Datasets for Computer Vision
The dataset is an integral part of an ML engineer’s toolkit. We recently compiled useful information about a range of these well known datasets. It’s all in one place, and hopefully useful to others as well.
Blog – page 4
Timeline
Type
Topics
CLA 13Reinforcement Learning 13HTM 11Hierarchical Generative Models 10Sparse Distributed Representations 10Neocortex 9Deep Learning 8Cortical Learning Algorithm 7Predictive Coding 7columns 6Hippocampus 6Temporal Pooling 6Thalamocortical 6unsupervised learning 6hierarchy 5neurobiology 5pyramidal cell 5
The case for Episodic Memory in Machine Learning
ML Today Today’s Machine Learning has demonstrated unprecedented performance in what seems like every application thrown at it. Almost all the success has been based on advanced memory systems that can learn to recognise an input based on a large number of training examples. This is the equivalent to memory for facts. The biological terminology…
2018 a Milestone for Project AGI
2018 is a fresh new year and an exciting milestone for Project AGI. Dave and I have been discussing, dreaming, playing around with and striving towards general purpose AI for over 6 years. It started with musings on the algorithmic underpinnings of consciousness and the nature of intelligence. We quickly moved into the more practical…
Choosing a Machine Learning Framework in 2018
There are plenty of established machine learning frameworks out there, and new frameworks are popping up frequently to address specific niches. We were interested in examining if one of these frameworks fits in our workflow. I surveyed the most popular frameworks, and aim to provide a helpful comparative analysis.
Getting Started with ‘Street View House Numbers’ (SVHN) Dataset
SVHN is relatively new and popular dataset, a natural next step to MNIST and complement to other popular computer vision datasets. This is an overview of the common preprocessing techniques used and the best performance benchmarks, as well as a look at the state-of-the-art neural network architectures used.
Open Sourcing SVHN Preprocessing Code
Releasing a set of tools for converting the Street View House Numbers (SVHN) dataset into images with additional preprocessing options such as grayscaling.
New approaches to Deep Networks – Capsules (Hinton), HTM (Numenta), Sparsey (Neurithmic Systems) and RCN (Vicarious)
Reproduced left to right from [8,10,1] Within a 5 day span in October, 4 papers came out that take a significantly different approach to AI hierarchical networks. They are all inspired by biological principles to varying degrees. It’s exciting to see different ways of thinking. Particularly at a time when there is growing skepticism…
New Ideas in Reinforcement Learning
Today’s blog post is about Reinforcement Learning (RL), a concept that is very relevant to Artificial General Intelligence. The goal of RL is to create an agent that can learn to behave optimally in an environment by observing the consequences – rewards – of its own actions. By incorporating deep neural networks into RL agents,…
Reading list – October 2017
This month’s reading list has two parts: a non-Reinforcement Learning list, and a Reinforcement Learning list. Since our next blog post will be on Reinforcement Learning, readers might like to refer to our RL reading list separately. Non-Reinforcement Learning reading list A Framework for searching for General Artificial Intelligence Authors: Marek Rosa, Jan Feyereisl, The…
Literature Review: ‘A Distributional Perspective on Reinforcement Learning’
This article assesses the research paper, ‘A Distributional Perspective on Reinforcement Learning’ by the authors, Marc G. Bellemare, Will Dabney and Remi Munos, published in the proceedings of the 34th International Conference on Machine Learning (ICML) in 2017. Bellemare et al.’s paper will be assessed on several criteria. Firstly, content is assessed through providing some…