Ghent University

research

∙ 05/06/2022

Optimal Control as Variational Inference

In this article we address the stochastic and risk sensitive optimal con...

Tom Lefebvre, et al. ∙

research

∙ 02/24/2020

FONDUE: A Framework for Node Disambiguation Using Network Embeddings

Real-world data often presents itself in the form of a network. Examples...

Ahmad Mel, et al. ∙

research

∙ 06/02/2020

Perturbation Analysis of Gradient-based Adversarial Attacks

After the discovery of adversarial examples and their adverse effects on...

Utku Ozbulak, et al. ∙

research

∙ 09/20/2019

FACE: Feasible and Actionable Counterfactual Explanations

Work in Counterfactual Explanations tends to focus on the principle of "...

Rafael Poyiadzi, et al. ∙

research

∙ 11/13/2019

Cost-efficient segmentation of electron microscopy images using active learning

Over the last decade, electron microscopy has improved up to a point tha...

Joris Roels, et al. ∙

research

∙ 08/08/2022

Dataset of Industrial Metal Objects

We present a diverse dataset of industrial metal objects. These objects ...

Peter De Roovere, et al. ∙

research

∙ 11/21/2018

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...

Utku Ozbulak, et al. ∙

research

∙ 03/06/2020

Deep Active Inference for Autonomous Robot Navigation

Active inference is a theory that underpins the way biological agent's p...

Ozan Çatal, et al. ∙

research

∙ 07/30/2019

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...

Utku Ozbulak, et al. ∙

research

∙ 05/19/2020

CSNE: Conditional Signed Network Embedding

Signed networks are mathematical structures that encode positive and neg...

Alexandru Mara, et al. ∙

research

∙ 10/06/2021

Adaptive control of a mechatronic system using constrained residual reinforcement learning

We propose a simple, practical and intuitive approach to improve the per...

Tom Staessens, et al. ∙

research

∙ 01/19/2022

Batch versus Sequential Active Learning for Recommender Systems

Recommender systems have been investigated for many years, with the aim ...

Toon De Pessemier, et al. ∙

research

∙ 05/27/2022

CEBaB: Estimating the Causal Effects of Real-World Concepts on NLP Model Behavior

The increasing size and complexity of modern ML systems has improved the...

Eldar David Abraham, et al. ∙

research

∙ 07/01/2021

Semi-Sparsity for Smoothing Filters

In this paper, we propose an interesting semi-sparsity smoothing algorit...

Junqing Huang, et al. ∙

research

∙ 05/31/2022

Exact Feature Collisions in Neural Networks

Predictions made by deep neural networks were shown to be highly sensiti...

Utku Ozbulak, et al. ∙

research

∙ 06/13/2018

SMHD: A Large-Scale Resource for Exploring Online Language Usage for Multiple Mental Health Conditions

Mental health is a significant and growing public health concern. As lan...

Arman Cohan, et al. ∙

research

∙ 06/20/2018

RSDD-Time: Temporal Annotation of Self-Reported Mental Health Diagnoses

Self-reported diagnosis statements have been widely employed in studying...

Sean MacAvaney, et al. ∙

research

∙ 02/04/2020

ALPINE: Active Link Prediction using Network Embedding

Many real-world problems can be formalized as predicting links in a part...

Xi Chen, et al. ∙

research

∙ 05/07/2020

Inference with Choice Functions Made Practical

We study how to infer new choices from previous choices in a conservativ...

Arne Decadt, et al. ∙

research

∙ 11/29/2017

Transfer Learning with Binary Neural Networks

Previous work has shown that it is possible to train deep neural network...

Sam Leroux, et al. ∙

research

∙ 11/10/2017

GPflowOpt: A Bayesian Optimization Library using TensorFlow

A novel Python framework for Bayesian optimization known as GPflowOpt is...

Nicolas Knudde, et al. ∙

research

∙ 10/23/2017

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

Visual exploration of high-dimensional real-valued datasets is a fundame...

Kai Puolamäki, et al. ∙

research

∙ 10/12/2017

Subjectively Interesting Subgroup Discovery on Real-valued Targets

Deriving insights from high-dimensional data is one of the core problems...

Jefrey Lijffijt, et al. ∙

research

∙ 12/03/2016

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...

Leen De Baets, et al. ∙

research

∙ 11/05/2016

A Differentiable Physics Engine for Deep Learning in Robotics

One of the most important fields in robotics is the optimization of cont...

