Machine Learning is
Welcome to our raggle taggle reading group for cool machine learning papers.
The next meeting will be Friday 18th October 16:00 at 1CS.
One Shot Domain Adaption for Re-Identification (pres. by Víctor Ponce-López).
Vicinal Risk Minimization (pres. by Alex Hepburn).
Neural Ordinary Differential Equations (pres. by Callum Mann).
Doubly Stochastic Variational Bayes for non-Conjugate Inference (pres. by Hao Song).
Quo Vadis, Action Recognition? (pres. by Alessandro Masullo).
Reinforcement Learning and Control as Probabilistic Inference: Tutorial and Review (pres. by Michal Kozlowski).
Network Embedding (with node2vec, Structural Deep Network Embedding and Deep Neural Networks for Learning Graph Representations) (pres. by Ryan McConville and Alex Hepburn).
Introduction to G.Ps and Dropout as a Bayesian Approximation (pres. by Niall Twomey and Hao Song).
Policy Shaping: Integrating Human Feedback with Reinforcement Learning (pres. by Taku Yamagata and Raul Santos-Rodriguez).
Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks (pres. by Alessandro Masullo and Víctor Ponce-López).
Generative Adverserial Networks (pres. by Hao Song).
Deep Learning and Distilling a Neural Network into a Soft Decision Tree (pres. by Alex Hepburn and Callum Mann).
Regularization: Regularization and variable selection via the elastic net (pres. by James Pope and Michal Kozlowski).
An Introduction to Graphical Models and Variational Inference (pres. by Niall Twomey and Rafael Poyiadzi).
Learning with labeled and unlabeled data (pres. by Miquel Perelló-Nieto and Raúl Santos-Rodríguez).
Scalable Nearest Neighbor Algorithms for High Dimensional Data (pres. by Ryan McConville)
If you would like to be added to the mailing list to receive notifications of the next meeting, and the occasional spam, send me an email at
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