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condLSTM-Q: A novel deep learning model for predicting Covid-19 mortality in fine geographical Scale
HyeongChanJo, JuhyunKim, Tzu-ChenHuang....
Published date-11/23/2020
Predictive models with a focus on different spatial-temporal scales benefit governments and healthcare systems to combat the COVID-19 pandemic. Here we present the conditional Long Short-Term Memory networks with Quantile …
Re-identification = Retrieval + Verification: Back to Essence and Forward with a New Metric
ZhengWang, XinYuan, ToshihikoYamasaki....
Published date-11/23/2020
ImageRetrieval
Re-identification (re-ID) is currently investigated as a closed-world image retrieval task, and evaluated by retrieval based metrics. The algorithms return ranking lists to users, but cannot tell which images are …
Time series classification for predictive maintenance on event logs
AntoineGuillaume, ChristelVrain, ElloumiWael....
Published date-11/22/2020
TimeSeries, TimeSeriesClassification
Time series classification (TSC) gained a lot of attention in the past decade and number of methods for representing and classifying time series have been proposed. Nowadays, methods based on …
Dense open-set recognition with synthetic outliers generated by Real NVP
MatejGrcić, PetraBevandić, SinišaŠegvić....
Published date-11/22/2020
AutonomousDriving, ImageClassification, OpenSetLearning, OutlierDetection, SemanticSegmentation
Today's deep models are often unable to detect inputs which do not belong to the training distribution. This gives rise to confident incorrect predictions which could lead to devastating consequences …
A Population-based Hybrid Approach to Hyperparameter Optimization for Neural Networks
MarcelloSerqueira, PedroGonzález, EduardoBezerra....
Published date-11/22/2020
HyperparameterOptimization
In recent years, large amounts of data have been generated, and computer power has kept growing. This scenario has led to a resurgence in the interest in artificial neural networks. …
A Homotopy-based Algorithm for Sparse Multiple Right-hand Sides Nonnegative Least Squares
NicolasNadisic, ArnaudVandaele, NicolasGillis....
Published date-11/22/2020
Nonnegative least squares (NNLS) problems arise in models that rely on additive linear combinations. In particular, they are at the core of nonnegative matrix factorization (NMF) algorithms. The nonnegativity constraint …