Filip De Turck

Artificial Intelligence · 2014–2020

A researcher in the Science corpus: 9 papers; trusts 46 researchers and is trusted by 38 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 88 citations into it; #23,922 of 35,345 by trust here, #17,110 by citations
• Reinforcement Learning — 65 citations into it; #643 of 2,242 by trust here, #382 by citations
• Computer Networks and Communications — 32 citations into it; #3,161 of 9,886 by trust here, #1,515 by citations
• Management Science and Operations Research — 26 citations into it
• Optimization & Training — 15 citations into it
• Statistical Learning Theory — 13 citations into it
• Cognitive Neuroscience — 12 citations into it
• Electrical and Electronic Engineering — 12 citations into it

Whom Filip De Turck trusts overall

Their strongest outbound trust, strongest first, inferred from their citations.

1. J. Urgen Schmidhuber
2. Ronald J. Williams
3. Geoffrey E. Hinton
4. Diederik P. Kingma
5. J. Ross Quinlan
6. Richard S. Sutton
7. Alex Graves
8. Jan Peters
9. Yoshua Bengio
10. Alex Krizhevsky

Who trusts Filip De Turck overall

1. Juliver Gil-Herrera
2. Sergey Levine
3. Md. Faizul Bari
4. Abhishek Gupta
5. Stefan Depeweg
6. Pieter Abbeel
7. Cédric Colas
8. Juan Felipe Botero
9. Deepak Pathak
10. Kumar Shridhar

Papers from Filip De Turck's perspective

Ordered by how strongly the researchers Filip De Turck trusts endorse each paper. Same corpus, a different order for every researcher.

1. ImageNet classification with deep convolutional neural networks
2. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
3. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
4. Gradient-based learning applied to document recognition
5. Long Short-Term Memory
6. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
7. Attention is All you Need (Neural Information Processing Systems)
8. ImageNet: A large-scale hierarchical image database
9. Learning Multiple Layers of Features from Tiny Images
10. Human-level control through deep reinforcement learning

Their papers

• Curiosity-driven Exploration in Deep Reinforcement Learning via Bayesian Neural Networks (2016) — 80 citations
• Network Function Virtualization: State-of-the-Art and Research Challenges (2015) — 1,831 citations
• GENESIM: genetic extraction of a single, interpretable model (2016) — 36 citations
• Performance analysis of single board computer clusters (2020) — 363 citations
• A Genetic Algorithm for Interpretable Model Extraction from Decision Tree Ensembles (2017) — 14 citations
• #Exploration: A Study of Count-Based Exploration for Deep Reinforcement Learning (2016) — 866 citations
• VIME: Variational Information Maximizing Exploration (2016) — 846 citations
• HTTP/2-Based Adaptive Streaming of HEVC Video Over 4G/LTE Networks (2016) — 321 citations
• VNF-P: A model for efficient placement of virtualized network functions (2014) — 496 citations