A Variational Recurrent Neural Network for Session-Based Recommendations using Bayesian Personalized Ranking

This work introduces VRNN-BPR, a novel deep learning model, which is utilized in session-based Recommender systems tackling the data sparsity problem. The proposed model combines a Recurrent Neural Network with an amortized variational inference setup (AVI) and a Bayesian Personalized Ranking in order to produce predictions on sequence-based data and generate recommendations. The model is assessed using a large real-world dataset and the results demonstrate its superiority over current state-of-the-art techniques

Local Competition and Stochasticity for Adversarial Robustness in Deep Learning

This work addresses adversarial robustness in deep learning by considering deep networks with stochastic local winner-takes-all (LWTA) activations. This type of network units result in sparse representations from each model layer, as the units are organized in blocks where only one unit generates a non-zero output. The main operating principle of the introduced units lies on stochastic arguments, as the network performs posterior sampling over competing units to select the winner. We combine these LWTA arguments with tools from the field of Bayesian non-parametrics, specifically the stick-breaking construction of the Indian Buffet Process, to allow for inferring the sub-part of each layer that is essential for modeling the data at hand. Then, inference is performed by means of stochastic variational Bayes. We perform a thorough experimental evaluation of our model using benchmark datasets. As we show, our method achieves high robustness to adversarial perturbations, with state-of-the-art performance in powerful adversarial attack schemes.Comment: Accepted AISTATS 2021. arXiv admin note: text overlap with arXiv:2006.1062

A Deep Learning Approach for Dynamic Balance Sheet Stress Testing

In the aftermath of the financial crisis, supervisory authorities have considerably improved their approaches in performing financial stress testing. However, they have received significant criticism by the market participants due to the methodological assumptions and simplifications employed, which are considered as not accurately reflecting real conditions. First and foremost, current stress testing methodologies attempt to simulate the risks underlying a financial institution's balance sheet by using several satellite models, making their integration a really challenging task with significant estimation errors. Secondly, they still suffer from not employing advanced statistical techniques, like machine learning, which capture better the nonlinear nature of adverse shocks. Finally, the static balance sheet assumption, that is often employed, implies that the management of a bank passively monitors the realization of the adverse scenario, but does nothing to mitigate its impact. To address the above mentioned criticism, we introduce in this study a novel approach utilizing deep learning approach for dynamic balance sheet stress testing. Experimental results give strong evidence that deep learning applied in big financial/supervisory datasets create a state of the art paradigm, which is capable of simulating real world scenarios in a more efficient way.Comment: Preprint submitted to Journal of Forecastin

Recurrent Latent Variable Networks for Session-Based Recommendation

In this work, we attempt to ameliorate the impact of data sparsity in the context of session-based recommendation. Specifically, we seek to devise a machine learning mechanism capable of extracting subtle and complex underlying temporal dynamics in the observed session data, so as to inform the recommendation algorithm. To this end, we improve upon systems that utilize deep learning techniques with recurrently connected units; we do so by adopting concepts from the field of Bayesian statistics, namely variational inference. Our proposed approach consists in treating the network recurrent units as stochastic latent variables with a prior distribution imposed over them. On this basis, we proceed to infer corresponding posteriors; these can be used for prediction and recommendation generation, in a way that accounts for the uncertainty in the available sparse training data. To allow for our approach to easily scale to large real-world datasets, we perform inference under an approximate amortized variational inference (AVI) setup, whereby the learned posteriors are parameterized via (conventional) neural networks. We perform an extensive experimental evaluation of our approach using challenging benchmark datasets, and illustrate its superiority over existing state-of-the-art techniques