Application-oriented automatic hyperparameter optimization for spiking neural network prototyping
Fuente:
arXiv
Guardado en:
| Autor principal: | Fra, Vittorio |
|---|---|
| Formato: | Preprint |
| Publicado: |
2025
|
| Materias: | |
| Acceso en línea: | |
| Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
Ejemplares similares
An algorithmic framework for the optimization of deep neural networks architectures and hyperparameters
por: Keisler, Julie, et al.
Publicado: (2023)
por: Keisler, Julie, et al.
Publicado: (2023)
Learning fast changing slow in spiking neural networks
por: Capone, Cristiano, et al.
Publicado: (2024)
por: Capone, Cristiano, et al.
Publicado: (2024)
Three factor delay learning rules for spiking neural networks
por: Vassallo, Luke, et al.
Publicado: (2026)
por: Vassallo, Luke, et al.
Publicado: (2026)
Iteration over event space in time-to-first-spike spiking neural networks for Twitter bot classification
por: Pabian, Mateusz, et al.
Publicado: (2024)
por: Pabian, Mateusz, et al.
Publicado: (2024)
Fast gradient-free activation maximization for neurons in spiking neural networks
por: Pospelov, Nikita, et al.
Publicado: (2023)
por: Pospelov, Nikita, et al.
Publicado: (2023)
Reconsidering the energy efficiency of spiking neural networks
por: Yan, Zhanglu, et al.
Publicado: (2024)
por: Yan, Zhanglu, et al.
Publicado: (2024)
Fractional-order spike-timing-dependent gradient descent for multi-layer spiking neural networks
por: Yang, Yi, et al.
Publicado: (2024)
por: Yang, Yi, et al.
Publicado: (2024)
Neuromorphic on-chip reservoir computing with spiking neural network architectures
por: Karki, Samip, et al.
Publicado: (2024)
por: Karki, Samip, et al.
Publicado: (2024)
Differentiable architecture search with multi-dimensional attention for spiking neural networks
por: Man, Yilei, et al.
Publicado: (2024)
por: Man, Yilei, et al.
Publicado: (2024)
Similarity-based context aware continual learning for spiking neural networks
por: Han, Bing, et al.
Publicado: (2024)
por: Han, Bing, et al.
Publicado: (2024)
Covariant spatio-temporal receptive fields for spiking neural networks
por: Pedersen, Jens Egholm, et al.
Publicado: (2024)
por: Pedersen, Jens Egholm, et al.
Publicado: (2024)
Decoding finger velocity from cortical spike trains with recurrent spiking neural networks
por: Liu, Tengjun, et al.
Publicado: (2024)
por: Liu, Tengjun, et al.
Publicado: (2024)
Scale-covariant spiking wavelets
por: Pedersen, Jens Egholm, et al.
Publicado: (2026)
por: Pedersen, Jens Egholm, et al.
Publicado: (2026)
Causal pieces: analysing and improving spiking neural networks piece by piece
por: Dold, Dominik, et al.
Publicado: (2025)
por: Dold, Dominik, et al.
Publicado: (2025)
Universality of reservoir systems with recurrent neural networks
por: Yasumoto, Hiroki, et al.
Publicado: (2024)
por: Yasumoto, Hiroki, et al.
Publicado: (2024)
Gated recurrent neural networks discover attention
por: Zucchet, Nicolas, et al.
Publicado: (2023)
por: Zucchet, Nicolas, et al.
Publicado: (2023)
Learning richness modulates equality reasoning in neural networks
por: Tong, William L., et al.
Publicado: (2025)
por: Tong, William L., et al.
Publicado: (2025)
Designing deep neural networks for driver intention recognition
por: Vellenga, Koen, et al.
Publicado: (2024)
por: Vellenga, Koen, et al.
Publicado: (2024)
Investigating the generative dynamics of energy-based neural networks
por: Tausani, Lorenzo, et al.
Publicado: (2023)
por: Tausani, Lorenzo, et al.
Publicado: (2023)
Benchmarking local Hebbian learning rules for memory storage and prototype extraction
por: Lansner, Anders, et al.
Publicado: (2026)
por: Lansner, Anders, et al.
Publicado: (2026)
Flexible inference for animal learning rules using neural networks
por: Liu, Yuhan Helena, et al.
Publicado: (2025)
por: Liu, Yuhan Helena, et al.
Publicado: (2025)
Improved weight initialization for deep and narrow feedforward neural network
por: Lee, Hyunwoo, et al.
