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Found 12 papers, 3 papers with code
WIKIBIAS: Detecting Multi-Span Subjective Biases in Language
Biases continue to be prevalent in modern text and media, especially subjective bias – a special type of bias that introduces improper attitudes or presents a statement with the presupposition of truth.
Modeling Intensification for Sign Language Generation: A Computational Approach
To employ our strategies, we first annotate a subset of the benchmark PHOENIX-14T, a German Sign Language dataset, with different levels of intensification.
Graph Neural Network Potential for Magnetic Materials
A spin-dependent ML interatomic potential approach based on the crystal graph neural network (GNN) has been developed for any magnetic system.
A Machine Learning Generative Method for Automating Antenna Design and Optimization
For a dual resonance antenna design with wide bandwidth, our proposed method is in par with Trust Region Framework and much better than the other mature machine learning algorithms including the widely used Genetic Algorithm and Particle Swarm Optimization.
Edge-based Tensor prediction via graph neural networks
Message-passing neural networks (MPNN) have shown extremely high efficiency and accuracy in predicting the physical properties of molecules and crystals, and are expected to become the next-generation material simulation tool after the density functional theory (DFT).
Neural CRF Model for Sentence Alignment in Text Simplification
The success of a text simplification system heavily depends on the quality and quantity of complex-simple sentence pairs in the training corpus, which are extracted by aligning sentences between parallel articles.
Ranked #1 on
Text Simplification
on Newsela
Discourse Level Factors for Sentence Deletion in Text Simplification
We inspect various document and discourse factors associated with sentence deletion, using a new manually annotated sentence alignment corpus we collected.
Regularizing CNN Transfer Learning with Randomised Regression
That is, a CNN is efficiently regularized without additional resources of data or prior domain expertise.
Target Aware Network Adaptation for Efficient Representation Learning
This paper presents an automatic network adaptation method that finds a ConvNet structure well-suited to a given target task, e. g., image classification, for efficiency as well as accuracy in transfer learning.
Face Attribute Prediction Using Off-the-Shelf CNN Features
Predicting attributes from face images in the wild is a challenging computer vision problem.
Leveraging Mid-Level Deep Representations For Predicting Face Attributes in the Wild
Predicting facial attributes from faces in the wild is very challenging due to pose and lighting variations in the real world.
Leveraging the Power of Gabor Phase for Face Identification: A Block Matching Approach
Different from face verification, face identification is much more demanding.
