal Sentiment Analysis with TensorFlow
Abstract: Anthony proposes a novel approach to multimodal sentiment analysis using deep neural networks combining visual analysis and natural language processing. The goal is different than the standard sentiment analysis goal of predicting whether a sentence expresses positive or negative sentiment; instead, his project aims to infer the latent emotional state of the user. Thus, it focuses on predicting the emotion word tags attached by users to their Tumblr posts, treating these as 'self-reported emotions'. Containing both convolutional and recurrent structures, the model was trained on TensorFlow that allows flexibility in term of neural network design and training (with multimodal inputs and transfer learning for instance) using the new TensorFlow Dataset which is a high-performance data pipeline that can easily handle different sources of data (text, images). Bio: Anthony is joining Machine Intelligence Laboratory (Ph.D.) at the University of Cambridge to work on Computer Vision and Machine Learning applied to autonomous vehicles, more precisely in scene understanding, and vehicle's interpretability. Previously, research scientist experience at Spotify where he worked on musical similarities at large-scale using audio. MSc in Applied Statistics from the University of Oxford, prior to that went to Telecom ParisTech, a French Engineering Grande Ecole. His recent work is published at KDD 2018 (https://arxiv.org/abs/1805.10205).
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