You are all cordially invited to the AMLab seminar on Thursday 14th Feb at 16:00 in C3.163, where Victor Garcia will give a talk titled “GRIN: Graphical Recurrent Inference Networks“. Afterwards there are the usual drinks and snacks!
Abstract: A graphical model is a structured representation of the data generating process. The traditional method to reason over random variables is to perform inference in this graphical model. However, in many cases the generating process is only a poor approximation of the much more complex true data generation process, leading to poor posterior estimates. The subtleties of the generative process are however captured in the data itself and we can “learn to infer”, that is, learn a direct mapping from observations to explanatory latent variables. In this work we propose a hybrid model that combines graphical inference with a learned inverse model, which we structure as a graph neural network. The iterative algorithm is formulated as a recurrent neural network. By using cross-validation we can automatically balance the amount of work performed by graphical inference versus learned inference. We apply our ideas to the Kalman filter, a Gaussian hidden Markov model for time sequences. We apply our “Graphical Recurrent Inference” method to a number of path estimation tasks and show that it successfully outperforms either learned or graphical inference run in isolation.
You are all cordially invited to the AMLab seminar on Thursday January 31 at 16:00 in C3.163, where Emiel Hoogeboom will give a talk titled “Emerging Convolutions for Generative Normalizing Flows”. Afterwards there are the usual drinks and snacks!
Abstract: Generative flows are attractive because they admit exact likelihood optimization and efficient image synthesis. Recently, Kingma & Dhariwal (2018) demonstrated with Glow that generative flows are capable of generating high quality images. We generalize the 1 x 1 convolutions proposed in Glow to invertible d x d convolutions, which are more flexible since they operate on both channel and spatial axes. We propose two methods to produce invertible convolutions that have receptive fields identical to standard convolutions: Emerging convolutions are obtained by chaining specific autoregressive convolutions, and periodic convolutions are decoupled in the frequency domain. Our experiments show that the flexibility of d x d convolutions significantly improves the performance of generative flow models on galaxy images, CIFAR10 and ImageNet.
You are all cordially invited to the AMLab seminar on Thursday January 17 at 16:00 in C3.163, where Herke van Hoof will give a talk titled “Learning Selective Coverage Strategies for Surveying and Search”. Afterwards there are the usual drinks and snacks!
Abstract: In this seminar, I’ll present a project I’ve been working on with Sandeep Manjanna and Gregory Dudek (Mobile Robotics Lab, McGill University). In this project, we investigated selective coverage strategies for a robot tasked with surveying or searching prioritised locations in a given area. This problem can be modelled as a Markov decision process and solved with reinforcement learning strategies, but the state space is extremely large, requiring these states to be aggregated. The proposed state aggregation method is shown to generalize well between different environments. In field tests over reefs at the Folkestone Marine Reserve, using this method an autonomous surface vehicle was able to improve the number of useable visual data samples.