Saved in:
| Main Authors: | Rochau, Dennis, Chan, Robin, Gottschalk, Hanno |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2411.08735 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
Necessary and sufficient conditions for universality of Kolmogorov-Arnold networks
by: Ismailov, Vugar
Published: (2026)
by: Ismailov, Vugar
Published: (2026)
Universality of shallow and deep neural networks on non-Euclidean spaces
by: Ismailov, Vugar
Published: (2026)
by: Ismailov, Vugar
Published: (2026)
Topological DeepONets and a generalization of the Chen-Chen operator approximation theorem
by: Ismailov, Vugar
Published: (2026)
by: Ismailov, Vugar
Published: (2026)
Stable Learning Using Spiking Neural Networks Equipped With Affine Encoders and Decoders
by: Neuman, A. Martina, et al.
Published: (2024)
by: Neuman, A. Martina, et al.
Published: (2024)
Universal Approximation Theorem for Input-Connected Multilayer Perceptrons
by: Ismailov, Vugar
Published: (2026)
by: Ismailov, Vugar
Published: (2026)
Approximation Rates in Besov Norms and Sample-Complexity of Kolmogorov-Arnold Networks with Residual Connections
by: Kratsios, Anastasis, et al.
Published: (2025)
by: Kratsios, Anastasis, et al.
Published: (2025)
Universal approximation results for neural networks with non-polynomial activation function over non-compact domains
by: Neufeld, Ariel, et al.
Published: (2024)
by: Neufeld, Ariel, et al.
Published: (2024)
SO-PIFRNN: Self-optimization physics-informed Fourier-features randomized neural network for solving partial differential equations
by: Linghu, Jiale, et al.
Published: (2025)
by: Linghu, Jiale, et al.
Published: (2025)
A Wachspress-based transfinite formulation for exactly enforcing Dirichlet boundary conditions on convex polygonal domains in physics-informed neural networks
by: Sukumar, N., et al.
Published: (2026)
by: Sukumar, N., et al.
Published: (2026)
Universality and approximation bounds for echo state networks with random weights
by: Li, Zhen, et al.
Published: (2022)
by: Li, Zhen, et al.
Published: (2022)
Unification of popular artificial neural network activation functions
by: Mostafanejad, Mohammad
Published: (2023)
by: Mostafanejad, Mohammad
Published: (2023)
A third-order finite difference weighted essentially non-oscillatory scheme with shallow neural network
by: Park, Kwanghyuk, et al.
Published: (2024)
by: Park, Kwanghyuk, et al.
Published: (2024)
A fast and accurate physics-informed neural network reduced order model with shallow masked autoencoder
by: Kim, Youngkyu, et al.
Published: (2020)
by: Kim, Youngkyu, et al.
Published: (2020)
Splitting physics-informed neural networks for inferring the dynamics of integer- and fractional-order neuron models
by: Shekarpaz, Simin, et al.
Published: (2023)
by: Shekarpaz, Simin, et al.
Published: (2023)
Exoplanet formation inference using conditional invertible neural networks
by: Burn, Remo, et al.
Published: (2025)
by: Burn, Remo, et al.
Published: (2025)
Evolutionary feature selection for spiking neural network pattern classifiers
by: Valko, Michal, et al.
Published: (2026)
by: Valko, Michal, et al.
Published: (2026)
Physics-informed neural network for modeling dynamic linear elasticity
by: Kag, Vijay, et al.
Published: (2023)
by: Kag, Vijay, et al.
Published: (2023)
DelRec: learning delays in recurrent spiking neural networks
by: Queant, Alexandre, et al.
Published: (2025)
by: Queant, Alexandre, et al.
Published: (2025)
Exploiting heterogeneous delays for efficient computation in low-bit neural networks
by: Sun, Pengfei, et al.
Published: (2025)
by: Sun, Pengfei, et al.
Published: (2025)
Do physics-informed neural networks (PINNs) need to be deep? Shallow PINNs using the Levenberg-Marquardt algorithm
by: Shahab, Muhammad Luthfi, et al.
Published: (2026)
by: Shahab, Muhammad Luthfi, et al.
Published: (2026)
TDE-3: An improved prior for optical flow computation in spiking neural networks
by: Yedutenko, Matthew, et al.
Published: (2024)
by: Yedutenko, Matthew, et al.
