Convolutional Neural Networks

Also known as CNN

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39 papers in the last four weeks, up 26% on the four weeks before. 0.2% of all new papers.

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On Making $\mathrm{SE}(2)$ -Invariant Networks Optimal
Oct 12, 2026Tomas Karella, Emily Shinkle, Alice Elisabeth Anastasia Allen +3Invariant Representation Learning Equivariant Neural Networks
CAI-3: Information Sciences Los Alamos National Laboratory · Machine Learning in Molecular Dynamics Max Planck Institute for Polymer Research · T-5: Applied Mathematics and Plasma Physics Los Alamos National Laboratory
Neural Network Preconditioning: A Case Study for the Solution of the Parametric Helmholtz Equation
Oct 12, 2026Luc Giraud, Carola Kruse, Paul Mycek +2PDE Solving Neural PDE Solvers
Inria, Concace joint team between Airbus CR&T, Cerfacs and Inria · Cerfacs, Concace joint team between Airbus CR&T, Cerfacs and Inria · Inria, Concace joint team between Airbus CR&T, Cerfacs and Inria. +5
Memorization in image classification: test set behavior and scaling laws
Oct 12, 2026Jeevesh Attri, Florentin GuthNeural Network Memorization Neural Network Training Dynamics
Center for Data Science New York University · Center for Computational Neuroscience Flatiron Institute · Work done while partially affiliated with Center for Data Science, New York University.
RIECNN: Real-Time Image Enhanced CNN for Traffic Sign Recognition
Oct 10, 2026Reem Abdel-Salam, Rana Mostafa, Ahmed H. Abdel-GawadConvolutional Neural Networks Autonomous Driving Perception
Computer Engineering Department, Cairo University, Giza, Egypt
Region Aware Squeeze-and-Excitation Networks for Image Segmentation
Oct 10, 2026Jack Roberts, Praneeth Tummala, Kavi Sarna +1Channel Attention Image Segmentation
Department of Computer Science, Brown University, Providence, RI, USA · Department of Computer Science, Bowdoin College, Brunswick, ME, USA
Use Of Convolutional Neural Networks In Detection Of Branch Dieback Disease In Pequi Trees
Oct 10, 2026Ronilson Rodrigues Pinho, Elua Ramos Coutinho, Nilza de Lima Pereira Sales +2Plant Disease Classification Convolutional Neural Networks
Federal University of Rio de Janeiro – UFRJ- Institute of Computing – IC - Postgraduate Program in Informatics - PPGI- Rio de Janeiro, 20010-974, Brazil · Federal Center for Technological Education Celso Suckow da Fonseca- CEFET-RJ-BSI- Rio de Janeiro, RJ, 20785-220, Brazil · Fluminense Federal University – UFF - Instituto de Ciência e Tecnologia – Rio das Ostras - RJ - 28895-532, Brazil +1
Layer-wise Input Recoverability in Deep Neural Networks
Oct 9, 2026Maxwell Spiridonov, Alireza Rahmansetayesh, Pierre BaldiConvolutional Neural Networks Neural Network Interpretability
Department of Computer Science, University of California, Irvine
SADL: A Size-Adaptive Decision Layer for Chronic Stroke Lesion Segmentation in ISLES 2026
Oct 9, 2026Marek Wodzinski, Gniewosz Drwiega, Wojciech SzymanskiMedical Image Segmentation Convolutional Neural Networks
Sano – Centre for Computational Personalised Medicine International Research Foundation, Krakow, Poland · AGH University of Krakow, Krakow, Poland
Mesh-Native Tokenisation: Retrofitting Foundation Models to Unstructured Data
Oct 8, 2026Georg Maierhofer, Miles Cranmer, Payel MukhopadhyayConvolutional Neural Networks
University of Cambridge
SV-TAD: Native Sparse Convs for Efficient Temporal Action Detection
Oct 8, 2026Ricardo Pizarro, Roberto Valle, José M. Buenaposada +2Temporal Action Detection Efficient ViTs
Universidad de Alcal´a, Alcal´a de Henares, Spain · Universidad Polit´ecnica de Madrid, Madrid, Spain · Universidad Rey Juan Carlos, M´ostoles, Spain
HAND: A Biologically-Inspired Activation Function that Improves Generalisation and Sample Efficiency in Image Classification
Oct 8, 2026Michael W. Spratling, Heiko H. SchüttNeural Network Activation Functions Neural Network Generalization
University of Luxembourg, Department of Behavioural and Cognitive Sciences, L-4366 Esch-sur-Alzette, Luxembourg.
MCL: Meta Convolution Layer
Oct 8, 2026Naim Reza, Md Al Amin, Ho Yub JungDynamic Convolution Convolutional Neural Networks
Department of Computer Engineering, Chosun University, Gwangju 61452, Republic of Korea
R-CNN-Based Chess Position Recognition
Oct 6, 2026Paras Govind, Ognjen ArandjelovićChess Convolutional Neural Networks
School of Computer Science University of St Andrews United Kingdom
MemFLoRA: Memory-Floor LoRA for CNN Adaptation at the Edge
Oct 6, 2026Mehmet Emre Akbulut, Johannes Geier, Ulf SchlichtmannOn-Device Training Memory-Efficient Fine-Tuning
Technical University of Munich Munich, Germany
CNet: A Complex-Valued Deep Learning Framework with Wirtinger Autodifferentiation and FFT--Hadamard Convolution
Oct 6, 2026Marcel CrasmaruComplex-Valued Neural Networks Autoregressive Language Modeling
2D Spatial Reasoning with Adaptive Neural Cellular Automata
