DEPARTMENT: Hello world
Neural networks and how machines learn meaning
By Oana Niculaescu, April 2019
Articles Tagged: Neural networks
Articles & Features
SECTION: Features
The Replica Project: Building a visual search engine for art historians
From the time of prehistoric etchings on the walls of the Lascaux cave to the present day, people have always been creating art. With millions of artistic artifacts filling museums, churches, cultural institutions, and private collections across the globe, connecting to our shared cultural and artistic past is no longer impossible.
By Benoit Seguin, April 2018
The burgeoning computer-art symbiosis
Computers help us understand art. Art helps us teach computers.
By Shiry Ginosar, Xi Shen, Karan Dwivedi, Elizabeth Honig, Mathieu Aubry, April 2018
Creation, curation, and classification: Mario Klingemann and Emily L. Spratt in conversation
Computer-generated art has long challenged traditional notions of the role of the artist and the curator in the creative process. In the age of machine learning these philosophical conceptions require even further consideration.
By Emily L. Spratt, April 2018
DEPARTMENT: Updates
"ANN" helps Mario rescue Princess Toadstool
By Daniel López Sánchez, September 2016
SECTION: Features
Profile: Geoffrey Hinton
Unlocking the language of the brain
By Adrian Scoică, October 2014
Storage capacity comparison of neural network models for memory recall
The physiology of how the human brain recalls memories is not well understood. Neural networks have been used in an attempt to model this process.
Two types of networks have been used in several models of temporal sequence memory for simple sequences of randomly generated and also of structured patterns: auto- and hetero-associative networks. Previous work has shown that a model with coupled auto- and hetero-associative continuous attractor networks can robustly recall learned simple sequences. In this paper, we compare Hebbian learning and pseudo-inverse learning in a model for recalling temporal sequences in terms of their storage capacities. The pseudo-inverse learning method is shown to have a much higher storage capacity, making the new network model 700% more efficient by reducing calculations.
By Kate Patterson, December 2007
Associative memory and the board game Quarto
By Zachary A. Kissel, December 2003
Using perception in managing unstructured documents
By Ching Kang Cheng, Xiaoshan Pan, December 2003