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20142025
most citedOn fundamental aspects of quantum extreme learning machines

29 citations · 149 across the 27 of their papers we have counts for

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Showing 2021Show all

6 papers · 1 filter

cond-mat.dis-nn2021★ 9 cited

Explainable Natural Language Processing with Matrix Product States

Jirawat Tangpanitanon, Chanatip Mangkang, Pradeep Bhadola +3

Despite empirical successes of recurrent neural networks (RNNs) in natural language processing (NLP), theoretical understanding of RNNs is still limited due to intrinsically comple…

quant-ph2021★ 2 cited

Hybrid Quantum-Classical Algorithms for Loan Collection Optimization with Loan Loss Provisions

Jirawat Tangpanitanon, Jirawat Saiphet, Pantita Palittapongarnpim +6

Banks are required to set aside funds in their income statement, known as a loan loss provision (LLP), to account for potential loan defaults and expenses. By treating the LLP as a…

cond-mat.dis-nn2021

Resistance distance distribution in large sparse random graphs

Pawat Akara-pipattana, Thiparat Chotibut, Oleg Evnin

We consider an Erdos-Renyi random graph consisting of N vertices connected by randomly and independently drawing an edge between every pair of them with probability c/N so that at…

quant-ph2021★ 14 cited

Quantum diffusion map for nonlinear dimensionality reduction

Apimuk Sornsaeng, Ninnat Dangniam, Pantita Palittapongarnpim +1

Inspired by random walk on graphs, diffusion map (DM) is a class of unsupervised machine learning that offers automatic identification of low-dimensional data structure hidden in a…

cs.GT2021★ 6 cited

Follow-the-Regularized-Leader Routes to Chaos in Routing Games

Jakub Bielawski, Thiparat Chotibut, Fryderyk Falniowski +3

We study the emergence of chaotic behavior of Follow-the-Regularized Leader (FoReL) dynamics in games. We focus on the effects of increasing the population size or the scale of cos…

cond-mat.dis-nn2021★ 9 cited

The birth of geometry in exponential random graphs

Pawat Akara-pipattana, Thiparat Chotibut, Oleg Evnin

Inspired by the prospect of having discretized spaces emerge from random graphs, we construct a collection of simple and explicit exponential random graph models that enjoy, in an…