Explore >> Select a destination
|
You are here 
|
proceedings.neurips.cc | ||
| | | | |
tomhume.org
|
|
| | | | | I don't remember how I came across it, but this is one of the most exciting papers I've read recently. The authors train a neural network that tries to identify the next in a sequence of MNIST samples, presented in digit order. The interesting part is that when they include a proxy for energy usage in the loss function (i.e. train it to be more energy-efficient), the resulting network seems to exhibit the characteristics of predictive coding: some units seem to be responsible for predictions, others for encoding prediction error. | |
| | | | |
automl.github.io
|
|
| | | | | ||
| | | | |
nicholas.carlini.com
|
|
| | | | | Abstract: We (again) broke a large collection of published defenses to adversarial examples. Here's how and why. | |
| | | | |
n9o.xyz
|
|
| | | In the last few years, the hype around artificial intelligence has been increasing (again). Most of it is due to truly groundbreaking research and innovative showcases in the field. From machines winning complex games like Go and Dota 2, to various content generation techniques, these technologies will impact our future. | ||
