Huszár Ferenc, a Twitter gépi tanulási kutatója 2019 április 17-én, szerdán 12.30-14.00 között a BME I. épület E szárny 224. termében tart előadást „Deep Learning for Visual Data Compression” címmel. Minden érdeklődőt szeretettel várunk.

 

Abstract: Deep learning has proven remarkably successful at modeling the statistics of dense, high-dimensional data, and dethroned traditional signal processing-based approaches as the state-of-the-art in in several domains from speech processing to object segmentation. It is likely that deep learning will eventually revolutionize video and image compression as well. Before this is possible, a number of key challenges remain: Convolutional neural networks are computationally demanding; Deep learning does not work well with the discrete, non-differentiable nature of data compression; We lack good inductive biases and priors for dealing with temporal aspects of video; Finally, we need more robust tools to measure, predict and directly optimize for subjective perceptual quality. In this talk I provide an overview of the work our team has done over the past couple years to address each of these challenges.

 

Biography: Ferenc Huszar (fhuszar@twitter.com) is a Senior Machine Learning Researcher at Twitter. He currently works on real-time recommender systems, and is also involved in Twitter’s ethical machine learning working group. Prior to this Ferenc joined Magic Pony Technology, a startup developing deep learning-based video processing technology, acquired by Twitter in 2016. Ferenc holds a PhD in Machine Learning from the University of Cambridge, and is an active contributor of his research blog inference.vc.