82 citations · 196 across the 22 of their papers we have counts for
23 papers
Steering Neural Network Training through Interpretable Constraints Based on Partial Dependence
Yann Claes, Pierre Geurts, Vân Anh Huynh-Thu
Over the last few years, there has been an increased interest in making machine learning models more interpretable. Although a great deal of effort goes into developing techniques…
Parallelizing Autoregressive Generation with Variational State Space Models
Gaspard Lambrechts, Yann Claes, Pierre Geurts +1
Attention-based models such as Transformers and recurrent models like state space models (SSMs) have emerged as successful methods for autoregressive sequence modeling. Although bo…
Hybrid additive modeling with partial dependence for supervised regression and dynamical systems forecasting
Yann Claes, Vân Anh Huynh-Thu, Pierre Geurts
Learning processes by exploiting restricted domain knowledge is an important task across a plethora of scientific areas, with more and more hybrid training methods additively combi…
Distillation from heterogeneous unlabeled collections
Jean-Michel Begon, Pierre Geurts
Compressing deep networks is essential to expand their range of applications to constrained settings. The need for compression however often arises long after the model was trained…
On The Transferability of Deep-Q Networks
Matthia Sabatelli, Pierre Geurts
Transfer Learning (TL) is an efficient machine learning paradigm that allows overcoming some of the hurdles that characterize the successful training of deep neural networks, rangi…
From global to local MDI variable importances for random forests and when they are Shapley values
Antonio Sutera, Gilles Louppe, Van Anh Huynh-Thu +2
Random forests have been widely used for their ability to provide so-called importance measures, which give insight at a global (per dataset) level on the relevance of input variab…