Talk by Ekaterina Kochmar, University of Cambridge, at the Cambridge Coding Academy Data Science Bootcamp:
14:00-14:20 Jes Frellsen, University of Cambridge Bayesian generalised ensemble Markov chain Monte Carlo 14:20-14:40 Adam Scibior, University of Cambridge Probabilistic programming with effect systems 14:40-15:00 Andy Gordon, Microsoft Research Fabular: Regression Formulas as Probabilistic Programming 15:00-15:20 John Hong, University of Cambridge Comparing Matrix Factorization algorithms on a level playing field
Dr Jess Whittlestone talks about how artificial intelligence can be used to most improve human decision-making. Her talk was held at the Cambridge Decision Summit 2018.
16:00-16:20 Alex Matthews, University of Cambridge A variational framework for approximate Gaussian process inference 16:20-16:40 Ferenc Huszár, Magic Pony Technology, Cambridge Generative Models for Image Processing 16:40-17:00 Sacha Krstulovic, Audio Analytic Ltd., Cambridge Automatic Environmental Sound Recognition: Performance versus Computational Cost
11:30-11:50 Antonio Criminisi, Microsoft Research Efficient machine learning for the quantitative analysis of medical images 11:50-12:10 Sebastian Nowozin, Microsoft Research Bayesian Time-of-Flight for Realtime Scene Decomposition into Geometry, Reflectance, and Light 12:10-12:30 Ryota Tomioka, Microsoft Research Understanding the role of invariances in training neural networks 12:30-12:50 Yoram Bachrach, Microsoft Research Analyzing factors behind hiring decisions based on online social network profiles
09:30-09:50 Carl Rasmussen, University of Cambridge Variational Inference in Gaussian Processes for non-linear time series 09:50-10:10 Yingzhen Li, University of Cambridge Variational inference with Rényi divergencee 10:10-10:30 Thang Bui, University of Cambridge Deep Gaussian Processes for Regression using Approximate Expectation Propagation 10:30-10:50 Adrian Weller, University of Cambridge Clamping variables and approximate inference