NIPS 2017 Mon Dec 4th through Sat the 9th, 2017 at Long Beach Convention Center Paper ID: 3085 Title: Predictive State Recurrent Neural Networks ### Reviewer 1 This paper proposes a new model for dynamical systems (called PSRNN), which combines the frameworks of PSR and RNN non-trivially. The model is learned from data in two steps: The first step initialize the model parameters using two-stage regression (2SR), a method previously proposed by Hefny et al for learning PSRs. The second step use Back-pro
Deep neural networks have emerged as a widely used and effective means for tackling complex, real-world problems. However, a major obstacle in applying them to safety-critical systems is the great difficulty in providing formal guarantees about their behavior. We
Do Graph Neural Network States Contain Graph Properties?Tom Pelletreau-Duris, Ruud van Bakel, Michael CochezDeep neural networks (DNNs) achieve sta
Blog Topics Advertise Join Newsletter Improving the Performance of a Neural Network There are many techniques available that could help us achieve that. Follow along to get to know them and to build your own accurate neural network. --> comments By Rohith Gandhi G Neural networks are machine learning algorithms that provide state of the accuracy on many use cases. But, a lot of times the accuracy of the network we are building might not be satisfactory or might not take us to the top positions on the leader
Recurrent Neural Networks (RNNs) have been widely used in sequence analysis and modeling. However, when processing high-dimensional data, RNNs typically require very large model sizes, thereby bringing a series of deployment challenges. Although the state-of-the-art tensor decomposition approaches can provide good model compression performance, these existing methods are still suffering some inherent limitations, such as restricted representation capability and insufficient model complexity reduction. To ov
Leverage Bayesian Theory to boost your performance
We talk a lot about AI, machine learning, and neural nets, but what's a neural net in the first place
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Skip to main content Breadcrumb Papers Benchmarking Predictive Coding Networks - Made Simple Benchmarking Predictive Coding Networks - Made Simple Pinchetti L Qi C Lokshyn O Oliviers G Emde C Tang M M'Charrak A Frieder S Menzat B Bogacz R Lukasiewicz T Salvatori T Predictive coding is an influential theory of information processing in the brain. However, until recently it was not feasible to simulate larger networks of neurons based on this theory. This paper describes an efficient computer implementation o
Learn about the different types of neural network architectures