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13,802 | Average Convergence Rate of Evolutionary Algorithms | cs.NE | In evolutionary optimization, it is important to understand how fast
evolutionary algorithms converge to the optimum per generation, or their
convergence rate. This paper proposes a new measure of the convergence rate,
called average convergence rate. It is a normalised geometric mean of the
reduction ratio of the fitn... | computer science |
13,803 | Microscopic approach of a time elapsed neural model | cs.NE | The spike trains are the main components of the information processing in the
brain. To model spike trains several point processes have been investigated in
the literature. And more macroscopic approaches have also been studied, using
partial differential equation models. The main aim of the present article is to
build... | computer science |
13,804 | Memory and information processing in neuromorphic systems | cs.NE | A striking difference between brain-inspired neuromorphic processors and
current von Neumann processors architectures is the way in which memory and
processing is organized. As Information and Communication Technologies continue
to address the need for increased computational power through the increase of
cores within ... | computer science |
13,805 | A review of landmark articles in the field of co-evolutionary computing | cs.NE | Coevolution is a powerful tool in evolutionary computing that mitigates some
of its endemic problems, namely stagnation in local optima and lack of
convergence in high dimensionality problems. Since its inception in 1990, there
are multiple articles that have contributed greatly to the development and
improvement of th... | computer science |
13,806 | Learning Spike time codes through Morphological Learning with Binary
Synapses | cs.NE | In this paper, a neuron with nonlinear dendrites (NNLD) and binary synapses
that is able to learn temporal features of spike input patterns is considered.
Since binary synapses are considered, learning happens through formation and
elimination of connections between the inputs and the dendritic branches to
modify the s... | computer science |
13,807 | Solving Problems with Unknown Solution Length at (Almost) No Extra Cost | cs.NE | Most research in the theory of evolutionary computation assumes that the
problem at hand has a fixed problem size. This assumption does not always apply
to real-world optimization challenges, where the length of an optimal solution
may be unknown a priori.
Following up on previous work of Cathabard, Lehre, and Yao [F... | computer science |
13,808 | A Tight Runtime Analysis of the $(1+(λ, λ))$ Genetic
Algorithm on OneMax | cs.NE | Understanding how crossover works is still one of the big challenges in
evolutionary computation research, and making our understanding precise and
proven by mathematical means might be an even bigger one. As one of few
examples where crossover provably is useful, the $(1+(\lambda, \lambda))$
Genetic Algorithm (GA) was... | computer science |
13,809 | A Feature-Based Analysis on the Impact of Set of Constraints for
e-Constrained Differential Evolution | cs.NE | Different types of evolutionary algorithms have been developed for
constrained continuous optimization. We carry out a feature-based analysis of
evolved constrained continuous optimization instances to understand the
characteristics of constraints that make problems hard for evolutionary
algorithm. In our study, we exa... | computer science |
13,810 | Learning Representations from Deep Networks Using Mode Synthesizers | cs.NE | Deep learning Networks play a crucial role in the evolution of a vast number
of current machine learning models for solving a variety of real world
non-trivial tasks. Such networks use big data which is generally unlabeled
unsupervised and multi-layered requiring no form of supervision for training
and learning data an... | computer science |
13,811 | ASOC: An Adaptive Parameter-free Stochastic Optimization Techinique for
Continuous Variables | cs.NE | Stochastic optimization is an important task in many optimization problems
where the tasks are not expressible as convex optimization problems. In the
case of non-convex optimization problems, various different stochastic
algorithms like simulated annealing, evolutionary algorithms, and tabu search
are available. Most ... | computer science |
13,812 | Evaluation of Genotypic Diversity Measurements Exploited in Real-Coded
Representation | cs.NE | Numerous genotypic diversity measures (GDMs) are available in the literature
to assess the convergence status of an evolutionary algorithm (EA) or describe
its search behavior. In a recent study, the authors of this paper drew
attention to the need for a GDM validation framework. In response, this study
proposes three ... | computer science |
13,813 | Developing Postfix-GP Framework for Symbolic Regression Problems | cs.NE | This paper describes Postfix-GP system, postfix notation based Genetic
Programming (GP), for solving symbolic regression problems. It presents an
object-oriented architecture of Postfix-GP framework. It assists the user in
