3 papers
cs.LG2026
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…
cs.LG2024
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…
cs.LG2023
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…