Search results for " Neural Networks"
showing 10 items of 390 documents
Mašīnmācīšanās uzdevumu risināšanai interaktīvās tekstuālās vidēs
2021
Interaktīvas tekstuālas piedzīvojumu spēles var izmantot, lai pārbaudītu mašīnmācīšanās aģentu spējas tikt galā ar dažādiem izaicinājumiem, kas saistīti ar dabiskās valodas izpratni, problēmu risināšanu un atbilžu meklēšanu, vai tādas darbības izvēles stratēģiju apgūšana, kas vispārinās uz iepriekš nesastaptām vidēm. TextWorld platforma ir šādiem pētījumiem domāts ietvars un palīgrīki, ar kuru palīdzību var darbināt daudzas iepriekšpublicētas teksta piedzīvojumu spēles, vai arī definēt un ģenerēt jaunas spēles, dažādās sarežģītības pakāpēs un gandrīz bezgalīgās variācijās. Šajā darbā aprakstīta tāda algoritmiska orākula (oracle) ieviešana, kas var veiksmīgi atrisināt spēles no 3 dažādām iep…
Mašīnmācīšanās pielietojums sporta notikumu prognozēšanā
2017
Dažādu notikumu prognozēšana cilvēcei ir vienmēr bijusi aktuāla. Mūsdienās ir attīstījušās tehnoloģijas, lai to būtu iespējams paveikt balstoties uz pagātnes datiem. Darbā tiek apskatīta sporta notikumu prognozēšana, konkrēti futbola maču iznākumi. Tiek apskatītas vairākas mašīnmācīšanās metodes, kas būtu piemērotākās šī uzdevuma veikšanai. Tiek realizēti un optimizēti divi multi-slāņu perceptrona tīkli un viens vairākkārtējā neironu tīkla, konkrēti LSTM algoritms. Ar tiem tiek veikta simulācija izmantojot reālus datus. Vienā no simulācijām tiek sasniegts pozitīvs rezultāts, sezonas laikā algoritms gūst 65% peļņu.
A sentence based system for measuring syntax complexity using a recurrent deep neural network
2018
In this paper we present a deep neural network model capable of inducing the rules that identify the syntax complexity of an Italian sentence. Our system, beyond the ability of choosing if a sentence needs of simplification, gives a score that represent the confidence of the model during the process of decision making which could be representative of the sentence complexity. Experiments have been carried out on one public corpus created specifically for the problem of text-simplification.
A recurrent deep neural network model to measure sentence complexity for the Italian Language
2019
Text simplification (TS) is a natural language processing task devoted to the modification of a text in such a way that the grammar and structure of the phrases is greatly simplified, preserving the underlying meaning and information contents. In this paper we give a contribution to the TS field presenting a deep neural network model able to detect the complexity of italian sentences. In particular, the system gives a score to an input text that identifies the confidence level during the decision making process and that could be interpreted as a measure of the sentence complexity. Experiments have been carried out on one public corpus of Italian texts created specifically for the task of TS…
Mapping and holistic design of natural hydraulic lime mortars
2020
Supplementary data to this article can be found online at https://doi.org/10.1016/j.cemconres.2020.106167.
Capabilities of Ultrametric Automata with One, Two, and Three States
2016
Ultrametric automata use p-adic numbers to describe the random branching of the process of computation. Previous research has shown that ultrametric automata can have a significant decrease in computing complexity. In this paper we consider the languages that can be recognized by one-way ultrametric automata with one, two, and three states. We also show an example of a promise problem that can be solved by ultrametric integral automaton with three states.
On the Hierarchy Classes of Finite Ultrametric Automata
2015
This paper explores the language classes that arise with respect to the head count of a finite ultrametric automaton. First we prove that in the one-way setting there is a language that can be recognized by a one-head ultrametric finite automaton and cannot be recognized by any k-head non-deterministic finite automaton. Then we prove that in the two-way setting the class of languages recognized by ultrametric finite k-head automata is a proper subclass of the class of languages recognized by (k + 1)-head automata. Ultrametric finite automata are similar to probabilistic and quantum automata and have only just recently been introduced by Freivalds. We introduce ultrametric Turing machines an…
Theory of heterogeneous viscoelasticity
2015
We review a new theory of viscoelasticity of a glass-forming viscous liquid near and below the glass transition. In our model we assume that each point in the material has a specific viscosity, which varies randomly in space according to a fluctuating activation free energy. We include a Maxwellian elastic term and assume that the corresponding shear modulus fluctuates as well with the same distribution as that of the activation barriers. The model is solved in coherent-potential approximation (CPA), for which a derivation is given. The theory predicts an Arrhenius-type temperature dependence of the viscosity in the vanishing-frequency limit, independent of the distribution of the activatio…
Consumer Neuroscience-Based Metrics Predict Recall, Liking and Viewing Rates in Online Advertising
2017
[EN] The purpose of the present study is to investigate whether the effectiveness of a new ad on digital channels (YouTube) can be predicted by using neural networks and neuroscience-based metrics (brain response, heart rate variability and eye tracking). Neurophysiological records from 35 participants were exposed to 8 relevant TV Super Bowl commercials. Correlations between neurophysiological-based metrics, ad recall, ad liking, the ACE metrix score and the number of views on YouTube during a year were investigated. Our findings suggest a significant correlation between neuroscience metrics and self-reported of ad effectiveness and the direct number of views on the YouTube channel. In add…
An Embedded Fingerprints Classification System based on Weightless Neural Networks
2009
Automatic fingerprint classification provides an important indexing scheme to facilitate efficient matching in large-scale fingerprint databases in Automatic Fingerprint Identification Systems (AFISs). The paper presents a new fast fingerprint classification module implementing on embedded Weightless Neural Network (RAM-based neural network). The proposed WNN architecture uses directional maps to classify fingerprint images in the five NIST classes (Left Loop, Right Loop, Whorl, Arch and Tented Arch) without anyone enhancement phase. Starting from the directional map, the WNN architecture computes the fingerprint classification rate. The proposed architecture is implemented on Celoxica RC20…