Oct 6, 2026Martin Spitznagel, Janis KeuperNeural Cellular Automata Visual Spatial Reasoning
Institute for Machine Learning and Analytics (IMLA), Offenburg University, Germany · University of Mannheim, Germany
Muon Is Theoretically Wrong For Convolutions, But Empirically Effective
Oct 5, 2026Thibaut Boissin, Thomas Massena, Mathieu Serrurier +1Newton-Schulz Iteration Neural Network Optimization
IRIT · IRIT, DTIPG - SNCF, UT3 · Institut de Recherche Technologique Saint-Exupery, France
Co-Optimizing Graph Sparsification and Approximate Computing for Energy-Efficient FPGA-Based GCN Inference
Oct 5, 2026Nathaniel Kaye Mellor, Shreejith Shanker, George FlorosEdge Computing Graph Sparsification
Department of Electronic and Electrical Engineering, Trinity College Dublin, Ireland
An Empirical Fault Vulnerability Exploration of ReRAM-based Process-in-Memory CNN Accelerators
Oct 4, 2026Aniseh Dorostkar, Hamed Farbeh, Hamid R. ZarandiNeural Network Robustness Convolutional Neural Networks
Amirkabir University of Technology (Tehran Polytechnic), Tehran, Iran
CGDD-Net: Context-Guided Dynamic Detail Modeling for Retinal Vessel Segmentation
Oct 4, 2026Xincheng Li, Xiaoqi Sheng, Xinyu ZhangRetinal Vessel Segmentation Multi-Scale Feature Fusion
School of Computer Science, The University of Auckland, Auckland, 1010, New Zealand · School of Future Technology, South China University of Technology, Guangzhou, 510641, Guangdong, China
One-Cycle Fault Classification and Faulted-Line Identification on the PROTECT-90 Dataset: An Initial Application Benchmark
Oct 2, 2026Emad Abukhousa, Abdulaziz Qwbaiban, Saman Zonouz +1Fault Diagnosis Power Systems
School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA, USA · Electrical Engineering Department, King Fahd University of Petroleum and Minerals, Dhahran, Saudi Arabia · Interdisciplinary Research Center for Sustainable Energy Systems (IRC-SES), KFUPM, Dhahran 31261, Saudi Arabia
Why Convolution Still Matters: Evaluating Inductive Biases in Cryospheric Image Classification
Oct 2, 2026Chhaya Kulkarni, Emam HossainConvolutional Neural Networks Remote Sensing Image Understanding
Department of Computer and Information Sciences Towson University · Department of Information Systems University of Maryland, Baltimore County
16-bit Precision of Convolutional Neural Networks on Microcontroller Units for 8-bit Costs
Oct 2, 2026Rui Liu, Benjamin PaaßenNeural Network Quantization Efficient Neural Network Inference
Faculty of Technology, Bielefeld University
A hybrid CNN-adjoint optimization framework for reconstruction of viscoelastic tissue properties in magnetic resonance elastography
Oct 2, 2026Anwesa Dey, Johann Rudi, Elena CherkaevImage Inverse Problems Magnetic Resonance Imaging
Department of Mathematics, The University of Utah, address USA. · Department of Mathematics, Virginia Tech, address USA. · Department of Mathematics, The University of Utah, address, address, USA.
Effects of interpulse-interval variation on deep-learning classification of bat vocalizations
Oct 1, 2026Welmoed R. Eversteijn, Burooj Ghani, A. Leonie Baier +1Classification Bioacoustics
Naturalis Biodiversity Centre, Darwinweg 2, Leiden, 2333 CR, The Netherlands · Department of Cognitive Science and Artificial Intelligence, Tilburg University, Warandelaan 2, Tilburg, 5037 AB, The Netherlands
Increasing Width Allows Greedy Layer-wise Training to Rival End-to-End Backpropagation in Self-Supervised Learning
Sep 30, 2026Syon Mansur, Joel ZylberbergDeep Learning Optimization Convolutional Neural Networks
Jules Stein Eye Institute University of California, Los Angeles Los Angeles, CA 90095, USA
Right In-Place (RiP) Convolution: A Simple, General, and Near-Optimal Strategy for Memory-Efficient CNN Inference
Sep 30, 2026Opegbemi Matthias Busoye, Tolulope Matthew Busoye, Eghonghon-aye EigbeMemory-Efficient Inference Convolutional Neural Networks
PowerLabs Technologies, Lagos, Nigeria
Sub-Model Short-Term Memory Convolutions for Keyword Spotting Systems on Device
Sep 28, 2026Paweł Warlewski, Artur Czeczko, Artur Szumaczuk +2Keyword Spotting Speech Processing
Samsung R&D Institute Poland · Samsung AI Center Warsaw
SincDPNet: Interpretable Raw-Waveform Bathroom Activity Recognition for Assistive Living
Sep 28, 2026Debolina Chowdhury, Suman Samui, Sujoy SahaTiny ML Sound Event Detection
Department of Computer Science and Engineering, National Institute of Technology Durgapur, Durgapur 713209, West Bengal, India · Department of Electronics and Communication Engineering, National Institute of Technology Durgapur, Durgapur 713209, West Bengal, India
Analytical and Convolutional Neural Network-Based Motion-Vector Propagation for Efficient Video Object Detection
Sep 28, 2026Ashiyana Abdul Majeed, Mahmoud Meribout, Neethu JosephEfficient Neural Network Inference Edge Computing
Department of Computer and Information Engineering, Khalifa University, Abu Dhabi, UAE

