Cover Image

Aliasing in Convnets: A Frame-Theoretic Perspective

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Haider, Daniel, Lostanlen, Vincent, Ehler, Martin, Holighaus, Nicki, Balazs, Peter
Format:	Preprint
Published:	2025
Subjects:	Machine Learning Numerical Analysis
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Instabilities in Convnets for Raw Audio
by: Haider, Daniel, et al.
Published: (2023)

Fitting Auditory Filterbanks with Multiresolution Neural Networks
by: Lostanlen, Vincent, et al.
Published: (2023)

Injectivity of ReLU-layers: Tools from Frame Theory
by: Haider, Daniel, et al.
Published: (2024)

Hold Me Tight: Stable Encoder-Decoder Design for Speech Enhancement
by: Haider, Daniel, et al.
Published: (2024)

Characterization of Analytic Wavelet Transforms and a New Phaseless Reconstruction Algorithm
by: Holighaus, Nicki, et al.
Published: (2019)

ISAC: An Invertible and Stable Auditory Filter Bank with Customizable Kernels for ML Integration
by: Haider, Daniel, et al.
Published: (2025)

Optimal lower Lipschitz bounds for ReLU layers, saturation, and phase retrieval
by: Freeman, Daniel, et al.
Published: (2025)

Construction of generalized samplets in Banach spaces
by: Balazs, Peter, et al.
Published: (2024)

Operator Learning of Lipschitz Operators: An Information-Theoretic Perspective
by: Lanthaler, Samuel
Published: (2024)

Continuous warped time-frequency representations - Coorbit spaces and discretization
by: Holighaus, Nicki, et al.
Published: (2015)

Phase-Based Signal Representations for Scattering
by: Haider, Daniel, et al.
Published: (2022)

Machine Learning Algorithms to Assess Site Closure Time Frames for Soil and Groundwater Contamination
by: Le, Vu-Anh, et al.
Published: (2024)

Sparsity via Hyperpriors: A Theoretical and Algorithmic Study under Empirical Bayes Framework
by: Li, Zhitao, et al.
Published: (2025)

Theoretical Refinement of CLIP by Utilizing Linear Structure of Optimal Similarity
by: Yoshida, Naoki, et al.
Published: (2025)

Number Theoretic Accelerated Learning of Physics-Informed Neural Networks
by: Matsubara, Takashi, et al.
Published: (2023)

FBSJNN: A Theoretically Interpretable and Efficiently Deep Learning method for Solving Partial Integro-Differential Equations
by: Ye, Zaijun, et al.
Published: (2024)

A Mean Field Games Perspective on Evolutionary Clustering
by: Basti, Alessio, et al.
Published: (2026)

Singularity Formation: Synergy in Theoretical, Numerical and Machine Learning Approaches
by: Wang, Yixuan
Published: (2026)

A Discrete Perspective Towards the Construction of Sparse Probabilistic Boolean Networks
by: Fok, Christopher H., et al.
Published: (2024)

Correction to "Wasserstein distance estimates for the distributions of numerical approximations to ergodic stochastic differential equations"
by: Paulin, Daniel, et al.
Published: (2024)

Expressivity of Bi-Lipschitz Normalizing Flows: A Score-Based Diffusion Perspective
by: Iske, Meira, et al.
Published: (2026)

A holomorphic neural network framework for 3D boundary value problems governed by harmonic potentials
by: Ballini, Enrico, et al.
Published: (2026)

Why Does Stochastic Gradient Descent Slow Down in Low-Precision Training?
by: Yun, Vincent-Daniel
Published: (2025)

Low-rank computation of the posterior mean in Multi-Output Gaussian Processes
by: Esche, Sebastian, et al.
Published: (2025)

Convolutional Surrogate for 3D Discrete Fracture-Matrix Tensor Upscaling
by: Špetlík, Martin, et al.
Published: (2026)

ANOVA-boosting for Random Fourier Features
by: Potts, Daniel, et al.
Published: (2024)

Multilevel CNNs for Parametric PDEs based on Adaptive Finite Elements
by: Schütte, Janina Enrica, et al.
Published: (2024)

Branching Strategies Based on Subgraph GNNs: A Study on Theoretical Promise versus Practical Reality
by: Zhou, Junru, et al.
Published: (2025)

Interpretable Spatial-Temporal Fusion Transformers: Multi-Output Prediction for Parametric Dynamical Systems with Time-Varying Inputs
by: Sun, Shuwen, et al.
Published: (2025)

Data-Augmented Predictive Deep Neural Network: Enhancing the extrapolation capabilities of non-intrusive surrogate models
by: Sun, Shuwen, et al.
Published: (2024)

Structure-preserving learning for multi-symplectic PDEs
by: Yıldız, Süleyman, et al.
Published: (2024)

Enhancing Gaussian Process Surrogates for Optimization and Posterior Approximation via Random Exploration
by: Kim, Hwanwoo, et al.
Published: (2024)

Fast Evaluation of Additive Kernels: Feature Arrangement, Fourier Methods, and Kernel Derivatives
by: Wagner, Theresa, et al.
Published: (2024)

Weighted quantization using MMD: From mean field to mean shift via gradient flows
by: Belhadji, Ayoub, et al.
Published: (2025)

Fast and interpretable Support Vector Classification based on the truncated ANOVA decomposition
by: Akhalaya, Kseniya, et al.
Published: (2024)

Discretization of Continuous Frames by Quasi-Monte Carlo Methods
by: Zimmermann, Jan, et al.
Published: (2025)

Approximation and learning with compositional tensor trains
by: Eigel, Martin, et al.
Published: (2025)

Vocabulary for Universal Approximation: A Linguistic Perspective of Mapping Compositions
by: Cai, Yongqiang
Published: (2023)

Back-Projection Diffusion: Solving the Wideband Inverse Scattering Problem with Diffusion Models
by: Zhang, Borong, et al.
Published: (2024)

Fourier Neural Operator with Learned Deformations for PDEs on General Geometries
by: Li, Zongyi, et al.
Published: (2022)