Although supervised deep learning has revolutionized speech and audio processing, it has
necessitated the building of specialist models for individual tasks and application scenarios …

Since their introduction about 25 years ago, machine learning (ML) potentials have become
an important tool in the field of atomistic simulations. After the initial decade, in which neural …

In the last decade, convolutional neural networks (ConvNets) have been a major focus of
research in medical image analysis. However, the performances of ConvNets may be limited …

Over the past decades, a tremendous amount of research has been done on the use of
machine learning for speech processing applications, especially speech recognition …

Deep neural networks have been successfully used in diverse emerging domains to solve
real world complex problems with may more deep learning (DL) architectures, being …

As the state-of-the-art machine learning methods in many fields rely on larger datasets,
storing datasets and training models on them become significantly more expensive. This …

Recent technological advancements in data acquisition tools allowed life scientists to
acquire multimodal data from different biological application domains. Categorized in three …

An open-source Mandarin speech corpus called AISHELL-1 is released. It is by far the
largest corpus which is suitable for conducting the speech recognition research and building …

This paper aims to develop a method that can accurately estimate the crowd count from an
individual image with arbitrary crowd density and arbitrary perspective. To this end, we have …

TensorFlow is a machine learning system that operates at large scale and in heterogeneous
environments. Tensor-Flow uses dataflow graphs to represent computation, shared state …