Search Results for author: Filip De Turck

Found 11 papers, 9 papers with code

GENDIS: GENetic DIscovery of Shapelets

1 code implementation13 Sep 2019 Gilles Vandewiele, Femke Ongenae, Filip De Turck

It has been shown that classifiers are able to achieve state-of-the-art results on a plethora of datasets by taking as input distances from the input time series to different discriminative shapelets.

Outlier Detection Time Series +2

GENESIM: genetic extraction of a single, interpretable model

1 code implementation17 Nov 2016 Gilles Vandewiele, Olivier Janssens, Femke Ongenae, Filip De Turck, Sofie Van Hoecke

The results show that GENESIM achieves a better predictive performance on most of these data sets than decision tree induction techniques and a predictive performance in the same order of magnitude as the ensemble techniques.

Decision Making Interpretable Machine Learning

#Exploration: A Study of Count-Based Exploration for Deep Reinforcement Learning

3 code implementations NeurIPS 2017 Haoran Tang, Rein Houthooft, Davis Foote, Adam Stooke, Xi Chen, Yan Duan, John Schulman, Filip De Turck, Pieter Abbeel

In this work, we describe a surprising finding: a simple generalization of the classic count-based approach can reach near state-of-the-art performance on various high-dimensional and/or continuous deep RL benchmarks.

Atari Games Continuous Control +2

VIME: Variational Information Maximizing Exploration

2 code implementations NeurIPS 2016 Rein Houthooft, Xi Chen, Yan Duan, John Schulman, Filip De Turck, Pieter Abbeel

While there are methods with optimality guarantees in the setting of discrete state and action spaces, these methods cannot be applied in high-dimensional deep RL scenarios.

Continuous Control Reinforcement Learning (RL) +1

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

no code implementations3 Aug 2015 Rein Houthooft, Filip De Turck

To improve prediction accuracy, this paper proposes: (i) Joint inference and learning by integration of back-propagation and loss-augmented inference in SSVM subgradient descent; (ii) Extending SSVM factors to neural networks that form highly nonlinear functions of input features.

Image Segmentation Semantic Segmentation +1

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