104 citations · 256 across the 12 of their papers we have counts for
13 papers
GR-RNN: Global-Context Residual Recurrent Neural Networks for Writer Identification
Sheng He, Lambert Schomaker
This paper presents an end-to-end neural network system to identify writers through handwritten word images, which jointly integrates global-context information and a sequence of l…
A fully automated end-to-end process for fluorescence microscopy images of yeast cells: From segmentation to detection and classification
Asmaa Haja, Lambert R. B. Schomaker
In recent years, an enormous amount of fluorescence microscopy images were collected in high-throughput lab settings. Analyzing and extracting relevant information from all images…
SChuBERT: Scholarly Document Chunks with BERT-encoding boost Citation Count Prediction
Thomas van Dongen, Gideon Maillette de Buy Wenniger, Lambert Schomaker
Predicting the number of citations of scholarly documents is an upcoming task in scholarly document processing. Besides the intrinsic merit of this information, it also has a wider…
DTGAN: Dual Attention Generative Adversarial Networks for Text-to-Image Generation
Zhenxing Zhang, Lambert Schomaker
Most existing text-to-image generation methods adopt a multi-stage modular architecture which has three significant problems: 1) Training multiple networks increases the run time a…
Artificial intelligence based writer identification generates new evidence for the unknown scribes of the Dead Sea Scrolls exemplified by the Great Isaiah Scroll (1QIsaa)
Mladen Popović, Maruf A. Dhali, Lambert Schomaker
The Dead Sea Scrolls are tangible evidence of the Bible's ancient scribal culture. Palaeography - the study of ancient handwriting - can provide access to this scribal culture. How…
Structure-Tags Improve Text Classification for Scholarly Document Quality Prediction
Gideon Maillette de Buy Wenniger, Thomas van Dongen, Eleri Aedmaa +3
Training recurrent neural networks on long texts, in particular scholarly documents, causes problems for learning. While hierarchical attention networks (HANs) are effective in sol…