Interaction-networks_tensorflow - Tensorflow Implementation of Interaction Networks for Learning about Objects, Relations and Physics

Similar to Wasserstein GAN,
this is another theory-motivated paper with neat
applications to deep learning. Once again, if you are looking for proof
details, you are better off reading the original paper. The goal
of this post is to give background and motivation.

[Project Resources] ICML 2017 Paper (Preprint): https://arxiv.org/abs/1703.06217 Poster: https://www.dropbox.com/s/svh5610fpfh7np1/drann-poster.pdf?dl=0 Soft...

tensorflow-cdcgan - A short Conditional DCGAN tensorflow implementation.

Check it out!

Deep learning is about creating machine learning models from compossible blocks, called layers. In this webinar you will learn convolutional neural network architectures for image classification.

This post is about finding the minimum and maximum eigenvalues and the corresponding eigenvectors of a matrix \(A\) using Projected Gradient Descent. There are several algorithms to find the eigenvalues of a given matrix (See Eigenvalue algorithms). Although…

In this post I provide a brief overview of the Generative Adversarial Networks paper and walk through some of the code.

We present Performance RNN, an LSTM-based recurrent neural network designed to model polyphonic music with expressive timing and dynamics. Here’s an example...

Neural network visualization toolkit for keras. Contribute to raghakot/keras-vis development by creating an account on GitHub.

We're open-sourcing a high-performance Python library for robotic simulation using the MuJoCo engine, developed over our past year of robotics research. View CodeView Docs This library is one of our core tools for deep learning robotics research, which we've…

The Encoder-Decoder architecture is popular because it has demonstrated state-of-the-art results across a range of domains. A limitation of the architecture is that it encodes the input sequence to a fixed length internal representation. This imposes limits…

Содержание Часть 1: Введение Часть 2: Manifold learning и скрытые (latent) переменные Часть 3: Вариационные автоэнкодеры (VAE) Часть 4: Conditional VAE...

Содержание Часть 1: Введение Часть 2: Manifold learning и скрытые ( latent ) переменные Часть 3: Вариационные автоэнкодеры ( VAE ) Часть 4: Conditional VAE Часть 5: GAN (Generative Adversarial...

Foreword

Creative Adversarial Networks for creating novel content.

A technical discussion and tutorial