450 citations · 755 across the 17 of their papers we have counts for
18 papers
Career Path Prediction using Resume Representation Learning and Skill-based Matching
Jens-Joris Decorte, Jeroen Van Hautte, Johannes Deleu +2
The impact of person-job fit on job satisfaction and performance is widely acknowledged, which highlights the importance of providing workers with next steps at the right time in t…
Computationally efficient joint coordination of multiple electric vehicle charging points using reinforcement learning
Manu Lahariya, Nasrin Sadeghianpourhamami, Chris Develder
A major challenge in todays power grid is to manage the increasing load from electric vehicle (EV) charging. Demand response (DR) solutions aim to exploit flexibility therein, i.e.…
Optimized cost function for demand response coordination of multiple EV charging stations using reinforcement learning
Manu Lahariya, Nasrin Sadeghianpourhamami, Chris Develder
Electric vehicle (EV) charging stations represent a substantial load with significant flexibility. The exploitation of that flexibility in demand response (DR) algorithms becomes i…
Defining a synthetic data generator for realistic electric vehicle charging sessions
Manu Lahariya, Dries Benoit, Chris Develder
Electric vehicle (EV) charging stations have become prominent in electricity grids in the past years. Analysis of EV charging sessions is useful for flexibility analysis, load bala…
Physics Informed Neural Networks for Control Oriented Thermal Modeling of Buildings
Gargya Gokhale, Bert Claessens, Chris Develder
This paper presents a data-driven modeling approach for developing control-oriented thermal models of buildings. These models are developed with the objective of reducing energy co…
DWIE: an entity-centric dataset for multi-task document-level information extraction
Klim Zaporojets, Johannes Deleu, Chris Develder +1
This paper presents DWIE, the 'Deutsche Welle corpus for Information Extraction', a newly created multi-task dataset that combines four main Information Extraction (IE) annotation…
Solving Arithmetic Word Problems by Scoring Equations with Recursive Neural Networks
Klim Zaporojets, Giannis Bekoulis, Johannes Deleu +2
Solving arithmetic word problems is a cornerstone task in assessing language understanding and reasoning capabilities in NLP systems. Recent works use automatic extraction and rank…
Block-wise Dynamic Sparseness
Amir Hadifar, Johannes Deleu, Chris Develder +1
Neural networks have achieved state of the art performance across a wide variety of machine learning tasks, often with large and computation-heavy models. Inducing sparseness as a…
Sub-event detection from Twitter streams as a sequence labeling problem
Giannis Bekoulis, Johannes Deleu, Thomas Demeester +1
This paper introduces improved methods for sub-event detection in social media streams, by applying neural sequence models not only on the level of individual posts, but also direc…
Definition and evaluation of model-free coordination of electrical vehicle charging with reinforcement learning
Nasrin Sadeghianpourhamami, Johannes Deleu, Chris Develder
Initial DR studies mainly adopt model predictive control and thus require accurate models of the control problem (e.g., a customer behavior model), which are to a large extent unce…
Predefined Sparseness in Recurrent Sequence Models
Thomas Demeester, Johannes Deleu, Fréderic Godin +1
Inducing sparseness while training neural networks has been shown to yield models with a lower memory footprint but similar effectiveness to dense models. However, sparseness is ty…
Adversarial training for multi-context joint entity and relation extraction
Giannis Bekoulis, Johannes Deleu, Thomas Demeester +1
Adversarial training (AT) is a regularization method that can be used to improve the robustness of neural network methods by adding small perturbations in the training data. We sho…
Prior Attention for Style-aware Sequence-to-Sequence Models
Lucas Sterckx, Johannes Deleu, Chris Develder +1
We extend sequence-to-sequence models with the possibility to control the characteristics or style of the generated output, via attention that is generated a priori (before decodin…
Joint entity recognition and relation extraction as a multi-head selection problem
Giannis Bekoulis, Johannes Deleu, Thomas Demeester +1
State-of-the-art models for joint entity recognition and relation extraction strongly rely on external natural language processing (NLP) tools such as POS (part-of-speech) taggers…
An attentive neural architecture for joint segmentation and parsing and its application to real estate ads
Giannis Bekoulis, Johannes Deleu, Thomas Demeester +1
In processing human produced text using natural language processing (NLP) techniques, two fundamental subtasks that arise are (i) segmentation of the plain text into meaningful sub…
Break it Down for Me: A Study in Automated Lyric Annotation
Lucas Sterckx, Jason Naradowsky, Bill Byrne +2
Comprehending lyrics, as found in songs and poems, can pose a challenge to human and machine readers alike. This motivates the need for systems that can understand the ambiguity an…
Predicting Relevance based on Assessor Disagreement: Analysis and Practical Applications for Search Evaluation
Thomas Demeester, Robin Aly, Djoerd Hiemstra +2
Evaluation of search engines relies on assessments of search results for selected test queries, from which we would ideally like to draw conclusions in terms of relevance of the re…
Knowledge Base Population using Semantic Label Propagation
Lucas Sterckx, Thomas Demeester, Johannes Deleu +1
A crucial aspect of a knowledge base population system that extracts new facts from text corpora, is the generation of training data for its relation extractors. In this paper, we…