49 citations · 122 across the 7 of their papers we have counts for
7 papers
Explicit Word Density Estimation for Language Modelling
Jovan Andonov, Octavian Ganea, Paulina Grnarova +2
Language Modelling has been a central part of Natural Language Processing for a very long time and in the past few years LSTM-based language models have been the go-to method for c…
Generative Minimization Networks: Training GANs Without Competition
Paulina Grnarova, Yannic Kilcher, Kfir Y. Levy +2
Many applications in machine learning can be framed as minimization problems and solved efficiently using gradient-based techniques. However, recent applications of generative mode…
A domain agnostic measure for monitoring and evaluating GANs
Paulina Grnarova, Kfir Y Levy, Aurelien Lucchi +4
Generative Adversarial Networks (GANs) have shown remarkable results in modeling complex distributions, but their evaluation remains an unsettled issue. Evaluations are essential f…
Defending Against Adversarial Attacks by Leveraging an Entire GAN
Gokula Krishnan Santhanam, Paulina Grnarova
Recent work has shown that state-of-the-art models are highly vulnerable to adversarial perturbations of the input. We propose cowboy, an approach to detecting and defending agains…
An Online Learning Approach to Generative Adversarial Networks
Paulina Grnarova, Kfir Y. Levy, Aurelien Lucchi +2
We consider the problem of training generative models with a Generative Adversarial Network (GAN). Although GANs can accurately model complex distributions, they are known to be di…
Neural Multi-Step Reasoning for Question Answering on Semi-Structured Tables
Till Haug, Octavian-Eugen Ganea, Paulina Grnarova
Advances in natural language processing tasks have gained momentum in recent years due to the increasingly popular neural network methods. In this paper, we explore deep learning t…
Neural Document Embeddings for Intensive Care Patient Mortality Prediction
Paulina Grnarova, Florian Schmidt, Stephanie L. Hyland +1
We present an automatic mortality prediction scheme based on the unstructured textual content of clinical notes. Proposing a convolutional document embedding approach, our empirica…