Unnamed: 0 int64 0 41k | title stringlengths 4 274 | category stringlengths 5 18 | summary stringlengths 22 3.66k | theme stringclasses 8
values |
|---|---|---|---|---|
13,902 | Anytime Bi-Objective Optimization with a Hybrid Multi-Objective CMA-ES
(HMO-CMA-ES) | cs.NE | We propose a multi-objective optimization algorithm aimed at achieving good
anytime performance over a wide range of problems. Performance is assessed in
terms of the hypervolume metric. The algorithm called HMO-CMA-ES represents a
hybrid of several old and new variants of CMA-ES, complemented by BOBYQA as a
warm start... | computer science |
13,903 | COCO: Performance Assessment | cs.NE | We present an any-time performance assessment for benchmarking numerical
optimization algorithms in a black-box scenario, applied within the COCO
benchmarking platform. The performance assessment is based on runtimes measured
in number of objective function evaluations to reach one or several quality
indicator target v... | computer science |
13,904 | Direct Method for Training Feed-forward Neural Networks using Batch
Extended Kalman Filter for Multi-Step-Ahead Predictions | cs.NE | This paper is dedicated to the long-term, or multi-step-ahead, time series
prediction problem. We propose a novel method for training feed-forward neural
networks, such as multilayer perceptrons, with tapped delay lines. Special
batch calculation of derivatives called Forecasted Propagation Through Time and
batch modif... | computer science |
13,905 | Combinatorially Generated Piecewise Activation Functions | cs.NE | In the neuroevolution literature, research has primarily focused on evolving
the number of nodes, connections, and weights in artificial neural networks.
Few attempts have been made to evolve activation functions. Research in
evolving activation functions has mainly focused on evolving function
parameters, and developi... | computer science |
13,906 | Chess Player by Co-Evolutionary Algorithm | cs.NE | A co-evolutionary algorithm (CA) based chess player is presented.
Implementation details of the algorithms, namely coding, population, variation
operators are described. The alpha-beta or mini-max like behaviour of the
player is achieved through two competitive or cooperative populations. Special
attention is given to ... | computer science |
13,907 | Evolutionary Demographic Algorithms | cs.NE | Most of the problems in genetic algorithms are very complex and demand a
large amount of resources that current technology can not offer. Our purpose
was to develop a Java-JINI distributed library that implements Genetic
Algorithms with sub-populations (coarse grain) and a graphical interface in
order to configure and ... | computer science |
13,908 | Improving energy efficiency and classification accuracy of neuromorphic
chips by learning binary synaptic crossbars | cs.NE | Deep Neural Networks (DNN) have achieved human level performance in many
image analytics tasks but DNNs are mostly deployed to GPU platforms that
consume a considerable amount of power. Brain-inspired spiking neuromorphic
chips consume low power and can be highly parallelized. However, for deploying
DNNs to energy effi... | computer science |
13,909 | On the performance of different mutation operators of a
subpopulation-based genetic algorithm for multi-robot task allocation
problems | cs.NE | The performance of different mutation operators is usually evaluated in
conjunc-tion with specific parameter settings of genetic algorithms and target
problems. Most studies focus on the classical genetic algorithm with different
parameters or on solving unconstrained combinatorial optimization problems such
as the tra... | computer science |
13,910 | A Spiking Network that Learns to Extract Spike Signatures from Speech
Signals | cs.NE | Spiking neural networks (SNNs) with adaptive synapses reflect core properties
of biological neural networks. Speech recognition, as an application involving
audio coding and dynamic learning, provides a good test problem to study SNN
functionality. We present a simple, novel, and efficient nonrecurrent SNN that
learns ... | computer science |
13,911 | Training a Hidden Markov Model with a Bayesian Spiking Neural Network | cs.NE | It is of some interest to understand how statistically based mechanisms for
signal processing might be integrated with biologically motivated mechanisms
such as neural networks. This paper explores a novel hybrid approach for
classifying segments of sequential data, such as individual spoken works. The
approach combine... | computer science |
13,912 | Acquisition of Visual Features Through Probabilistic
Spike-Timing-Dependent Plasticity | cs.NE | The final version of this paper has been published in IEEEXplore available at
http://ieeexplore.ieee.org/document/7727213. Please cite this paper as:
Amirhossein Tavanaei, Timothee Masquelier, and Anthony Maida, Acquisition of
visual features through probabilistic spike-timing-dependent plasticity. IEEE
International J... | computer science |
13,913 | Critical Echo State Networks that Anticipate Input using Morphable
Transfer Functions | cs.NE | The paper investigates a new type of truly critical echo state networks where
individual transfer functions for every neuron can be modified to anticipate
the expected next input. Deviations from expected input are only forgotten
slowly in power law fashion. The paper outlines the theory, numerically
analyzes a one neu... | computer science |
13,914 | Viral Search algorithm | cs.NE | The article, after a brief introduction on genetic algorithms and their
functioning, presents a kind of genetic algorithm called Viral Search. We
present the key concepts, we formally derive the algorithm and we perform
numerical tests designed to illustrate the potential and limits. | computer science |
13,915 | Neural Networks and Continuous Time | cs.NE | The fields of neural computation and artificial neural networks have
developed much in the last decades. Most of the works in these fields focus on
implementing and/or learning discrete functions or behavior. However,
technical, physical, and also cognitive processes evolve continuously in time.
