Ghent University

∙ Gent , Belgium ∙ 0 followers

Ghent University is one of the largest universities in the Dutch language area and presents itself as open, socially engaged and pluralistic.

FONDUE: A Framework for Node Disambiguation Using Network Embeddings

Real-world data often presents itself in the form of a network. Examples...
02/24/2020 ∙ by Ahmad Mel, et al. ∙ 17 ∙ share

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Perturbation Analysis of Gradient-based Adversarial Attacks

After the discovery of adversarial examples and their adverse effects on...
06/02/2020 ∙ by Utku Ozbulak, et al. ∙ 17 ∙ share

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FACE: Feasible and Actionable Counterfactual Explanations

Work in Counterfactual Explanations tends to focus on the principle of "...
09/20/2019 ∙ by Rafael Poyiadzi, et al. ∙ 14 ∙ share

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Cost-efficient segmentation of electron microscopy images using active learning

Over the last decade, electron microscopy has improved up to a point tha...
11/13/2019 ∙ by Joris Roels, et al. ∙ 13 ∙ share

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How the Softmax Output is Misleading for Evaluating the Strength of Adversarial Examples

Even before deep learning architectures became the de facto models for c...
11/21/2018 ∙ by Utku Ozbulak, et al. ∙ 12 ∙ share

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Deep Active Inference for Autonomous Robot Navigation

Active inference is a theory that underpins the way biological agent's p...
03/06/2020 ∙ by Ozan Çatal, et al. ∙ 11 ∙ share

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Not All Adversarial Examples Require a Complex Defense: Identifying Over-optimized Adversarial Examples with IQR-based Logit Thresholding

Detecting adversarial examples currently stands as one of the biggest ch...
07/30/2019 ∙ by Utku Ozbulak, et al. ∙ 8 ∙ share

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CSNE: Conditional Signed Network Embedding

Signed networks are mathematical structures that encode positive and neg...
05/19/2020 ∙ by Alexandru Mara, et al. ∙ 7 ∙ share

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ALPINE: Active Link Prediction using Network Embedding

Many real-world problems can be formalized as predicting links in a part...
02/04/2020 ∙ by Xi Chen, et al. ∙ 1 ∙ share

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Transfer Learning with Binary Neural Networks

Previous work has shown that it is possible to train deep neural network...
11/29/2017 ∙ by Sam Leroux, et al. ∙ 0 ∙ share

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GPflowOpt: A Bayesian Optimization Library using TensorFlow

A novel Python framework for Bayesian optimization known as GPflowOpt is...
11/10/2017 ∙ by Nicolas Knudde, et al. ∙ 0 ∙ share

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Interactive Visual Data Exploration with Subjective Feedback: An Information-Theoretic Approach

Visual exploration of high-dimensional real-valued datasets is a fundame...
10/23/2017 ∙ by Kai Puolamäki, et al. ∙ 0 ∙ share

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Subjectively Interesting Subgroup Discovery on Real-valued Targets

Deriving insights from high-dimensional data is one of the core problems...
10/12/2017 ∙ by Jefrey Lijffijt, et al. ∙ 0 ∙ share

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Positive blood culture detection in time series data using a BiLSTM network

The presence of bacteria or fungi in the bloodstream of patients is abno...
12/03/2016 ∙ by Leen De Baets, et al. ∙ 0 ∙ share

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A Differentiable Physics Engine for Deep Learning in Robotics

One of the most important fields in robotics is the optimization of cont...
11/05/2016 ∙ by Jonas Degrave, et al. ∙ 0 ∙ share

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Lexicographic choice functions

We investigate a generalisation of the coherent choice functions conside...
07/10/2017 ∙ by Arthur Van Camp, et al. ∙ 0 ∙ share

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Integrated Inference and Learning of Neural Factors in Structural Support Vector Machines

Tackling pattern recognition problems in areas such as computer vision, ...
08/03/2015 ∙ by Rein Houthooft, et al. ∙ 0 ∙ share

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GENESIM: genetic extraction of a single, interpretable model

Models obtained by decision tree induction techniques excel in being int...
11/17/2016 ∙ by Gilles Vandewiele, et al. ∙ 0 ∙ share

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Fast Calculation of the Knowledge Gradient for Optimization of Deterministic Engineering Simulations

A novel efficient method for computing the Knowledge-Gradient policy for...
08/16/2016 ∙ by Joachim van der Herten, et al. ∙ 0 ∙ share

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Fast Face-swap Using Convolutional Neural Networks

We consider the problem of face swapping in images, where an input ident...
11/29/2016 ∙ by Iryna Korshunova, et al. ∙ 0 ∙ share

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Characterizing and Extending Answer Set Semantics using Possibility Theory

Answer Set Programming (ASP) is a popular framework for modeling combina...
11/30/2013 ∙ by Kim Bauters, et al. ∙ 0 ∙ share

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Modeling Stable Matching Problems with Answer Set Programming

The Stable Marriage Problem (SMP) is a well-known matching problem first...
02/28/2013 ∙ by Sofie De Clercq, et al. ∙ 0 ∙ share

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Credal nets under epistemic irrelevance

We present a new approach to credal nets, which are graphical models tha...
08/06/2012 ∙ by Jasper De Bock, et al. ∙ 0 ∙ share

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Search Combinators

The ability to model search in a constraint solver can be an essential a...
03/06/2012 ∙ by Tom Schrijvers, et al. ∙ 0 ∙ share

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Improved texture image classification through the use of a corrosion-inspired cellular automaton

In this paper, the problem of classifying synthetic and natural texture ...
12/26/2014 ∙ by Núbia Rosa da Silva, et al. ∙ 0 ∙ share

