Here we demonstrate a new deep generative model for classification. We introduce `semi-unsupervised learning', a problem regime related to transfer learning and zero/few shot learning where, in the training data, some classes are sparsely labelled and others entirely unlabelled. Models able to learn from training data of this type are potentially of great use, as many medical datasets are `semi-unsupervised'. Our model demonstrates superior semi-unsupervised classification performance on MNIST to model M2 from Kingma and Welling (2014). We apply the model to human accelerometer data, performing activity classification and structure discovery on windows of time series data.
Each node asynchronously trains an unsupervised representation of text. Each trains its own model on its own dataset and learns a representations of language (a projection from raw text to embedding) which their neighbours use as inputs to their own model. As they train, they also validate the representations produced by their neighbours, producing a score using a Fishers information metric. We use distillation to extract knowledge from the peers. The result is a local, transfer capable language model at each node.
Current recommendation engines attempt to answer the same question: given a user with some activity in the system, which is the next entity, be it a restaurant, a book or a movie, that the user should visit or buy next. The presumption is that the user would favorably review the item being recommended. The goal of our project is to predict how a user would rate an item he/she never rated, which is a generalization of the task recommendation engines perform. Previous work successfully employs machine learning techniques, particularly statistical methods. However, there are some outlier situations which are more difficult to predict, such as new users. In this paper we present a rating prediction approach targeted for entities for which little prior information exists in the database.We put forward and test a number of hypotheses, exploring recommendations based on nearest neighbor-like methods. We adapt existing common sense topic modeling methods to compute similarity measures between users and then use a relatively small set of key users to predict how the target user will rate a given business. We implemented and tested our system for recommending businesses using the Yelp Academic Dataset. We report initial results for topic-based rating predictions, which perform consistently across a broad range of parameters.