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#p-kutyniok

EDM+26

ExPLAIND: Unifying Model, Data, and Training Attribution to Study Model Behavior

ICML 2026

#p-hedderich #p-kutyniok #p-plank

MFC+26

Conflicting Biases at the Edge of Stability: Norm Versus Sharpness Regularization

ICML 2026

#p-kutyniok #p-maly

AHC+26

Neural Poisson Surface Reconstruction: Resolution-Agnostic Shape Reconstruction From Point Clouds

3D4S @CVPR 2026

#p-cremers #p-kutyniok

ESA+26

VideoWeaver: Multimodal Multi-View Video-to-Video Transfer for Embodied Agents

CVPR 2026

#p-kutyniok

BYE+26

DRAW2ACT: Turning Depth-Encoded Trajectories Into Robotic Demonstration Videos

ICRA 2026

#p-kutyniok

YBE+26

ICRA 2026

#p-kutyniok

YBW+26

Lightweight Learning From Actuation-Space Demonstrations via Flow Matching for Whole-Body Soft Robotic Grasping

IEEE Robotics and Automation Letters 11.6. Jun. 2026

#p-kutyniok

BEL+26

Physics-Informed Video Diffusion for Shallow Water Equations

ICASSP 2026

#p-kutyniok

ADK26

Random Spiking Neural Networks Are Stable and Spectrally Simple

ICLR 2026

#p-kutyniok

MPJ+26

Graph Representational Learning: When Does More Expressivity Hurt Generalization?

ICLR 2026

#p-kutyniok

SCV+26

GradPCA: Leveraging NTK Alignment for Reliable Out-of-Distribution Detection

ICLR 2026

#p-kutyniok

BFD+26

The Price of Robustness: Stable Classifiers Need Overparameterization

ICLR 2026

#p-kutyniok

NDA+26

Expressive Power of Recurrent Spiking Neural Networks for Sequence Modeling

TSALM @ICLR 2026

#p-kutyniok

FWB+26

Complexity Theory Meets Ordinary Differential Equations

Preprint (Apr. 2026)

#p-kutyniok

KME+26

Copy First, Translate Later: Interpreting Translation Dynamics in Multilingual Pretraining

Preprint (Apr. 2026)

#p-hedderich #p-kutyniok #p-plank

LHF+26

Sparse-Aware Neural Networks for Nonlinear Functionals: Mitigating the Exponential Dependence on Dimension

Preprint (Apr. 2026)

#p-kutyniok

FSA+26

Mathematical Foundations of Spiking Neural Networks: Strengths, Challenges, and Computational Paradigm Potential [Special Issue on the Mathematics of Deep Learning]

IEEE Signal Processing Magazine 43.2. Mar. 2026

#p-kutyniok

BKH+26

Adaptive-CaRe: Adaptive Causal Regularization for Robust Outcome Prediction

Preprint (Feb. 2026)

#p-kutyniok

LHS+26

Conditional Deep Generative Modeling of Blood-Based Infrared Spectra Enables Controlled In-Silico Phenotyping Studies

Preprint (Feb. 2026)

#p-kutyniok

FKB26

How to Realize Efficient Spiking Neural Networks?

MATH4AI @AAAI 2026

#p-kutyniok

KBS+26

Improved Probabilistic Regression Using Diffusion Models

NLDL 2026

#p-kutyniok

KBS+25

Uncertainty-Aware Diffusion Models for Probabilistic Regression

EIML @EurIPS 2025

#p-kutyniok

KBK+25

The Ethics of Analog AI

AI and Ethics 6.27. Dec. 2025

#p-kutyniok #p-nyholm

LRK+25

CHEM: Estimating and Understanding Hallucinations in Deep Learning for Image Processing

Preprint (Dec. 2025)

#p-kutyniok

BSA+25

Revisiting Glorot Initialization for Long-Range Linear Recurrences

NeurIPS 2025

#p-kutyniok

SZS+25

When Is a System Discoverable From Data? Discovery Requires Chaos

Preprint (Nov. 2025)

#p-kutyniok

KCC+25

Learning Interpretable Queries for Explainable Image Classification With Information Pursuit

