Cover Image

Improving Set Function Approximation with Quasi-Arithmetic Neural Networks

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Tokar, Tomas, Sanner, Scott
Format:	Preprint
Published:	2026
Subjects:	Machine Learning
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Large Neighborhood Search meets Iterative Neural Constraint Heuristics
by: Xu, Yudong W., et al.
Published: (2026)

Near-optimal Linear Predictive Clustering in Non-separable Spaces via MIP and QPBO Reductions
by: Liang, Jiazhou, et al.
Published: (2025)

On the Lipschitz Continuity of Set Aggregation Functions and Neural Networks for Sets
by: Nikolentzos, Giannis, et al.
Published: (2025)

Approximation of RKHS Functionals by Neural Networks
by: Zhou, Tian-Yi, et al.
Published: (2024)

Improved Particle Approximation Error for Mean Field Neural Networks
by: Nitanda, Atsushi
Published: (2024)

Optimal Neural Network Approximation for High-Dimensional Continuous Functions
by: Maiti, Ayan, et al.
Published: (2024)

On the Approximation of Phylogenetic Distance Functions by Artificial Neural Networks
by: Rosenzweig, Benjamin K., et al.
Published: (2025)

Mitigating Partial Observability in Adaptive Traffic Signal Control with Transformers
by: Wang, Xiaoyu, et al.
Published: (2024)

On Expressive Power of Quantized Neural Networks under Fixed-Point Arithmetic
by: Park, Yeachan, et al.
Published: (2024)

Deep Neural Networks are Adaptive to Function Regularity and Data Distribution in Approximation and Estimation
by: Liu, Hao, et al.
Published: (2024)

Spectrum-Informed Multistage Neural Networks: Multiscale Function Approximators of Machine Precision
by: Ng, Jakin, et al.
Published: (2024)

Constraint-Generation Policy Optimization (CGPO): Nonlinear Programming for Policy Optimization in Mixed Discrete-Continuous MDPs
by: Gimelfarb, Michael, et al.
Published: (2024)

An Overview of Arithmetic Adaptations for Inference of Convolutional Neural Networks on Re-configurable Hardware
by: Wunderlich, Ilkay, et al.
Published: (2025)

On the Dimension-Free Approximation of Deep Neural Networks for Symmetric Korobov Functions
by: Lu, Yulong, et al.
Published: (2025)

Approximating Matrix Functions with Deep Neural Networks and Transformers
by: Padmanabhan, Rahul, et al.
Published: (2026)

SetPINNs: Set-based Physics-informed Neural Networks
by: Nagda, Mayank, et al.
Published: (2024)

Neural Network Symmetrisation in Concrete Settings
by: Cornish, Rob
Published: (2024)

Hamiltonian Neural PDE Solvers through Functional Approximation
by: Zhou, Anthony, et al.
Published: (2025)

Pychop: Emulating Low-Precision Arithmetic in Numerical Methods and Neural Networks
by: Carson, Erin, et al.
Published: (2025)

Reflect-then-Plan: Offline Model-Based Planning through a Doubly Bayesian Lens
by: Jeong, Jihwan, et al.
Published: (2025)

Generalized Multi-hop Traffic Pressure for Heterogeneous Traffic Perimeter Control
by: Li, Xiaocan, et al.
Published: (2024)

Blocked Gibbs meets Diffusion Transformers: Unsupervised Learning for Constraint Optimization
by: Xu, Yudong W., et al.
Published: (2026)

Multigrade Neural Network Approximation
by: Zhang, Shijun, et al.
Published: (2026)

Neural Networks With Dense Weights Are Not Universal Approximators
by: Rauchwerger, Levi, et al.
Published: (2026)

A Quasi-Wasserstein Loss for Learning Graph Neural Networks
by: Cheng, Minjie, et al.
Published: (2023)

Neural Network Training with Approximate Logarithmic Computations
by: Sanyal, Arnab, et al.
Published: (2019)

Fourier Circuits in Neural Networks and Transformers: A Case Study of Modular Arithmetic with Multiple Inputs
by: Li, Chenyang, et al.
Published: (2024)

Arithmetic Circuits and Neural Networks for Regular Matroids
by: Hertrich, Christoph, et al.
Published: (2025)

Random-Set Neural Networks (RS-NN)
by: Manchingal, Shireen Kudukkil, et al.
Published: (2023)

Simultaneous Approximation of the Score Function and Its Derivatives by Deep Neural Networks
by: Yakovlev, Konstantin, et al.
Published: (2025)

Approximation Bounds for Recurrent Neural Networks with Application to Regression
by: Jiao, Yuling, et al.
Published: (2024)

Training Deep Morphological Neural Networks as Universal Approximators
by: Fotopoulos, Konstantinos, et al.
Published: (2025)

QoS-Nets: Adaptive Approximate Neural Network Inference
by: Trommer, Elias, et al.
Published: (2024)

Approximation Capabilities of Feedforward Neural Networks with GELU Activations
by: Yakovlev, Konstantin, et al.
Published: (2025)

Approximation by Steklov Neural Network Operators
by: Karaman, S. N., et al.
Published: (2024)

Dash: Accelerating Distributed Private Convolutional Neural Network Inference with Arithmetic Garbled Circuits
by: Sander, Jonas, et al.
Published: (2023)

On the Topology of Neural Network Superlevel Sets
by: Gharesifard, Bahman
Published: (2026)

Approximation Rates of Shallow Neural Networks: Barron Spaces, Activation Functions and Optimality Analysis
by: Lu, Jian, et al.
Published: (2025)

Probabilistic Functional Neural Networks
by: Wang, Haixu, et al.
Published: (2025)

Enhancing Neural Function Approximation: The XNet Outperforming KAN
by: Li, Xin, et al.
Published: (2025)