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pymgrid: An Open-Source Python Microgrid Simulator for Applied Artificial Intelligence Research
GonzagueHenri, TanguyLevent, AvishaiHalev....
Published date-11/11/2020
Microgrids, self contained electrical grids that are capable of disconnecting from the main grid, hold potential in both tackling climate change mitigation via reducing CO2 emissions and adaptation by increasing …
Open-Source Morphology for Endangered Mordvinic Languages
JackRueter, MikaHämäläinen, NikoPartanen....
Published date-11/11/2020
This document describes shared development of finite-state description of two closely related but endangered minority languages, Erzya and Moksha. It touches upon morpholexical unity and diversity of the two languages …
FPGA: Fast Patch-Free Global Learning Framework for Fully End-to-End Hyperspectral Image Classification
ZhuoZheng, YanfeiZhong, AilongMa....
Published date-11/11/2020
HyperspectralImageClassification, ImageClassification
Deep learning techniques have provided significant improvements in hyperspectral image (HSI) classification. The current deep learning based HSI classifiers follow a patch-based learning framework by dividing the image into overlapping …
Automatic Open-World Reliability Assessment
MohsenJafarzadeh, TouqeerAhmad, AkshayRajDhamija....
Published date-11/11/2020
ImageClassification
Image classification in the open-world must handle out-of-distribution (OOD) images. Systems should ideally reject OOD images, or they will map atop of known classes and reduce reliability. Using open-set classifiers …
Optimized Loss Functions for Object detection: A Case Study on Nighttime Vehicle Detection
ShangJiang, HaoranQin, BingliZhang....
Published date-11/11/2020
ObjectDetection
Loss functions is a crucial factor that affecting the detection precision in object detection task. In this paper, we optimize both two loss functions for classification and localization simultaneously. Firstly, …
Text Augmentation for Language Models in High Error Recognition Scenario
KarelBeneš, LukášBurget....
Published date-11/11/2020
DataAugmentation, SpeechRecognition, TextAugmentation
We examine the effect of data augmentation for training of language models for speech recognition. We compare augmentation based on global error statistics with one based on per-word unigram statistics …