Copertina

Designing deep neural networks for driver intention recognition

Fuente: arXiv

Salvato in:

Dettagli Bibliografici
Autori principali:	Vellenga, Koen, Steinhauer, H. Joe, Karlsson, Alexander, Falkman, Göran, Rhodin, Asli, Koppisetty, Ashok
Natura:	Preprint
Pubblicazione:	2024
Soggetti:	Machine Learning Neural and Evolutionary Computing
Accesso online:	Acceder al recurso
Tags:	Aggiungi Tag Nessun Tag, puoi essere il primo ad aggiungerne!!

Documenti analoghi

Improved weight initialization for deep and narrow feedforward neural network
di: Lee, Hyunwoo, et al.
Pubblicazione: (2023)

An algorithmic framework for the optimization of deep neural networks architectures and hyperparameters
di: Keisler, Julie, et al.
Pubblicazione: (2023)

Universality of reservoir systems with recurrent neural networks
di: Yasumoto, Hiroki, et al.
Pubblicazione: (2024)

Gated recurrent neural networks discover attention
di: Zucchet, Nicolas, et al.
Pubblicazione: (2023)

Learning fast changing slow in spiking neural networks
di: Capone, Cristiano, et al.
Pubblicazione: (2024)

Learning richness modulates equality reasoning in neural networks
di: Tong, William L., et al.
Pubblicazione: (2025)

Investigating the generative dynamics of energy-based neural networks
di: Tausani, Lorenzo, et al.
Pubblicazione: (2023)

A developmental approach for training deep belief networks
di: Zambra, Matteo, et al.
Pubblicazione: (2022)

From Frege to chatGPT: Compositionality in language, cognition, and deep neural networks
di: Russin, Jacob, et al.
Pubblicazione: (2024)

Three factor delay learning rules for spiking neural networks
di: Vassallo, Luke, et al.
Pubblicazione: (2026)

Spike-based computation using classical recurrent neural networks
di: De Geeter, Florent, et al.
Pubblicazione: (2023)

Hypercomplex neural network in time series forecasting of stock data
di: Kycia, Radosław, et al.
Pubblicazione: (2024)

Flexible inference for animal learning rules using neural networks
di: Liu, Yuhan Helena, et al.
Pubblicazione: (2025)

Optimal feature rescaling in machine learning based on neural networks
di: Vitrò, Federico Maria, et al.
Pubblicazione: (2024)

Application-oriented automatic hyperparameter optimization for spiking neural network prototyping
di: Fra, Vittorio
Pubblicazione: (2025)

Fast gradient-free activation maximization for neurons in spiking neural networks
di: Pospelov, Nikita, et al.
Pubblicazione: (2023)

A rationale from frequency perspective for grokking in training neural network
di: Zhou, Zhangchen, et al.
Pubblicazione: (2024)

Gradient-based inference of abstract task representations for generalization in neural networks
di: Hummos, Ali, et al.
Pubblicazione: (2024)

Prototype-based interpretation of the functionality of neurons in winner-take-all neural networks
di: Sabzevar, Ramin Zarei, et al.
Pubblicazione: (2020)

Last Layer Hamiltonian Monte Carlo
di: Vellenga, Koen, et al.
Pubblicazione: (2025)

On the Influence of the Feature Computation Budget on Per-Instance Algorithm Selection for Black-Box Optimization
di: van der Blom, Koen, et al.
Pubblicazione: (2026)

Akkumula: Evidence accumulation driver models with Spiking Neural Networks
di: Morando, Alberto
Pubblicazione: (2025)

Iteration over event space in time-to-first-spike spiking neural networks for Twitter bot classification
di: Pabian, Mateusz, et al.
Pubblicazione: (2024)

Pruning-induced phases in fully-connected neural networks: the eumentia, the dementia, and the amentia
di: Pan, Haining, et al.
Pubblicazione: (2026)

Sufficient conditions for offline reactivation in recurrent neural networks
di: Krishna, Nanda H., et al.
Pubblicazione: (2025)

Provably robust learning of regression neural networks using $β$-divergences
di: Ghosh, Abhik, et al.
Pubblicazione: (2026)

Universality of shallow and deep neural networks on non-Euclidean spaces
di: Ismailov, Vugar
Pubblicazione: (2026)

The boundary of neural network trainability is fractal
di: Sohl-Dickstein, Jascha
Pubblicazione: (2024)

Temporal and Spatial Reservoir Ensembling Techniques for Liquid State Machines
di: Biswas, Anmol, et al.
Pubblicazione: (2024)

Continual learning with the neural tangent ensemble
di: Benjamin, Ari S., et al.
Pubblicazione: (2024)

Reconsidering the energy efficiency of spiking neural networks
di: Yan, Zhanglu, et al.
Pubblicazione: (2024)

Who invented deep residual learning?
di: Schmidhuber, Juergen
Pubblicazione: (2025)

Banach neural operator for Navier-Stokes equations
di: Zhang, Bo
Pubblicazione: (2025)

Activation degree thresholds and expressiveness of polynomial neural networks
di: Finkel, Bella, et al.
Pubblicazione: (2024)

Experimental verification of the quantum nature of a neural network
di: Patrascu, Andrei T.
Pubblicazione: (2022)

Method for noise-induced regularization in quantum neural networks
di: Kuzmin, Viacheslav, et al.
Pubblicazione: (2024)

Fractional-order spike-timing-dependent gradient descent for multi-layer spiking neural networks
di: Yang, Yi, et al.
Pubblicazione: (2024)

Gate-level boolean evolutionary geometric attention neural networks
di: Shi, Xianshuai, et al.
Pubblicazione: (2025)

Neuromorphic on-chip reservoir computing with spiking neural network architectures
di: Karki, Samip, et al.
Pubblicazione: (2024)

Parametric Taylor series based latent dynamics identification neural networks
di: Lin, Xinlei, et al.
Pubblicazione: (2024)