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Bibliografski detalji
Glavni autori:	Fong, Robert Simon, Li, Boyu, Tiňo, Peter
Format:	Preprint
Izdano:	2024
Teme:	Machine Learning Neural and Evolutionary Computing
Online pristup:	https://arxiv.org/abs/2408.08071
Oznake:	Dodaj oznaku Bez oznaka, Budi prvi tko označuje ovaj zapis!

Slični predmeti

Simple Cycle Reservoirs are Universal
od: Li, Boyu, i dr.
Izdano: (2023)

Predictive Modeling in the Reservoir Kernel Motif Space
od: Tino, Peter, i dr.
Izdano: (2024)

Linear Simple Cycle Reservoirs at the edge of stability perform Fourier decomposition of the input driving signals
od: Fong, Robert Simon, i dr.
Izdano: (2024)

Structuring Multiple Simple Cycle Reservoirs with Particle Swarm Optimization
od: Li, Ziqiang, i dr.
Izdano: (2025)

Reservoir Computation with Networks of Differentiating Neuron Ring Oscillators
od: Yeung, Alexander, i dr.
Izdano: (2025)

Sharpness-Aware Minimization and the Edge of Stability
od: Long, Philip M., i dr.
Izdano: (2023)

Time Shifts to Reduce the Size of Reservoir Computers
od: Carroll, Thomas L., i dr.
Izdano: (2022)

Comparison of Reservoir Computing topologies using the Recurrent Kernel approach
od: D'Inverno, Giuseppe Alessio, i dr.
Izdano: (2024)

Temporal and Spatial Reservoir Ensembling Techniques for Liquid State Machines
od: Biswas, Anmol, i dr.
Izdano: (2024)

Scalable Memristive-Friendly Reservoir Computing for Time Series Classification
od: Horuz, Coşku Can, i dr.
Izdano: (2026)

Sequential Reservoir Computing for Efficient High-Dimensional Spatiotemporal Forecasting
od: Asanjan, Ata Akbari, i dr.
Izdano: (2026)

NeuroPareto: Calibrated Acquisition for Costly Many-Goal Search in Vast Parameter Spaces
od: Fu, Rong, i dr.
Izdano: (2026)

ReLiCADA -- Reservoir Computing using Linear Cellular Automata Design Algorithm
od: Kantic, Jonas, i dr.
Izdano: (2023)

Predicting concentration levels of air pollutants by transfer learning and recurrent neural network
od: Fong, Iat Hang, i dr.
Izdano: (2025)

Minimally Supervised Learning using Topological Projections in Self-Organizing Maps
od: Lyu, Zimeng, i dr.
Izdano: (2024)

Universality of Linear Recurrences Followed by Non-linear Projections: Finite-Width Guarantees and Benefits of Complex Eigenvalues
od: Orvieto, Antonio, i dr.
Izdano: (2023)

Sharpness-Aware Minimization in Genetic Programming
od: Bakurov, Illya, i dr.
Izdano: (2024)

Reservoir Computing via Multi-Scale Random Fourier Features for Forecasting Fast-Slow Dynamical Systems
od: Laha, S. K.
Izdano: (2025)

Active Sensing with Predictive Coding and Uncertainty Minimization
od: Sharafeldin, Abdelrahman, i dr.
Izdano: (2023)

Minimal Convolutional RNNs Accelerate Spatiotemporal Learning
od: Horuz, Coşku Can, i dr.
Izdano: (2025)

Sharpness-Aware Minimization for Evolutionary Feature Construction in Regression
od: Zhang, Hengzhe, i dr.
Izdano: (2024)

Evolving Reservoirs for Meta Reinforcement Learning
od: Léger, Corentin, i dr.
Izdano: (2023)

Minimally Supervised Topological Projections of Self-Organizing Maps for Phase of Flight Identification
od: Lyu, Zimeng, i dr.
Izdano: (2024)

Repair Brain Damage: Real-Numbered Error Correction Code for Neural Network
od: Li, Ziqing, i dr.
Izdano: (2026)

Enhancing Generalization in Evolutionary Feature Construction for Symbolic Regression through Vicinal Jensen Gap Minimization
od: Zhang, Hengzhe, i dr.
Izdano: (2026)

A binary PSO based ensemble under-sampling model for rebalancing imbalanced training data
od: Li, Jinyan, i dr.
Izdano: (2025)

Reservoir Computing Benchmarks: a tutorial review and critique
od: Wringe, Chester, i dr.
Izdano: (2024)

Insect-Wing Structured Microfluidic System for Reservoir Computing
od: Clouse, Jacob, i dr.
Izdano: (2025)

Boosting Reservoir Computing with Brain-inspired Adaptive Dynamics
od: Srinivasan, Keshav, i dr.
Izdano: (2025)

Generalization Bounds and Model Complexity for Kolmogorov-Arnold Networks
od: Zhang, Xianyang, i dr.
Izdano: (2024)

Recurrent Complex-Weighted Autoencoders for Unsupervised Object Discovery
od: Gopalakrishnan, Anand, i dr.
Izdano: (2024)

Denoising and Reconstruction of Nonlinear Dynamics using Truncated Reservoir Computing
od: Sedehi, Omid, i dr.
Izdano: (2025)

Universality of reservoir systems with recurrent neural networks
od: Yasumoto, Hiroki, i dr.
Izdano: (2024)

On the Universal Representation Property of Spiking Neural Networks
od: Hundrieser, Shayan, i dr.
Izdano: (2025)

Data-Informed Model Complexity Metric for Optimizing Symbolic Regression Models
od: Haut, Nathan, i dr.
Izdano: (2025)

Transforming Datasets to Requested Complexity with Projection-based Many-Objective Genetic Algorithm
od: Komorniczak, Joanna
Izdano: (2025)

Universal Approximation Theorem for Vector- and Hypercomplex-Valued Neural Networks
od: Valle, Marcos Eduardo, i dr.
Izdano: (2024)

Scaling can lead to compositional generalization
od: Redhardt, Florian, i dr.
Izdano: (2025)

Unsupervised Sparse Coding-based Spiking Neural Network for Real-time Spike Sorting
od: Melot, Alexis, i dr.
Izdano: (2025)

Understanding Vector-Valued Neural Networks and Their Relationship with Real and Hypercomplex-Valued Neural Networks
od: Valle, Marcos Eduardo
Izdano: (2023)