3260 papers • 126 benchmarks • 313 datasets
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This paper presents an analysis of Transformer-based language model performance across a wide range of model scales -- from models with tens of millions of parameters up to a 280 billion parameter model called Gopher.
An innovative image transformation technique is introduced that effectively renders facial images unrecognizable to the eye while maintaining their identifiability by neural network models, which allows the distorted photo version to be stored for further verification.
This work introduces new guessing techniques that make dictionary attacks consistently more resilient to inadequate configurations, and introduces automatic dynamic strategies within dictionary attacks to mimic experts' ability to adapt their guessing strategies on the fly by incorporating knowledge on their targets.
The capability of a 3D Fully Convolutional Neural Network to map crop types from multi-temporal images and the Intersection Over Union (IOU) loss function for increasing the overlap between the predicted classes and ground reference data are explored.
An innovative image distortion technique is introduced that makes facial images unrecognizable to the eye but still identifiable by any custom embedding neural network model, and is tested by determining the maximum image distortion that does not change the predicted identity.
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