understanding of the implementation details of various components of
Postfix-GP. Postfix-GP provi... | computer science |
13,814 | Design of OFDM radar pulses using genetic algorithm based techniques | cs.NE | The merit of evolutionary algorithms (EA) to solve convex optimization
problems is widely acknowledged. In this paper, a genetic algorithm (GA)
optimization based waveform design framework is used to improve the features of
radar pulses relying on the orthogonal frequency division multiplexing (OFDM)
structure. Our opt... | computer science |
13,815 | Parameter Sensitivity Analysis of Social Spider Algorithm | cs.NE | Social Spider Algorithm (SSA) is a recently proposed general-purpose
real-parameter metaheuristic designed to solve global numerical optimization
problems. This work systematically benchmarks SSA on a suite of 11 functions
with different control parameters. We conduct parameter sensitivity analysis of
SSA using advance... | computer science |
13,816 | Adaptive Chemical Reaction Optimization for Global Numerical
Optimization | cs.NE | A newly proposed chemical-reaction-inspired metaheurisic, Chemical Reaction
Optimization (CRO), has been applied to many optimization problems in both
discrete and continuous domains. To alleviate the effort in tuning parameters,
this paper reduces the number of optimization parameters in canonical CRO and
develops an ... | computer science |
13,817 | A Trainable Neuromorphic Integrated Circuit that Exploits Device
Mismatch | cs.NE | Random device mismatch that arises as a result of scaling of the CMOS
(complementary metal-oxide semi-conductor) technology into the deep submicron
regime degrades the accuracy of analogue circuits. Methods to combat this
increase the complexity of design. We have developed a novel neuromorphic
system called a Trainabl... | computer science |
13,818 | A neuromorphic hardware architecture using the Neural Engineering
Framework for pattern recognition | cs.NE | We present a hardware architecture that uses the Neural Engineering Framework
(NEF) to implement large-scale neural networks on Field Programmable Gate
Arrays (FPGAs) for performing pattern recognition in real time. NEF is a
framework that is capable of synthesising large-scale cognitive systems from
subnetworks. We wi... | computer science |
13,819 | STICK: Spike Time Interval Computational Kernel, A Framework for General
Purpose Computation using Neurons, Precise Timing, Delays, and Synchrony | cs.NE | There has been significant research over the past two decades in developing
new platforms for spiking neural computation. Current neural computers are
primarily developed to mimick biology. They use neural networks which can be
trained to perform specific tasks to mainly solve pattern recognition problems.
These machin... | computer science |
13,820 | Neural NILM: Deep Neural Networks Applied to Energy Disaggregation | cs.NE | Energy disaggregation estimates appliance-by-appliance electricity
consumption from a single meter that measures the whole home's electricity
demand. Recently, deep neural networks have driven remarkable improvements in
classification performance in neighbouring machine learning fields such as
image classification and ... | computer science |
13,821 | Multi-objective analysis of computational models | cs.NE | Computational models are of increasing complexity and their behavior may in
particular emerge from the interaction of different parts. Studying such models
becomes then more and more difficult and there is a need for methods and tools
supporting this process. Multi-objective evolutionary algorithms generate a set
of tr... | computer science |
13,822 | A Neural Prototype for a Virtual Chemical Spectrophotometer | cs.NE | A virtual chemical spectrophotometer for the simultaneous analysis of nickel
(Ni) and cobalt (Co) was developed based on an artificial neural network (ANN).
The developed ANN correlates the respective concentrations of Co and Ni given
the absorbance profile of a Co-Ni mixture based on the Beer's Law. The virtual
chemic... | computer science |
13,823 | A Social Spider Algorithm for Solving the Non-convex Economic Load
Dispatch Problem | cs.NE | Economic Load Dispatch (ELD) is one of the essential components in power
system control and operation. Although conventional ELD formulation can be
solved using mathematical programming techniques, modern power system
introduces new models of the power units which are non-convex,
non-differentiable, and sometimes non-c... | computer science |
13,824 | On Proportions of Fit Individuals in Population of Evolutionary
Algorithm with Tournament Selection | cs.NE | In this paper, we consider a fitness-level model of a non-elitist
mutation-only evolutionary algorithm (EA) with tournament selection. The model
provides upper and lower bounds for the expected proportion of the individuals
with fitness above given thresholds. In the case of so-called monotone
mutation, the obtained bo... | computer science |
13,825 | The Interactive Effects of Operators and Parameters to GA Performance
Under Different Problem Sizes | cs.NE | The complex effect of genetic algorithm's (GA) operators and parameters to