Publicado: (2023)
por: Lee, Hyunwoo, et al.
Publicado: (2023)
Spike-based computation using classical recurrent neural networks
por: De Geeter, Florent, et al.
Publicado: (2023)
por: De Geeter, Florent, et al.
Publicado: (2023)
Hypercomplex neural network in time series forecasting of stock data
por: Kycia, Radosław, et al.
Publicado: (2024)
por: Kycia, Radosław, et al.
Publicado: (2024)
Optimal feature rescaling in machine learning based on neural networks
por: Vitrò, Federico Maria, et al.
Publicado: (2024)
por: Vitrò, Federico Maria, et al.
Publicado: (2024)
Towards a "universal translator" for neural dynamics at single-cell, single-spike resolution
por: Zhang, Yizi, et al.
Publicado: (2024)
por: Zhang, Yizi, et al.
Publicado: (2024)
Towards graph neural networks for provably solving convex optimization problems
por: Qian, Chendi, et al.
Publicado: (2025)
por: Qian, Chendi, et al.
Publicado: (2025)
A survey on learning models of spiking neural membrane systems and spiking neural networks
por: Paul, Prithwineel, et al.
Publicado: (2024)
por: Paul, Prithwineel, et al.
Publicado: (2024)
A rationale from frequency perspective for grokking in training neural network
por: Zhou, Zhangchen, et al.
Publicado: (2024)
por: Zhou, Zhangchen, et al.
Publicado: (2024)
Gradient-based inference of abstract task representations for generalization in neural networks
por: Hummos, Ali, et al.
Publicado: (2024)
por: Hummos, Ali, et al.
Publicado: (2024)
DelRec: learning delays in recurrent spiking neural networks
por: Queant, Alexandre, et al.
Publicado: (2025)
por: Queant, Alexandre, et al.
Publicado: (2025)
Evolutionary feature selection for spiking neural network pattern classifiers
por: Valko, Michal, et al.
Publicado: (2026)
por: Valko, Michal, et al.
Publicado: (2026)
STCSNN: High energy efficiency spike-train level spiking neural networks with spatio-temporal conversion
por: Xu, Changqing, et al.
Publicado: (2023)
por: Xu, Changqing, et al.
Publicado: (2023)
Scalable neuromorphic computing from autonomous spiking dynamics in a clockless reconfigurable chip
por: Gomes, Eric Oliveira, et al.
Publicado: (2026)
por: Gomes, Eric Oliveira, et al.
Publicado: (2026)
Prototype-based interpretation of the functionality of neurons in winner-take-all neural networks
por: Sabzevar, Ramin Zarei, et al.
Publicado: (2020)
por: Sabzevar, Ramin Zarei, et al.
Publicado: (2020)
Storing overlapping associative memories on latent manifolds in low-rank spiking networks
por: Podlaski, William F., et al.
Publicado: (2024)
por: Podlaski, William F., et al.
Publicado: (2024)
Natively neuromorphic LMU architecture for encoding-free SNN-based HAR on commercial edge devices
por: Fra, Vittorio, et al.
Publicado: (2024)
por: Fra, Vittorio, et al.
Publicado: (2024)
TDE-3: An improved prior for optical flow computation in spiking neural networks
por: Yedutenko, Matthew, et al.
Publicado: (2024)
por: Yedutenko, Matthew, et al.
Publicado: (2024)
ISAM-MTL: Cross-subject multi-task learning model with identifiable spikes and associative memory networks
por: Li, Junyan, et al.
Publicado: (2025)
por: Li, Junyan, et al.
Publicado: (2025)
Provably robust learning of regression neural networks using $β$-divergences
por: Ghosh, Abhik, et al.
Publicado: (2026)
por: Ghosh, Abhik, et al.
Publicado: (2026)
Ejemplares similares
-
An algorithmic framework for the optimization of deep neural networks architectures and hyperparameters
por: Keisler, Julie, et al.
Publicado: (2023) -
Learning fast changing slow in spiking neural networks
por: Capone, Cristiano, et al.
Publicado: (2024) -
Three factor delay learning rules for spiking neural networks
por: Vassallo, Luke, et al.
Publicado: (2026) -
Iteration over event space in time-to-first-spike spiking neural networks for Twitter bot classification
por: Pabian, Mateusz, et al.
Publicado: (2024) -
Fast gradient-free activation maximization for neurons in spiking neural networks
por: Pospelov, Nikita, et al.
Publicado: (2023)