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On Making $\mathrm{SE}(2)$ -Invariant Networks Optimal

Tomas Karella, Emily Shinkle, Alice Elisabeth Anastasia Allen, Pieter J. Swart+2

CAI-3: Information Sciences Los Alamos National Laboratory · Machine Learning in Molecular Dynamics Max Planck Institute for Polymer Research · T-5: Applied Mathematics and Plasma Physics Los Alamos National Laboratory

Invariant Representation Learning Equivariant Neural Networks Convolutional Neural Networks

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Respecting a-priori known symmetries of underlying data is a key principle in designing efficient neural architectures. Convolutional neural networks are a canonical example, encoding translation equivariance. Extending this inductive bias to richer symmetry groups such as

\mathrm{SE}(2)

in the most effective way possible is an ongoing architectural and computational challenge. Building on classical invariant theory, we derive invariant feature maps that are provably optimal: minimal and information-preserving with respect to the symmetry group. Our construction is designed to integrate seamlessly into standard architectures, requiring no modification to nonlinearities or normalization layers. As a concrete implementation, we introduce a plug-and-play module for convolutional neural networks that enforces

\mathrm{SE}(2)

invariance, capturing both translations and rotations within standard architectures. The resulting models are both efficient and expressive. Experiments show competitive or improved accuracy compared to state-of-the-art equivariant methods at significantly lower computational cost. Finally, we provide a theoretical and empirical analysis of the relationship between equivariance and invariance, offering insight into their roles in controlling robustness and flexibility.