Recurrent neural network - Wikipedia
A major advantage over conventional discrete-time recurrent neural networks with fixed time steps, as well as Kalman filters and time-delay neural network (TDNN) models with fixed time steps, is that the distribution of time steps is now arbitrary, allowing for smaller time steps during steep signal transitions for much better trade-offs between accuracy and CPU time, while there is also still freedom in the choice of time steps after the neural network model has been generated.
The Neural Network Zoo - The Asimov Institute
As platform we used a wireless smart camera system containing an SIMD (Single-Instruction Multiple-Data) processorfor real-time detection of moving persons and an 8051 microcontroller for implementing the neural network.
Weiguang Ding and Graham Taylor. Mental rotation by optimizing transforming distance. In Neural Information Processing Systems 27 (NIPS) Workshop on Deep Learning and Representation Learning, 2014.[ | ]
Neural Network Learns SDR Ham Radio | Hackaday
The resulting formalism represents a wide class of nonlinear and dynamic systems, including arbitrary nonlinear static systems, arbitrary quasi-static systems, and arbitrary lumped linear dynamical systems.Envisioned application areas include the representation and control of nonlinear neural dynamicsand its use in neuroengineering, oscillatory brain dynamics, neuromodulation, and computational neuroscience.
My Deep Learning Library 1.0: Fast Neural Network …
With feedback from output to input, attractor neural networks can be represented for modeling arbitrarily complex brain dynamics (including various forms of chaotic behavior).
Neural networks and deep learning
Fan Li and Graham Taylor. Alter-CNN: An approach to learning from label proportions with application to ice-water classification. In Neural Information Processing Systems 28 (NIPS) Deep Learning and Representation Learning Workshop on Learning and Privacy with Incomplete Data and Weak Supervision, 2015.[ | ]
Deep learning in neural networks: An overview - …
This is possible because the neural formalism with external feedback can represent any dynamical system described by implicit nonlinear vector equations of the general form f(x,dx/dt,t)=0.
A Brief Introduction to Neural Networks [D. Kriesel]
Note that the approach may also be applied to non-deterministic and noisy systems that arecharacterized by differential-algebraic equations for the deterministic statistical models, e.g., dynamic Bayesian networks (DBN).
Text Generation With LSTM Recurrent Neural Networks …
ISBN 90-74445-26-8.Since the methods described in this thesis generalize multilayer perception networks, theymay similarly be readily extended to incorporate modern methods for layer-by-layer pre-training with stacked autoencoders, e.g.