103 research outputs found
Evolino for recurrent support vector machines
Traditional Support Vector Machines (SVMs) need pre-wired finite time windows
to predict and classify time series. They do not have an internal state
necessary to deal with sequences involving arbitrary long-term dependencies.
Here we introduce a new class of recurrent, truly sequential SVM-like devices
with internal adaptive states, trained by a novel method called EVOlution of
systems with KErnel-based outputs (Evoke), an instance of the recent Evolino
class of methods. Evoke evolves recurrent neural networks to detect and
represent temporal dependencies while using quadratic programming/support
vector regression to produce precise outputs. Evoke is the first SVM-based
mechanism learning to classify a context-sensitive language. It also
outperforms recent state-of-the-art gradient-based recurrent neural networks
(RNNs) on various time series prediction tasks.Comment: 10 pages, 2 figure
Evolving Graphs by Graph Programming
Graphs are a ubiquitous data structure in computer science and can be used to represent solutions to difficult problems in many distinct domains. This motivates the use of Evolutionary Algorithms to search over graphs and efficiently find approximate solutions. However, existing techniques often represent and manipulate graphs in an ad-hoc manner. In contrast, rule-based graph programming offers a formal mechanism for describing relations over graphs.
This thesis proposes the use of rule-based graph programming for representing and implementing genetic operators over graphs. We present the Evolutionary Algorithm Evolving Graphs by Graph Programming and a number of its extensions which are capable of learning stateful and stateless digital circuits, symbolic expressions and Artificial Neural Networks. We demonstrate that rule-based graph programming may be used to implement new and effective constraint-respecting mutation operators and show that these operators may strictly generalise others found in the literature. Through our proposal of Semantic Neutral Drift, we accelerate the search process by building plateaus into the fitness landscape using domain knowledge of equivalence. We also present Horizontal Gene Transfer, a mechanism whereby graphs may be passively recombined without disrupting their fitness.
Through rigorous evaluation and analysis of over 20,000 independent executions of Evolutionary Algorithms, we establish numerous benefits of our approach. We find that on many problems, Evolving Graphs by Graph Programming and its variants may significantly outperform other approaches from the literature. Additionally, our empirical results provide further evidence that neutral drift aids the efficiency of evolutionary search
Artificial Neurogenesis: An Introduction and Selective Review
International audienceIn this introduction and review—like in the book which follows—we explore the hypothesis that adaptive growth is a means of producing brain-like machines. The emulation of neural development can incorporate desirable characteristics of natural neural systems into engineered designs. The introduction begins with a review of neural development and neural models. Next, artificial development— the use of a developmentally-inspired stage in engineering design—is introduced. Several strategies for performing this " meta-design " for artificial neural systems are reviewed. This work is divided into three main categories: bio-inspired representations ; developmental systems; and epigenetic simulations. Several specific network biases and their benefits to neural network design are identified in these contexts. In particular, several recent studies show a strong synergy, sometimes interchange-ability, between developmental and epigenetic processes—a topic that has remained largely under-explored in the literature
Evolutionary design of deep neural networks
MenciĂłn Internacional en el tĂtulo de doctorFor three decades, neuroevolution has applied evolutionary computation to the optimization of
the topology of artificial neural networks, with most works focusing on very simple architectures.
However, times have changed, and nowadays convolutional neural networks are the industry and
academia standard for solving a variety of problems, many of which remained unsolved before the
discovery of this kind of networks.
Convolutional neural networks involve complex topologies, and the manual design of these
topologies for solving a problem at hand is expensive and inefficient. In this thesis, our aim is to
use neuroevolution in order to evolve the architecture of convolutional neural networks.
To do so, we have decided to try two different techniques: genetic algorithms and grammatical
evolution. We have implemented a niching scheme for preserving the genetic diversity, in order
to ease the construction of ensembles of neural networks. These techniques have been validated
against the MNIST database for handwritten digit recognition, achieving a test error rate of 0.28%,
and the OPPORTUNITY data set for human activity recognition, attaining an F1 score of 0.9275.
Both results have proven very competitive when compared with the state of the art. Also, in all
cases, ensembles have proven to perform better than individual models.
Later, the topologies learned for MNIST were tested on EMNIST, a database recently introduced
in 2017, which includes more samples and a set of letters for character recognition. Results have
shown that the topologies optimized for MNIST perform well on EMNIST, proving that architectures
can be reused across domains with similar characteristics.
In summary, neuroevolution is an effective approach for automatically designing topologies for
convolutional neural networks. However, it still remains as an unexplored field due to hardware
limitations. Current advances, however, should constitute the fuel that empowers the emergence of
this field, and further research should start as of today.This Ph.D. dissertation has been partially supported by the Spanish Ministry of Education, Culture and Sports under FPU fellowship with identifier FPU13/03917.
