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ZORB: A Derivative-Free Backpropagation Algorithm for Neural Networks
VarunRanganathan, AlexLewandowski....
Published date-11/17/2020
Gradient descent and backpropagation have enabled neural networks to achieve remarkable results in many real-world applications. Despite ongoing success, training a neural network with gradient descent can be a slow …
A Deep Neural Network for SSVEP-based Brain Computer Interfaces
OsmanBerkeGuney, MuhtashamOblokulov, HuseyinOzkan....
Published date-11/17/2020
EEG, Multi-classClassification
The target identification in brain-computer interface (BCI) speller systems refers to the multi-channel electroencephalogram (EEG) classification for predicting the target character that the user intends to spell. The EEG in …
Measuring the Novelty of Natural Language Text Using the Conjunctive Clauses of a Tsetlin Machine Text Classifier
BimalBhattarai, Ole-ChristofferGranmo, LeiJiao....
Published date-11/17/2020
TextClassification
Most supervised text classification approaches assume a closed world, counting on all classes being present in the data at training time. This assumption can lead to unpredictable behaviour during operation, …
RELLIS-3D Dataset: Data, Benchmarks and Analysis
PengJiang, PhilipOsteen, MaggieWigness....
Published date-11/17/2020
3DSemanticSegmentation, AutonomousNavigation, SceneUnderstanding, SemanticSegmentation
Semantic scene understanding is crucial for robust and safe autonomous navigation, particularly so in off-road environments. Recent deep learning advances for 3D semantic segmentation rely heavily on large sets of …
Exploring Neural Entity Representations for Semantic Information
AndrewRunge, EduardHovy....
Published date-11/17/2020
EntityLinking
Neural methods for embedding entities are typically extrinsically evaluated on downstream tasks and, more recently, intrinsically using probing tasks. Downstream task-based comparisons are often difficult to interpret due to differences …
EEG-GCNN: Augmenting Electroencephalogram-based Neurological Disease Diagnosis using a Domain-guided Graph Convolutional Neural Network
NeerajWagh, YogatheesanVaratharajah....
Published date-11/17/2020
EEG
This paper presents a novel graph convolutional neural network (GCNN)-based approach for improving the diagnosis of neurological diseases using scalp-electroencephalograms (EEGs). Although EEG is one of the main tests used …