b'TU Eindhoven'

research

∙ 01/15/2020

Causal Discovery from Incomplete Data: A Deep Learning Approach

As systems are getting more autonomous with the development of artificia...

Yuhao Wang, et al. ∙

research

∙ 09/30/2020

ResGCN: Attention-based Deep Residual Modeling for Anomaly Detection on Attributed Networks

Effectively detecting anomalous nodes in attributed networks is crucial ...

Yulong Pei, et al. ∙

research

∙ 02/25/2019

A Survey of Crowdsourcing in Medical Image Analysis

Rapid advances in image processing capabilities have been seen across ma...

Silas Ørting, et al. ∙

research

∙ 06/24/2020

Topological Insights in Sparse Neural Networks

Sparse neural networks are effective approaches to reduce the resource r...

Shiwei Liu, et al. ∙

research

∙ 01/07/2020

Resource-Efficient Neural Networks for Embedded Systems

While machine learning is traditionally a resource intensive task, embed...

Wolfgang Roth, et al. ∙

research

∙ 08/27/2021

ProtoInfoMax: Prototypical Networks with Mutual Information Maximization for Out-of-Domain Detection

The ability to detect Out-of-Domain (OOD) inputs has been a critical req...

Iftitahu Ni'mah, et al. ∙

research

∙ 01/13/2022

Automated Reinforcement Learning: An Overview

Reinforcement Learning and recently Deep Reinforcement Learning are popu...

Reza Refaei Afshar, et al. ∙

research

∙ 06/16/2020

Solving the Order Batching and Sequencing Problem using Deep Reinforcement Learning

In e-commerce markets, on time delivery is of great importance to custom...

Bram Cals, et al. ∙

research

∙ 12/05/2018

An empirical study on hyperparameter tuning of decision trees

Machine learning algorithms often contain many hyperparameters whose val...

Rafael Gomes Mantovani, et al. ∙

research

∙ 07/26/2021

Geometric Deep Learning on Molecular Representations

Geometric deep learning (GDL), which is based on neural network architec...

Kenneth Atz, et al. ∙

research

∙ 01/30/2022

Augmented Business Process Management Systems: A Research Manifesto

Augmented Business Process Management Systems (ABPMSs) are an emerging c...

Marlon Dumas, et al. ∙

research

∙ 03/22/2019

Learning with Delayed Synaptic Plasticity

The plasticity property of biological neural networks allows them to per...

Anil Yaman, et al. ∙

research

∙ 07/07/2021

A Graph-based Approach for Mitigating Multi-sided Exposure Bias in Recommender Systems

Fairness is a critical system-level objective in recommender systems tha...

Masoud Mansoury, et al. ∙

research

∙ 11/03/2021

The Impact of Batch Learning in Stochastic Bandits

We consider a special case of bandit problems, namely batched bandits. M...

Danil Provodin, et al. ∙

research

∙ 04/04/2022

Event Log Sampling for Predictive Monitoring

Predictive process monitoring is a subfield of process mining that aims ...

Mohammadreza Fani Sani, et al. ∙

research

∙ 07/25/2022

AMLB: an AutoML Benchmark

Comparing different AutoML frameworks is notoriously challenging and oft...

Pieter Gijsbers, et al. ∙

research

∙ 07/07/2021

Hierarchical Semantic Segmentation using Psychometric Learning

Assigning meaning to parts of image data is the goal of semantic image s...

Lu Yin, et al. ∙

research

∙ 02/17/2022

Does the End Justify the Means? On the Moral Justification of Fairness-Aware Machine Learning

Despite an abundance of fairness-aware machine learning (fair-ml) algori...

Hilde Weerts, et al. ∙

research

∙ 09/08/2019

Back to the Future -- Sequential Alignment of Text Representations

Language evolves over time in many ways relevant to natural language pro...

Johannes Bjerva, et al. ∙

research

∙ 04/18/2020

Dual Embedding Expansion for Vehicle Re-identification

Vehicle re-identification plays a crucial role in the management of tran...

Clint Sebastian, et al. ∙

research

∙ 11/05/2021

Frugal Machine Learning

Machine learning, already at the core of increasingly many systems and a...

Mikhail Evchenko, et al. ∙

research

∙ 12/24/2021

Ultrasound Speckle Suppression and Denoising using MRI-derived Normalizing Flow Priors

Ultrasonography offers an inexpensive, widely-accessible and compact med...

Vincent van de Schaft, et al. ∙

research

∙ 07/08/2022

Memory-free Online Change-point Detection: A Novel Neural Network Approach

Change-point detection (CPD), which detects abrupt changes in the data d...

Zahra Atashgahi, et al. ∙

research

∙ 01/24/2020

PDE-based Group Equivariant Convolutional Neural Networks

We present a PDE-based framework that generalizes Group equivariant Conv...

Bart Smets, et al. ∙

research

∙ 09/10/2019

Semantic Foreground Inpainting from Weak Supervision

Semantic scene understanding is an essential task for self-driving vehic...

Chenyang Lu, et al. ∙

research

∙ 07/06/2021

On Generalization of Graph Autoencoders with Adversarial Training

Adversarial training is an approach for increasing model's resilience ag...

