:: Library Catalog

Cover Image

Saved in:

Bibliographic Details
Main Authors:	Chiu, Shao-Ting, Nambiar, Aditya, Syed, Ali, Siegel, Jonathan W., Braga-Neto, Ulisses
Format:	Preprint
Published:	2025
Subjects:	Machine Learning
Online Access:	https://arxiv.org/abs/2512.16074
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

DeepOSets: Non-Autoregressive In-Context Learning with Permutation-Invariance Inductive Bias
by: Chiu, Shao-Ting, et al.
Published: (2024)

Embedding Dimension Lower Bounds for Universality of Deep Sets and Janossy Pooling
by: Syed, Ali, et al.
Published: (2026)

BumpNet: A Sparse MLP Framework for Learning PDE Solutions
by: Chiu, Shao-Ting, et al.
Published: (2025)

Free-RBF-KAN: Kolmogorov-Arnold Networks with Adaptive Radial Basis Functions for Efficient Function Learning
by: Chiu, Shao-Ting, et al.
Published: (2026)

Generalized Resubstitution for Regression Error Estimation
by: Marcondes, Diego, et al.
Published: (2024)

Self-Adaptive Physics-Informed Neural Networks using a Soft Attention Mechanism
by: McClenny, Levi, et al.
Published: (2020)

Convolution Operator Network for Forward and Inverse Problems (FI-Conv): Application to Plasma Turbulence Simulations
by: Chen, Xingzhuo, et al.
Published: (2026)

Label Propagation Training Schemes for Physics-Informed Neural Networks and Gaussian Processes
by: Zhong, Ming, et al.
Published: (2024)

A State-Space Approach to Nonstationary Discriminant Analysis
by: Xie, Shuilian, et al.
Published: (2025)

Quantitative Approximation Rates for Group Equivariant Learning
by: Siegel, Jonathan W., et al.
Published: (2026)

Optimal Approximation Rates for Deep ReLU Neural Networks on Sobolev and Besov Spaces
by: Siegel, Jonathan W.
Published: (2022)

Sharp Lower Bounds on Interpolation by Deep ReLU Neural Networks at Irregularly Spaced Data
by: Siegel, Jonathan W.
Published: (2023)

Advancing Welding Defect Detection in Maritime Operations via Adapt-WeldNet and Defect Detection Interpretability Analysis
by: S, Kamal Basha, et al.
Published: (2025)

SegXAL: Explainable Active Learning for Semantic Segmentation in Driving Scene Scenarios
by: Mandalika, Sriram, et al.
Published: (2024)

The AI Scientific Community: Agentic Virtual Lab Swarms
by: Braga-Neto, Ulisses
Published: (2026)

When Network Architecture Meets Physics: Deep Operator Learning for Coupled Multiphysics
by: Kobayashi, Kazuma, et al.
Published: (2025)

Optimizing 2D+1 Packing in Constrained Environments Using Deep Reinforcement Learning
by: Pugliese, Victor Ulisses, et al.
Published: (2025)

Equivariant Frames and the Impossibility of Continuous Canonicalization
by: Dym, Nadav, et al.
Published: (2024)

Sharp Convergence Rates for Matching Pursuit
by: Klusowski, Jason M., et al.
Published: (2023)

Orion-MSP: Multi-Scale Sparse Attention for Tabular In-Context Learning
by: Bouadi, Mohamed, et al.
Published: (2025)

Optimal Approximation of Zonoids and Uniform Approximation by Shallow Neural Networks
by: Siegel, Jonathan W.
Published: (2023)

Data-Efficient Operator Learning via Unsupervised Pretraining and In-Context Learning
by: Chen, Wuyang, et al.
Published: (2024)

Deep Neural Operator Driven Real Time Inference for Nuclear Systems to Enable Digital Twin Solutions
by: Kobayashi, Kazuma, et al.
Published: (2023)

In-Context Learning Operates as Concept Subspace Learning
by: Tang, Wei, et al.
Published: (2026)

Dual Operating Modes of In-Context Learning
by: Lin, Ziqian, et al.
Published: (2024)

Context-Aware Deep Learning for Multi Modal Depression Detection
by: Lam, Genevieve, et al.
Published: (2024)

Replay to Remember (R2R): An Efficient Uncertainty-driven Unsupervised Continual Learning Framework Using Generative Replay
by: Mandalika, Sriram, et al.
Published: (2025)

Evaluating the Performance of Large Language Models for SDG Mapping (Technical Report)
by: Yin, Hui, et al.
Published: (2024)

Sharp Bounds on the Approximation Rates, Metric Entropy, and $n$-widths of Shallow Neural Networks
by: Siegel, Jonathan W., et al.
Published: (2021)

Nesterov acceleration despite very noisy gradients
by: Gupta, Kanan, et al.
Published: (2023)

Can Transformers Break Encryption Schemes via In-Context Learning?
by: Korrapati, Jathin, et al.
Published: (2025)

Deep Reinforcement Learning for Dynamic Order Picking in Warehouse Operations
by: Mahmoudinazlou, Sasan, et al.
Published: (2024)

On the expressiveness and spectral bias of KANs
by: Wang, Yixuan, et al.
Published: (2024)

Dynamic Latent Separation for Deep Learning
by: Tuan, Yi-Lin, et al.
Published: (2022)

A Deep Learning Framework for Multi-Operator Learning: Architectures and Approximation Theory
by: Weihs, Adrien, et al.
Published: (2025)

Operator-Based Generalization Bound for Deep Learning: Insights on Multi-Task Learning
by: Mohammadigohari, Mahdi, et al.
Published: (2025)

Training In-Context and In-Weights Mixtures Via Contrastive Context Sampling
by: Malu, Deeptanshu, et al.
Published: (2026)

Look Into the LITE in Deep Learning for Time Series Classification
by: Ismail-Fawaz, Ali, et al.
Published: (2024)

A Multi-Layer Cloud-IDS Pipeline with LLM and Adaptive Q-Learning Calibration
by: Ali, Syed Waqas, et al.
Published: (2026)

Energy-Efficient Prediction in Textile Manufacturing: Enhancing Accuracy and Data Efficiency With Ensemble Deep Transfer Learning
by: Chen, Yan-Chen, et al.
Published: (2026)