no code implementations • 30 Mar 2022 • Jonathan Warrell, Mark Gerstein
Here, we offer a framework for representing and learning flexible PAC-Bayes bounds as stochastic programs using DPP-based methods.
no code implementations • 30 Mar 2022 • Jonathan Warrell, Alexey Potapov, Adam Vandervorst, Ben Goertzel
We introduce a formal meta-language for probabilistic programming, capable of expressing both programs and the type systems in which they are embedded.
no code implementations • 1 Dec 2018 • Jonathan Warrell, Hussein Mohsen, Mark Gerstein
A variety of methods have been proposed for interpreting nodes in deep neural networks, which typically involve scoring nodes at lower layers with respect to their effects on the output of higher-layer nodes (where lower and higher layers are closer to the input and output layers, respectively).
no code implementations • CVPR 2014 • Shuai Zheng, Ming-Ming Cheng, Jonathan Warrell, Paul Sturgess, Vibhav Vineet, Carsten Rother, Philip H. S. Torr
The concepts of objects and attributes are both important for describing images precisely, since verbal descriptions often contain both adjectives and nouns (e. g. "I see a shiny red chair').
no code implementations • 25 Mar 2014 • Vibhav Vineet, Jonathan Warrell, Philip H. S. Torr
The algorithm converges to a local minimum for any general pairwise potential, and we give a theoretical analysis of the properties of the algorithm, characterizing the situations in which we can expect good performance.
no code implementations • 16 Oct 2013 • Ming-Ming Cheng, Shuai Zheng, Wen-Yan Lin, Jonathan Warrell, Vibhav Vineet, Paul Sturgess, Nigel Crook, Niloy Mitra, Philip Torr
This allows us to formulate the image parsing problem as one of jointly estimating per-pixel object and attribute labels from a set of training images.
no code implementations • CVPR 2013 • Julien P. C. Valentin, Sunando Sengupta, Jonathan Warrell, Ali Shahrokni, Philip H. S. Torr
We then define a CRF over this mesh, which is able to capture the consistency of geometric properties of the objects present in the scene.