神经与进化计算学术速递[1.10]
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cs.NE神经与进化计算，共计5篇
【1】 Neural Network Optimization for Reinforcement Learning Tasks Using Sparse Computations
标题：基于稀疏计算的强化学习任务神经网络优化
链接：https://arxiv.org/abs/2201.02571
摘要：This article proposes a sparse computation-based method for optimizing neural
networks for reinforcement learning (RL) tasks. This method combines two ideas:
neural network pruning and taking into account input data correlations; it
makes it possible to update neuron states only when changes in them exceed a
certain threshold. It significantly reduces the number of multiplications when
running neural networks. We tested different RL tasks and achieved 20-150x
reduction in the number of multiplications. There were no substantial
performance losses; sometimes the performance even improved.
【2】 Improving Surrogate Gradient Learning in Spiking Neural Networks via Regularization and Normalization
标题：用正则化和归一化改进尖峰神经网络的代理梯度学习
链接：https://arxiv.org/abs/2201.02538
备注：Bachelor Thesis
摘要：Spiking neural networks (SNNs) are different from the classical networks used
in deep learning: the neurons communicate using electrical impulses called
spikes, just like biological neurons. SNNs are appealing for AI technology,
because they could be implemented on low power neuromorphic chips. However,
SNNs generally remain less accurate than their analog counterparts. In this
report, we examine various regularization and normalization techniques with the
goal of improving surrogate gradient learning in SNNs.
【3】 Time Series Forecasting Using Fuzzy Cognitive Maps: A Survey
标题：基于模糊认知图的时间序列预测研究综述
链接：https://arxiv.org/abs/2201.02297
摘要：Among various soft computing approaches for time series forecasting, Fuzzy
Cognitive Maps (FCM) have shown remarkable results as a tool to model and
analyze the dynamics of complex systems. FCM have similarities to recurrent
neural networks and can be classified as a neuro-fuzzy method. In other words,
FCMs are a mixture of fuzzy logic, neural network, and expert system aspects,
which act as a powerful tool for simulating and studying the dynamic behavior
of complex systems. The most interesting features are knowledge
interpretability, dynamic characteristics and learning capability. The goal of
this survey paper is mainly to present an overview on the most relevant and
recent FCM-based time series forecasting models proposed in the literature. In
addition, this article considers an introduction on the fundamentals of FCM
model and learning methodologies. Also, this survey provides some ideas for
future research to enhance the capabilities of FCM in order to cover some
challenges in the real-world experiments such as handling non-stationary data
and scalability issues. Moreover, equipping FCMs with fast learning algorithms
is one of the major concerns in this area.
【4】 A unified software/hardware scalable architecture for brain-inspired computing based on self-organizing neural models
标题：基于自组织神经模型的脑启发计算软硬件统一可扩展体系结构
链接：https://arxiv.org/abs/2201.02262
摘要：The field of artificial intelligence has significantly advanced over the past
decades, inspired by discoveries from the fields of biology and neuroscience.
The idea of this work is inspired by the process of self-organization of
cortical areas in the human brain from both afferent and lateral/internal
connections. In this work, we develop an original brain-inspired neural model
associating Self-Organizing Maps (SOM) and Hebbian learning in the Reentrant
SOM (ReSOM) model. The framework is applied to multimodal classification
problems. Compared to existing methods based on unsupervised learning with
post-labeling, the model enhances the state-of-the-art results. This work also
demonstrates the distributed and scalable nature of the model through both
simulation results and hardware execution on a dedicated FPGA-based platform
named SCALP (Self-configurable 3D Cellular Adaptive Platform). SCALP boards can
be interconnected in a modular way to support the structure of the neural
model. Such a unified software and hardware approach enables the processing to
be scaled and allows information from several modalities to be merged
dynamically. The deployment on hardware boards provides performance results of
parallel execution on several devices, with the communication between each
board through dedicated serial links. The proposed unified architecture,
composed of the ReSOM model and the SCALP hardware platform, demonstrates a
significant increase in accuracy thanks to multimodal association, and a good
trade-off between latency and power consumption compared to a centralized GPU
implementation.
【5】 Projective Embedding of Dynamical Systems: uniform mean field equations
标题：动力系统的射影嵌入：一致平均场方程
链接：https://arxiv.org/abs/2201.02355
备注：45 pages; one column; 10 figures;
摘要：We study embeddings of continuous dynamical systems in larger dimensions via
projector operators. We call this technique PEDS, projective embedding of
dynamical systems, as the stable fixed point of the dynamics are recovered via
projection from the higher dimensional space. In this paper we provide a
general definition and prove that for a particular type of projector operator
of rank-1, the uniform mean field projector, the equations of motion become a
mean field approximation of the dynamical system. While in general the
embedding depends on a specified variable ordering, the same is not true for
the uniform mean field projector. In addition, we prove that the original
stable fixed points remain stable fixed points of the dynamics, saddle points
remain saddle, but unstable fixed points become saddles.
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