Accurately predicting the arrival times of buses is of key importance, particularly in hectic urban environments. Providing people with efficient and timely transportation can discourage them from using private vehicles, consequently reducing both fuel consumption and...
In recent years, researchers have developed deep neural networks that can perform a variety of tasks, including visual recognition and natural language processing (NLP) tasks. Although many of these models achieved remarkable results, they typically only perform well...
Researchers at UC Berkeley and Ecole des Ponts Paris Tech have recently developed a deep learning approach for discovering recurring visual patterns in art collections. Their paper, pre-published on arXiv, will be presented at CVPR 2019, a renowned computer vision...
Researchers at Colorado State University (CSU) have developed an approximate dynamic programming approach to improve the food security of communities affected by natural disasters. Their work exploring methods to aid community recovery spans several papers. Their most...
A team of researchers at NVIDIA has recently developed WaveGlow, a flow-based network that can generate high-quality speech from melspectrograms, which are acoustic time-frequency representations of sound. Their method, outlined in a paper pre-published on arXiv, uses...
Researchers at the University of Pompeu Fabra, Cardiff University and the Technical University of Madrid used machine-learning algorithms to discover new things about the history of music.
A team of researchers at Yonsei University and École Polytechnique Fédérale de Lausanne (EPFL) has recently developed a new technique that can recognize emotions by analyzing people's faces in images along with contextual features. They presented and outlined their...
Researchers at Taif University, Birzeit University and RMIT University have developed a new approach for software fault prediction (SFP), which addresses some of the limitations of existing machine learning SFP techniques. Their approach employs feature selection (FS)...
Researchers from the University of Pittsburgh have recently developed a conditional variational autoencoder that can produce unique faces for advertisements. Their study is grounded on their previous work, which explored automated methods of better understanding...
Researchers from the Karlsruhe Institute of Technology (KIT), in Germany, have recently applied multi-task machine learning to low-latency neural speech translation. Their study, which was pre-published on ArXiv, addresses some of the limitations of existing neural...