Density Encoding Enables Resource-Efficient Randomly Connected Neural Networks
University of California, Berkeley（加州大学伯克利分校）；Research Institutes of Sweden（瑞典研究所）；Netlight Consulting AB（Netlight咨询公司）；Umeå University（于默奥大学）；Luleå University of Technology（吕勒奥理工大学）
The deployment of machine learning algorithms on resource-constrained edge devices is an important challenge from both theoretical and applied points of view. In this article, we focus on resource-efficient randomly connected neural networks known as Random Vector Functional Link (RVFL) networks since their simple design and extremely fast training time make them very attractive for solving many applied classification tasks. We propose to represent input features via the density-based encoding known in the area of stochastic computing and use the operations of binding and bundling from the area of hyperdimensional computing for obtaining the activations of the hidden neurons. Using a collection of 121 real-world datasets from the UCI Machine Learning Repository, we empirically show that the proposed approach demonstrates higher average accuracy than the conventional RVFL. We also demonstrate that it is possible to represent the readout matrix using only integers in a limited range with minimal loss in the accuracy. In this case, the proposed approach operates only on small n-bits integers, which results in a computationally efficient architecture. Finally, through hardware Field-Programmable Gate Array (FPGA) implementations, we show that such an approach consumes approximately eleven times less energy than that of the conventional RVFL.
PEOPL: Characterizing Privately Encoded Open Datasets with Public Labels
Massachusetts Institute of Technology (MIT)（麻省理工学院）；University of California, Berkeley（加州大学伯克利分校）；University of California, San Francisco（加州大学旧金山分校）；Clemson University（克莱姆森大学）；Maynooth University（梅努斯大学）
Introduction to dynamical mean-field theory of randomly connected neural networks with bidirectionally correlated couplings
School of Physics, Sun Yat-sen University（中山大学物理学院）；Guangdong Provincial Key Laboratory of Magnetoelectric Physics and Devices, Sun Yat-sen University（中山大学广东省磁电物理与器件重点实验室）
Deep Learning in Random Neural Fields: Numerical Experiments via Neural Tangent Kernel
LPIXEL Inc（LPIXEL公司）；The Institute of Statistical Mathematics（统计数学研究所）；AIST（产业技术综合研究所）；RIKEN AIP（理化学研究所人工智能项目中心）；RIKEN CBS（理化学研究所脑科学中心）；Teikyo University（帝京大学）；Araya Inc（Araya公司）
It's All Connected: Topology-Aware Structural Graph Encoding Improves Performance on Polymer Prediction
Faculty of Engineering Science, University of Bayreuth, Germany（拜罗伊特大学工程科学学院）；Faculty of Mathematics, Physics & Computer Science, University of Bayreuth, Germany（拜罗伊特大学数学、物理与计算机科学学院）；Laboratory of Organic and Macromolecular Chemistry (IOMC), Friedrich Schiller University Jena, Germany（耶拿弗里德里希·施莱尔大学有机与大分子化学实验室）；Jena Center for Soft Matter (JCSM), Friedrich Schiller University Jena, Germany（耶拿软物质中心（JCSM））；Helmholtz Institute for Polymers in Energy Applications Jena (HIPOLE Jena), Germany（耶拿聚合物能源应用研究所（HIPOLE 耶拿））；Helmholtz Zentrum Berlin für Materialien und Energie GmbH (HZB), Germany（柏林材料与能源研究中心（HZB））
Exploring Randomly Wired Neural Networks for Climate Model Emulation
Harvey Mudd College（哈维穆德学院）；University of Southern California（南加州大学）；Pacific Northwest National Laboratory（太平洋西北国家实验室）；University of California San Diego（加州大学圣迭戈分校）