This cannot be describe... | computer science |
13,916 | Learning from Non-Stationary Stream Data in Multiobjective Evolutionary
Algorithm | cs.NE | Evolutionary algorithms (EAs) have been well acknowledged as a promising
paradigm for solving optimisation problems with multiple conflicting objectives
in the sense that they are able to locate a set of diverse approximations of
Pareto optimal solutions in a single run. EAs drive the search for approximated
solutions ... | computer science |
13,917 | Hitting times of local and global optima in genetic algorithms with very
high selection pressure | cs.NE | The paper is devoted to upper bounds on the expected first hitting times of
the sets of local or global optima for non-elitist genetic algorithms with very
high selection pressure. The results of this paper extend the range of
situations where the upper bounds on the expected runtime are known for genetic
algorithms an... | computer science |
13,918 | Neural networks with differentiable structure | cs.NE | While gradient descent has proven highly successful in learning connection
weights for neural networks, the actual structure of these networks is usually
determined by hand, or by other optimization algorithms. Here we describe a
simple method to make network structure differentiable, and therefore
accessible to gradie... | computer science |
13,919 | Evolutionary computation for multicomponent problems: opportunities and
future directions | cs.NE | Over the past 30 years many researchers in the field of evolutionary
computation have put a lot of effort to introduce various approaches for
solving hard problems. Most of these problems have been inspired by major
industries so that solving them, by providing either optimal or near optimal
solution, was of major sign... | computer science |
13,920 | A Modified Activation Function with Improved Run-Times For Neural
Networks | cs.NE | In this paper we present a modified version of the Hyperbolic Tangent
Activation Function as a learning unit generator for neural networks. The
function uses an integer calibration constant as an approximation to the Euler
number, e, based on a quadratic Real Number Formula (RNF) algorithm and an
adaptive normalization... | computer science |
13,921 | The BioDynaMo Project | cs.NE | Computer simulations have become a very powerful tool for scientific
research. Given the vast complexity that comes with many open scientific
questions, a purely analytical or experimental approach is often not viable.
For example, biological systems (such as the human brain) comprise an extremely
complex organization ... | computer science |
13,922 | Forward Table-Based Presynaptic Event-Triggered Spike-Timing-Dependent
Plasticity | cs.NE | Spike-timing-dependent plasticity (STDP) incurs both causal and acausal
synaptic weight updates, for negative and positive time differences between
pre-synaptic and post-synaptic spike events. For realizing such updates in
neuromorphic hardware, current implementations either require forward and
reverse lookup access t... | computer science |
13,923 | Update Strength in EDAs and ACO: How to Avoid Genetic Drift | cs.NE | We provide a rigorous runtime analysis concerning the update strength, a
vital parameter in probabilistic model-building GAs such as the step size $1/K$
in the compact Genetic Algorithm (cGA) and the evaporation factor $\rho$ in
ACO. While a large update strength is desirable for exploitation, there is a
general trade-... | computer science |
13,924 | Enhanced Boolean Correlation Matrix Memory | cs.NE | This paper introduces an Enhanced Boolean version of the Correlation Matrix
Memory (CMM), which is useful to work with binary memories. A novel Boolean
Orthonormalization Process (BOP) is presented to convert a non-orthonormal
Boolean basis, i.e., a set of non-orthonormal binary vectors (in a Boolean
sense) to an ortho... | computer science |
13,925 | mpEAd: Multi-Population EA Diagrams | cs.NE | Multi-population evolutionary algorithms are, by nature, highly complex and
difficult to describe. Even two populations working in concert (or opposition)
present a myriad of potential configurations that are often difficult to relate
using text alone. Little effort has been made, however, to depict these kinds
of syst... | computer science |
13,926 | Genetic Transfer or Population Diversification? Deciphering the Secret
Ingredients of Evolutionary Multitask Optimization | cs.NE | Evolutionary multitasking has recently emerged as a novel paradigm that
enables the similarities and/or latent complementarities (if present) between
distinct optimization tasks to be exploited in an autonomous manner simply by
solving them together with a unified solution representation scheme. An
important matter und... | computer science |
13,927 | Stochastic Backpropagation through Mixture Density Distributions | cs.NE | The ability to backpropagate stochastic gradients through continuous latent
distributions has been crucial to the emergence of variational autoencoders and
stochastic gradient variational Bayes. The key ingredient is an unbiased and
low-variance way of estimating gradients with respect to distribution
parameters from g... | computer science |
13,928 | Effective Connectivity-Based Neural Decoding: A Causal
Interaction-Driven Approach | cs.NE | We propose a geometric model-free causality measurebased on multivariate
delay embedding that can efficiently detect linear and nonlinear causal
interactions between time series with no prior information. We then exploit the
proposed causal interaction measure in real MEG data analysis. The results are
used to construc... | computer science |
13,929 | Heterogeneous Strategy Particle Swarm Optimization | cs.NE | PSO is a widely recognized optimization algorithm inspired by social swarm.
