The proposed method accelerates the convergence of the genetic algorithm and increases the system's performance.
Language models built using semi-supervised machine learning on large corpora of natural language have very quickly enveloped the fields of natural language generation and understanding.
A false contract is more likely to be rejected than a contract is, yet a false key is less likely than a key to open doors.
The Winograd Schema Challenge (WSC) and variants inspired by it have become important benchmarks for common-sense reasoning (CSR).
Reduction of the number of channels could reduce the complexity of brain-computer-interface devices.
Intracranial tumors are groups of cells that usually grow uncontrollably.
One of the routine examinations that are used for prenatal care in many countries is ultrasound imaging.
Histopathology images contain essential information for medical diagnosis and prognosis of cancerous disease.
Ultrasound imaging is a standard examination during pregnancy that can be used for measuring specific biometric parameters towards prenatal diagnosis and estimating gestational age.
Recent studies have significantly improved the state-of-the-art on common-sense reasoning (CSR) benchmarks like the Winograd Schema Challenge (WSC) and SWAG.
To explain this performance gap, we show empirically that state-of-the art models often fail to capture context, instead relying on the gender or number of candidate antecedents to make a decision.
Due to the rapid growth of machine learning tools and specifically deep networks in various computer vision and image processing areas, application of Convolutional Neural Networks for watermarking have recently emerged.
We introduce an automatic system that achieves state-of-the-art results on the Winograd Schema Challenge (WSC), a common sense reasoning task that requires diverse, complex forms of inference and knowledge.
The effect of using separate networks for segmentation of MR images is evaluated by comparing the results with a single network.
We introduce an automatic system that performs well on two common-sense reasoning tasks, the Winograd Schema Challenge (WSC) and the Choice of Plausible Alternatives (COPA).
Deep neural networks have shown great achievements in solving complex problems.
By increasing the volume of telemedicine information, the need for medical image compression has become more important.
Recent advances in capsule endoscopy systems have introduced new methods and capabilities.