its performance has been studied extensively by researchers in the past but
none studied their interactive effects while the GA is under different problem
sizes. In this paper, We present the use of experimental model (1)~to
investigate whether ... | computer science |
13,826 | INsight: A Neuromorphic Computing System for Evaluation of Large Neural
Networks | cs.NE | Deep neural networks have been demonstrated impressive results in various
cognitive tasks such as object detection and image classification. In order to
execute large networks, Von Neumann computers store the large number of weight
parameters in external memories, and processing elements are timed-shared,
which leads t... | computer science |
13,827 | Benchmarking of LSTM Networks | cs.NE | LSTM (Long Short-Term Memory) recurrent neural networks have been highly
successful in a number of application areas. This technical report describes
the use of the MNIST and UW3 databases for benchmarking LSTM networks and
explores the effect of different architectural and hyperparameter choices on
performance. Signif... | computer science |
13,828 | Genetic Algorithms for multimodal optimization: a review | cs.NE | In this article we provide a comprehensive review of the different
evolutionary algorithm techniques used to address multimodal optimization
problems, classifying them according to the nature of their approach. On the
one hand there are algorithms that address the issue of the early convergence
to a local optimum by di... | computer science |
13,829 | Population Synthesis via k-Nearest Neighbor Crossover Kernel | cs.NE | The recent development of multi-agent simulations brings about a need for
population synthesis. It is a task of reconstructing the entire population from
a sampling survey of limited size (1% or so), supplying the initial conditions
from which simulations begin. This paper presents a new kernel density
estimator for th... | computer science |
13,830 | A Cognitive Architecture Based on a Learning Classifier System with
Spiking Classifiers | cs.NE | Learning Classifier Systems (LCS) are population-based reinforcement learners
that were originally designed to model various cognitive phenomena. This paper
presents an explicitly cognitive LCS by using spiking neural networks as
classifiers, providing each classifier with a measure of temporal dynamism. We
employ a co... | computer science |
13,831 | Evolving Unipolar Memristor Spiking Neural Networks | cs.NE | Neuromorphic computing --- brainlike computing in hardware --- typically
requires myriad CMOS spiking neurons interconnected by a dense mesh of
nanoscale plastic synapses. Memristors are frequently citepd as strong synapse
candidates due to their statefulness and potential for low-power
implementations. To date, plenti... | computer science |
13,832 | A Telescopic Binary Learning Machine for Training Neural Networks | cs.NE | This paper proposes a new algorithm based on multi-scale stochastic local
search with binary representation for training neural networks.
In particular, we study the effects of neighborhood evaluation strategies,
the effect of the number of bits per weight and that of the maximum weight
range used for mapping binary ... | computer science |
13,833 | A compact aVLSI conductance-based silicon neuron | cs.NE | We present an analogue Very Large Scale Integration (aVLSI) implementation
that uses first-order lowpass filters to implement a conductance-based silicon
neuron for high-speed neuromorphic systems. The aVLSI neuron consists of a soma
(cell body) and a single synapse, which is capable of linearly summing both the
excita... | computer science |
13,834 | A Reconfigurable Mixed-signal Implementation of a Neuromorphic ADC | cs.NE | We present a neuromorphic Analogue-to-Digital Converter (ADC), which uses
integrate-and-fire (I&F) neurons as the encoders of the analogue signal, with
modulated inhibitions to decohere the neuronal spikes trains. The architecture
consists of an analogue chip and a control module. The analogue chip comprises
two scan c... | computer science |
13,835 | Training of CC4 Neural Network with Spread Unary Coding | cs.NE | This paper adapts the corner classification algorithm (CC4) to train the
neural networks using spread unary inputs. This is an important problem as
spread unary appears to be at the basis of data representation in biological
learning. The modified CC4 algorithm is tested using the pattern classification
experiment and ... | computer science |
13,836 | Transfer learning approach for financial applications | cs.NE | Artificial neural networks learn how to solve new problems through a
computationally intense and time consuming process. One way to reduce the
amount of time required is to inject preexisting knowledge into the network. To
make use of past knowledge, we can take advantage of techniques that transfer
the knowledge learn... | computer science |
13,837 | Regular expressions for decoding of neural network outputs | cs.NE | This article proposes a convenient tool for decoding the output of neural