In this brief we present a heterogeneous strategy particle swarm optimization
(HSPSO), in which a proportion of particles adopt a fully informed strategy to
enhance the converging speed while the rest are singly informed to maintain the
diversi... | computer science |
13,930 | The Evolutionary Process of Image Transition in Conjunction with Box and
Strip Mutation | cs.NE | Evolutionary algorithms have been used in many ways to generate digital art.
We study how evolutionary processes are used for evolutionary art and present a
new approach to the transition of images. Our main idea is to define
evolutionary processes for digital image transition, combining different
variants of mutation ... | computer science |
13,931 | The BioDynaMo Project: a platform for computer simulations of biological
dynamics | cs.NE | This paper is a brief update on developments in the BioDynaMo project, a new
platform for computer simulations for biological research. We will discuss the
new capabilities of the simulator, important new concepts simulation
methodology as well as its numerous applications to the computational biology
and nanoscience c... | computer science |
13,932 | Student's t Distribution based Estimation of Distribution Algorithms for
Derivative-free Global Optimization | cs.NE | In this paper, we are concerned with a branch of evolutionary algorithms
termed estimation of distribution (EDA), which has been successfully used to
tackle derivative-free global optimization problems. For existent EDA
algorithms, it is a common practice to use a Gaussian distribution or a mixture
of Gaussian componen... | computer science |
13,933 | Dynamic Multi-Objectives Optimization with a Changing Number of
Objectives | cs.NE | Existing studies on dynamic multi-objective optimization focus on problems
with time-dependent objective functions, while the ones with a changing number
of objectives have rarely been considered in the literature. Instead of
changing the shape or position of the Pareto-optimal front/set when having
time-dependent obje... | computer science |
13,934 | Recurrent Neural Networks With Limited Numerical Precision | cs.NE | Recurrent Neural Networks (RNNs) produce state-of-art performance on many
machine learning tasks but their demand on resources in terms of memory and
computational power are often high. Therefore, there is a great interest in
optimizing the computations performed with these models especially when
considering developmen... | computer science |
13,935 | About Learning in Recurrent Bistable Gradient Networks | cs.NE | Recurrent Bistable Gradient Networks are attractor based neural networks
characterized by bistable dynamics of each single neuron. Coupled together
using linear interaction determined by the interconnection weights, these
networks do not suffer from spurious states or very limited capacity anymore.