Jonas Degrave, et al. ∙

research

∙ 07/10/2017

Lexicographic choice functions

We investigate a generalisation of the coherent choice functions conside...

Arthur Van Camp, et al. ∙

research

∙ 08/03/2015

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

Tackling pattern recognition problems in areas such as computer vision, ...

Rein Houthooft, et al. ∙

research

∙ 11/17/2016

GENESIM: genetic extraction of a single, interpretable model

Models obtained by decision tree induction techniques excel in being int...

Gilles Vandewiele, et al. ∙

research

∙ 08/16/2016

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

A novel efficient method for computing the Knowledge-Gradient policy for...

Joachim van der Herten, et al. ∙

research

∙ 11/29/2016

Fast Face-swap Using Convolutional Neural Networks

We consider the problem of face swapping in images, where an input ident...

Iryna Korshunova, et al. ∙

research

∙ 11/30/2013

Characterizing and Extending Answer Set Semantics using Possibility Theory

Answer Set Programming (ASP) is a popular framework for modeling combina...

Kim Bauters, et al. ∙

research

∙ 02/28/2013

Modeling Stable Matching Problems with Answer Set Programming

The Stable Marriage Problem (SMP) is a well-known matching problem first...

Sofie De Clercq, et al. ∙

research

∙ 08/06/2012

Credal nets under epistemic irrelevance

We present a new approach to credal nets, which are graphical models tha...

Jasper De Bock, et al. ∙

research

∙ 03/06/2012

Search Combinators

The ability to model search in a constraint solver can be an essential a...

Tom Schrijvers, et al. ∙

research

∙ 12/26/2014

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

In this paper, the problem of classifying synthetic and natural texture ...

Núbia Rosa da Silva, et al. ∙

research

∙ 10/01/2014

Pattern Encoding on the Poincare Sphere

This paper presents a convenient graphical tool for encoding visual patt...

Aleksandra Pizurica, et al. ∙

research

∙ 11/08/2011

On the stability of bootstrap estimators

It is shown that bootstrap approximations of an estimator which is based...

Andreas Christmann, et al. ∙

research

∙ 01/08/2008

Imprecise probability trees: Bridging two theories of imprecise probability

We give an overview of two approaches to probability theory where lower ...

Gert de Cooman, et al. ∙

research

∙ 10/05/2014

Real-time animation of human characters with fuzzy controllers

The production of animation is a resource intensive process in game comp...

Koen Samyn, et al. ∙

research

∙ 08/18/2017

Assessing the Stylistic Properties of Neurally Generated Text in Authorship Attribution

Recent applications of neural language models have led to an increased i...

E. Manjavacas, et al. ∙

research

∙ 08/11/2017

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

Comprehending lyrics, as found in songs and poems, can pose a challenge ...

Lucas Sterckx, et al. ∙

research

∙ 07/02/2016

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...

Cedric De Boom, et al. ∙

research

∙ 12/02/2015

Learning Semantic Similarity for Very Short Texts

Levering data on social media, such as Twitter and Facebook, requires in...

Cedric De Boom, et al. ∙

research

∙ 12/11/2017

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...

Merima Kulin, et al. ∙

research

∙ 12/21/2017

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

The family of snarks -- connected bridgeless cubic graphs that cannot be...

Jan Goedgebeur, et al. ∙

research

∙ 12/14/2017

Structural and computational results on platypus graphs

A platypus graph is a non-hamiltonian graph for which every vertex-delet...

Jan Goedgebeur, et al. ∙

research

∙ 11/16/2017

Bounding the convergence time of local probabilistic evolution

Isoperimetric inequalities form a very intuitive yet powerful characteri...

Simon Apers, et al. ∙

research

∙ 02/02/2017

Sensitivity Analysis of Expensive Black-Box Systems Using Metamodeling

Simulations are becoming ever more common as a tool for designing comple...

Tom Van Steenkiste, et al. ∙

research

∙ 10/02/2017

On the smallest snarks with oddness 4 and connectivity 2

A snark is a bridgeless cubic graph which is not 3-edge-colourable. The ...

Jan Goedgebeur, et al. ∙

research

∙ 09/13/2017

Generation and properties of nut graphs

A nut graph is a graph on at least 2 vertices whose adjacency matrix has...

Kris Coolsaet, et al. ∙

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