OPENNN Flood is a comprehensive implementation of the multilayer perceptron neural network in the C++ programming language. It includes several objective functionals and training algorithms, as well as different utilities for the solution of a wide range of problems. Flood also provides an effective framework for the research and development of neural networks algorithms and applications.
RESEARCH | Hasegawa Lab., Imaging Science and Engineering Laboratory. Tokyo Institute of Technology In this page, we introduce our unsupervised online incremental learning method, Self-Organizing Incremental Neural Network (SOINN). We will release the 2nd-Generation SOINN, which is designed based on the Bayes Theory. What is SOINN? The SOINN is an unsupervised online-learning method, which is capable of incremental learning, based on Growing Neural Gas (GNG) and Self-Organizing Map (SOM).
Bayesian Computation Emerges in Generic Cortical Microcircuits through Spike-Timing-Dependent Plasticity
RoboEarth offers a Cloud Robotics infrastructure, which includes everything needed to close the loop from robot to the cloud and back to the robot. RoboEarth’s World-Wide-Web style database stores knowledge generated by humans – and robots – in a machine-readable format. Data stored in the RoboEarth knowledge base include software components, maps for navigation (e.g., object locations, world models), task knowledge (e.g., action recipes, manipulation strategies), and object recognition models (e.g., images, object models). What is RoboEarth ?
Resurgence in Neural Networks - tjake.blog If you’ve been paying attention, you’ll notice there has been a lot of news recently about neural networks and the brain. A few years ago the idea of virtual brains seemed so far from reality, especially for me, but in the past few years there has been a breakthrough that has turned neural networks from nifty little toys to actual useful things that keep getting better at doing tasks computers are traditionally very bad at. In this post I’ll cover some background on Neural networks and my experience with them. Then go over the recent discoveries I’ve learned about. At the end of the post I’ll share a sweet little github project I wrote that implements this new neural network approach.
This paper presents an artificial homeostatic system (AHS) devoted to the autonomous navigation of mobile robots, with emphasis on neuro-endocrine interactions. The AHS is composed of two modules, each one associated with a particular reactive task and both implemented using an extended version of the GasNet neural model, denoted spatially unconstrained GasNet model or simply non-spatial GasNet (NSGasNet). There is a coordination system, which is responsible for the specific role of each NSGasNet at a given operational condition. Towards the evolution of an artificial homeostatic system
PyBrain is a modular Machine Learning Library for Python. Its goal is to offer flexible, easy-to-use yet still powerful algorithms for Machine Learning Tasks and a variety of predefined environments to test and compare your algorithms. PyBrain is short for Python-Based Reinforcement Learning, Artificial Intelligence and Neural Network Library. In fact, we came up with the name first and later reverse-engineered this quite descriptive "Backronym". How is PyBrain different?
Programming Collective Intelligence Subscriber Reviews Average Rating: Based on 8 Ratings "Useful book on machine learning, etc." - by Alex Ott on 20-JUL-2011Reviewer Rating:
Temporal difference learning Temporal difference (TD) learning is an approach to learning how to predict a quantity that depends on future values of a given signal. The name TD derives from its use of changes, or differences, in predictions over successive time steps to drive the learning process. The prediction at any given time step is updated to bring it closer to the prediction of the same quantity at the next time step. It is a supervised learning process in which the training signal for a prediction is a future prediction.
Heuristic search project