This research stay has been partially co-funded by the Spanish Ministry of Education, Culture and Sports under FPU short stay grant with identifier EST15/00260.Programa Oficial de Doctorado en Ciencia y TecnologĂa InformáticaPresidente: MarĂa Araceli SanchĂs de Miguel.- Secretario: Francisco Javier Segovia PĂ©rez.- Vocal: Simon Luca
Neuroevolution in Deep Neural Networks: Current Trends and Future Challenges
A variety of methods have been applied to the architectural configuration and
learning or training of artificial deep neural networks (DNN). These methods
play a crucial role in the success or failure of the DNN for most problems and
applications. Evolutionary Algorithms (EAs) are gaining momentum as a
computationally feasible method for the automated optimisation and training of
DNNs. Neuroevolution is a term which describes these processes of automated
configuration and training of DNNs using EAs. While many works exist in the
literature, no comprehensive surveys currently exist focusing exclusively on
the strengths and limitations of using neuroevolution approaches in DNNs.
Prolonged absence of such surveys can lead to a disjointed and fragmented field
preventing DNNs researchers potentially adopting neuroevolutionary methods in
their own research, resulting in lost opportunities for improving performance
and wider application within real-world deep learning problems. This paper
presents a comprehensive survey, discussion and evaluation of the
state-of-the-art works on using EAs for architectural configuration and
training of DNNs. Based on this survey, the paper highlights the most pertinent
current issues and challenges in neuroevolution and identifies multiple
promising future research directions.Comment: 20 pages (double column), 2 figures, 3 tables, 157 reference
Is Neuro-Symbolic AI Meeting its Promise in Natural Language Processing? A Structured Review
Advocates for Neuro-Symbolic Artificial Intelligence (NeSy) assert that
combining deep learning with symbolic reasoning will lead to stronger AI than
either paradigm on its own. As successful as deep learning has been, it is
generally accepted that even our best deep learning systems are not very good
at abstract reasoning. And since reasoning is inextricably linked to language,
it makes intuitive sense that Natural Language Processing (NLP), would be a
particularly well-suited candidate for NeSy. We conduct a structured review of
studies implementing NeSy for NLP, with the aim of answering the question of
whether NeSy is indeed meeting its promises: reasoning, out-of-distribution
generalization, interpretability, learning and reasoning from small data, and
transferability to new domains. We examine the impact of knowledge
representation, such as rules and semantic networks, language structure and
relational structure, and whether implicit or explicit reasoning contributes to
higher promise scores. We find that systems where logic is compiled into the
neural network lead to the most NeSy goals being satisfied, while other factors
such as knowledge representation, or type of neural architecture do not exhibit
a clear correlation with goals being met. We find many discrepancies in how
reasoning is defined, specifically in relation to human level reasoning, which
impact decisions about model architectures and drive conclusions which are not
always consistent across studies. Hence we advocate for a more methodical
approach to the application of theories of human reasoning as well as the
development of appropriate benchmarks, which we hope can lead to a better
understanding of progress in the field. We make our data and code available on
github for further analysis.Comment: Surve
Genetic Evolution of Neural Networks
Evolutionary computation has been around ever since the late 50s. This thesis aims at elaborate on genetic algorithms, a subset of evolutionary computation, with particular regard to the field of neuroevolution, which is the application of GAs to the generation of functioning neural networks. The most widely adopted techniques are thereby explained and contrasted. The experimentation chapter finally shows an implementation of a genetic algorithm, inspired by existing algorithms, with the objective of optimizing a novel kind of artificial neural network
Fast Neural Models for Symbolic Regression at Scale
Deep learning owes much of its success to the astonishing expressiveness of
neural networks. However, this comes at the cost of complex, black-boxed models
that extrapolate poorly beyond the domain of the training dataset, conflicting
with goals of finding analytic expressions to describe science, engineering and
real world data. Under the hypothesis that the hierarchical modularity of such
laws can be captured by training a neural network, we introduce OccamNet, a
neural network model that finds interpretable, compact, and sparse solutions
for fitting data, \`{a} la Occam's razor. Our model defines a probability
distribution over a non-differentiable function space. We introduce a two-step
optimization method that samples functions and updates the weights with
backpropagation based on cross-entropy matching in an evolutionary strategy: we
train by biasing the probability mass toward better fitting solutions. OccamNet
is able to fit a variety of symbolic laws including simple analytic functions,
recursive programs, implicit functions, simple image classification, and can
outperform noticeably state-of-the-art symbolic regression methods on real
world regression datasets. Our method requires minimal memory footprint, does
not require AI accelerators for efficient training, fits complicated functions
in minutes of training on a single CPU, and demonstrates significant
performance gains when scaled on a GPU. Our implementation, demonstrations and
instructions for reproducing the experiments are available at
https://github.com/druidowm/OccamNet_Public
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