Vladimir Chinarov an... | computer science |
13,936 | Matching-Based Selection with Incomplete Lists for Decomposition
Multi-Objective Optimization | cs.NE | The balance between convergence and diversity is a key issue of evolutionary
multi-objective optimization. The recently proposed stable matching-based
selection provides a new perspective to handle this balance under the framework
of decomposition multi-objective optimization. In particular, the stable
matching between... | computer science |
13,937 | Training Deep Spiking Neural Networks using Backpropagation | cs.NE | Deep spiking neural networks (SNNs) hold great potential for improving the
latency and energy efficiency of deep neural networks through event-based
computation. However, training such networks is difficult due to the
non-differentiable nature of asynchronous spike events. In this paper, we
introduce a novel technique,... | computer science |
13,938 | Uniform Transformation of Non-Separable Probability Distributions | cs.NE | A theoretical framework is developed to describe the transformation that
distributes probability density functions uniformly over space. In one
dimension, the cumulative distribution can be used, but does not generalize to
higher dimensions, or non-separable distributions. A potential function is
shown to link probabil... | computer science |
13,939 | Fast and Efficient Asynchronous Neural Computation with Adapting Spiking
Neural Networks | cs.NE | Biological neurons communicate with a sparing exchange of pulses - spikes. It
is an open question how real spiking neurons produce the kind of powerful
neural computation that is possible with deep artificial neural networks, using
only so very few spikes to communicate. Building on recent insights in
neuroscience, we ... | computer science |
13,940 | A Tutorial about Random Neural Networks in Supervised Learning | cs.NE | Random Neural Networks (RNNs) are a class of Neural Networks (NNs) that can
also be seen as a specific type of queuing network. They have been successfully
used in several domains during the last 25 years, as queuing networks to
analyze the performance of resource sharing in many engineering areas, as
learning tools an... | computer science |
13,941 | Low Complexity Multiply Accumulate Unit for Weight-Sharing Convolutional
Neural Networks | cs.NE | Convolutional Neural Networks (CNNs) are one of the most successful deep
machine learning technologies for processing image, voice and video data. CNNs
require large amounts of processing capacity and memory, which can exceed the
resources of low power mobile and embedded systems. Several designs for
hardware accelerat... | computer science |
13,942 | Sooner than Expected: Hitting the Wall of Complexity in Evolution | cs.NE | In evolutionary robotics an encoding of the control software, which maps
sensor data (input) to motor control values (output), is shaped by stochastic
optimization methods to complete a predefined task. This approach is assumed to
be beneficial compared to standard methods of controller design in those cases
where no a... | computer science |
13,943 | Reactive Collision Avoidance using Evolutionary Neural Networks | cs.NE | Collision avoidance systems can play a vital role in reducing the number of
accidents and saving human lives. In this paper, we introduce and validate a
novel method for vehicles reactive collision avoidance using evolutionary
neural networks (ENN). A single front-facing rangefinder sensor is the only
input required by... | computer science |
13,944 | Proposal for a Leaky-Integrate-Fire Spiking Neuron based on
Magneto-Electric Switching of Ferro-magnets | cs.NE | The efficiency of the human brain in performing classification tasks has
attracted considerable research interest in brain-inspired neuromorphic
computing. Hardware implementations of a neuromorphic system aims to mimic the
computations in the brain through interconnection of neurons and synaptic
weights. A leaky-integ... | computer science |
13,945 | Adaptive Neuron Apoptosis for Accelerating Deep Learning on Large Scale
Systems | cs.NE | We present novel techniques to accelerate the convergence of Deep Learning
algorithms by conducting low overhead removal of redundant neurons -- apoptosis
of neurons -- which do not contribute to model learning, during the training
phase itself. We provide in-depth theoretical underpinnings of our heuristics
(bounding ... | computer science |
13,946 | LAYERS: Yet another Neural Network toolkit | cs.NE | Layers is an open source neural network toolkit aim at providing an easy way
to implement modern neural networks. The main user target are students and to
this end layers provides an easy scriptting language that can be early adopted.
The user has to focus only on design details as network totpology and parameter
tunni... | computer science |
13,947 | Nonlinear Systems Identification Using Deep Dynamic Neural Networks | cs.NE | Neural networks are known to be effective function approximators. Recently,
deep neural networks have proven to be very effective in pattern recognition,
classification tasks and human-level control to model highly nonlinear
realworld systems. This paper investigates the effectiveness of deep neural
networks in the mod... | computer science |
13,948 | Investigating the effects Diversity Mechanisms have on Evolutionary
Algorithms in Dynamic Environments | cs.NE | Evolutionary algorithms have been successfully applied to a variety of