ICCV 2025

#p-cremers #p-kutyniok

BSK+25

Uncertainty Quantification for Regression: A Unified Framework Based on Kernel Scores

Preprint (Oct. 2025)

#p-huellermeier #p-kutyniok

RSV+25

A Continuous and Interpretable Morphometric for Robust Quantification of Dynamic Biological Shapes

Preprint (Oct. 2025)

#p-kutyniok

SBK25

A Variational Framework for the Algorithmic Complexity of PDE Solutions

Preprint (Oct. 2025)

#p-kutyniok

BFD+25

The Price of Robustness: Stable Classifiers Need Overparameterization

HiLD @ICML 2025

#p-kutyniok

NAF+25

Time to Spike? Understanding the Representational Power of Spiking Neural Networks in Discrete Time

ICML 2025

#p-kutyniok

LK25

Expressivity of Deep Neural Networks

Preprint (Jul. 2025)

#p-kutyniok

BFK25

Mathematical Algorithm Design for Deep Learning Under Societal and Judicial Constraints: The Algorithmic Transparency Requirement

Applied and Computational Harmonic Analysis 77.101763. Jun. 2025

#p-kutyniok

MKL25

Generalization Bounds for Message Passing Networks on Mixture of Graphons

SIAM Journal on Mathematics of Data Science 7.2. Jun. 2025

#p-kutyniok

BFF+25

Computability of Classification and Deep Learning: From Theoretical Limits to Practical Feasibility Through Quantization

Journal of Fourier Analysis and Applications 31.35. May. 2025

#p-kutyniok

SDW+25

Interpretable Robotic Friction Learning via Symbolic Regression

Preprint (May. 2025)

#p-kutyniok

BMS+25

Graph Neural Networks for Enhancing Ensemble Forecasts of Extreme Rainfall

Climate Change AI @ICLR 2025

#p-kutyniok

SBH+25

ParFam -- (Neural Guided) Symbolic Regression Based on Continuous Global Optimization

ICLR 2025

#p-kutyniok

HSK25

Robust Identifiability for Symbolic Recovery of Differential Equations

ICASSP 2025

#p-kutyniok

Kut25

How Can Reliability of Artificial Intelligence Be Ensured?

Harvard Data Science Review 7.2. Apr. 2025

#p-kutyniok

BSL+25

An Axiomatic Assessment of Entropy- And Variance-Based Uncertainty Quantification in Regression

Preprint (Apr. 2025)

#p-huellermeier #p-kutyniok

BSK25

Probabilistic Neural Operators for Functional Uncertainty Quantification

Transactions on Machine Learning Research. Mar. 2025

#p-kutyniok

BKS+24

Compositional Construction of Barrier Functions for Switched Impulsive Systems

CDC 2024

#p-kutyniok

BSK24

Probabilistic Predictions With Fourier Neural Operators

BDU @NeurIPS 2024

#p-kutyniok

PMW+24

Weisfeiler and Leman Go Loopy: A New Hierarchy for Graph Representational Learning

NeurIPS 2024

#p-kutyniok

BBF+24

Computing-Model and Computing-Hardware Selection for ICT Under Societal and Judicial Constraints

IEEE Access 12. Dec. 2024

#p-kutyniok

SIW+24

Learning-Based Adaption of Robotic Friction Models

Robotics and Computer-Integrated Manufacturing 89. Oct. 2024

#p-kutyniok

SBB+24

Symbolic Recovery of Differential Equations: The Identifiability Problem

Preprint (Oct. 2024)

#p-kutyniok

YLK+24

Dataset of Pathloss and ToA Radio Maps With Localization Application

Preprint (Sep. 2024)

#p-kutyniok

BFK24

A Mathematical Framework for Computability Aspects of Algorithmic Transparency

ISIT 2024

#p-kutyniok

YFL+24

Overview of the First Pathloss Radio Map Prediction Challenge

IEEE Open Journal of Signal Processing 5. Jun. 2024

#p-kutyniok

LBK24

Computability of Optimizers

IEEE Transactions on Information Theory 70.4. Apr. 2024

#p-kutyniok

LBK24a

Error Estimation for Physics-Informed Neural Networks Approximating Semilinear Wave Equations

