Search results for "Computer Science::Neural and Evolutionary Computation"
showing 10 items of 61 documents
Forecasting Exchange Rates Volatilities Using Artificial Neural Networks
2000
This paper employs Artificial Neural Networks to forecast volatilities of the exchange rates of six currencies against the Spanish peseta. First, we propose to use ANN as an alternative to parametric volatility models, then, we employ them as an aggregation procedure to build hybrid models. Though we do not find a systematic superiority of ANN, our results suggest that they are an interesting alternative to classical parametric volatility models.
Neural Networks, Inside Out: Solving for Inputs Given Parameters (A Preliminary Investigation)
2021
Artificial neural network (ANN) is a supervised learning algorithm, where parameters are learned by several back-and-forth iterations of passing the inputs through the network, comparing the output with the expected labels, and correcting the parameters. Inspired by a recent work of Boer and Kramer (2020), we investigate a different problem: Suppose an observer can view how the ANN parameters evolve over many iterations, but the dataset is oblivious to him. For instance, this can be an adversary eavesdropping on a multi-party computation of an ANN parameters (where intermediate parameters are leaked). Can he form a system of equations, and solve it to recover the dataset?
Quantum autoencoders via quantum adders with genetic algorithms
2017
The quantum autoencoder is a recent paradigm in the field of quantum machine learning, which may enable an enhanced use of resources in quantum technologies. To this end, quantum neural networks with less nodes in the inner than in the outer layers were considered. Here, we propose a useful connection between quantum autoencoders and quantum adders, which approximately add two unknown quantum states supported in different quantum systems. Specifically, this link allows us to employ optimized approximate quantum adders, obtained with genetic algorithms, for the implementation of quantum autoencoders for a variety of initial states. Furthermore, we can also directly optimize the quantum autoe…
Scalability of using Restricted Boltzmann Machines for Combinatorial Optimization
2014
Abstract Estimation of Distribution Algorithms (EDAs) require flexible probability models that can be efficiently learned and sampled. Restricted Boltzmann Machines (RBMs) are generative neural networks with these desired properties. We integrate an RBM into an EDA and evaluate the performance of this system in solving combinatorial optimization problems with a single objective. We assess how the number of fitness evaluations and the CPU time scale with problem size and complexity. The results are compared to the Bayesian Optimization Algorithm (BOA), a state-of-the-art multivariate EDA, and the Dependency Tree Algorithm (DTA), which uses a simpler probability model requiring less computati…
Depth-Adapted CNN for RGB-D cameras
2020
Conventional 2D Convolutional Neural Networks (CNN) extract features from an input image by applying linear filters. These filters compute the spatial coherence by weighting the photometric information on a fixed neighborhood without taking into account the geometric information. We tackle the problem of improving the classical RGB CNN methods by using the depth information provided by the RGB-D cameras. State-of-the-art approaches use depth as an additional channel or image (HHA) or pass from 2D CNN to 3D CNN. This paper proposes a novel and generic procedure to articulate both photometric and geometric information in CNN architecture. The depth data is represented as a 2D offset to adapt …
"Table 35" of "Centrality dependence of Pi, K, p production in Pb-Pb collisions at sqrt(sNN) = 2.76 TeV"
2018
PBAR/P ratio in Pb-Pb collisions at sqrt(sNN) = 2.76 TeV.
"Table 33" of "Centrality dependence of Pi, K, p production in Pb-Pb collisions at sqrt(sNN) = 2.76 TeV"
2018
pi-/pi+ ratio in Pb-Pb collisions at sqrt(sNN) = 2.76 TeV.
"Table 34" of "Centrality dependence of Pi, K, p production in Pb-Pb collisions at sqrt(sNN) = 2.76 TeV"
2018
K-/K+ ratio in Pb-Pb collisions at sqrt(sNN) = 2.76 TeV.
An ant colony optimization-based fuzzy predictive control approach for nonlinear processes
2015
In this paper, a new approach for designing an adaptive fuzzy model predictive control (AFMPC) based on the ant colony optimization (ACO) is proposed. On-line adaptive fuzzy identification is introduced to identify the system parameters. These parameters are used to calculate the objective function based on a predictive approach and structure of RST control. Then the optimization problem is solved based on an ACO algorithm, used at the optimization process in AFMPC to determine optimal controller parameters of RST control. The utility of the proposed controller is demonstrated by applying it to two nonlinear processes, where the proposed approach provides better performances compared with p…
Enabling XCSF to cope with dynamic environments via an adaptive error threshold
2020
The learning classifier system XCSF is a variant of XCS employed for function approximation. Although XCSF is a promising candidate for deployment in autonomous systems, its parameter dependability imposes a significant hurdle, as a-priori parameter optimization is not feasible for complex and changing environmental conditions. One of the most important parameters is the error threshold, which can be interpreted as a target bound on the approximation error and has to be set according to the approximated function. To enable XCSF to reliably approximate functions that change during runtime, we propose the use of an error threshold, which is adapted at run-time based on the currently achieved …