This paper is a review of the evolutionary history of deep learning models. It covers from the
genesis of neural networks when associationism modeling of the brain is studied, to the …

Deep Learning (DL) techniques for Natural Language Processing have been evolving
remarkably fast. Recently, the DL advances in language modeling, machine translation, and …

Single-molecule localization microscopy (SMLM) has had remarkable success in imaging
cellular structures with nanometer resolution, but standard analysis algorithms require …

An introduction to a broad range of topics in deep learning, covering mathematical and
conceptual background, deep learning techniques used in industry, and research …

Representation learning seeks to expose certain aspects of observed data in a learned
representation that's amenable to downstream tasks like classification. For instance, a good …

The variational autoencoder (VAE; Kingma, Welling (2014)) is a recently proposed
generative model pairing a top-down generative network with a bottom-up recognition …

A central problem in machine learning involves modeling complex data-sets using highly
flexible families of probability distributions in which learning, sampling, inference, and …

Latent variable models (LVMs) with discrete compositional latents are an important but
challenging setting due to a combinatorially large number of possible configurations of the …

We present Neural Autoregressive Distribution Estimation (NADE) models, which are neural
network architectures applied to the problem of unsupervised distribution and density …

Despite the successes in capturing continuous distributions, the application of generative
adversarial networks (GANs) to discrete settings, like natural language tasks, is rather …