353 citations · 1.3k across the 38 of their papers we have counts for
13 papers · 1 filter
Wasserstein Dropout
Joachim Sicking, Maram Akila, Maximilian Pintz +3
Despite of its importance for safe machine learning, uncertainty quantification for neural networks is far from being solved. State-of-the-art approaches to estimate neural uncerta…
The ELBO of Variational Autoencoders Converges to a Sum of Three Entropies
Simon Damm, Dennis Forster, Dmytro Velychko +3
The central objective function of a variational autoencoder (VAE) is its variational lower bound (the ELBO). Here we show that for standard (i.e., Gaussian) VAEs the ELBO converges…
Investigating maximum likelihood based training of infinite mixtures for uncertainty quantification
Sina Däubener, Asja Fischer
Uncertainty quantification in neural networks gained a lot of attention in the past years. The most popular approaches, Bayesian neural networks (BNNs), Monte Carlo dropout, and de…
Improving the Long-Range Performance of Gated Graph Neural Networks
Denis Lukovnikov, Jens Lehmann, Asja Fischer
Many popular variants of graph neural networks (GNNs) that are capable of handling multi-relational graphs may suffer from vanishing gradients. In this work, we propose a novel GNN…
Characteristics of Monte Carlo Dropout in Wide Neural Networks
Joachim Sicking, Maram Akila, Tim Wirtz +2
Monte Carlo (MC) dropout is one of the state-of-the-art approaches for uncertainty estimation in neural networks (NNs). It has been interpreted as approximately performing Bayesian…
On the convergence of the Metropolis algorithm with fixed-order updates for multivariate binary probability distributions
Kai Brügge, Asja Fischer, Christian Igel
The Metropolis algorithm is arguably the most fundamental Markov chain Monte Carlo (MCMC) method. But the algorithm is not guaranteed to converge to the desired distribution in the…
Bringing Light Into the Dark: A Large-scale Evaluation of Knowledge Graph Embedding Models Under a Unified Framework
Mehdi Ali, Max Berrendorf, Charles Tapley Hoyt +6
The heterogeneity in recently published knowledge graph embedding models' implementations, training, and evaluation has made fair and thorough comparisons difficult. In order to as…
Unsupervised Cross-Domain Speech-to-Speech Conversion with Time-Frequency Consistency
Mohammad Asif Khan, Fabien Cardinaux, Stefan Uhlich +2
In recent years generative adversarial network (GAN) based models have been successfully applied for unsupervised speech-to-speech conversion.The rich compact harmonic view of the…
Detecting Adversarial Examples for Speech Recognition via Uncertainty Quantification
Sina Däubener, Lea Schönherr, Asja Fischer +1
Machine learning systems and also, specifically, automatic speech recognition (ASR) systems are vulnerable against adversarial attacks, where an attacker maliciously changes the in…
Thresholded Adaptive Validation: Tuning the Graphical Lasso for Graph Recovery
Mike Laszkiewicz, Asja Fischer, Johannes Lederer
Many Machine Learning algorithms are formulated as regularized optimization problems, but their performance hinges on a regularization parameter that needs to be calibrated to each…
Leveraging Frequency Analysis for Deep Fake Image Recognition
Joel Frank, Thorsten Eisenhofer, Lea Schönherr +3
Deep neural networks can generate images that are astonishingly realistic, so much so that it is often hard for humans to distinguish them from actual photos. These achievements ha…
End-to-End Entity Linking and Disambiguation leveraging Word and Knowledge Graph Embeddings
Rostislav Nedelchev, Debanjan Chaudhuri, Jens Lehmann +1
Entity linking - connecting entity mentions in a natural language utterance to knowledge graph (KG) entities is a crucial step for question answering over KGs. It is often based on…
Pretrained Transformers for Simple Question Answering over Knowledge Graphs
D. Lukovnikov, A. Fischer, J. Lehmann
Answering simple questions over knowledge graphs is a well-studied problem in question answering. Previous approaches for this task built on recurrent and convolutional neural netw…