You are all cordially invited to the next AMLab colloquium on Tuesday, January 26 at 16:00 in C3.163, where Mijung Park will give a talk titled “Bayesian methodologies for efficient data analysis”.
Abstract: Machine learning and data science can greatly benefit from Bayesian methodologies, not only because they improve generalisation performance compared to point estimates that are prone to overfitting, but also they provide efficient and principled ways to solve a broad range of statistical problems. In this talk, I will describe several concrete examples where using Bayesian approaches greatly benefit in tackling problems occurring in many areas of science. These examples include (a) designing priors using domain knowledge for structurally sparse high-dimensional parameters with application to functional neuroimaging data and neural spike data; (b) Bayesian manifold learning that enables evaluating the quality of estimated latent manifold as well as learning the latent dimension from statistical evidence; and (c) approximate Bayesian computation (ABC) for models with intractable likelihoods, where we employ kernel mean embeddings to measure data similarities, which is an essential step in ABC.
You are all cordially invited to the next AMLab colloquium on Tuesday, January 19 at 16:00 in C3.163, where Deepak Geetha Viswanathan, will give a talk titled “Generalized parts-based models for unrectified images”.
Abstract: Parts-based detectors are a widely used class of models in object recognition. We propose a novel approach which takes lens distortion into account and generalizes parts-based detectors to unrectified images. Standard parts-based detectors are typically applied to unrectified images, which is sub-optimal, or to rectified images, which is time-consuming. By modifying the feature-extraction and the distance transform function to account for the distortion, we have developed a principled method to generalize parts-based detectors to unrectified images. We validate our approach on omni-directional images with a large amount of distortion, and empirically verify that our method outperforms the standard parts based detector trained on raw omni-directional images.
Thijs van Ommen joined AMLab as a postdoc. Thijs studied mathematics and
computer science in Leiden and did his PhD on model selection and prediction
at the CWI. After that, he was lecturer for a Machine Learning course in
Utrecht, and will now work on causal inference in the CAFES project.