Preprint (Mar. 2024)

#p-kutyniok

SFK24b

Expressivity of Spiking Neural Networks

Preprint (Mar. 2024)

#p-kutyniok

MPB+23

A Fractional Graph Laplacian Approach to Oversmoothing

NeurIPS 2023

#p-kutyniok

SWG+23

Neural (Tangent Kernel) Collapse

NeurIPS 2023

#p-kutyniok

SFK23

Are Spiking Neural Networks More Expressive Than Artificial Neural Networks?

UniReps @NeurIPS 2023

#p-kutyniok

BFK23

Limitations of Deep Learning for Inverse Problems on Digital Hardware

IEEE Transactions on Information Theory 69.12. Dec. 2023

#p-kutyniok

YLK+23

Real-Time Outdoor Localization Using Radio Maps: A Deep Learning Approach

IEEE Transactions on Wireless Communications 22.12. Dec. 2023

#p-kutyniok

YJR+23

Overview of the Urban Wireless Localization Competition

MLSP 2023

#p-kutyniok

BBK23a

Complexity Blowup for Solutions of the Laplace and the Diffusion Equation

Preprint (Sep. 2023)

#p-kutyniok

ADT+23

Sumformer: Universal Approximation for Efficient Transformers

TAG-ML @ICML 2023

#p-kutyniok

Kut23

An Introduction to the Mathematics of Deep Learning

European Congress of Mathematics. Jul. 2023

#p-kutyniok

BBK23

Reliable AI: Does the Next Generation Require Quantum Computing?

Preprint (Jul. 2023)

#p-kutyniok

SBB+23

The Uniqueness Problem of Physical Law Learning

ICASSP 2023

#p-kutyniok

YJL+23

The First Pathloss Radio Map Prediction Challenge

ICASSP 2023

#p-kutyniok

PBG+23

Unveiling the Sampling Density in Non-Uniform Geometric Graphs

ICLR 2023

#p-guennemann #p-kutyniok

BFK22

Non-Computability of the Pseudoinverse on Digital Computers

Preprint (Dec. 2022)

#p-kutyniok

KK22a

Graph Scattering Beyond Wavelet Shackles

NeurIPS 2022

#p-cremers #p-kutyniok

MLL+22

Generalization Analysis of Message Passing Neural Networks on Large Random Graphs

NeurIPS 2022

#p-kutyniok

ZKR22

OOD Link Prediction Generalization Capabilities of Message-Passing GNNs in Larger Test Graphs

NeurIPS 2022

#p-kutyniok

#p-kutyniok

ExPLAIND: Unifying Model, Data, and Training Attribution to Study Model Behavior

Conflicting Biases at the Edge of Stability: Norm Versus Sharpness Regularization

Neural Poisson Surface Reconstruction: Resolution-Agnostic Shape Reconstruction From Point Clouds

VideoWeaver: Multimodal Multi-View Video-to-Video Transfer for Embodied Agents

DRAW2ACT: Turning Depth-Encoded Trajectories Into Robotic Demonstration Videos

CoVAR: Co-Generation of Video and Action for Robotic Manipulation via Multi-Modal Diffusion

Lightweight Learning From Actuation-Space Demonstrations via Flow Matching for Whole-Body Soft Robotic Grasping

Physics-Informed Video Diffusion for Shallow Water Equations

Random Spiking Neural Networks Are Stable and Spectrally Simple

Graph Representational Learning: When Does More Expressivity Hurt Generalization?

GradPCA: Leveraging NTK Alignment for Reliable Out-of-Distribution Detection

The Price of Robustness: Stable Classifiers Need Overparameterization

Expressive Power of Recurrent Spiking Neural Networks for Sequence Modeling

Complexity Theory Meets Ordinary Differential Equations

Copy First, Translate Later: Interpreting Translation Dynamics in Multilingual Pretraining

Sparse-Aware Neural Networks for Nonlinear Functionals: Mitigating the Exponential Dependence on Dimension

Mathematical Foundations of Spiking Neural Networks: Strengths, Challenges, and Computational Paradigm Potential [Special Issue on the Mathematics of Deep Learning]

Adaptive-CaRe: Adaptive Causal Regularization for Robust Outcome Prediction

Conditional Deep Generative Modeling of Blood-Based Infrared Spectra Enables Controlled In-Silico Phenotyping Studies

How to Realize Efficient Spiking Neural Networks?

