… deep learning prediction performs better than shallow learning predictions (b) EHR results:
deep learning … In addition, during combination deep model outperforms shallow models such …

… STL is a deep learning approach that is based on … deep learning approach STLIDS (a
self-taught learning based intrusion detection system) based on the STL framework by combining …

… IDC and deep learning and demonstrates that deep learning can obtain … combinations is
a challenging task. By extracting features of injected drugs in vivo and in vitro, a deep learning …

… combination of the features built from DNN, rVSM, and project’s bug-fixing history, achieves
higher accuracy than the state-of-theart IR and machine learning … and a feature combination …

… Deep learning and neuromorphic modelling share many … (this is on a continuum with deep
learning, rather than a sharp … We usually think of deep learning in terms of abstract nonlinear …

… aims at exploring deep learning architectures for combining time-… Over the last decade, deep
learning has made major advances … It is precisely this success of deep learning in handling …

… Importantly, the errors made by our deep learning system were not strongly correlated with
… superior to our deep learning system alone, combining deep learning with the pathologist pro…

… In this work, we deal with the problem of combining images and metadata using deep learning
models. We propose an approach named Metadata Processing Block (MetaBlock), which …

… The other type of hyperparameters in deep learning is related to how we design the deep
neural networks. For example, some important design questions include: How many layers we …

… Inspired by the advantages of CS and deep learning, this paper proposes a novel intelligent
diagnosis method combining compressed measurements with DNN for fault classification of …