Abstract: Generator-based adversarial attack methods aim to fool deep neural networks (DNNs) by training a generator for crafting adversarial examples (AEs). However, as DNNs evolve from Convolutional ...

This library brings Spatially-sparse convolutional networks to PyTorch. Moreover, it introduces Submanifold Sparse Convolutions, that can be used to build computationally efficient sparse ...

Quantum Convolutional Neural Network (QCNN) has achieved significant success in solving various complex problems, such as quantum many-body physics and image recognition. In comparison to the ...

To address the difficulties in fusing multi-mode sensor data for complex industrial machinery, an adaptive deep coupling convolutional auto-encoder (ADCCAE) fusion method was proposed. First, the ...

We present a method for understanding the functional relationships among large populations of neurons in complex circuits such as the retina and primary visual cortex that allows a global comparison ...

Multiclass classification is of great interest for various applications, for example, it is a common task in computer vision, where one needs to categorize an image into three or more classes. Here we ...

Abstract: The life cycle data of machines are difficult to collected in practice. Therefore, the data-driven remaining useful life (RUL) prognostic face following problems: (1) under-fitting problem ...

Rapid progress in technologies such as calcium imaging and electrophysiology has seen a dramatic increase in the size and extent of neural recordings. Even so, interpretation of this data requires ...