Please remember to register here: http://bit.ly/2FqUtPI
6:00 - Doors open. Networking. Wine, beer & snacks.
6:45 - Opening remarks.
7:00 - Hassle Free, Scalable, Machine Learning with Kubeflow
7:20 - Architectures for big scale 2D imagery
7:40 - Q&A break
8:00 - Wrap-up
Speaker: Barbara Fusinska
Title: Hassle Free, Scalable, Machine Learning with Kubeflow
Abstract: Kubeflow uses Kubernetes strengths to build a toolkit for data scientists where they can create, train and publish the models in a hassle-free and scalable way. The goal is to run machine learning workflow without a need to think about the infrastructure. In this talk, Barbara will discuss the capabilities of Kubeflow from the data scientist perspective. The presentation will introduce how you can use the platform to build the models and deploy it adjusting the computation environment.
Bio: Barbara is a Machine Learning Strategic Cloud Engineer at Google with strong software development background. While working with a variety of different companies, she gained experience in building diverse software systems. This experience brought her focus to the Data Science and Big Data field. She believes in the importance of the data and metrics when growing a successful business. Alongside collaborating around data architectures, Barbara still enjoys programming activities. Currently speaking at conferences in-between working in London. She tweets at @BasiaFusinska and you can follow her blog.
Speaker: Zbigniew Wojna
Title: Architectures for big scale 2D imagery
Abstract: Zbigniew will present research he conducted during his Ph.D. at University College London and in collaboration with Google. His primary interest lays in the development of neural architectures for 2D imagery problems in big scale. He will present the recently published analysis of different upsampling methods in the decoder part of visual architectures, together with last week ongoing extension for GANs. Will discuss attention mechanism for text recognition and review for what kind of application it can be useful (automatically updating Google Maps based on Google Street View imagery). He will explain the idea behind inception and change in Inception-v3 to have it the best single model on ImageNet 2015 and how does it compare to Resnet architecture which was published 2 weeks after. Together with inception, will present his winning submission to MS COCO 2016 detection challenge and the extensive analysis of different models and backbone architectures inside. At the end will shortly review UCL effort working with 4096x4096 images at The Digital Mammography DREAM Challenge for breast cancer recognition, where they achieved 9th among 1375 teams worldwide and 2nd place in the community phase.
Bio: Zbigniew Wojna is deep learning researcher and founder of TensorFlight Inc. company providing instant remote commercial property inspection (for risk factors for reinsurance enterprises) based on satellite and street view type imagery. Zbigniew is currently in the final stage of his Ph.D. (already with more than 1000 citations) at the University College London under the supervision of Professor Iasonas Kokkinos and professor John Shawe-Taylor. His primary interest lies in finding and solving research problems around 2D machine vision applications usually in big scale. Zbigniew in his Ph.D. career spent most of the time working across different groups in DeepMind, Google Research, and Facebook Research. It includes DeepMind Health Team, Deep Learning Team for Google Maps in collaboration with Google Brain, Machine Perception with Kevin Murphy, Weak Localization Team with Vittorio Ferrari and Facebook AI Research Lab in Paris. His company TensorFlight Inc. was featured as top 2 AI startups among few hundreds by InnovatorsRace50 and closed seed funding last year.