Bayesian deep learning for accurate characterisation of uncertainties in time series analysis
At alpha-i we are developing deep learning models for accurate characterisation of uncertainties in time series analysis. We achieve this by combining deep learning methodologies with powerful Bayesian formalism. The alpha-i deep learning network is able not only to make forecasts from time series but also to associate each prediction with a confidence level, which is derived from the information about the model and the data available. One of the key aspect of this Bayesian deep learning methodology is its aversion to over-fitting obtained thanks to the robust probabilistic inference framework. We are also developing novel Bayesian inference methodologies to significantly boost the online performance of our machinery.
Christopher Bonnett has a Masters in Astronomy from the University of Leiden and a PhD in Cosmology from the University of Pierre et Marie Curie. He has 6 years of post-doctoral experience as a key member of several large international collaborations measuring the accelerated expansion of the universe. He has extensive experience in applying deep learning to inverse problems in astronomy. He attended the Insight data science fellowship program in NYC.