networks trained by Connectionist Temporal Classification (CTC) for handwritten
text recognition. We use regular expressions to describe the complex structures
expected in the writing. The corresponding finite automata are employed to
build a dec... | computer science |
13,838 | Some Theorems for Feed Forward Neural Networks | cs.NE | In this paper we introduce a new method which employs the concept of
"Orientation Vectors" to train a feed forward neural network and suitable for
problems where large dimensions are involved and the clusters are
characteristically sparse. The new method is not NP hard as the problem size
increases. We `derive' the met... | computer science |
13,839 | Deep Boltzmann Machines in Estimation of Distribution Algorithms for
Combinatorial Optimization | cs.NE | Estimation of Distribution Algorithms (EDAs) require flexible probability
models that can be efficiently learned and sampled. Deep Boltzmann Machines
(DBMs) are generative neural networks with these desired properties. We
integrate a DBM into an EDA and evaluate the performance of this system in
solving combinatorial o... | computer science |
13,840 | A hybrid COA$ε$-constraint method for solving multi-objective
problems | cs.NE | In this paper, a hybrid method for solving multi-objective problem has been
provided. The proposed method is combining the {\epsilon}-Constraint and the
Cuckoo algorithm. First the multi objective problem transfers into a
single-objective problem using $\epsilon$-Constraint, then the Cuckoo
optimization algorithm will ... | computer science |
13,841 | Multi-objective Differential Evolution with Helper Functions for
Constrained Optimization | cs.NE | Solving constrained optimization problems by multi-objective evolutionary
algorithms has scored tremendous achievements in the last decade. Standard
multi-objective schemes usually aim at minimizing the objective function and
also the degree of constraint violation simultaneously. This paper proposes a
new multi-object... | computer science |
13,842 | Generative Adversarial Networks in Estimation of Distribution Algorithms
for Combinatorial Optimization | cs.NE | Estimation of Distribution Algorithms (EDAs) require flexible probability
models that can be efficiently learned and sampled. Generative Adversarial
Networks (GAN) are generative neural networks which can be trained to
implicitly model the probability distribution of given data, and it is possible
to sample this distri... | computer science |
13,843 | An Asynchronous Implementation of the Limited Memory CMA-ES | cs.NE | We present our asynchronous implementation of the LM-CMA-ES algorithm, which
is a modern evolution strategy for solving complex large-scale continuous
optimization problems. Our implementation brings the best results when the
number of cores is relatively high and the computational complexity of the
fitness function is... | computer science |
13,844 | A novel mutation operator based on the union of fitness and design
spaces information for Differential Evolution | cs.NE | Differential Evolution (DE) is one of the most successful and powerful
evolutionary algorithms for global optimization problem. The most important
operator in this algorithm is mutation operator which parents are selected
randomly to participate in it. Recently, numerous papers are tried to make this
operator more inte... | computer science |
13,845 | Differential Evolution with Generalized Mutation Operator for Parameters
Optimization in Gene Selection for Cancer Classification | cs.NE | Differential Evolution (DE) proved to be one of the most successful
evolutionary algorithms for global optimization purposes in continuous
problems. The core operator in DE is mutation which can provide the algorithm
with both exploration and exploitation. In this article, a new notation for DE
is proposed which has a ... | computer science |
13,846 | Nonlinear memory capacity of parallel time-delay reservoir computers in
the processing of multidimensional signals | cs.NE | This paper addresses the reservoir design problem in the context of
delay-based reservoir computers for multidimensional input signals, parallel
architectures, and real-time multitasking. First, an approximating reservoir
model is presented in those frameworks that provides an explicit functional
link between the reser... | computer science |
13,847 | Exploring the Space of Adversarial Images | cs.NE | Adversarial examples have raised questions regarding the robustness and
security of deep neural networks. In this work we formalize the problem of
adversarial images given a pretrained classifier, showing that even in the
linear case the resulting optimization problem is nonconvex. We generate
adversarial images using ... | computer science |
13,848 | Evolutionary Landscape and Management of Population Diversity | cs.NE | The search ability of an Evolutionary Algorithm (EA) depends on the variation
among the individuals in the population [3, 4, 8]. Maintaining an optimal level
of diversity in the EA population is imperative to ensure that progress of the
EA search is unhindered by premature convergence to suboptimal solutions.
