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Tell Me What You Read: Automatic Expertise-Based Annotator Assignment for Text Annotation in Expert Domains
To address the issue, we propose a method to estimate the domain expertise of each annotator before the annotation process using information easily available from the annotators beforehand.
Deep generative model super-resolves spatially correlated multiregional climate data
Additionally, we present the outcomes of another variant of the deep generative model-based downscaling approach in which the low-resolution precipitation field is substituted with the pressure field, referred to as $\psi$SRGAN (Precipitation Source Inaccessible SRGAN).
SapientML: Synthesizing Machine Learning Pipelines by Learning from Human-Written Solutions
In this work we propose an AutoML technique SapientML, that can learn from a corpus of existing datasets and their human-written pipelines, and efficiently generate a high-quality pipeline for a predictive task on a new dataset.
Reservoir Computing with Diverse Timescales for Prediction of Multiscale Dynamics
To develop an efficient machine learning method dedicated to modeling and prediction of multiscale dynamics, we propose a reservoir computing (RC) model with diverse timescales by using a recurrent network of heterogeneous leaky integrator (LI) neurons.
