Representing concepts efficiently
This week’s post is about “Semantic Folding Theory and its Application in Semantic Fingerprinting” by Webber . The basic ideas were also discussed in this Braininspired podcast, and also presented and recorded at the HVB Forum in Munich. You don’t need any particular prior knowledge to understand this post. In my own words The space […]
Predicting with world models
This week’s article is “World Models” by Ha and Schmidhuber . You can find a fancy interactive version of the article here. To understand this post, you need to have a basic understanding of neural networks, recurrent neural networks / LSTMs and reinforcement learning. In my own words Let’s say you want to train a […]
Conditional neural processes
This week’s article is “Conditional Neural Processes” by Garnelo et al. . To understand this post, you need to have a basic understanding of neural networks and Gaussian processes. In my own words A neural process (NP) is a novel framework for regression and classification tasks that combines the strengths of neural networks (NNs) and […]
Gaussian processes
A Gaussian Process is a mathematical tool that you can use to model a probability distribution from data, i.e. to do regression, classification, and inference.
Reinforcement learning and Rubik’s Cube
This week’s article is “Solving the Rubik’s Cube Without Human Knowledge” by McAleer, Agostinelli, Shmakov, and Baldi , which was submitted to NeurIPS 2018. To understand this article, you need to have a basic understanding of neural networks and be familiar with reinforcement learning. In my own words The Rubik’s Cube is a 3-dimensional combination […]