127 research outputs found
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
- …