In supervised learning, the most prevalent, the data is labeled to tell the machine exactly what patterns it should look for. Think of it as something like a sniffer dog that will hunt down ...

Abstract: In response to the problems of difficulty in information fusion, complex feature learning and difficulty in deep network training in multimodal data, a deep supervised learning algorithm is ...

We present DistillFlow, a knowledge distillation approach to learning optical flow. DistillFlow trains multiple teacher models and a student model, where challenging transformations are applied to the ...

In the formal paper, "data2vec: A General Framework for Self-supervised Learning in Speech, Vision and Language," Baevski et al., train the Transformer for image data, speech audio waveforms, and ...

Machine learning methods are becoming increasingly important in the analysis of large-scale genomic, epigenomic, proteomic and metabolic data sets. In this Review, the authors consider the ...