Digests » 105


A foolproof way to shrink deep learning models

​Researchers unveil a pruning algorithm to make artificial intelligence applications run faster.

Efficient Neural Audio Synthesis

Sequential models achieve state-of-the-art results in audio, visual and textual domains with respect to both estimating the data distribution and generating high-quality samples. Efficient sampling for this class of models has however remained an elusive problem. With a focus on text-to-speech synthesis, we describe a set of general techniques for reducing sampling time while maintaining high output quality.

Speeding Up Neural Network Training with Data Echoing

Over the past decade, dramatic increases in neural network training speed have made it possible to apply deep learning techniques to many important problems. In the twilight of Moore's law, as improvements in general purpose processors plateau, the machine learning community has increasingly turned to specialized hardware to produce additional speedups.

Exploring Bayesian Optimization

Many modern machine learning algorithms have a large number of hyperparameters. To effectively use these algorithms, we need to pick good hyperparameter values. In this article, we talk about Bayesian Optimization, a suite of techniques often used to tune hyperparameters. More generally, Bayesian Optimization can be used to optimize any black-box function.

How Doom's Enemy AI Works

200 IQ demons explained.