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Found 11 papers, 2 papers with code
What’s the Difference Between Professional Human and Machine Translation? A Blind Multi-language Study on Domain-specific MT
Machine translation (MT) has been shown to produce a number of errors that require human post-editing, but the extent to which professional human translation (HT) contains such errors has not yet been compared to MT.
Nunc profana tractemus. Detecting Code-Switching in a Large Corpus of 16th Century Letters
This paper is based on a collection of 16th century letters from and to the Zurich reformer Heinrich Bullinger.
Machine Translation of 16Th Century Letters from Latin to German
This paper outlines our work in collecting training data for and developing a Latin–German Neural Machine Translation (NMT) system, for translating 16th century letters.
On Mitigating the Utility-Loss in Differentially Private Learning: A new Perspective by a Geometrically Inspired Kernel Approach
Privacy-utility tradeoff remains as one of the fundamental issues of differentially private machine learning.
Membership-Mappings for Practical Secure Distributed Deep Learning
This study leverages the data representation capability of fuzzy based membership-mappings for practical secure distributed deep learning using fully homomorphic encryption.
Information Theoretic Evaluation of Privacy-Leakage, Interpretability, and Transferability for Trustworthy AI
A variational membership-mapping Bayesian model is used for the analytical approximations of the defined information theoretic measures for privacy-leakage, interpretability, and transferability.
Membership-Mappings for Data Representation Learning: Measure Theoretic Conceptualization
An analytical approach to the variational learning of a membership-mappings based data representation model is considered.
What's the Difference Between Professional Human and Machine Translation? A Blind Multi-language Study on Domain-specific MT
Machine translation (MT) has been shown to produce a number of errors that require human post-editing, but the extent to which professional human translation (HT) contains such errors has not yet been compared to MT.
Deep Learning architectures for generalized immunofluorescence based nuclear image segmentation
In this work, we aim to evaluate the performance of state-of-the-art deep learning architectures to segment nuclei in fluorescence images of various tissue origins and sample preparation types without post-processing.
On Conditioning GANs to Hierarchical Ontologies
Additionally, we show that the O-GAN achieves better conditioning results evaluated by implicit similarity between the text and the generated image.
Deep SNP: An End-to-end Deep Neural Network with Attention-based Localization for Break-point Detection in SNP Array Genomic data
We show, that Deep SNP is capable of successfully predicting the presence or absence of a breakpoint in large genomic windows and outperforms state-of-the-art neural network models.
