Hendrik Blockeel
Artificial Intelligence · 1999–2019
A researcher in the Science corpus: 10 papers; trusts 38 researchers and is trusted by 37 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 46 citations into it; #5,637 of 35,345 by trust here, #11,490 by citations
- Graph Learning — 22 citations into it
- Representation & Self-Supervised Learning — 20 citations into it
- Computer Vision and Pattern Recognition — 17 citations into it
- Information Systems — 8 citations into it; #1,628 of 8,771 by trust here, #3,098 by citations
- Natural Language Processing — 8 citations into it
- Model Architectures — 7 citations into it
- Computational Theory and Mathematics — 6 citations into it
Whom Hendrik Blockeel trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- J. Ross Quinlan
- Jon M. Kleinberg
- Yoshua Bengio
- Leo Breiman
- Thomas G. Dietterich
- Geoffrey E. Hinton
- Yann LeCun
- Antoine Bordes
- Mark W. Craven
- Eric Bauer
Who trusts Hendrik Blockeel overall
- Kurt Driessens
- Kristian Kersting
- Sriraam Natarajan
- Prasad Tadepalli
- Phillip Odom
- Andrew Cropper
- Anneleen Van Assche
- Sebastijan Dumancic
- Saso Dzeroski
- Thomas Gärtner
Papers from Hendrik Blockeel's perspective
Ordered by how strongly the researchers Hendrik Blockeel trusts endorse each paper. Same corpus, a different order for every researcher.
- ImageNet classification with deep convolutional neural networks
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- Gradient-based learning applied to document recognition
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- Long Short-Term Memory
- Attention is All you Need (Neural Information Processing Systems)
- ImageNet: A large-scale hierarchical image database
- Learning Multiple Layers of Features from Tiny Images
- Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
- Going deeper with convolutions
Their papers
- Decision trees for hierarchical multi-label classification (2008) — 718 citations
- Clustering-Based Relational Unsupervised Representation Learning with an Explicit Distributed Representation (2016) — 15 citations
- Demystifying Relational Latent Representations (2017) — 7 citations
- Learning Relational Representations with Auto-encoding Logic Programs (2019) — 30 citations
- Neural networks for relational learning: an experimental comparison (2011) — 39 citations
- Frontiers in Artificial Intelligence and Applications (2009) — 556 citations
- Seeing the Forest Through the Trees (2007) — 79 citations
- Speeding Up Relational Reinforcement Learning through the Use of an Incremental First Order Decision Tree Learner (2001) — 96 citations
- Web mining research: a survey (2000) — 1,913 citations
- Top‐down induction of first order logical decision trees (1999)