4 papers
Disentangling Curriculum Learning in NLP: Towards a Unifying Taxonomy
Vanessa Toborek, Florian Seiffarth, Sebastian Müller +3
Despite more than a decade of curriculum learning (CL) research in NLP, the field lacks a principled account of which difficulty function or scheduler to use for a given problem. T…
Beyond Shallow Heuristics: Leveraging Human Intuition for Curriculum Learning
Vanessa Toborek, Sebastian Müller, Tim Selbach +2
Curriculum learning (CL) aims to improve training by presenting data from "easy" to "hard", yet defining and measuring linguistic difficulty remains an open challenge. We investiga…
CFIRE: A General Method for Combining Local Explanations
Sebastian Müller, Vanessa Toborek, Tamás Horváth +1
We propose a novel eXplainable AI algorithm to compute faithful, easy-to-understand, and complete global decision rules from local explanations for tabular data by combining XAI me…
A Divergence-Free and -Conforming Embedded-Hybridized DG Method for the Incompressible Resistive MHD equations
Jau-Uei Chen, Tamás L. Horváth, Tan Bui-Thanh
We present a divergence-free and -conforming hybridized discontinuous Galerkin (HDG) method and a computationally efficient variant called embedded-HDG (E-HDG) for solving…