Search Results for author: Hiroaki Yoshida

Found 5 papers, 0 papers with code

Tell Me What You Read: Automatic Expertise-Based Annotator Assignment for Text Annotation in Expert Domains

no code implementations RANLP 2021 Hiyori Yoshikawa, Tomoya Iwakura, Kimi Kaneko, Hiroaki Yoshida, Yasutaka Kumano, Kazutaka Shimada, Rafal Rzepka, Patrycja Swieczkowska

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.

text annotation

Predicting unobserved climate time series data at distant areas via spatial correlation using reservoir computing

no code implementations5 Jun 2024 Shihori Koyama, Daisuke Inoue, Hiroaki Yoshida, Kazuyuki Aihara, Gouhei Tanaka

This study focuses on a prediction of climatic elements, specifically near-surface temperature and pressure, at a target location apart from a data observation point.

Time Series

Deep generative model super-resolves spatially correlated multiregional climate data

no code implementations26 Sep 2022 Norihiro Oyama, Noriko N. Ishizaki, Satoshi Koide, Hiroaki Yoshida

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).

Generative Adversarial Network Super-Resolution

SapientML: Synthesizing Machine Learning Pipelines by Learning from Human-Written Solutions

no code implementations18 Feb 2022 Ripon K. Saha, Akira Ura, Sonal Mahajan, Chenguang Zhu, Linyi Li, Yang Hu, Hiroaki Yoshida, Sarfraz Khurshid, Mukul R. Prasad

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.

AutoML BIG-bench Machine Learning +1

Reservoir Computing with Diverse Timescales for Prediction of Multiscale Dynamics

no code implementations21 Aug 2021 Gouhei Tanaka, Tadayoshi Matsumori, Hiroaki Yoshida, Kazuyuki Aihara

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.

BIG-bench Machine Learning Time Series +1

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