Clearer u... | computer science |
13,849 | Increasing Behavioral Complexity for Evolved Virtual Creatures with the
ESP Method | cs.NE | Since their introduction in 1994 (Sims), evolved virtual creatures (EVCs)
have employed the coevolution of morphology and control to produce high-impact
work in multiple fields, including graphics, evolutionary computation,
robotics, and artificial life. However, in contrast to fixed-morphology
creatures, there has bee... | computer science |
13,850 | Spiking Analog VLSI Neuron Assemblies as Constraint Satisfaction Problem
Solvers | cs.NE | Solving constraint satisfaction problems (CSPs) is a notoriously expensive
computational task. Recently, it has been proposed that efficient stochastic
solvers can be obtained through appropriately configured spiking neural
networks performing Markov Chain Monte Carlo (MCMC) sampling. The possibility
to run such models... | computer science |
13,851 | Turing Computation with Recurrent Artificial Neural Networks | cs.NE | We improve the results by Siegelmann & Sontag (1995) by providing a novel and
parsimonious constructive mapping between Turing Machines and Recurrent
Artificial Neural Networks, based on recent developments of Nonlinear Dynamical
Automata. The architecture of the resulting R-ANNs is simple and elegant,
stemming from it... | computer science |
13,852 | An Analytic Expression of Relative Approximation Error for a Class of
Evolutionary Algorithms | cs.NE | An important question in evolutionary computation is how good solutions
evolutionary algorithms can produce. This paper aims to provide an analytic
analysis of solution quality in terms of the relative approximation error,
which is defined by the error between 1 and the approximation ratio of the
solution found by an e... | computer science |
13,853 | Stochastic Synapses Enable Efficient Brain-Inspired Learning Machines | cs.NE | Recent studies have shown that synaptic unreliability is a robust and
sufficient mechanism for inducing the stochasticity observed in cortex. Here,
we introduce Synaptic Sampling Machines, a class of neural network models that
uses synaptic stochasticity as a means to Monte Carlo sampling and unsupervised
learning. Sim... | computer science |
13,854 | Recurrent Neural Networks Hardware Implementation on FPGA | cs.NE | Recurrent Neural Networks (RNNs) have the ability to retain memory and learn
data sequences. Due to the recurrent nature of RNNs, it is sometimes hard to
parallelize all its computations on conventional hardware. CPUs do not
currently offer large parallelism, while GPUs offer limited parallelism due to
sequential compo... | computer science |
13,855 | MOEA/D-GM: Using probabilistic graphical models in MOEA/D for solving
combinatorial optimization problems | cs.NE | Evolutionary algorithms based on modeling the statistical dependencies
(interactions) between the variables have been proposed to solve a wide range
of complex problems. These algorithms learn and sample probabilistic graphical
models able to encode and exploit the regularities of the problem. This paper
investigates t... | computer science |
13,856 | Critical Parameters in Particle Swarm Optimisation | cs.NE | Particle swarm optimisation is a metaheuristic algorithm which finds
reasonable solutions in a wide range of applied problems if suitable parameters
are used. We study the properties of the algorithm in the framework of random
dynamical systems which, due to the quasi-linear swarm dynamics, yields
analytical results fo... | computer science |
13,857 | Evolutionary algorithms | cs.NE | This manuscript contains an outline of lectures course "Evolutionary
Algorithms" read by the author in Omsk State University n.a. F.M.Dostoevsky.
The course covers Canonic Genetic Algorithm and various other genetic
algorithms as well as evolutionary strategies, genetic programming, tabu search
and the class of evoluti... | computer science |
13,858 | rnn : Recurrent Library for Torch | cs.NE | The rnn package provides components for implementing a wide range of
Recurrent Neural Networks. It is built withing the framework of the Torch
distribution for use with the nn package. The components have evolved from 3
iterations, each adding to the flexibility and capability of the package. All
component modules inhe... | computer science |
13,859 | On randomization of neural networks as a form of post-learning strategy | cs.NE | Today artificial neural networks are applied in various fields - engineering,
data analysis, robotics. While they represent a successful tool for a variety
of relevant applications, mathematically speaking they are still far from being
conclusive. In particular, they suffer from being unable to find the best
configurat... | computer science |
13,860 | Duelist Algorithm: An Algorithm Inspired by How Duelist Improve Their
Capabilities in a Duel | cs.NE | This paper proposes an optimization algorithm based on how human fight and
learn from each duelist. Since this algorithm is based on population, the
proposed algorithm starts with an initial set of duelists. The duel is to
determine the winner and loser. The loser learns from the winner, while the
winner try their new ... | computer science |
13,861 | Cleaning Schedule Optimization of Heat Exchanger Networks Using Particle
Swarm Optimization | cs.NE | Oil refinery is one of industries that require huge energy consumption. The
today technology advance requires energy saving. Heat integration is a method
used to minimize the energy comsumption though the implementation of Heat
Exchanger Network (HEN). CPT is one of types of Heat Exchanger Network (HEN)
that functions ... | computer science |
13,862 | Triplet Spike Time Dependent Plasticity: A floating-gate Implementation | cs.NE | Synapse plays an important role of learning in a neural network; the learning
rules which modify the synaptic strength based on the timing difference between
the pre- and post-synaptic spike occurrence is termed as Spike Time Dependent
Plasticity (STDP). The most commonly used rule posits weight change based on
time di... | computer science |
13,863 | An Online Unsupervised Structural Plasticity Algorithm for Spiking
Neural Networks | cs.NE | In this article, we propose a novel Winner-Take-All (WTA) architecture
employing neurons with nonlinear dendrites and an online unsupervised
structural plasticity rule for training it. Further, to aid hardware
implementations, our network employs only binary synapses. The proposed
learning rule is inspired by spike tim... | computer science |
13,864 | Level-Based Analysis of Genetic Algorithms for Combinatorial
Optimization | cs.NE | The paper is devoted to upper bounds on run-time of Non-Elitist Genetic
Algorithms until some target subset of solutions is visited for the first time.