Improved Probabilistic Regression Using Diffusion Models

Uncertainty-Aware Diffusion Models for Probabilistic Regression

The Ethics of Analog AI

CHEM: Estimating and Understanding Hallucinations in Deep Learning for Image Processing

Revisiting Glorot Initialization for Long-Range Linear Recurrences

When Is a System Discoverable From Data? Discovery Requires Chaos

Learning Interpretable Queries for Explainable Image Classification With Information Pursuit

Uncertainty Quantification for Regression: A Unified Framework Based on Kernel Scores

A Continuous and Interpretable Morphometric for Robust Quantification of Dynamic Biological Shapes

A Variational Framework for the Algorithmic Complexity of PDE Solutions

The Price of Robustness: Stable Classifiers Need Overparameterization

Time to Spike? Understanding the Representational Power of Spiking Neural Networks in Discrete Time

Expressivity of Deep Neural Networks

Mathematical Algorithm Design for Deep Learning Under Societal and Judicial Constraints: The Algorithmic Transparency Requirement

Generalization Bounds for Message Passing Networks on Mixture of Graphons

Computability of Classification and Deep Learning: From Theoretical Limits to Practical Feasibility Through Quantization

Interpretable Robotic Friction Learning via Symbolic Regression

Graph Neural Networks for Enhancing Ensemble Forecasts of Extreme Rainfall

ParFam -- (Neural Guided) Symbolic Regression Based on Continuous Global Optimization

Robust Identifiability for Symbolic Recovery of Differential Equations

How Can Reliability of Artificial Intelligence Be Ensured?

An Axiomatic Assessment of Entropy- And Variance-Based Uncertainty Quantification in Regression

Probabilistic Neural Operators for Functional Uncertainty Quantification

Compositional Construction of Barrier Functions for Switched Impulsive Systems

Probabilistic Predictions With Fourier Neural Operators

Weisfeiler and Leman Go Loopy: A New Hierarchy for Graph Representational Learning

Computing-Model and Computing-Hardware Selection for ICT Under Societal and Judicial Constraints

Learning-Based Adaption of Robotic Friction Models

Symbolic Recovery of Differential Equations: The Identifiability Problem

Dataset of Pathloss and ToA Radio Maps With Localization Application

A Mathematical Framework for Computability Aspects of Algorithmic Transparency

Overview of the First Pathloss Radio Map Prediction Challenge

Computability of Optimizers

Error Estimation for Physics-Informed Neural Networks Approximating Semilinear Wave Equations

Expressivity of Spiking Neural Networks

A Fractional Graph Laplacian Approach to Oversmoothing

Neural (Tangent Kernel) Collapse

Are Spiking Neural Networks More Expressive Than Artificial Neural Networks?

Limitations of Deep Learning for Inverse Problems on Digital Hardware

Real-Time Outdoor Localization Using Radio Maps: A Deep Learning Approach

Overview of the Urban Wireless Localization Competition

Complexity Blowup for Solutions of the Laplace and the Diffusion Equation

Sumformer: Universal Approximation for Efficient Transformers

An Introduction to the Mathematics of Deep Learning

Reliable AI: Does the Next Generation Require Quantum Computing?

The Uniqueness Problem of Physical Law Learning

The First Pathloss Radio Map Prediction Challenge

Unveiling the Sampling Density in Non-Uniform Geometric Graphs

Non-Computability of the Pseudoinverse on Digital Computers

Graph Scattering Beyond Wavelet Shackles

Generalization Analysis of Message Passing Neural Networks on Large Random Graphs

OOD Link Prediction Generalization Capabilities of Message-Passing GNNs in Larger Test Graphs