miscellaneous-7

Air Quality Inference

3260 papers • 126 benchmarks • 313 datasets

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Most implemented papers

A Neural Attention Model for Urban Air Quality Inference: Learning the Weights of Monitoring Stations

Yanmin Zhu, Weiyu Cheng, Yanyan Shen, Linpeng Huang•Wed Apr 25 2018

A generic neural approach, named ADAIN, for urban air quality inference, that combines feedforward and recurrent neural networks for modeling static and sequential features as well as capturing deep feature interactions effectively is proposed.

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Increasing the Robustness of Model Predictions to Missing Sensors in Earth Observation

F. Mena, Diego Arenas, A. Dengel•Sun Jul 21 2024

This work studies two novel methods tailored for multi-sensor scenarios, namely Input Sensor Dropout (ISensD) and Ensemble Sensor Invariant (ESensI), and observes that ensemble multi-sensor models are the most robust to the lack of sensors.

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Deep Gaussian Processes for Air Quality Inference

Aadesh Desai, Eshan Gujarathi, Saagar Parikh, Sachin Yadav, Zeel B Patel, Nipun Batra•Thu Nov 17 2022

This work implements Doubly Stochastic Variational Inference, a DGP algorithm, and shows that it performs comparably to the state-of-the-art models.

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