In particular, we consider the sets of optimal solutions and the sets of local
optima as the target subsets. Previously known upper bounds are improved by
means of drif... | computer science |
13,865 | Digital Genesis: Computers, Evolution and Artificial Life | cs.NE | The application of evolution in the digital realm, with the goal of creating
artificial intelligence and artificial life, has a history as long as that of
the digital computer itself. We illustrate the intertwined history of these
ideas, starting with the early theoretical work of John von Neumann and the
pioneering ex... | computer science |
13,866 | Computing factorized approximations of Pareto-fronts using
mNM-landscapes and Boltzmann distributions | cs.NE | NM-landscapes have been recently introduced as a class of tunable rugged
models. They are a subset of the general interaction models where all the
interactions are of order less or equal $M$. The Boltzmann distribution has
been extensively applied in single-objective evolutionary algorithms to
implement selection and s... | computer science |
13,867 | Synthesis of recurrent neural networks for dynamical system simulation | cs.NE | We review several of the most widely used techniques for training recurrent
neural networks to approximate dynamical systems, then describe a novel
algorithm for this task. The algorithm is based on an earlier theoretical
result that guarantees the quality of the network approximation. We show that a
feedforward neural... | computer science |
13,868 | Diversity Enhancement for Micro-Differential Evolution | cs.NE | The differential evolution (DE) algorithm suffers from high computational
time due to slow nature of evaluation. In contrast, micro-DE (MDE) algorithms
employ a very small population size, which can converge faster to a reasonable
solution. However, these algorithms are vulnerable to a premature convergence
as well as ... | computer science |
13,869 | Feedforward Sequential Memory Networks: A New Structure to Learn
Long-term Dependency | cs.NE | In this paper, we propose a novel neural network structure, namely
\emph{feedforward sequential memory networks (FSMN)}, to model long-term
dependency in time series without using recurrent feedback. The proposed FSMN
is a standard fully-connected feedforward neural network equipped with some
learnable memory blocks in... | computer science |
13,870 | An Improved Intelligent Agent for Mining Real-Time Databases Using
Modified Cortical Learning Algorithms | cs.NE | Cortical Learning Algorithms based on the Hierarchical Temporal Memory, HTM
have been developed by Numenta Incorporation from which variations and
modifications are currently being investigated upon. HTM offers better promises
as a future computational model of the neocortex the seat of intelligence in
the brain. Curre... | computer science |
13,871 | A Fuzzy MLP Approach for Non-linear Pattern Classification | cs.NE | In case of decision making problems, classification of pattern is a complex
and crucial task. Pattern classification using multilayer perceptron (MLP)
trained with back propagation learning becomes much complex with increase in
number of layers, number of nodes and number of epochs and ultimate increases
computational ... | computer science |
13,872 | Conversion of Artificial Recurrent Neural Networks to Spiking Neural
Networks for Low-power Neuromorphic Hardware | cs.NE | In recent years the field of neuromorphic low-power systems that consume
orders of magnitude less power gained significant momentum. However, their
wider use is still hindered by the lack of algorithms that can harness the
strengths of such architectures. While neuromorphic adaptations of
representation learning algori... | computer science |
13,873 | Orthogonal Echo State Networks and stochastic evaluations of likelihoods | cs.NE | We report about probabilistic likelihood estimates that are performed on time
series using an echo state network with orthogonal recurrent connectivity. The
results from tests using synthetic stochastic input time series with temporal
inference indicate that the capability of the network to infer depends on the
balance... | computer science |
13,874 | A continuum among logarithmic, linear, and exponential functions, and
its potential to improve generalization in neural networks | cs.NE | We present the soft exponential activation function for artificial neural
networks that continuously interpolates between logarithmic, linear, and
exponential functions. This activation function is simple, differentiable, and
parameterized so that it can be trained as the rest of the network is trained.
