Search Results for author: Asja Fischer

Found 33 papers, 15 papers with code

Copula-Based Normalizing Flows

1 code implementation15 Jul 2021 Mike Laszkiewicz, Johannes Lederer, Asja Fischer

Normalizing flows, which learn a distribution by transforming the data to samples from a Gaussian base distribution, have proven powerful density approximations.

A Novel Regression Loss for Non-Parametric Uncertainty Optimization

no code implementations7 Jan 2021 Joachim Sicking, Maram Akila, Maximilian Pintz, Tim Wirtz, Asja Fischer, Stefan Wrobel

One of the most commonly used approaches so far is Monte Carlo dropout, which is computationally cheap and easy to apply in practice.

Gated Relational Graph Attention Networks

no code implementations1 Jan 2021 Denis Lukovnikov, Asja Fischer

Relational Graph Neural Networks (GNN) are a class of GNN that are capable of handling multi-relational graphs.

Graph Attention

SmoothLRP: Smoothing Explanations of Neural Network Decisions by Averaging over Stochastic Input Variations

no code implementations1 Jan 2021 Arne Peter Raulf, Ben Luis Hack, Sina Däubener, Axel Mosig, Asja Fischer

With the excessive use of neural networks in safety critical domains the need for understandable explanations of their predictions is rising.

Second-Moment Loss: A Novel Regression Objective for Improved Uncertainties

1 code implementation23 Dec 2020 Joachim Sicking, Maram Akila, Maximilian Pintz, Tim Wirtz, Asja Fischer, Stefan Wrobel

One of the most commonly used approaches so far is Monte Carlo dropout, which is computationally cheap and easy to apply in practice.

Object Detection

Improving the Long-Range Performance of Gated Graph Neural Networks

no code implementations19 Jul 2020 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.

Characteristics of Monte Carlo Dropout in Wide Neural Networks

no code implementations10 Jul 2020 Joachim Sicking, Maram Akila, Tim Wirtz, Sebastian Houben, Asja Fischer

Monte Carlo (MC) dropout is one of the state-of-the-art approaches for uncertainty estimation in neural networks (NNs).

Bayesian Inference Gaussian Processes

On the convergence of the Metropolis algorithm with fixed-order updates for multivariate binary probability distributions

no code implementations26 Jun 2020 Kai Brügge, Asja Fischer, Christian Igel

We propose a modified Metropolis transition operator that behaves almost always identically to the standard Metropolis operator and prove that it ensures irreducibility and convergence to the limiting distribution in the multivariate binary case with fixed-order updates.

Bringing Light Into the Dark: A Large-scale Evaluation of Knowledge Graph Embedding Models Under a Unified Framework

2 code implementations23 Jun 2020 Mehdi Ali, Max Berrendorf, Charles Tapley Hoyt, Laurent Vermue, Mikhail Galkin, Sahand Sharifzadeh, Asja Fischer, Volker Tresp, Jens Lehmann

The heterogeneity in recently published knowledge graph embedding models' implementations, training, and evaluation has made fair and thorough comparisons difficult.

Knowledge Graph Embedding

Detecting Adversarial Examples for Speech Recognition via Uncertainty Quantification

1 code implementation24 May 2020 Sina Däubener, Lea Schönherr, Asja Fischer, Dorothea Kolossa

The neural networks for uncertainty quantification simultaneously diminish the vulnerability to the attack, which is reflected in a lower recognition accuracy of the malicious target text in comparison to a standard hybrid ASR system.

automatic-speech-recognition Speech Recognition

Unsupervised Cross-Domain Speech-to-Speech Conversion with Time-Frequency Consistency

no code implementations15 May 2020 Mohammad Asif Khan, Fabien Cardinaux, Stefan Uhlich, Marc Ferras, Asja Fischer

This procedure bears the problem that the generated magnitude spectrogram may not be consistent, which is required for finding a phase such that the full spectrogram has a natural-sounding speech waveform.

Thresholded Adaptive Validation: Tuning the Graphical Lasso for Graph Recovery

1 code implementation1 May 2020 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 application at hand.

Leveraging Frequency Analysis for Deep Fake Image Recognition

1 code implementation ICML 2020 Joel Frank, Thorsten Eisenhofer, Lea Schönherr, Asja Fischer, Dorothea Kolossa, Thorsten Holz

Based on this analysis, we demonstrate how the frequency representation can be used to identify deep fake images in an automated way, surpassing state-of-the-art methods.

Image Forensics

End-to-End Entity Linking and Disambiguation leveraging Word and Knowledge Graph Embeddings

no code implementations25 Feb 2020 Rostislav Nedelchev, Debanjan Chaudhuri, Jens Lehmann, Asja Fischer

Entity linking - connecting entity mentions in a natural language utterance to knowledge graph (KG) entities is a crucial step for question answering over KGs.

Entity Disambiguation Entity Linking +4

Introduction to Neural Network based Approaches for Question Answering over Knowledge Graphs

no code implementations22 Jul 2019 Nilesh Chakraborty, Denis Lukovnikov, Gaurav Maheshwari, Priyansh Trivedi, Jens Lehmann, Asja Fischer

Question answering has emerged as an intuitive way of querying structured data sources, and has attracted significant advancements over the years.

