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20162026
most citedLoGANv2: Conditional Style-Based Logo Generation with Generative Adversarial Networks

36 citations · 154 across the 55 of their papers we have counts for

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Showing 2020Show all

6 papers · 1 filter

cs.CV2020

Can we detect harmony in artistic compositions? A machine learning approach

Adam Vandor, Marie van Vollenhoven, Gerhard Weiss +1

Harmony in visual compositions is a concept that cannot be defined or easily expressed mathematically, even by humans. The goal of the research described in this paper was to find…

cs.CL2020

"Thy algorithm shalt not bear false witness": An Evaluation of Multiclass Debiasing Methods on Word Embeddings

Thalea Schlender, Gerasimos Spanakis

With the vast development and employment of artificial intelligence applications, research into the fairness of these algorithms has been increased. Specifically, in the natural la…

cs.CL2020★ 11 cited

Evaluating Bias In Dutch Word Embeddings

Rodrigo Alejandro Chávez Mulsa, Gerasimos Spanakis

Recent research in Natural Language Processing has revealed that word embeddings can encode social biases present in the training data which can affect minorities in real world app…

cs.CL2020★ 1 cited

Adapting End-to-End Speech Recognition for Readable Subtitles

Danni Liu, Jan Niehues, Gerasimos Spanakis

Automatic speech recognition (ASR) systems are primarily evaluated on transcription accuracy. However, in some use cases such as subtitling, verbatim transcription would reduce out…

cs.CL2020★ 9 cited

Low-Latency Sequence-to-Sequence Speech Recognition and Translation by Partial Hypothesis Selection

Danni Liu, Gerasimos Spanakis, Jan Niehues

Encoder-decoder models provide a generic architecture for sequence-to-sequence tasks such as speech recognition and translation. While offline systems are often evaluated on qualit…

cs.CL2020★ 1 cited

Hybrid Tiled Convolutional Neural Networks for Text Sentiment Classification

Maria Mihaela Trusca, Gerasimos Spanakis

The tiled convolutional neural network (tiled CNN) has been applied only to computer vision for learning invariances. We adjust its architecture to NLP to improve the extraction of…