We hypothesize ... | computer science |
13,875 | Unsupervised Regenerative Learning of Hierarchical Features in Spiking
Deep Networks for Object Recognition | cs.NE | We present a spike-based unsupervised regenerative learning scheme to train
Spiking Deep Networks (SpikeCNN) for object recognition problems using
biologically realistic leaky integrate-and-fire neurons. The training
methodology is based on the Auto-Encoder learning model wherein the
hierarchical network is trained lay... | computer science |
13,876 | Computing with hardware neurons: spiking or classical? Perspectives of
applied Spiking Neural Networks from the hardware side | cs.NE | While classical neural networks take a position of a leading method in the
machine learning community, spiking neuromorphic systems bring attention and
large projects in neuroscience. Spiking neural networks were shown to be able
to substitute networks of classical neurons in applied tasks. This work
explores recent ha... | computer science |
13,877 | Reducing training requirements through evolutionary based dimension
reduction and subject transfer | cs.NE | Training Brain Computer Interface (BCI) systems to understand the intention
of a subject through Electroencephalogram (EEG) data currently requires
multiple training sessions with a subject in order to develop the necessary
expertise to distinguish signals for different tasks. Conventionally the task
of training the su... | computer science |
13,878 | A Feature-Based Prediction Model of Algorithm Selection for Constrained
Continuous Optimisation | cs.NE | With this paper, we contribute to the growing research area of feature-based
analysis of bio-inspired computing. In this research area, problem instances
are classified according to different features of the underlying problem in
terms of their difficulty of being solved by a particular algorithm. We
investigate the im... | computer science |
13,879 | Associative Long Short-Term Memory | cs.NE | We investigate a new method to augment recurrent neural networks with extra
memory without increasing the number of network parameters. The system has an
associative memory based on complex-valued vectors and is closely related to
Holographic Reduced Representations and Long Short-Term Memory networks.
Holographic Redu... | computer science |
13,880 | Learning Over Long Time Lags | cs.NE | The advantage of recurrent neural networks (RNNs) in learning dependencies
between time-series data has distinguished RNNs from other deep learning
models. Recently, many advances are proposed in this emerging field. However,
there is a lack of comprehensive review on memory models in RNNs in the
literature. This paper... | computer science |
13,881 | Greedy Ants Colony Optimization Strategy for Solving the Curriculum
Based University Course Timetabling Problem | cs.NE | Timetabling is a problem faced in all higher education institutions. The
International Timetabling Competition (ITC) has published a dataset that can be
used to test the quality of methods used to solve this problem. A number of
meta-heuristic approaches have obtained good results when tested on the ITC
dataset, howeve... | computer science |
13,882 | A Nonparametric Framework for Quantifying Generative Inference on
Neuromorphic Systems | cs.NE | Restricted Boltzmann Machines and Deep Belief Networks have been successfully
used in probabilistic generative model applications such as image occlusion
removal, pattern completion and motion synthesis. Generative inference in such
algorithms can be performed very efficiently on hardware using a Markov Chain
Monte Car... | computer science |
13,883 | Firefly Algorithm for optimization problems with non-continuous
variables: A Review and Analysis | cs.NE | Firefly algorithm is a swarm based metaheuristic algorithm inspired by the
flashing behavior of fireflies. It is an effective and an easy to implement
algorithm. It has been tested on different problems from different disciplines
and found to be effective. Even though the algorithm is proposed for
optimization problems... | computer science |
13,884 | Deep Spiking Networks | cs.NE | We introduce an algorithm to do backpropagation on a spiking network. Our
network is "spiking" in the sense that our neurons accumulate their activation
into a potential over time, and only send out a signal (a "spike") when this
potential crosses a threshold and the neuron is reset. Neurons only update
their states wh... | computer science |
13,885 | Significance Driven Hybrid 8T-6T SRAM for Energy-Efficient Synaptic
Storage in Artificial Neural Networks | cs.NE | Multilayered artificial neural networks (ANN) have found widespread utility
in classification and recognition applications. The scale and complexity of
such networks together with the inadequacies of general purpose computing
platforms have led to a significant interest in the development of efficient
hardware implemen... | computer science |
13,886 | Multiplier-less Artificial Neurons Exploiting Error Resiliency for
Energy-Efficient Neural Computing | cs.NE | Large-scale artificial neural networks have shown significant promise in
addressing a wide range of classification and recognition applications.
