We’ve created MuseNet, a deep neural network that can generate 4-minute musical compositions with 10 different instruments, and can combine styles from country to Mozart to the Beatles. MuseNet was not explicitly programmed with our understanding of music…

This article will analyze runtimes of sparse matrices and their dense counterparts in Pytorch.

📝 The paper "Fast Example-Based Stylization with Local Guidance" is available here:
https://dcgi.fel.cvut.cz/home/sykorad/styleblit.html

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Artificial Intelligence driving a model of the Universe learned science it was never taught, and no one knows why.

I've designed a free natural language processing curriculum for anyone interested in improving their skills in order to start a startup, get consulting work, or find full-time work related to NLP. This curriculum is for beginners and starts with basic NLP…

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📝 The paper "Few-Shot Adversarial Learning of Realistic Neural Talking Head Models" is available here:
https://arxiv.org/abs/1905.08233v1
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📝 The paper "Moving Least Squares MPM with Compatible Particle-in-Cell" and its source code is available here:
http://taichi.graphics/wp-content/uploads/2019/03/mls-mpm-cpic.pdf
https://github.com/yuanming-hu/taichi_mpm

The Taichi framework: http://taichi.graphics/…

Making neural nets uncool again

In many real-world prediction tasks, class labels include information about the relative ordering between labels, which is not captured by commonly-used loss functions such as multi-category...

A step-by-step Jupyter notebook walkthrough on hyperparameter optimization.

What do we gain by automating a pastime?

Top 100 trending deep learning repositories sorted by the number of stars gained on a specific day. - mbadry1/Trending-Deep-Learning

Many state-of-the-art neural models for NLP are heavily parameterized and thus memory inefficient. This paper proposes a series of lightweight and memory efficient neural architectures for a potpourri of natural language processing (NLP) tasks.