130 citations · 671 across the 42 of their papers we have counts for
11 papers · 1 filter
Unsupervised anomaly detection algorithms on real-world data: how many do we need?
Roel Bouman, Zaharah Bukhsh, Tom Heskes
In this study we evaluate 32 unsupervised anomaly detection algorithms on 52 real-world multivariate tabular datasets, performing the largest comparison of unsupervised anomaly det…
Machine Learning Meets The Herbrand Universe
Jelle Piepenbrock, Josef Urban, Konstantin Korovin +3
The appearance of strong CDCL-based propositional (SAT) solvers has greatly advanced several areas of automated reasoning (AR). One of the directions in AR is thus to apply SAT sol…
Learning Equational Theorem Proving
Jelle Piepenbrock, Tom Heskes, Mikoláš Janota +1
We develop Stratified Shortest Solution Imitation Learning (3SIL) to learn equational theorem proving in a deep reinforcement learning (RL) setting. The self-trained models achieve…
Constraining the Parameters of High-Dimensional Models with Active Learning
Sascha Caron, Tom Heskes, Sydney Otten +1
Constraining the parameters of physical models with parameters is a widespread problem in fields like particle physics and astronomy. The generation of data to explore this…
The Artificial Mind's Eye: Resisting Adversarials for Convolutional Neural Networks using Internal Projection
Harm Berntsen, Wouter Kuijper, Tom Heskes
We introduce a novel artificial neural network architecture that integrates robustness to adversarial input in the network structure. The main idea of our approach is to force the…
Properties of Bethe Free Energies and Message Passing in Gaussian Models
Botond Cseke, Tom Heskes
We address the problem of computing approximate marginals in Gaussian probabilistic models by using mean field and fractional Bethe approximations. We define the Gaussian fractiona…