optimisation problems in stationary environments. However, many real world
optimisation problems are set in dynamic environments where the success
criteria shifts regularly. Population diversity affects algorithmic
performance, particularly on multi... | computer science |
13,949 | Evolving the Structure of Evolution Strategies | cs.NE | Various variants of the well known Covariance Matrix Adaptation Evolution
Strategy (CMA-ES) have been proposed recently, which improve the empirical
performance of the original algorithm by structural modifications. However, in
practice it is often unclear which variation is best suited to the specific
optimization pro... | computer science |
13,950 | Using Centroidal Voronoi Tessellations to Scale Up the Multi-dimensional
Archive of Phenotypic Elites Algorithm | cs.NE | The recently introduced Multi-dimensional Archive of Phenotypic Elites
(MAP-Elites) is an evolutionary algorithm capable of producing a large archive
of diverse, high-performing solutions in a single run. It works by discretizing
a continuous feature space into unique regions according to the desired
discretization per... | computer science |
13,951 | Building Energy Load Forecasting using Deep Neural Networks | cs.NE | Ensuring sustainability demands more efficient energy management with
minimized energy wastage. Therefore, the power grid of the future should
provide an unprecedented level of flexibility in energy management. To that
end, intelligent decision making requires accurate predictions of future energy
demand/load, both at ... | computer science |
13,952 | Surrogate-Assisted Partial Order-based Evolutionary Optimisation | cs.NE | In this paper, we propose a novel approach (SAPEO) to support the survival
selection process in multi-objective evolutionary algorithms with surrogate
models - it dynamically chooses individuals to evaluate exactly based on the
model uncertainty and the distinctness of the population. We introduce variants
that differ ... | computer science |
13,953 | Alternating Direction Method of Multipliers for Sparse Convolutional
Neural Networks | cs.NE | The storage and computation requirements of Convolutional Neural Networks
(CNNs) can be prohibitive for exploiting these models over low-power or
embedded devices. This paper reduces the computational complexity of the CNNs
by minimizing an objective function, including the recognition loss that is
augmented with a spa... | computer science |
13,954 | Sigma Delta Quantized Networks | cs.NE | Deep neural networks can be obscenely wasteful. When processing video, a
convolutional network expends a fixed amount of computation for each frame with
no regard to the similarity between neighbouring frames. As a result, it ends
up repeatedly doing very similar computations. To put an end to such waste, we
introduce ... | computer science |
13,955 | Bio-Inspired Spiking Convolutional Neural Network using Layer-wise
Sparse Coding and STDP Learning | cs.NE | Hierarchical feature discovery using non-spiking convolutional neural
networks (CNNs) has attracted much recent interest in machine learning and
computer vision. However, it is still not well understood how to create a
biologically plausible network of brain-like, spiking neurons with multi-layer,
unsupervised learning... | computer science |
13,956 | Computing threshold functions using dendrites | cs.NE | Neurons, modeled as linear threshold unit (LTU), can in theory compute all
thresh- old functions. In practice, however, some of these functions require
synaptic weights of arbitrary large precision. We show here that dendrites can
alleviate this requirement. We introduce here the non-Linear Threshold Unit
(nLTU) that i... | computer science |
13,957 | Intrinsic Geometric Information Transfer Learning on Multiple
Graph-Structured Datasets | cs.NE | Graphs provide a powerful means for representing complex interactions between
entities. Recently, deep learning approaches are emerging for representing and
modeling graph-structured data, although the conventional deep learning methods
(such as convolutional neural networks and recurrent neural networks) have
mainly f... | computer science |
13,958 | Differentiable Genetic Programming | cs.NE | We introduce the use of high order automatic differentiation, implemented via
the algebra of truncated Taylor polynomials, in genetic programming. Using the
Cartesian Genetic Programming encoding we obtain a high-order Taylor
representation of the program output that is then used to back-propagate errors
during learnin... | computer science |
13,959 | Recurrent Neural Networks With Limited Numerical Precision | cs.NE | Recurrent Neural Networks (RNNs) produce state-of-art performance on many
machine learning tasks but their demand on resources in terms of memory and
computational power are often high. Therefore, there is a great interest in
optimizing the computations performed with these models especially when
considering developmen... | computer science |
13,960 | PlatEMO: A MATLAB Platform for Evolutionary Multi-Objective Optimization | cs.NE | Over the last three decades, a large number of evolutionary algorithms have
been developed for solving multiobjective optimization problems. However, there
lacks an up-to-date and comprehensive software platform for researchers to
properly benchmark existing algorithms and for practitioners to apply selected
