Results 21 to 30 of about 11,673,454 (251)

A novel approach to attention mechanism using kernel functions: Kerformer

Frontiers in Neurorobotics, 2023
Artificial Intelligence (AI) is driving advancements across various fields by simulating and enhancing human intelligence. In Natural Language Processing (NLP), transformer models like the Kerformer, a linear transformer based on a kernel approach, have ...
Yao Gan, Yanyun Fu, Deyong Wang, Yongming Li   +3 more
doaj   +1 more source

Further enumeration results concerning a recent equivalence of restricted inversion sequences [PDF]

Discrete Mathematics & Theoretical Computer Science, 2022
Let asc and desc denote respectively the statistics recording the number of ascents or descents in a sequence having non-negative integer entries.
Toufik Mansour, Mark Shattuck
doaj   +1 more source

Applying the possibilistic C-means algorithm in kernel-induced spaces [PDF]

, 2010
In this paper, we study a kernel extension of the classic possibilistic c-means. In the proposed extension, we implicitly map input patterns into a possibly high-dimensional space by means of positive semidefinite kernels. In this new space, we model the
Masulli, F., M. Filippone, Filippone, M., ROVETTA, STEFANO, MASULLI, FRANCESCO, Rovetta, S.   +5 more
core   +1 more source

Biologically-Inspired Spike-Based Automatic Speech Recognition of Isolated Digits Over a Reproducing Kernel Hilbert Space

Frontiers in Neuroscience, 2018
This paper presents a novel real-time dynamic framework for quantifying time-series structure in spoken words using spikes. Audio signals are converted into multi-channel spike trains using a biologically-inspired leaky integrate-and-fire (LIF) spike ...
Kan Li, José C. Príncipe
doaj   +1 more source

Intelligent Fault Identification for Rolling Element Bearings in Impulsive Noise Environments Based on Cyclic Correntropy Spectra and LSSVM

IEEE Access, 2020
Rolling element bearings are important components in various types of industrial equipment. It is necessary to develop advanced fault diagnosis techniques to prevent unexpected accidents caused by bearing failures.
Xuejun Zhao, Yong Qin, Changbo He, Limin Jia   +3 more
doaj   +1 more source

Synthesis of neural networks for spatio-temporal spike pattern recognition and processing

Frontiers in Neuroscience, 2013
The advent of large scale neural computational platforms has highlighted the lack of algorithms for synthesis of neural structures to perform predefined cognitive tasks.
Jonathan C Tapson, Greg Kevin Cohen, Saeed eAfshar, Klaus M Stiefel, Yossi eBuskila, Tara Julia Hamilton, André evan Schaik   +6 more
doaj   +1 more source

Multivariate Time Series Clustering with State Space Dynamical Modeling and Grassmann Manifold Learning: A Systematic Review on Human Motion Data

Applied Sciences
Multivariate time series (MTS) clustering has been an essential research topic in various domains over the past decades. However, inherent properties of MTS data—namely, temporal dynamics and inter-variable correlations—make MTS clustering challenging ...
Sebin Heo, Andrew Beng Jin Teoh, Sunjin Yu, Beom-Seok Oh   +3 more
doaj   +1 more source

Enumeration of convex polyominoes using the ECO method [PDF]

Discrete Mathematics & Theoretical Computer Science, 2003
ECO is a method for the enumeration of classes of combinatorial objects based on recursive constructions of such classes. In the first part of this paper we present a construction for the class of convex polyominoes based on the ECO method.
A. Del Lungo, E. Duchi, A. Frosini, S. Rinaldi   +3 more
doaj   +1 more source

A distance-based kernel for classification via Support Vector Machines

Frontiers in Artificial Intelligence
Support Vector Machines (SVMs) are a type of supervised machine learning algorithm widely used for classification tasks. In contrast to traditional methods that split the data into separate training and testing sets, here we propose an innovative ...
Nazhir Amaya-Tejera, Margarita Gamarra, Jorge I. Vélez, Eduardo Zurek   +3 more
doaj   +1 more source

Distributed Kernel Extreme Learning Machines for Aircraft Engine Failure Diagnostics

Applied Sciences, 2019
Kernel extreme learning machine (KELM) has been widely studied in the field of aircraft engine fault diagnostics due to its easy implementation. However, because its computational complexity is proportional to the training sample size, its application in
Junjie Lu, Jinquan Huang, Feng Lu
doaj   +1 more source