1,167 research outputs found
Metaheuristic design of feedforward neural networks: a review of two decades of research
Over the past two decades, the feedforward neural network (FNN) optimization has been a key interest among the researchers and practitioners of multiple disciplines. The FNN optimization is often viewed from the various perspectives: the optimization of weights, network architecture, activation nodes, learning parameters, learning environment, etc. Researchers adopted such different viewpoints mainly to improve the FNN's generalization ability. The gradient-descent algorithm such as backpropagation has been widely applied to optimize the FNNs. Its success is evident from the FNN's application to numerous real-world problems. However, due to the limitations of the gradient-based optimization methods, the metaheuristic algorithms including the evolutionary algorithms, swarm intelligence, etc., are still being widely explored by the researchers aiming to obtain generalized FNN for a given problem. This article attempts to summarize a broad spectrum of FNN optimization methodologies including conventional and metaheuristic approaches. This article also tries to connect various research directions emerged out of the FNN optimization practices, such as evolving neural network (NN), cooperative coevolution NN, complex-valued NN, deep learning, extreme learning machine, quantum NN, etc. Additionally, it provides interesting research challenges for future research to cope-up with the present information processing era
ES-ENAS: Blackbox Optimization over Hybrid Spaces via Combinatorial and Continuous Evolution
We consider the problem of efficient blackbox optimization over a large
hybrid search space, consisting of a mixture of a high dimensional continuous
space and a complex combinatorial space. Such examples arise commonly in
evolutionary computation, but also more recently, neuroevolution and
architecture search for Reinforcement Learning (RL) policies. Unfortunately
however, previous mutation-based approaches suffer in high dimensional
continuous spaces both theoretically and practically. We thus instead propose
ES-ENAS, a simple joint optimization procedure by combining Evolutionary
Strategies (ES) and combinatorial optimization techniques in a highly scalable
and intuitive way, inspired by the one-shot or supernet paradigm introduced in
Efficient Neural Architecture Search (ENAS). Through this relatively simple
marriage between two different lines of research, we are able to gain the best
of both worlds, and empirically demonstrate our approach by optimizing BBOB
functions over hybrid spaces as well as combinatorial neural network
architectures via edge pruning and quantization on popular RL benchmarks. Due
to the modularity of the algorithm, we also are able incorporate a wide variety
of popular techniques ranging from use of different continuous and
combinatorial optimizers, as well as constrained optimization.Comment: 22 pages. See
https://github.com/google-research/google-research/tree/master/es_enas for
associated cod
Learning to infer: RL-based search for DNN primitive selection on Heterogeneous Embedded Systems
Deep Learning is increasingly being adopted by industry for computer vision
applications running on embedded devices. While Convolutional Neural Networks'
accuracy has achieved a mature and remarkable state, inference latency and
throughput are a major concern especially when targeting low-cost and low-power
embedded platforms. CNNs' inference latency may become a bottleneck for Deep
Learning adoption by industry, as it is a crucial specification for many
real-time processes. Furthermore, deployment of CNNs across heterogeneous
platforms presents major compatibility issues due to vendor-specific technology
and acceleration libraries. In this work, we present QS-DNN, a fully automatic
search based on Reinforcement Learning which, combined with an inference engine
optimizer, efficiently explores through the design space and empirically finds
the optimal combinations of libraries and primitives to speed up the inference
of CNNs on heterogeneous embedded devices. We show that, an optimized
combination can achieve 45x speedup in inference latency on CPU compared to a
dependency-free baseline and 2x on average on GPGPU compared to the best vendor
library. Further, we demonstrate that, the quality of results and time
"to-solution" is much better than with Random Search and achieves up to 15x
better results for a short-time search
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