Tianjin Huang, et al. ∙

research

∙ 09/21/2021

Beyond Discriminant Patterns: On the Robustness of Decision Rule Ensembles

Local decision rules are commonly understood to be more explainable, due...

Xin Du, et al. ∙

research

∙ 02/05/2022

The Unreasonable Effectiveness of Random Pruning: Return of the Most Naive Baseline for Sparse Training

Random pruning is arguably the most naive way to attain sparsity in neur...

Shiwei Liu, et al. ∙

research

∙ 05/17/2022

Federated learning for violence incident prediction in a simulated cross-institutional psychiatric setting

Inpatient violence is a common and severe problem within psychiatry. Kno...

Thomas Borger, et al. ∙

research

∙ 07/23/2019

Hallucinating Beyond Observation: Learning to Complete with Partial Observation and Unpaired Prior Knowledge

We propose a novel single-step training strategy that allows convolution...

Chenyang Lu, et al. ∙

research

∙ 07/27/2019

Multi-task Self-Supervised Learning for Human Activity Detection

Deep learning methods are successfully used in applications pertaining t...

Aaqib Saeed, et al. ∙

research

∙ 02/01/2022

Advances in MetaDL: AAAI 2021 challenge and workshop

To stimulate advances in metalearning using deep learning techniques (Me...

Adrian El Baz, et al. ∙

research

∙ 05/05/2020

Approximation Algorithms for Multi-Robot Patrol-Scheduling with Min-Max Latency

We consider the problem of finding patrol schedules for k robots to visi...

Peyman Afshani, et al. ∙

research

∙ 06/19/2021

Sparse Training via Boosting Pruning Plasticity with Neuroregeneration

Works on lottery ticket hypothesis (LTH) and single-shot network pruning...

Shiwei Liu, et al. ∙

research

∙ 10/01/2021

Calibrated Adversarial Training

Adversarial training is an approach of increasing the robustness of mode...

Tianjin Huang, et al. ∙

research

∙ 04/29/2022

Making sense of violence risk predictions using clinical notes

Violence risk assessment in psychiatric institutions enables interventio...

Pablo Mosteiro, et al. ∙

research

∙ 08/01/2018

A Deep Neural Model Of Emotion Appraisal

Emotional concepts play a huge role in our daily life since they take pa...

Pablo Barros, et al. ∙

research

∙ 11/20/2018

Deep Unfolded Robust PCA with Application to Clutter Suppression in Ultrasound

Contrast enhanced ultrasound is a radiation-free imaging modality which ...

Oren Solomon, et al. ∙

research

∙ 07/14/2021

Federated Self-Training for Semi-Supervised Audio Recognition

Federated Learning is a distributed machine learning paradigm dealing wi...

Vasileios Tsouvalas, et al. ∙

research

∙ 07/17/2019

Machine Learning based Simulation Optimisation for Trailer Management

In many situations, simulation models are developed to handle complex re...

Dylan Rijnen, et al. ∙

research

∙ 08/09/2019

Synthetic Elastography using B-mode Ultrasound through a Deep Fully-Convolutional Neural Network

Shear-wave elastography (SWE) permits local estimation of tissue elastic...

R. R. Wildeboer, et al. ∙

research

∙ 11/03/2017

Discovering More Precise Process Models from Event Logs by Filtering Out Chaotic Activities

Process Discovery is concerned with the automatic generation of a proces...

Niek Tax, et al. ∙

research

∙ 12/07/2016

Predictive Business Process Monitoring with LSTM Neural Networks

Predictive business process monitoring methods exploit logs of completed...

Niek Tax, et al. ∙

research

∙ 04/17/2018

Not-so-supervised: a survey of semi-supervised, multi-instance, and transfer learning in medical image analysis

Machine learning (ML) algorithms have made a tremendous impact in the fi...

Veronika Cheplygina, et al. ∙

research

∙ 06/19/2018

Instance-Level Explanations for Fraud Detection: A Case Study

Fraud detection is a difficult problem that can benefit from predictive ...

Dennis Collaris, et al. ∙

research

∙ 02/18/2022

Towards a Numerical Proof of Turbulence Closure

The development of turbulence closure models, parametrizing the influenc...

Giulio Ortali, et al. ∙

research

∙ 12/12/2017

Mining Non-Redundant Sets of Generalizing Patterns from Sequence Databases

Sequential pattern mining techniques extract patterns corresponding to f...

Niek Tax, et al. ∙

research

∙ 07/15/2017

Evolutionary Training of Sparse Artificial Neural Networks: A Network Science Perspective

Through the success of deep learning, Artificial Neural Networks (ANNs) ...

Decebal Constantin Mocanu, et al. ∙

research

∙ 06/09/2017

Weakly supervised training of deep convolutional neural networks for overhead pedestrian localization in depth fields

Overhead depth map measurements capture sufficient amount of information...

Alessandro Corbetta, et al. ∙

research

∙ 09/12/2017

Meta-QSAR: a large-scale application of meta-learning to drug design and discovery

We investigate the learning of quantitative structure activity relations...

Ivan Olier, et al. ∙

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