Search results for " neural network"
showing 10 items of 1232 documents
Review of Non-English Corpora Annotated for Emotion Classification in Text
2020
In this paper we try to systematize the information about the available corpora for emotion classification in text for languages other than English with the goal to find what approaches could be used for low-resource languages with close to no existing works in the field. We analyze the corresponding volume, emotion classification schema, language of each corresponding corpus and methods employed for data preparation and annotation automation. We’ve systematized twenty-four papers representing the corpora and found that corpora were mostly for the most spoken world languages: Hindi, Chinese, Turkish, Arabic, Japanese etc. A typical corpus contained several thousand of manually-annotated ent…
A Controllable Text Simplification System for the Italian Language
2021
Text simplification is a non-trivial task that aims at reducing the linguistic complexity of written texts. Researchers have studied the problem by proposing new methodologies for addressing the English language, but other languages, like the Italian one, are almost unexplored. In this paper, we give a contribution to the enhancement of the Automated Text Simplification research by presenting a deep learning-based system, inspired by a state of the art system for the English language, capable of simplifying Italian texts. The system has been trained and tested by leveraging the Italian version of Newsela; it has shown promising results by achieving a SARI value of 30.17.
Increasing the Inference and Learning Speed of Tsetlin Machines with Clause Indexing
2020
The Tsetlin Machine (TM) is a machine learning algorithm founded on the classical Tsetlin Automaton (TA) and game theory. It further leverages frequent pattern mining and resource allocation principles to extract common patterns in the data, rather than relying on minimizing output error, which is prone to overfitting. Unlike the intertwined nature of pattern representation in neural networks, a TM decomposes problems into self-contained patterns, represented as conjunctive clauses. The clause outputs, in turn, are combined into a classification decision through summation and thresholding, akin to a logistic regression function, however, with binary weights and a unit step output function. …
Diffusive neural network
2002
Abstract A non-connectionist model of a neuronal network based on passive diffusion of neurotransmitters is presented as an alternative to hard-wired artificial neural networks. Classic thermodynamical approach shows that the diffusive network is capable of exhibiting asymptotic stability and a dynamics resembling that of a chaotic system. Basic computational capabilities of the net are discussed based on the equivalence with a Turing machine. The model offers a way to represent mass-sustained brain functions in terms of recurrent behaviors in the phase space.
The computational power of continuous time neural networks
1997
We investigate the computational power of continuous-time neural networks with Hopfield-type units. We prove that polynomial-size networks with saturated-linear response functions are at least as powerful as polynomially space-bounded Turing machines.
Comparative study of modelling the thermal efficiency of a novel straight through evacuated tube collector with MLR, SVR, BP and RBF methods
2021
Abstract Data-based methods are useful for accurate modelling of solar thermal systems. In this work, several artificial neural network (ANN) techniques are proposed to predict the thermal performance of an all-glass straight through evacuated tube solar collector. These are compared to support vector regression analysis. Extensive experimental data sets were collected for training the ANN models. Solar radiation intensity, ambient temperature, wind speed, mass flow rate and collector inlet temperature were selected as the input layer to predict the thermal efficiency of the solar collector. The prediction precision of the ANN models was compared to the multiple linear regression and suppor…
ChemInform Abstract: Relaxation Phenomena of a Triplet Spin Probe in Glassy and Crystalline o-Terphenyl.
2010
The authors used quinoxaline in its photoexcited triplet state as a spin probe in order to measure the spin-lattice relaxation rate in o-terphenyl glass as a function of temperature. They found a power law with an exponent close to 2. Since o-terphenyl can easily be crystallized, they investigated the crystal, too. Below 3.5 K the spin is highly polarized, contrary to the behavior in the glass, where it reaches thermal equilibrium down to the lowest temperatures of their experiment (1.4 K). Around 3.5 K the polarization in the crystal vanishes. Above it appears with opposite sign due to thermal equilibration.
Computational evidence that frequency trajectory theory does not oppose but emerges from age-of-acquisition theory.
2012
International audience; According to the age-of-acquisition hypothesis, words acquired early in life are processed faster and more accurately than words acquired later. Connectionist models have begun to explore the influence of the age/order of acquisition of items (and also their frequency of encounter). This study attempts to reconcile two different methodological and theoretical approaches (proposed by Lambon Ralph & Ehsan, 2006 and Zevin & Seidenberg, 2002) to age-limited learning effects. The current simulations extend the findings reported by Zevin and Seidenberg (2002) that have shown that frequency trajectories (FTs) have limited and specific effects on word-reading tasks. Using th…
Neural Network Based Finite-Time Stabilization for Discrete-Time Markov Jump Nonlinear Systems with Time Delays
2013
Published version of an article in the journal: Abstract and Applied Analysis. Also available from the publisher at: http://dx.doi.org/10.1155/2013/359265 Open Access This paper deals with the finite-time stabilization problem for discrete-time Markov jump nonlinear systems with time delays and norm-bounded exogenous disturbance. The nonlinearities in different jump modes are parameterized by neural networks. Subsequently, a linear difference inclusion state space representation for a class of neural networks is established. Based on this, sufficient conditions are derived in terms of linear matrix inequalities to guarantee stochastic finite-time boundedness and stochastic finite-time stabi…
Synchronization of Uncertain Neural Networks with H8 Performance and Mixed Time-Delays
2011
An exponential H8 synchronization method is addressed for a class of uncertain master and slave neural networks with mixed time-delays, where the mixed delays comprise different neutral, discrete and distributed time-delays. An appropriate discretized Lyapunov-Krasovskii functional and some free weighting matrices are utilized to establish some delay-dependent sufficient conditions for designing a delayed state-feedback control as a synchronization law in terms of linear matrix inequalities under less restrictive conditions. The controller guarantees the exponential H8 synchronization of the two coupled master and slave neural networks regardless of their initial states. Numerical simulatio…