Knowledge Graphs Question Answering

Compound Density Networks

no code implementations ICLR 2019 Agustinus Kristiadi, Asja Fischer

Despite the huge success of deep neural networks (NNs), finding good mechanisms for quantifying their prediction uncertainty is still an open problem.

Predictive Uncertainty Quantification with Compound Density Networks

no code implementations4 Feb 2019 Agustinus Kristiadi, Sina Däubener, Asja Fischer

Despite the huge success of deep neural networks (NNs), finding good mechanisms for quantifying their prediction uncertainty is still an open problem.

Bayesian Inference

Translating Natural Language to SQL using Pointer-Generator Networks and How Decoding Order Matters

no code implementations13 Nov 2018 Denis Lukovnikov, Nilesh Chakraborty, Jens Lehmann, Asja Fischer

Translating natural language to SQL queries for table-based question answering is a challenging problem and has received significant attention from the research community.

Question Answering Semantic Parsing

Learning to Rank Query Graphs for Complex Question Answering over Knowledge Graphs

1 code implementation2 Nov 2018 Gaurav Maheshwari, Priyansh Trivedi, Denis Lukovnikov, Nilesh Chakraborty, Asja Fischer, Jens Lehmann

In this paper, we conduct an empirical investigation of neural query graph ranking approaches for the task of complex question answering over knowledge graphs.

Graph Ranking Knowledge Graphs +3

On the Relation Between the Sharpest Directions of DNN Loss and the SGD Step Length

1 code implementation ICLR 2019 Stanisław Jastrzębski, Zachary Kenton, Nicolas Ballas, Asja Fischer, Yoshua Bengio, Amos Storkey

When studying the SGD dynamics in relation to the sharpest directions in this initial phase, we find that the SGD step is large compared to the curvature and commonly fails to minimize the loss along the sharpest directions.

Incorporating Literals into Knowledge Graph Embeddings

1 code implementation3 Feb 2018 Agustinus Kristiadi, Mohammad Asif Khan, Denis Lukovnikov, Jens Lehmann, Asja Fischer

Most of the existing work on embedding (or latent feature) based knowledge graph analysis focuses mainly on the relations between entities.

Entity Embeddings Knowledge Graph Embeddings +2

Three Factors Influencing Minima in SGD

no code implementations ICLR 2018 Stanisław Jastrzębski, Zachary Kenton, Devansh Arpit, Nicolas Ballas, Asja Fischer, Yoshua Bengio, Amos Storkey

In particular we find that the ratio of learning rate to batch size is a key determinant of SGD dynamics and of the width of the final minima, and that higher values of the ratio lead to wider minima and often better generalization.

On the regularization of Wasserstein GANs

2 code implementations ICLR 2018 Henning Petzka, Asja Fischer, Denis Lukovnicov

Since their invention, generative adversarial networks (GANs) have become a popular approach for learning to model a distribution of real (unlabeled) data.

Graph-based Predictable Feature Analysis

no code implementations1 Feb 2016 Björn Weghenkel, Asja Fischer, Laurenz Wiskott

We propose graph-based predictable feature analysis (GPFA), a new method for unsupervised learning of predictable features from high-dimensional time series, where high predictability is understood very generically as low variance in the distribution of the next data point given the previous ones.

Graph Embedding Time Series

Early Inference in Energy-Based Models Approximates Back-Propagation

no code implementations9 Oct 2015 Yoshua Bengio, Asja Fischer

We show that Langevin MCMC inference in an energy-based model with latent variables has the property that the early steps of inference, starting from a stationary point, correspond to propagating error gradients into internal layers, similarly to back-propagation.

Population-Contrastive-Divergence: Does Consistency help with RBM training?

no code implementations6 Oct 2015 Oswin Krause, Asja Fischer, Christian Igel

Compared to CD, it leads to a consistent estimate and may have a significantly lower bias.

STDP as presynaptic activity times rate of change of postsynaptic activity

no code implementations19 Sep 2015 Yoshua Bengio, Thomas Mesnard, Asja Fischer, Saizheng Zhang, Yuhuai Wu

We introduce a weight update formula that is expressed only in terms of firing rates and their derivatives and that results in changes consistent with those associated with spike-timing dependent plasticity (STDP) rules and biological observations, even though the explicit timing of spikes is not needed.

Bidirectional Helmholtz Machines

1 code implementation12 Jun 2015 Jorg Bornschein, Samira Shabanian, Asja Fischer, Yoshua Bengio

We present a lower-bound for the likelihood of this model and we show that optimizing this bound regularizes the model so that the Bhattacharyya distance between the bottom-up and top-down approximate distributions is minimized.

Difference Target Propagation

1 code implementation23 Dec 2014 Dong-Hyun Lee, Saizheng Zhang, Asja Fischer, Yoshua Bengio

Back-propagation has been the workhorse of recent successes of deep learning but it relies on infinitesimal effects (partial derivatives) in order to perform credit assignment.

How to Center Binary Deep Boltzmann Machines

1 code implementation6 Nov 2013 Jan Melchior, Asja Fischer, Laurenz Wiskott

This work analyzes centered binary Restricted Boltzmann Machines (RBMs) and binary Deep Boltzmann Machines (DBMs), where centering is done by subtracting offset values from visible and hidden variables.

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