algorithms... | computer science |
13,961 | Subpopulation Diversity Based Selecting Migration Moment in Distributed
Evolutionary Algorithms | cs.NE | In distributed evolutionary algorithms, migration interval is used to decide
migration moments. Nevertheless, migration moments predetermined by intervals
cannot match the dynamic situation of evolution. In this paper, a scheme of
setting the success rate of migration based on subpopulation diversity at each
interval i... | computer science |
13,962 | Membrane-Dependent Neuromorphic Learning Rule for Unsupervised Spike
Pattern Detection | cs.NE | Several learning rules for synaptic plasticity, that depend on either spike
timing or internal state variables, have been proposed in the past imparting
varying computational capabilities to Spiking Neural Networks. Due to design
complications these learning rules are typically not implemented on
neuromorphic devices l... | computer science |
13,963 | Classification Accuracy Improvement for Neuromorphic Computing Systems
with One-level Precision Synapses | cs.NE | Brain inspired neuromorphic computing has demonstrated remarkable advantages
over traditional von Neumann architecture for its high energy efficiency and
parallel data processing. However, the limited resolution of synaptic weights
degrades system accuracy and thus impedes the use of neuromorphic systems. In
this work,... | computer science |
13,964 | Using LLVM-based JIT Compilation in Genetic Programming | cs.NE | The paper describes an approach to implementing genetic programming, which
uses the LLVM library to just-in-time compile/interpret the evolved abstract
syntax trees. The solution is described in some detail, including a parser
(based on FlexC++ and BisonC++) that can construct the trees from a simple toy
language with ... | computer science |
13,965 | Integration of Preferences in Decomposition Multi-Objective Optimization | cs.NE | Most existing studies on evolutionary multi-objective optimization focus on
approximating the whole Pareto-optimal front. Nevertheless, rather than the
whole front, which demands for too many points (especially in a
high-dimensional space), the decision maker might only interest in a partial
region, called the region o... | computer science |
13,966 | Robust Particle Swarm Optimizer based on Chemomimicry | cs.NE | A particle swarm optimizer (PSO) loosely based on the phenomena of
crystallization and a chaos factor which follows the complimentary error
function is described. The method features three phases: diffusion, directed
motion, and nucleation. During the diffusion phase random walk is the only
contributor to particle moti... | computer science |
13,967 | Multitask Evolution with Cartesian Genetic Programming | cs.NE | We introduce a genetic programming method for solving multiple Boolean
circuit synthesis tasks simultaneously. This allows us to solve a set of
elementary logic functions twice as easily as with a direct, single-task
approach. | computer science |
13,968 | Stochastic Configuration Networks: Fundamentals and Algorithms | cs.NE | This paper contributes to a development of randomized methods for neural
networks. The proposed learner model is generated incrementally by stochastic
configuration (SC) algorithms, termed as Stochastic Configuration Networks
(SCNs). In contrast to the existing randomised learning algorithms for single
layer feed-forwa... | computer science |
13,969 | Whale swarm algorithm for function optimization | cs.NE | Increasing nature-inspired metaheuristic algorithms are applied to solving
the real-world optimization problems, as they have some advantages over the
classical methods of numerical optimization. This paper has proposed a new
nature-inspired metaheuristic called Whale Swarm Algorithm for function
optimization, which is... | computer science |
13,970 | Hierarchy Influenced Differential Evolution: A Motor Operation Inspired
Approach | cs.NE | Operational maturity of biological control systems have fuelled the
inspiration for a large number of mathematical and logical models for control,
automation and optimisation. The human brain represents the most sophisticated
control architecture known to us and is a central motivation for several
research attempts acr... | computer science |
13,971 | Toward Abstraction from Multi-modal Data: Empirical Studies on Multiple
Time-scale Recurrent Models | cs.NE | The abstraction tasks are challenging for multi- modal sequences as they
require a deeper semantic understanding and a novel text generation for the
data. Although the recurrent neural networks (RNN) can be used to model the
context of the time-sequences, in most cases the long-term dependencies of
multi-modal data mak... | computer science |
13,972 | Analyzing and Exploiting NARX Recurrent Neural Networks for Long-Term
Dependencies | cs.NE | Recurrent neural networks (RNNs) have achieved state-of-the-art performance
on many diverse tasks, from machine translation to surgical activity
recognition, yet training RNNs to capture long-term dependencies remains
difficult. To date, the vast majority of successful RNN architectures alleviate
this problem by facili... | computer science |
13,973 | Improving the Neural GPU Architecture for Algorithm Learning | cs.NE | Algorithm learning is a core problem in artificial intelligence with
significant implications on automation level that can be achieved by machines.
