SIMBRAIN is a free tool for building, running, and analyzing neural-networks (computer simulations of brain circuitry). Simbrain aims to be as visual and easy-to-use as possible.
Fast Artificial Neural Network Library is a free open source neural network library, which implements multilayer artificial neural networks in C with support for both fully connected and sparsely connected networks. Cross-platform execution
What are Convolutional Neural Networks and why are they important? Convolutional Neural Networks (ConvNets or CNNs) are a category of Neural Networks that have proven very effective in areas such as image recognition and classification. ConvNets have been successful in identifying faces, objects and traffic signs apart from powering vision in robots and self driving cars. Figure 1:…
In this project, we provide our implementations of CNN [Zeng et al., 2014] and PCNN [Zeng et al.,2015] and their extended version with sentence-level attention scheme [Lin et al., 2016] .
Torch is a scientific computing framework with wide support for machine learning algorithms. It is easy to use and efficient, thanks to an easy and fast scripting language, LuaJIT, and an underlying C/CUDA implementation.
Neuroph is lightweight Java neural network framework to develop common neural network architectures. It contains well designed, open source Java library with small number of basic classes which correspond to basic NN concepts. Also has nice GUI neural network editor to quickly create Java neural network components. It has been released as open source under the LGPL license, and it's FREE for you to use it.
Engineer friends often ask me: Graph Deep Learning sounds great, but are there any big commercial success stories? Is it being deployed in practical applications? Besides the obvious ones–recommendation systems at Pinterest, Alibaba and Twitter–a slightly nuanced success story is the Transformer architecture, which has taken the NLP industry by storm. Through this post, I want to establish links between Graph Neural Networks (GNNs) and Transformers. I’ll talk about the intuitions behind model architectures in the NLP and GNN communities, make connections using equations and figures, and discuss how we could work together to drive progress.
COBOSLAB: Cognitive Bodyspaces: Learning and Behavior:
Laboratory that investigates and models the Self-organized Learning of and Behavior within Integrated Multimodal Multimodular Bodyspace Representations.
*NOTE: These videos were recorded in Fall 2015 to update the Neural Nets portion of the class. MIT 6.034 Artificial Intelligence, Fall 2010 View the complete...
S. Sapkota, A. Shakya, and B. Joshi. Proceedings of the 26th International Conference of the ORIENTAL- COCOSDA (O-COCOSDA 2023), page 1-6. IEEE, (December 2023)