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Found 6 papers, 1 papers with code
Reducing Computational Costs in Sentiment Analysis: Tensorized Recurrent Networks vs. Recurrent Networks
We compare traditional recurrent models with their tensorized version, and we show that with the tensorized models, we reach comparable performances with respect to the traditional models while using fewer resources for the training.
Emotion Analysis using Multi-Layered Networks for Graphical Representation of Tweets
State of the art Graph Neural Networks (GNNs) are used to extract information from the Tweet-MLN and make predictions based on the extracted graph features.
Explaining Convolutional Neural Networks by Tagging Filters
Finally, we show that the tags are helpful in analyzing classification errors caused by noisy input images and that the tags can be further processed by machines.
Knowledge Graphs Evolution and Preservation -- A Technical Report from ISWS 2019
Although linked open data (LOD) is one knowledge graph, it is the closest realisation (and probably the only one) to a public FAIR Knowledge Graph (KG) of everything.
Right for the Right Reason: Making Image Classification Robust
To this end, we propose a new explanation quality metric to measure object aligned explanation in image classification which we refer to as theObAlExmetric.
Making Neural Networks FAIR
In this paper, we first present the neural network ontology FAIRnets Ontology, an ontology to make existing neural network models findable, accessible, interoperable, and reusable according to the FAIR principles.
