High accuracy data representation via sequence of neural networks
نویسندگان
چکیده
منابع مشابه
Representation of functional data in neural networks
Functional Data Analysis (FDA) is an extension of traditional data analysis to functional data, for example spectra, temporal series, spatio-temporal images, gesture recognition data, etc. Functional data are rarely known in practice; usually a regular or irregular sampling is known. For this reason, some processing is needed in order to benefit from the smooth character of functional data in t...
متن کاملConvolutional Neural Networks using Logarithmic Data Representation
Recent advances in convolutional neural networks have considered model complexity and hardware efficiency to enable deployment onto embedded systems and mobile devices. For example, it is now well-known that the arithmetic operations of deep networks can be encoded down to 8-bit fixed-point without significant deterioration in performance. However, further reduction in precision down to as low ...
متن کاملNonparametric Weight Initialization of Neural Networks via Integral Representation
A new initialization method for hidden parameters in a neural network is proposed. Derived from the integral representation of neural networks, a nonparametric probability distribution of hidden parameters is introduced. In this proposal, hidden parameters are initialized by samples drawn from this distribution, and output parameters are fitted by ordinary linear regression. Numerical experimen...
متن کاملOptimal compressed representation of high throughput sequence data via light assembly
The most effective genomic data compression methods either assemble reads into contigs, or replace them with their alignment positions on a reference genome. Such methods require significant computational resources, but faster alternatives that avoid using explicit or de novo-constructed references fail to match their performance. Here, we introduce a new reference-free compressed representatio...
متن کاملData Clustering Via Spiking Neural Networks
A new spiking-neural-network model for partitioning data into clusters has been developed. The learning process is based on the Spike Timing-Dependent Plasticity rule under the Hebbian Learning framework. With temporally encoded inputs, the synaptic efficiencies of the delays between the pre and postsynaptic spikes can store the information of different data clusters. Various simulation results...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Acta Geodaetica et Geophysica Hungarica
سال: 2003
ISSN: 1217-8977,1587-1037
DOI: 10.1556/ageod.38.2003.3.4