Recently deep learning methods are emerging for synthesizing an algorithm from
its input-output examples, the most successful being the Neural GPU, capable of
learning mult... | computer science |
13,974 | Co-evolutionary multi-task learning for dynamic time series prediction | cs.NE | Multi-task learning employs shared representation of knowledge for learning
multiple instances from the same or related problems. Time series prediction
consists of several instances that are defined by the way they are broken down
into fixed windows known as embedding dimension. Finding the optimal values for
embeddin... | computer science |
13,975 | Neuromorphic Hardware In The Loop: Training a Deep Spiking Network on
the BrainScaleS Wafer-Scale System | cs.NE | Emulating spiking neural networks on analog neuromorphic hardware offers
several advantages over simulating them on conventional computers, particularly
in terms of speed and energy consumption. However, this usually comes at the
cost of reduced control over the dynamics of the emulated networks. In this
paper, we demo... | computer science |
13,976 | Fast Genetic Algorithms | cs.NE | For genetic algorithms using a bit-string representation of length~$n$, the
general recommendation is to take $1/n$ as mutation rate. In this work, we
discuss whether this is really justified for multimodal functions. Taking jump
functions and the $(1+1)$ evolutionary algorithm as the simplest example, we
observe that ... | computer science |
13,977 | Evolutionary Image Composition Using Feature Covariance Matrices | cs.NE | Evolutionary algorithms have recently been used to create a wide range of
artistic work. In this paper, we propose a new approach for the composition of
new images from existing ones, that retain some salient features of the
original images. We introduce evolutionary algorithms that create new images
based on a fitness... | computer science |
13,978 | Comparison of echo state network output layer classification methods on
noisy data | cs.NE | Echo state networks are a recently developed type of recurrent neural network
where the internal layer is fixed with random weights, and only the output
layer is trained on specific data. Echo state networks are increasingly being
used to process spatiotemporal data in real-world settings, including speech
recognition,... | computer science |
13,979 | Large Scale Evolution of Convolutional Neural Networks Using Volunteer
Computing | cs.NE | This work presents a new algorithm called evolutionary exploration of
augmenting convolutional topologies (EXACT), which is capable of evolving the
structure of convolutional neural networks (CNNs). EXACT is in part modeled
after the neuroevolution of augmenting topologies (NEAT) algorithm, with
notable exceptions to a... | computer science |
13,980 | Reservoir Computing and Extreme Learning Machines using Pairs of
Cellular Automata Rules | cs.NE | A framework for implementing reservoir computing (RC) and extreme learning
machines (ELMs), two types of artificial neural networks, based on 1D
elementary Cellular Automata (CA) is presented, in which two separate CA rules
explicitly implement the minimum computational requirements of the reservoir
layer: hyperdimensi... | computer science |
13,981 | An Adaptive Framework to Tune the Coordinate Systems in Evolutionary
Algorithms | cs.NE | In the evolutionary computation research community, the performance of most
evolutionary algorithms (EAs) depends strongly on their implemented coordinate
system. However, the commonly used coordinate system is fixed and not well
suited for different function landscapes, EAs thus might not search
efficiently. To overco... | computer science |
13,982 | Empirical Analysis of the Necessary and Sufficient Conditions of the
Echo State Property | cs.NE | The Echo State Network (ESN) is a specific recurrent network, which has
gained popularity during the last years. The model has a recurrent network
named reservoir, that is fixed during the learning process. The reservoir is
used for transforming the input space in a larger space. A fundamental property
that provokes an... | computer science |
13,983 | Evolving Parsimonious Networks by Mixing Activation Functions | cs.NE | Neuroevolution methods evolve the weights of a neural network, and in some
cases the topology, but little work has been done to analyze the effect of
evolving the activation functions of individual nodes on network size, which is
important when training networks with a small number of samples. In this work
we extend th... | computer science |
13,984 | Long-Term Evolution of Genetic Programming Populations | cs.NE | We evolve binary mux-6 trees for up to 100000 generations evolving some
programs with more than a hundred million nodes. Our unbounded Long-Term
Evolution Experiment LTEE GP appears not to evolve building blocks but does
suggests a limit to bloat. We do see periods of tens even hundreds of
generations where the populat... | computer science |
13,985 | PonyGE2: Grammatical Evolution in Python | cs.NE | Grammatical Evolution (GE) is a population-based evolutionary algorithm,
where a formal grammar is used in the genotype to phenotype mapping process.
PonyGE2 is an open source implementation of GE in Python, developed at UCD's
Natural Computing Research and Applications group. It is intended as an
advertisement and a s... | computer science |
13,986 | Balancing Selection Pressures, Multiple Objectives, and Neural
Modularity to Coevolve Cooperative Agent Behavior | cs.NE | Previous research using evolutionary computation in Multi-Agent Systems
indicates that assigning fitness based on team vs.\ individual behavior has a
strong impact on the ability of evolved teams of artificial agents to exhibit
teamwork in challenging tasks. However, such research only made use of
single-objective evol... | computer science |
13,987 | Deep Deterministic Policy Gradient for Urban Traffic Light Control | cs.NE | Traffic light timing optimization is still an active line of research despite
the wealth of scientific literature on the topic, and the problem remains
unsolved for any non-toy scenario. One of the key issues with traffic light
optimization is the large scale of the input information that is available for
the controlli... | computer science |
13,988 | Experimental Analysis of Design Elements of Scalarizing Functions-based
Multiobjective Evolutionary Algorithms | cs.NE | In this paper we systematically study the importance, i.e., the influence on
performance, of the main design elements that differentiate scalarizing
functions-based multiobjective evolutionary algorithms (MOEAs). This class of
MOEAs includes Multiobjecitve Genetic Local Search (MOGLS) and Multiobjective
Evolutionary Al... | computer science |
13,989 | Experience-based Optimization: A Coevolutionary Approach | cs.NE | This paper studies improving solvers based on their past solving experiences,
and focuses on improving solvers by offline training. Specifically, the key
issues of offline training methods are discussed, and research belonging to
this category but from different areas are reviewed in a unified framework.
