Convolutional networks for images, speech, and time series

TL;DR: The review begins by covering fundamental concepts in ML and modern statistics such as the bias-variance tradeoff, overfitting, regularization, generalization, and gradient descent before moving on to more advanced topics in both supervised and unsupervised learning.

TL;DR: This review paper depicts the application of various deep learning algorithms used till recently, but in future it will be used for more healthcare areas to improve the quality of diagnosis.

TL;DR: A novel deep Convolutional Neural Network (CNN) based regression approach for estimating the Remaining Useful Life (RUL) of a subsystem or a component using sensor data, which has many real world applications.

TL;DR: This paper proposes the use of deep learning approaches for breast ultrasound lesion detection and investigates three different methods: a Patch-based LeNet, a U-Net, and a transfer learning approach with a pretrained FCN-AlexNet.

TL;DR: Minimal preprocessing of the data was required, but architecture of the network was highly constrained and specifically designed for the task, and has 1% error rate and about a 9% reject rate on zipcode digits provided by the U.S. Postal Service.

TL;DR: The authors present a time-delay neural network (TDNN) approach to phoneme recognition which is characterized by two important properties: using a three-layer arrangement of simple computing units, a hierarchy can be constructed that allows for the formation of arbitrary nonlinear decision surfaces, which the TDNN learns automatically using error backpropagation.

TL;DR: The results confirm the idea that minimizing the number of free parameters in the network enhances generalization, and show that Multtlayer constrained networks perform very well on this task when orgamzed in a hierarchical structure with shift invariant feature detectors.

TL;DR: A feed-forward network architecture for recognizing an unconstrained handwritten multi-digit string with segmentation done on the feature maps developed in the Space Displacement Neural Network rather than the input (pixel) space.