Results 11 to 20 of about 25,600 (268)
Backpropagation Neural Tree [PDF]
Neural Networks, 2022 We propose a novel algorithm called Backpropagation Neural Tree (BNeuralT), which is a stochastic computational dendritic tree. BNeuralT takes random repeated inputs through its leaves and imposes dendritic nonlinearities through its internal connections like a biological dendritic tree would do. Considering the dendritic-tree like plausible biological
Varun Ojha 0001, Giuseppe Nicosia
openaire +5 more sources
Backpropagation and the brain [PDF]
Nature Reviews Neuroscience, 2020 During learning, the brain modifies synapses to improve behaviour. In the cortex, synapses are embedded within multilayered networks, making it difficult to determine the effect of an individual synaptic modification on the behaviour of the system. The backpropagation algorithm solves this problem in deep artificial neural networks, but historically it
Timothy P. Lillicrap +4 more
openaire +2 more sources
Memorized sparse backpropagation [PDF]
Neurocomputing, 2020 Accepted to ...
Zhiyuan Zhang 0001 +4 more
openaire +2 more sources
CoRR, 2022 Quaternion valued neural networks experienced rising popularity and interest from researchers in the last years, whereby the derivatives with respect to quaternions needed for optimization are calculated as the sum of the partial derivatives with respect to the real and imaginary parts.
Johannes Pöppelbaum, Andreas Schwung
openaire +2 more sources
Gradients without Backpropagation
CoRR, 2022 Using backpropagation to compute gradients of objective functions for optimization has remained a mainstay of machine learning. Backpropagation, or reverse-mode differentiation, is a special case within the general family of automatic differentiation algorithms that also includes the forward mode.
Baydin, AG +4 more
openaire +3 more sources
Prediction of Salinity Based on Meteorological Data Using the Backpropagation Neural Network Method
Ilmu Kelautan, 2021 Salinity is the level of salt dissolved in water. The salinity level of seawater can affect the hydrological balance and climate change. The salinity level of seawater in each area varies depending on the influencing factors, that is evaporation and ...
Anisa Nur Azizah +4 more
doaj +1 more source
Backpropagation on Dynamical Networks
CoRR, 2022 Dynamical networks are versatile models that can describe a variety of behaviours such as synchronisation and feedback. However, applying these models in real world contexts is difficult as prior information pertaining to the connectivity structure or local dynamics is often unknown and must be inferred from time series observations of network states ...
Eugene Tan +3 more
openaire +2 more sources
Earth and Space Science, 2021 As the amount of published geoscience literature grows, reading and summarizing texts of large collections has become a challenging task. Publication keywords can be considered basic components of knowledge structure representations and have been used to
Qinjun Qiu +3 more
doaj +1 more source
Water quality prediction method based on preferred classification
IET Cyber-Physical Systems, 2020 Water quality monitoring and prediction are important parts of Cyber Physical Systems. Considering the complexity, diversity, and strong non-linearity of water quality data, a single water quality prediction model is difficult to have a significant ...
Liming Sheng +4 more
doaj +1 more source
Spiking Autoencoders With Temporal Coding
Frontiers in Neuroscience, 2021 Spiking neural networks with temporal coding schemes process information based on the relative timing of neuronal spikes. In supervised learning tasks, temporal coding allows learning through backpropagation with exact derivatives, and achieves ...
Iulia-Maria Comşa +3 more
doaj +1 more source