Explainable neural networks that simulate reasoning
The success of deep neural networks suggests that cognition may emerge from
indecipherable patterns of distributed neural activity. Yet these networks are pattern …
indecipherable patterns of distributed neural activity. Yet these networks are pattern …
Review on R&D task integrated management of intelligent manufacturing equipment
T Ren, T Luo, S Li, L Xing, S Xiang - Neural Computing and Applications, 2022 - Springer
With the rapid development of various types of industrial big data technologies, in the
context of industrial big data and systems science, intelligent optimization algorithms and …
context of industrial big data and systems science, intelligent optimization algorithms and …
A hybrid genetic algorithm with bidirectional mutation for maximizing lifetime of heterogeneous wireless sensor networks
J Li, Z Luo, J Xiao - IEEE Access, 2020 - ieeexplore.ieee.org
Sleep scheduling is an effective mechanism to extend the lifetime of energy-constrained
Wireless Sensor Networks (WSNs). It is often that the sensors are divided into sets with …
Wireless Sensor Networks (WSNs). It is often that the sensors are divided into sets with …
Optimal streaming approximations for all boolean max-2csps and max-ksat
CN Chou, A Golovnev… - 2020 IEEE 61st Annual …, 2020 - ieeexplore.ieee.org
We prove tight upper and lower bounds on approximation ratios of all Boolean Max-2CSP
problems in the streaming model. Specifically, for every type of Max-2CSP problem, we give …
problems in the streaming model. Specifically, for every type of Max-2CSP problem, we give …
A novel multi-objective hybrid election algorithm for higher-order random satisfiability in discrete hopfield neural network
Hybridized algorithms are commonly employed to improve the performance of any existing
method. However, an optimal learning algorithm composed of evolutionary and swarm …
method. However, an optimal learning algorithm composed of evolutionary and swarm …
Learning from survey propagation: a neural network for MAX-E-3-SAT
R Marino - Machine Learning: Science and Technology, 2021 - iopscience.iop.org
Many natural optimization problems are NP-hard, which implies that they are probably hard
to solve exactly in the worst-case. However, it suffices to get reasonably good solutions for …
to solve exactly in the worst-case. However, it suffices to get reasonably good solutions for …
[PDF][PDF] Metaheuristics approach for maximum k satisfiability in restricted neural symbolic integration
S Sathasivam, M Mamat… - Pertanika J. Sci …, 2020 - journals-jd.upm.edu.my
Maximum k Satisfiability logical rule (MAX-kSAT) is a language that bridges real life
application to neural network optimization. MAX-kSAT is an interesting paradigm because …
application to neural network optimization. MAX-kSAT is an interesting paradigm because …
Automated discovery of algorithms from data
To automate the discovery of new scientific and engineering principles, artificial intelligence
must distill explicit rules from experimental data. This has proven difficult because existing …
must distill explicit rules from experimental data. This has proven difficult because existing …
Adapting local sequential algorithms to the distributed setting
K Kawarabayashi, G Schwartzman - arXiv preprint arXiv:1711.10155, 2017 - arxiv.org
It is a well known fact that sequential algorithms which exhibit a strong" local" nature can be
adapted to the distributed setting given a legal graph coloring. The running time of the …
adapted to the distributed setting given a legal graph coloring. The running time of the …
A refined branching algorithm for the maximum satisfiability problem
Abstract The Maximum satisfiability problem (MaxSAT) is a fundamental NP-hard problem
which has significant applications in many areas. Based on refined observations, we derive …
which has significant applications in many areas. Based on refined observations, we derive …