1 citations · 2 across the 4 of their papers we have counts for
5 papers
Disentangling Curriculum Learning in NLP: Towards a Unifying Taxonomy
Vanessa Toborek, Florian Seiffarth, Sebastian Müller +1
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…
Iterative Graph Neural Network Enhancement via Frequent Subgraph Mining of Explanations
Harish G. Naik, Jan Polster, Raj Shekhar +2
We formulate an XAI-based model improvement approach for Graph Neural Networks (GNNs) for node classification, called Explanation Enhanced Graph Learning (EEGL). The goal is to imp…
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…