Top /r/MachineLearning Posts, October: NSFW Image Recognition, Differentiable Neural Computers, Hinton on Coursera

NSFW Image Recognition, Differentiable Neural Computers, Hinton's Neural Networks for Machine Learning Coursera course; Introducing the AI Open Network; Making a Self-driving RC Car

Reddit MXLearnIn October on /r/MachineLearning, things get NSFW, DeepMind continues to innovate, Hinton is in the headlines (again), the AI Open Network is introduced, and Stuart Little gets an autonomous car of his very own.

The top 5 /r/MachineLearning posts of the past month, not including subreddit meta-discussions, are:

1. Image Synthesis from Yahoo's open_nsfw

In case the title wasn't clear enough...

Warning: This post contains abstract depictions of nudity and may be unsuitable for the workplace


This project on GitLab explores Yahoo's open_nsfw, a deep neural network for "Not Suitable for Work (NSFW) classification using deep neural network Caffe models." The post explains:

Yahoo's recently open sourced neural network, open_nsfw, is a fine tuned Residual Network which scores images on a scale of 0 to 1 on its suitability for use in the workplace. In the documentation, Yahoo notes:

"Defining NSFW material is subjective and the task of identifying these images is non-trivial. Moreover, what may be objectionable in one context can be suitable in another."

As a double disclaimer, the synthesized images contained in the post are oddly disturbing in an abstract way, along with being NSFW.

The project and article, however, are legitimate explorations of the technology, and are far from gratuitous or pornographic in nature. This is an actual presentation of research.

2. New Nature paper by DeepMind: Hybrid Computing using a Neural Network with Dynamic External Memory

This post from DeepMind provides an overview to a recent paper published in Nature, introducing differentiable neural computers. From the article:

In a recent study in Nature, we introduce a form of memory-augmented neural network called a differentiable neural computer, and show that it can learn to use its memory to answer questions about complex, structured data, including artificially generated stories, family trees, and even a map of the London Underground. We also show that it can solve a block puzzle game using reinforcement learning.

Watch the differentiable neural computer answer a family tree relation question:

3. Neural Network for Machine Learning by Geoffrey Hinton has started. We've also got a sub for it (r/nn4ml)

Geoffrey Hinton's Neural Networks for Machine Learning is running again on Coursera. You may have missed the first several weeks, but if you're interested in learning the material, this is a great starter course, and all of the previous weeks' material and videos are still available.

As mentioned in the title, the subreddit /r/nn4ml is also a thing, and may be useful for someone taking the course. Of particular note, a discussion on the subreddit centers on forming a study group to implement the coursework in TensorFlow. There is activity on the thread, and it makes mention of a Slack channel that members are using, with instructions to join. Active collaborative communities implementing neural network models for educational purposes. Sounds like a good thing.

4. Introducing the AI Open Network: a 100% open-source AI research community

AI Open Network

Directly from AI•ON's website:

AI•ON is an open community dedicated to advancing Artificial Intelligence by:

  • Drawing attention to important yet under-appreciated research problems.
  • Connecting researchers and encouraging open scientific collaboration.
  • Providing a learning environment for students looking to gain machine learning experience.

Collaborative AI research with a focus on applied research projects. Sounds like a good thing.

5. Made a self-driving RC car during my free time

Self-driving cars make the headlines continuously, but have been making more waves than usual recently.

This project brings autonomous driving to the RC car. The researcher has implemented Nvidia's recent paper in compact form, and the results are encouraging:

For details on how the project was implemented, and for a look at the code, check out this post.