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07/13/2023
Layerwise Linear Mode Connectivity
In the federated setup one performs an aggregation of separate local mod...
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05/26/2023
Leveraging characteristics of the output probability distribution for identifying adversarial audio examples
Adversarial attacks represent a security threat to machine learning base...
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05/22/2023
Uncertainty-based Detection of Adversarial Attacks in Semantic Segmentation
State-of-the-art deep neural networks have proven to be highly powerful ...
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03/01/2023
Information Plane Analysis for Dropout Neural Networks
The information-theoretic framework promises to explain the predictive p...
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10/26/2022
Towards the Detection of Diffusion Model Deepfakes
Diffusion models (DMs) have recently emerged as a promising method in im...
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06/21/2022
Marginal Tail-Adaptive Normalizing Flows
Learning the tail behavior of a distribution is a notoriously difficult ...
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04/22/2022
How Sampling Impacts the Robustness of Stochastic Neural Networks
Stochastic neural networks (SNNs) are random functions and predictions a...
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12/14/2021
Robustifying automatic speech recognition by extracting slowly varying features
In the past few years, it has been shown that deep learning systems are ...
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07/15/2021
Copula-Based Normalizing Flows
Normalizing flows, which learn a distribution by transforming the data t...
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01/07/2021
A Novel Regression Loss for Non-Parametric Uncertainty Optimization
Quantification of uncertainty is one of the most promising approaches to...
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12/23/2020
Second-Moment Loss: A Novel Regression Objective for Improved Uncertainties
Quantification of uncertainty is one of the most promising approaches to...
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08/07/2020
Investigating maximum likelihood based training of infinite mixtures for uncertainty quantification
Uncertainty quantification in neural networks gained a lot of attention ...
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07/19/2020
Improving the Long-Range Performance of Gated Graph Neural Networks
Many popular variants of graph neural networks (GNNs) that are capable o...
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07/10/2020
Characteristics of Monte Carlo Dropout in Wide Neural Networks
Monte Carlo (MC) dropout is one of the state-of-the-art approaches for u...
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06/26/2020
On the convergence of the Metropolis algorithm with fixed-order updates for multivariate binary probability distributions
The Metropolis algorithm is arguably the most fundamental Markov chain M...
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05/24/2020
Detecting Adversarial Examples for Speech Recognition via Uncertainty Quantification
Machine learning systems and also, specifically, automatic speech recogn...
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05/15/2020
Unsupervised Cross-Domain Speech-to-Speech Conversion with Time-Frequency Consistency
In recent years generative adversarial network (GAN) based models have b...
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05/01/2020
Thresholded Adaptive Validation: Tuning the Graphical Lasso for Graph Recovery
The graphical lasso is the most popular estimator in Gaussian graphical ...
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03/19/2020
Leveraging Frequency Analysis for Deep Fake Image Recognition
Deep neural networks can generate images that are astonishingly realisti...
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02/25/2020
End-to-End Entity Linking and Disambiguation leveraging Word and Knowledge Graph Embeddings
Entity linking - connecting entity mentions in a natural language uttera...
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07/22/2019
Introduction to Neural Network based Approaches for Question Answering over Knowledge Graphs
Question answering has emerged as an intuitive way of querying structure...
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02/04/2019
Predictive Uncertainty Quantification with Compound Density Networks
Despite the huge success of deep neural networks (NNs), finding good mec...
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11/13/2018
Translating Natural Language to SQL using Pointer-Generator Networks and How Decoding Order Matters
Translating natural language to SQL queries for table-based question ans...
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11/02/2018
Learning to Rank Query Graphs for Complex Question Answering over Knowledge Graphs
In this paper, we conduct an empirical investigation of neural query gra...
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09/10/2018
Improving Response Selection in Multi-turn Dialogue Systems
Building systems that can communicate with humans is a core problem in A...
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07/13/2018
DNN's Sharpest Directions Along the SGD Trajectory
Recent work has identified that using a high learning rate or a small ba...
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02/03/2018
Incorporating Literals into Knowledge Graph Embeddings
Knowledge graphs, on top of entities and their relationships, contain an...
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11/13/2017
Three Factors Influencing Minima in SGD
We study the properties of the endpoint of stochastic gradient descent (...
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09/26/2017
On the regularization of Wasserstein GANs
Since their invention, generative adversarial networks (GANs) have becom...
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06/16/2017
A Closer Look at Memorization in Deep Networks
We examine the role of memorization in deep learning, drawing connection...
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10/09/2015
Early Inference in Energy-Based Models Approximates Back-Propagation
We show that Langevin MCMC inference in an energy-based model with laten...
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10/06/2015
Population-Contrastive-Divergence: Does Consistency help with RBM training?
Estimating the log-likelihood gradient with respect to the parameters of...
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09/19/2015
STDP as presynaptic activity times rate of change of postsynaptic activity
We introduce a weight update formula that is expressed only in terms of ...
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06/12/2015
Bidirectional Helmholtz Machines
Efficient unsupervised training and inference in deep generative models ...
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12/23/2014