Over the past decades, a tremendous amount of research has been done on the use of
machine learning for speech processing applications, especially speech recognition …

This is a survey of neural network applications in the real-world scenario. It provides a
taxonomy of artificial neural networks (ANNs) and furnish the reader with knowledge of …

Transformers are state-of-the-art models for a variety of sequence modeling tasks. At their
core is an attention function which models pairwise interactions between the inputs at every …

How well does a classic deep net architecture like AlexNet or VGG19 classify on a standard
dataset such as CIFAR-10 when its “width”—namely, number of channels in convolutional …

Breakthroughs in machine learning are rapidly changing science and society, yet our
fundamental understanding of this technology has lagged far behind. Indeed, one of the …

A longstanding goal in deep learning research has been to precisely characterize training
and generalization. However, the often complex loss landscapes of neural networks have …

Deep learning, in particular the deep convolutional neural networks, has received
increasing interests in face recognition recently, and a number of deep learning methods …

In a series of recent theoretical works, it was shown that strongly over-parameterized neural
networks trained with gradient-based methods could converge exponentially fast to zero …

Single-index models are a class of functions given by an unknown univariate``link''function
applied to an unknown one-dimensional projection of the input. These models are …

At initialization, artificial neural networks (ANNs) are equivalent to Gaussian processes in
the infinite-width limit, thus connecting them to kernel methods. We prove that the evolution …