However, their large computational requirements stretch the capabilities of
computing platforms. The fundamental components of these neural networks are
the neurons and its sy... | computer science |
13,887 | On Complex Valued Convolutional Neural Networks | cs.NE | Convolutional neural networks (CNNs) are the cutting edge model for
supervised machine learning in computer vision. In recent years CNNs have
outperformed traditional approaches in many computer vision tasks such as
object detection, image classification and face recognition. CNNs are
vulnerable to overfitting, and a l... | computer science |
13,888 | Real time error detection in metal arc welding process using Artificial
Neural Netwroks | cs.NE | Quality assurance in production line demands reliable weld joints. Human made
errors is a major cause of faulty production. Promptly Identifying errors in
the weld while welding is in progress will decrease the post inspection cost
spent on the welding process. Electrical parameters generated during welding,
could able... | computer science |
13,889 | A Stochastic Approach to STDP | cs.NE | We present a digital implementation of the Spike Timing Dependent Plasticity
(STDP) learning rule. The proposed digital implementation consists of an
exponential decay generator array and a STDP adaptor array. On the arrival of a
pre- and post-synaptic spike, the STDP adaptor will send a digital spike to the
decay gene... | computer science |
13,890 | Adaptive Parameter Selection in Evolutionary Algorithms by Reinforcement
Learning with Dynamic Discretization of Parameter Range | cs.NE | Online parameter controllers for evolutionary algorithms adjust values of
parameters during the run of an evolutionary algorithm. Recently a new
efficient parameter controller based on reinforcement learning was proposed by
Karafotias et al. In this method ranges of parameters are discretized into
several intervals bef... | computer science |
13,891 | A Draft Memory Model on Spiking Neural Assemblies | cs.NE | A draft memory model (DM) for neural networks with spike propagation delay
(SNNwD) is described. Novelty in this approach are that the DM learns
immediately, with stimuli presented once, without synaptic weight changes, and
without external learning algorithm. Basal on this model is to trap spikes
within neural loops. ... | computer science |
13,892 | Convolutional Networks for Fast, Energy-Efficient Neuromorphic Computing | cs.NE | Deep networks are now able to achieve human-level performance on a broad
spectrum of recognition tasks. Independently, neuromorphic computing has now
demonstrated unprecedented energy-efficiency through a new chip architecture
based on spiking neurons, low precision synapses, and a scalable communication
network. Here,... | computer science |
13,893 | Hybrid Ant Colony Optimization in solving Multi-Skill
Resource-Constrained Project Scheduling Problem | cs.NE | In this paper Hybrid Ant Colony Optimization (HAntCO) approach in solving
Multi--Skill Resource Constrained Project Scheduling Problem (MS--RCPSP) has
been presented. We have proposed hybrid approach that links classical heuristic
priority rules for project scheduling with Ant Colony Optimization (ACO).
Furthermore, a ... | computer science |
13,894 | Adaptive Computation Time for Recurrent Neural Networks | cs.NE | This paper introduces Adaptive Computation Time (ACT), an algorithm that
allows recurrent neural networks to learn how many computational steps to take
between receiving an input and emitting an output. ACT requires minimal changes
to the network architecture, is deterministic and differentiable, and does not
add any n... | computer science |
13,895 | Dataflow Matrix Machines as a Generalization of Recurrent Neural
Networks | cs.NE | Dataflow matrix machines are a powerful generalization of recurrent neural
networks. They work with multiple types of arbitrary linear streams, multiple
types of powerful neurons, and allow to incorporate higher-order constructions.
We expect them to be useful in machine learning and probabilistic programming,
and in t... | computer science |
13,896 | Application of artificial neural networks and genetic algorithms for
crude fractional distillation process modeling | cs.NE | This work presents the application of the artificial neural networks, trained
and structurally optimized by genetic algorithms, for modeling of crude
distillation process at PKN ORLEN S.A. refinery. Models for the main
fractionator distillation column products were developed using historical data.
Quality of the fracti... | computer science |
13,897 | Fitness-based Adaptive Control of Parameters in Genetic Programming:
Adaptive Value Setting of Mutation Rate and Flood Mechanisms | cs.NE | This paper concerns applications of genetic algorithms and genetic
programming to tasks for which it is difficult to find a representation that
does not map to a highly complex and discontinuous fitness landscape. In such
cases the standard algorithm is prone to getting trapped in local extremes. The
paper proposes sev... | computer science |
13,898 | Biobjective Performance Assessment with the COCO Platform | cs.NE | This document details the rationales behind assessing the performance of
numerical black-box optimizers on multi-objective problems within the COCO
platform and in particular on the biobjective test suite bbob-biobj. The
evaluation is based on a hypervolume of all non-dominated solutions in the
archive of candidate sol... | computer science |
13,899 | Resource allocation using metaheuristic search | cs.NE | This research is focused on solving problems in the area of software project
management using metaheuristic search algorithms and as such is research in the
field of search based software engineering. The main aim of this research is to
evaluate the performance of different metaheuristic search techniques in
resource a... | computer science |
13,900 | LSTM with Working Memory | cs.NE | Previous RNN architectures have largely been superseded by LSTM, or "Long
Short-Term Memory". Since its introduction, there have been many variations on
this simple design. However, it is still widely used and we are not aware of a
gated-RNN architecture that outperforms LSTM in a broad sense while still being
as simpl... | computer science |
13,901 | Efficiency Evaluation of Character-level RNN Training Schedules | cs.NE | We present four training and prediction schedules from the same
character-level recurrent neural network. The efficiency of these schedules is
tested in terms of model effectiveness as a function of training time and
amount of training data seen. We show that the choice of training and
prediction schedule potentially h... | computer science |
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