Published: (2024)
A two-stage algorithm in evolutionary product unit neural networks for classification
by: Tallón-Ballesteros, Antonio J., et al.
Published: (2024)
by: Tallón-Ballesteros, Antonio J., et al.
Published: (2024)
Towards free-response paradigm: a theory on decision-making in spiking neural networks
by: Zhu, Zhichao, et al.
Published: (2024)
by: Zhu, Zhichao, et al.
Published: (2024)
A survey on learning models of spiking neural membrane systems and spiking neural networks
by: Paul, Prithwineel, et al.
Published: (2024)
by: Paul, Prithwineel, et al.
Published: (2024)
When Spiking neural networks meet temporal attention image decoding and adaptive spiking neuron
by: Qiu, Xuerui, et al.
Published: (2024)
by: Qiu, Xuerui, et al.
Published: (2024)
STCSNN: High energy efficiency spike-train level spiking neural networks with spatio-temporal conversion
by: Xu, Changqing, et al.
Published: (2023)
by: Xu, Changqing, et al.
Published: (2023)
Parameter efficient hybrid spiking-quantum convolutional neural network with surrogate gradient and quantum data-reupload
by: Nhan, Luu Trong, et al.
Published: (2025)
by: Nhan, Luu Trong, et al.
Published: (2025)
Efficient Design of Compliant Mechanisms Using Multi-Objective Optimization
by: Humer, Alexander, et al.
Published: (2025)
by: Humer, Alexander, et al.
Published: (2025)
From LIF to QIF: Toward Differentiable Spiking Neurons for Scientific Machine Learning
by: Wan, Ruyin, et al.
Published: (2025)
by: Wan, Ruyin, et al.
Published: (2025)
Implementation of high-efficiency, lightweight residual spiking neural network processor based on field-programmable gate arrays
by: Yue, Hou, et al.
Published: (2025)
by: Yue, Hou, et al.
Published: (2025)
CMOS-based area-and-power-efficient neuron and synapse circuits for time-domain analog spiking neural networks
by: Chen, Xiangyu, et al.
Published: (2022)
by: Chen, Xiangyu, et al.
Published: (2022)
Universality of reservoir systems with recurrent neural networks
by: Yasumoto, Hiroki, et al.
Published: (2024)
by: Yasumoto, Hiroki, et al.
Published: (2024)
Gated recurrent neural networks discover attention
by: Zucchet, Nicolas, et al.
Published: (2023)
by: Zucchet, Nicolas, et al.
Published: (2023)
Uniform-in-time concentration in two-layer neural networks via transportation inequalities
by: Guillin, Arnaud, et al.
Published: (2026)
by: Guillin, Arnaud, et al.
Published: (2026)
Structure of activity in multiregion recurrent neural networks
by: Clark, David G., et al.
Published: (2024)
by: Clark, David G., et al.
Published: (2024)
Bridging the Gap Between Approximation and Learning via Optimal Approximation by ReLU MLPs of Maximal Regularity
by: Hong, Ruiyang, et al.
Published: (2024)
by: Hong, Ruiyang, et al.
Published: (2024)
Training neural networks without backpropagation using particles
by: Kumar, Deepak
Published: (2024)
by: Kumar, Deepak
Published: (2024)
Learning fast changing slow in spiking neural networks
by: Capone, Cristiano, et al.
Published: (2024)
by: Capone, Cristiano, et al.
Published: (2024)
Designing deep neural networks for driver intention recognition
by: Vellenga, Koen, et al.
Published: (2024)
by: Vellenga, Koen, et al.
Published: (2024)
Heterogeneous quantization regularizes spiking neural network activity
by: Moyal, Roy, et al.
Published: (2024)
by: Moyal, Roy, et al.
Published: (2024)
Similar Items
-
Necessary and sufficient conditions for universality of Kolmogorov-Arnold networks
by: Ismailov, Vugar
Published: (2026) -
Universality of shallow and deep neural networks on non-Euclidean spaces
by: Ismailov, Vugar
Published: (2026) -
Topological DeepONets and a generalization of the Chen-Chen operator approximation theorem
by: Ismailov, Vugar
Published: (2026) -
Stable Learning Using Spiking Neural Networks Equipped With Affine Encoders and Decoders
by: Neuman, A. Martina, et al.
Published: (2024) -
Universal Approximation Theorem for Input-Connected Multilayer Perceptrons
by: Ismailov, Vugar
Published: (2026)