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Cross‐Scale Hierarchical Targeted Delivery System Based on Small‐Scale Magnetic Robots
Advanced Robotics Research, EarlyView.This article reviews a cross‐scale hierarchical targeted delivery system that integrates magnetic continuum robots and magnetic microrobots. By combining rapid long‐range navigation with precise microscale targeting, the system overcomes key limitations of single‐scale approaches.
Junjian Zhou +4 more
wiley +1 more source
From Lab to Landscape: Environmental Biohybrid Robotics for Ecological Futures
Advanced Robotics Research, EarlyView.This Perspective explores environmental biohybrid robotics, integrating living tissues, microorganisms, and insects for operation in real‐world ecosystems. It traces the leap from laboratory experiments to forests, wetlands, and urban environments and discusses key challenges, development pathways, and opportunities for ecological monitoring and ...
Miriam Filippi
wiley +1 more source
Functional Fibers in Soft Robotics: Advances in Material, Structural, and Systemic Tactics
Advanced Robotics Research, EarlyView.Fiber‐form robotic systems offer a scalable pathway toward embodied intelligence in soft robotics. This review surveys functional fibers as material, structural, and systemic elements, highlighting advances in responsive materials, architectural programing, and fabrication strategies.
Joonhee Won +5 more
wiley +1 more source
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Information Bottleneck and Aggregated Learning
IEEE Transactions on Pattern Analysis and Machine Intelligence, 2023We consider the problem of learning a neural network classifier. Under the information bottleneck (IB) principle, we associate with this classification problem a representation learning problem, which we call "IB learning". We show that IB learning is, in fact, equivalent to a special class of the quantization problem.
Soflaei, Masoumeh +4 more
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A Spiking Neuron as Information Bottleneck
Neural Computation, 2010Neurons receive thousands of presynaptic input spike trains while emitting a single output spike train. This drastic dimensionality reduction suggests considering a neuron as a bottleneck for information transmission. Extending recent results, we propose a simple learning rule for the weights of spiking neurons derived from the information bottleneck (
Buesing L., Maass W.
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Information Bottleneck Problem Revisited
2019 57th Annual Allerton Conference on Communication, Control, and Computing (Allerton), 2019In this paper, we revisit the information bottleneck problem whose formulation and solution are of great importance in both information theory and statistical learning applications. We go into details as to why the problem was first introduced and how the algorithm proposed using Lagrangian method to solve such problems fell short of an exact solution.
Farhang Bayat, Shuangqing Wei
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Neural Information Bottleneck Decoding
2020 14th International Conference on Signal Processing and Communication Systems (ICSPCS), 2020Receiver-sided channel decoding is a crucial, but computationally very demanding task. Recently, information-bottleneck-based decoding received considerable attention in the literature, as it achieves very good performance with coarse quantization and low complexity.
Stark, Maximilian +2 more
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Distributed cooperative information bottleneck
2017 IEEE International Symposium on Information Theory (ISIT), 2017This paper investigates a scenario where two distant nodes separately observe memoryless process, namely X 1 and X 2 , and can cooperate through multiple exchanges of messages with the goal of enabling a third node to learn “relevant information” (measured in terms of a multi-letter mutual information) about some hidden memoryless process Y, which is ...
Vera, Matias +2 more
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A Survey on Information Bottleneck
IEEE Transactions on Pattern Analysis and Machine IntelligenceThis survey is for the remembrance of one of the creators of the information bottleneck theory, Prof. Naftali Tishby, passing away at the age of 68 on August, 2021. Information bottleneck (IB), a novel information theoretic approach for pattern analysis and representation learning, has gained widespread popularity since its birth in 1999.
Shizhe Hu +3 more
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