Demo Data from VLDB 2026 / reviewers in the wild / expert
Pieter Buteneers
dblp:66/7231
· DBLP profile ↗
5ranked-venue papers
3as first author
1since 2021 · last 2021
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Artificial intelligence and machine learning · 5 · 3 first-author · 1 since 2021
Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.
| Artificial intelligence
2 papers |
Transfer learning and domain adaptation · 53% Representation and self-supervised learning · 16% Deep learning architectures and training · 15% |
Topics — the 4 heaviest of 4, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Machine learning › Transfer learning and domain adaptation
cross-lingual transfer |
0.5 | 1 | 2021 | A Simple Geometric Method for Cross-Lingual Linguistic Transformations with Pre-trained Autoencoders · EMNLP (1) 2021 |
Machine learning › Representation and self-supervised learning › text embedding
sentence embedding |
0.1 | 1 | 2021 | A Simple Geometric Method for Cross-Lingual Linguistic Transformations with Pre-trained Autoencoders · EMNLP (1) 2021 |
Machine learning › Deep learning architectures and training
modular neural network |
0.1 | 1 | 2012 | Oger: modular learning architectures for large-scale sequential processing · J. Mach. Learn. Res. 2012 |
Machine learning › Efficient and distributed learning
scalable learning |
0.1 | 1 | 2012 | Oger: modular learning architectures for large-scale sequential processing · J. Mach. Learn. Res. 2012 |
Methods — techniques the papers use, named apart from their topics
geometric mapping · 0.5autoencoder · 0.5online learning · 0.1modular learning · 0.1
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2021 | A Simple Geometric Method for Cross-Lingual Linguistic Transformations with Pre-trained AutoencodersabstractPowerful sentence encoders trained for multiple languages are on the rise.These systems are capable of embedding a wide range of linguistic properties into vector representations.While explicit probing tasks can be used to verify the presence of specific linguistic properties, it is unclear whether the vector representations can be manipulated to indirectly steer such properties.For efficient learning, we investigate the use of a geometric mapping in embedding space to transform linguistic properties, without any tuning of the pre-trained sentence encoder or decoder.We validate our approach on three linguistic properties using a pre-trained multilingual autoencoder and analyze the results in both monolingual and crosslingual settings. Maarten De Raedt, Fréderic Godin, Pieter Buteneers, Chris Develder, Thomas Demeester |
EMNLP (1) | 3 |
| 2013 | Optimized Parameter Search for Large Datasets of the Regularization Parameter and Feature Selection for Ridge Regression
Pieter Buteneers, Ken Caluwaerts, Joni Dambre, David Verstraeten, Benjamin Schrauwen |
Neural Process. Lett. | 1 |
| 2012 | Oger: modular learning architectures for large-scale sequential processing
David Verstraeten, Benjamin Schrauwen, Sander Dieleman, Philemon Brakel, Pieter Buteneers, Dejan Pecevski |
J. Mach. Learn. Res. | 5 |
| 2011 | Automatic detection of epileptic seizures on the intra-cranial electroencephalogram of rats using reservoir computing
Pieter Buteneers, David Verstraeten, Pieter van Mierlo, Tine Wyckhuys, Dirk Stroobandt, Robrecht Raedt, Hans Hallez, Benjamin Schrauwen |
Artif. Intell. Medicine | 1 |
| 2008 | Real-Time Epileptic Seizure Detection on Intra-cranial Rat Data Using Reservoir Computing
Pieter Buteneers, Benjamin Schrauwen, David Verstraeten, Dirk Stroobandt |
ICONIP (1) | 1 |