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Pattern Encoding on the Poincare Sphere

This paper presents a convenient graphical tool for encoding visual patt...
10/01/2014 ∙ by Aleksandra Pizurica, et al. ∙ 0 ∙ share

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On the stability of bootstrap estimators

It is shown that bootstrap approximations of an estimator which is based...
11/08/2011 ∙ by Andreas Christmann, et al. ∙ 0 ∙ share

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Imprecise probability trees: Bridging two theories of imprecise probability

We give an overview of two approaches to probability theory where lower ...
01/08/2008 ∙ by Gert de Cooman, et al. ∙ 0 ∙ share

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Real-time animation of human characters with fuzzy controllers

The production of animation is a resource intensive process in game comp...
10/05/2014 ∙ by Koen Samyn, et al. ∙ 0 ∙ share

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Assessing the Stylistic Properties of Neurally Generated Text in Authorship Attribution

Recent applications of neural language models have led to an increased i...
08/18/2017 ∙ by E. Manjavacas, et al. ∙ 0 ∙ share

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Break it Down for Me: A Study in Automated Lyric Annotation

Comprehending lyrics, as found in songs and poems, can pose a challenge ...
08/11/2017 ∙ by Lucas Sterckx, et al. ∙ 0 ∙ share

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Representation learning for very short texts using weighted word embedding aggregation

Short text messages such as tweets are very noisy and sparse in their us...
07/02/2016 ∙ by Cedric De Boom, et al. ∙ 0 ∙ share

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Learning Semantic Similarity for Very Short Texts

Levering data on social media, such as Twitter and Facebook, requires in...
12/02/2015 ∙ by Cedric De Boom, et al. ∙ 0 ∙ share

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End-to-end Learning from Spectrum Data: A Deep Learning approach for Wireless Signal Identification in Spectrum Monitoring applications

This paper presents end-to-end learning from spectrum data - an umbrella...
12/11/2017 ∙ by Merima Kulin, et al. ∙ 0 ∙ share

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Smallest snarks with oddness 4 and cyclic connectivity 4 have order 44

The family of snarks -- connected bridgeless cubic graphs that cannot be...
12/21/2017 ∙ by Jan Goedgebeur, et al. ∙ 0 ∙ share

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Structural and computational results on platypus graphs

A platypus graph is a non-hamiltonian graph for which every vertex-delet...
12/14/2017 ∙ by Jan Goedgebeur, et al. ∙ 0 ∙ share

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Bounding the convergence time of local probabilistic evolution

Isoperimetric inequalities form a very intuitive yet powerful characteri...
11/16/2017 ∙ by Simon Apers, et al. ∙ 0 ∙ share

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Sensitivity Analysis of Expensive Black-Box Systems Using Metamodeling

Simulations are becoming ever more common as a tool for designing comple...
02/02/2017 ∙ by Tom Van Steenkiste, et al. ∙ 0 ∙ share

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On the smallest snarks with oddness 4 and connectivity 2

A snark is a bridgeless cubic graph which is not 3-edge-colourable. The ...
10/02/2017 ∙ by Jan Goedgebeur, et al. ∙ 0 ∙ share

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Generation and properties of nut graphs

A nut graph is a graph on at least 2 vertices whose adjacency matrix has...
09/13/2017 ∙ by Kris Coolsaet, et al. ∙ 0 ∙ share

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Instrumental variables estimation with competing risk data

Time-to-event analyses are often plagued by both -- possibly unmeasured ...
12/30/2017 ∙ by Torben Martinussen, et al. ∙ 0 ∙ share

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A Generative Deep Recurrent Model for Exchangeable Data

We present a novel model architecture which leverages deep learning tool...
02/21/2018 ∙ by Iryna Korshunova, et al. ∙ 0 ∙ share

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Optimal subspace codes in PG(4,q)

We investigate subspace codes whose codewords are subspaces of PG(4,q) ...
02/27/2018 ∙ by Antonio Cossidente, et al. ∙ 0 ∙ share

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A general measure of the impact of priors in Bayesian statistics via Stein's Method

We propose a measure of the impact of any two choices of prior distribut...
02/28/2018 ∙ by Fatemeh Ghaderinezhad, et al. ∙ 0 ∙ share

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A Comparative Study of Pairwise Learning Methods based on Kernel Ridge Regression

Many machine learning problems can be formulated as predicting labels fo...
03/05/2018 ∙ by Michiel Stock, et al. ∙ 0 ∙ share

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Randomization inference with general interference and censoring

Interference occurs between individuals when the treatment (or exposure)...
03/06/2018 ∙ by Wen Wei Loh, et al. ∙ 0 ∙ share

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Bisimulations for intuitionistic temporal logics

We introduce bisimulations for the logic ITL^e with `next', `until' and ...
03/14/2018 ∙ by Philippe Balbiani, et al. ∙ 0 ∙ share

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How robust are Structural Equation Models to model miss-specification? A simulation study

Structural Equation Models (SEMs) are routinely used in the analysis of ...
03/16/2018 ∙ by Lionel R. Hertzog, et al. ∙ 0 ∙ share

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Speech-Driven Facial Reenactment Using Conditional Generative Adversarial Networks

We present a novel approach to generating photo-realistic images of a fa...
03/20/2018 ∙ by Seyed Ali Jalalifar, et al. ∙ 0 ∙ share

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High-dimensional doubly robust tests for regression parameters

After variable selection, standard inferential procedures for regression...
05/17/2018 ∙ by Oliver Dukes, et al. ∙ 0 ∙ share

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