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Found 7 papers, 1 papers with code
Graph Neural Networks for the Offline Nanosatellite Task Scheduling Problem
This study examines the use of GNNs in this context, which has been effectively applied to optimization problems such as the traveling salesman, scheduling, and facility placement problems.
Combinatorial Optimization
Explainable Artificial Intelligence (XAI)
+1
Short-term forecasting COVID-19 cumulative confirmed cases: Perspectives for Brazil
In the stacking learning approach, the cubist, RF, RIDGE, and SVR models are adopted as base-learners and Gaussian process (GP) as meta-learner.
Forecasting Brazilian and American COVID-19 cases based on artificial intelligence coupled with climatic exogenous variables
Due to the efficiency of evaluated models to forecasting cumulative COVID-19 cases up to six-days-ahead, the adopted models can be recommended as a promising models for forecasting and be used to assist in the development of public policies to mitigate the effects of COVID-19 outbreak.
Short-term forecasting of Amazon rainforest fires based on ensemble decomposition model
Accurate forecasting is important for decision-makers.
Multi-Stage Transfer Learning with an Application to Selection Process
In this work, we proposed a \textit{Multi-StaGe Transfer Learning} (MSGTL) approach that uses knowledge from simple classifiers trained in early stages to improve the performance of classifiers in the latter stages.
Adversarial Encoder-Multi-Task-Decoder for Multi-Stage Processes
We also introduce a sequence constraint in the output of an MLSSL classifier to guarantee the sequential pattern in the predictions.
Unified Multi-Domain Learning and Data Imputation using Adversarial Autoencoder
We present a novel framework that can combine multi-domain learning (MDL), data imputation (DI) and multi-task learning (MTL) to improve performance for classification and regression tasks in different domains.
