The Information Age paper review: a new commitment

Hello everyone with an interest in The Information Age. I will announce today that I’ll start to post regular reviews of papers that I deem interesting developments in fields of research this blog cares about, namely to do with Information Technology, Computer Science and Data Science.

My first choice for today is a paper on the thriving and important topic of Neural Networks, Deep Neural Networks and Evolutionary Computing, topics that are undergoing a period of massive improvements and feeding the Artificial Intelligence resurgence.

Horn: A System for Parallel Training and Regularizing of Large-Scale Neural Networks

I describe here today a project called Horn and its newest development around large-scale neural networks and the performance in regularization and training of such networks. Experiments were performed on MNIST handwritten digits classification including results, as depicted in the abstract:

I introduce a new distributed system for effective training and regularizing of Large-Scale Neural Networks on distributed computing architectures. The experiments demonstrate the effectiveness of flexible model partitioning and parallelization strategies based on neuron-centric computation model, with an implementation of the collective and parallel dropout neural networks training. Experiments are performed on MNIST handwritten digits classification including results.

The developments that are and will be occurring in the field of Deep Neural Networks are exciting stuff, with the promise of broad real world applications beyond AI. Applications are as diverse as Health systems monitoring, Social Networks, Algorithm Formal Proof checking, Self-driving vehicles, advanced distributed computing, natural language processing, image recognition… And so on… This site will certainly come back on this topic. This paper is one contribution to the possible enhancement of distributed computing architectures.

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