Existing train... | computer science |
13,990 | Hierarchical Surrogate Modeling for Illumination Algorithms | cs.NE | Evolutionary illumination is a recent technique that allows producing many
diverse, optimal solutions in a map of manually defined features. To support
the large amount of objective function evaluations, surrogate model assistance
was recently introduced. Illumination models need to represent many more,
diverse optimal... | computer science |
13,991 | Upper Bounds on the Runtime of the Univariate Marginal Distribution
Algorithm on OneMax | cs.NE | A runtime analysis of the Univariate Marginal Distribution Algorithm (UMDA)
is presented on the OneMax function for wide ranges of its parameters $\mu$ and
$\lambda$. If $\mu\ge c\log n$ for some constant $c>0$ and
$\lambda=(1+\Theta(1))\mu$, a general bound $O(\mu n)$ on the expected runtime
is obtained. This bound cr... | computer science |
13,992 | A Brownian Motion Model and Extreme Belief Machine for Modeling Sensor
Data Measurements | cs.NE | As the title suggests, we will describe (and justify through the presentation
of some of the relevant mathematics) prediction methodologies for sensor
measurements. This exposition will mainly be concerned with the mathematics
related to modeling the sensor measurements. | computer science |
13,993 | A Genetic Programming Approach to Designing Convolutional Neural Network
Architectures | cs.NE | The convolutional neural network (CNN), which is one of the deep learning
models, has seen much success in a variety of computer vision tasks. However,
designing CNN architectures still requires expert knowledge and a lot of trial
and error. In this paper, we attempt to automatically construct CNN
architectures for an ... | computer science |
13,994 | A Software-equivalent SNN Hardware using RRAM-array for Asynchronous
Real-time Learning | cs.NE | Spiking Neural Network (SNN) naturally inspires hardware implementation as it
is based on biology. For learning, spike time dependent plasticity (STDP) may
be implemented using an energy efficient waveform superposition on memristor
based synapse. However, system level implementation has three challenges.
First, a clas... | computer science |
13,995 | The (1+$λ$) Evolutionary Algorithm with Self-Adjusting Mutation
Rate | cs.NE | We propose a new way to self-adjust the mutation rate in population-based
evolutionary algorithms in discrete search spaces. Roughly speaking, it
consists of creating half the offspring with a mutation rate that is twice the
current mutation rate and the other half with half the current rate. The
mutation rate is then ... | computer science |
13,996 | Evolutionary Many-Objective Optimization Based on Adversarial
Decomposition | cs.NE | The decomposition-based method has been recognized as a major approach for
multi-objective optimization. It decomposes a multi-objective optimization
problem into several single-objective optimization subproblems, each of which
is usually defined as a scalarizing function using a weight vector. Due to the
characteristi... | computer science |
13,997 | Automated Curriculum Learning for Neural Networks | cs.NE | We introduce a method for automatically selecting the path, or syllabus, that
a neural network follows through a curriculum so as to maximise learning
efficiency. A measure of the amount that the network learns from each data
sample is provided as a reward signal to a nonstationary multi-armed bandit
algorithm, which t... | computer science |
13,998 | Improving Fitness Functions in Genetic Programming for Classification on
Unbalanced Credit Card Datasets | cs.NE | Credit card fraud detection based on machine learning has recently attracted
considerable interest from the research community. One of the most important
tasks in this area is the ability of classifiers to handle the imbalance in
credit card data. In this scenario, classifiers tend to yield poor accuracy on
the fraud c... | computer science |
13,999 | ApproxDBN: Approximate Computing for Discriminative Deep Belief Networks | cs.NE | Probabilistic generative neural networks are useful for many applications,
such as image classification, speech recognition and occlusion removal.
However, the power budget for hardware implementations of neural networks can
be extremely tight. To address this challenge we describe a design methodology
for using approx... | computer science |
14,000 | A Search for Improved Performance in Regular Expressions | cs.NE | The primary aim of automated performance improvement is to reduce the running
time of programs while maintaining (or improving on) functionality. In this
paper, Genetic Programming is used to find performance improvements in regular
expressions for an array of target programs, representing the first application
of auto... | computer science |
14,001 | Runtime Analysis of the $(1+(λ,λ))$ Genetic Algorithm on
Random Satisfiable 3-CNF Formulas | cs.NE | The $(1+(\lambda,\lambda))$ genetic algorithm, first proposed at GECCO 2013,
showed a surprisingly good performance on so me optimization problems. The
theoretical analysis so far was restricted to the OneMax test function, where
this GA profited from the perfect fitness-distance correlation. In this work,
we conduct a... | computer science |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.