… The progress of some AI paradigms such as deep learning is said to be linked to an exponential
growth in the number of parameters. There are many studies corroborating these trends, …

… intelligence in the context deep learning. A brief overview of … on “Reduction of parameters
in deep-learning models”. Cao … unnecessary factors and parameters in deep-learning models. …

Deep learning (DL) has solved a problem that a few years ago was thought to be intractable
— the automatic recognition of patterns in spatial and temporal data with an accuracy …

… parameters of the HMMs, the most likely acoustic features are predicted using the speech
parameter-… Finally, we discuss the remaining issues associated with current deep learning meth…

… where x stands for unknown parameters, y stands for observation which we partially know,
and L is a forward or degraded operator in geophysical data observation, such as noise …

… Applying deep learning generally needs to manually pre-collect a large set of supervised
training data for network parameter learning. However, labeling large amount of samples is …

… accurate MR parameters. In Section 4, we present how deep learning can be applied to
improve rapid MR relaxometry with specific examples and how the use of deep learning is linked …

… trend of diminishing returns as the overhead of coordinating between the machines grows.
Other approaches to distributed learning … to study techniques for learning larger networks on a …

… Finally, this research showed the feasibility of using deep learning based Seq2Seq model
to predict future trends during abnormal operating condition or emergencies at NPPs and the …

… Other deep learning architectures considered in this study … of neuroevolution and deep
learning from many years ago to … (ii) parameter sharing refers to learning one set of parameters …