Image de couverture de livre

A foundation for exact binarized morphological neural networks

Fuente: arXiv

Enregistré dans:

Détails bibliographiques
Auteurs principaux:	Aouad, Theodore, Talbot, Hugues
Format:	Preprint
Publié:	2024
Sujets:	Machine Learning Artificial Intelligence Computer Vision and Pattern Recognition
Accès en ligne:	Acceder al recurso
Tags:	Ajouter un tag Pas de tags, Soyez le premier à ajouter un tag!

Documents similaires

Can neural networks count digit frequency?
par: Khandelwal, Padmaksh
Publié: (2023)

Kinematic analysis of structural mechanics based on convolutional neural network
par: Zhang, Leye, et autres
Publié: (2024)

Learning Fourier shapes to probe the geometric world of deep neural networks
par: Wang, Jian, et autres
Publié: (2025)

Revisiting semi-supervised learning in the era of foundation models
par: Zhang, Ping, et autres
Publié: (2025)

Sign language recognition from skeletal data using graph and recurrent neural networks
par: Mederos, B., et autres
Publié: (2025)

Exploring possible vector systems for faster training of neural networks with preconfigured latent spaces
par: Gabdullin, Nikita
Publié: (2025)

Evaluating alignment between humans and neural network representations in image-based learning tasks
par: Demircan, Can, et autres
Publié: (2023)

An approach based on class activation maps for investigating the effects of data augmentation on neural networks for image classification
par: Dorneles, Lucas M., et autres
Publié: (2025)

Large language models for artificial general intelligence (AGI): A survey of foundational principles and approaches
par: Mumuni, Alhassan, et autres
Publié: (2025)

Human alignment of neural network representations
par: Muttenthaler, Lukas, et autres
Publié: (2022)

Ditch the Denoiser: Emergence of Noise Robustness in Self-Supervised Learning from Data Curriculum
par: Lu, Wenquan, et autres
Publié: (2025)

Using predefined vector systems as latent space configuration for neural network supervised training on data with arbitrarily large number of classes
par: Gabdullin, Nikita
Publié: (2025)

Towards scientific discovery with dictionary learning: Extracting biological concepts from microscopy foundation models
par: Donhauser, Konstantin, et autres
Publié: (2024)

Permutation-equivariant quantum convolutional neural networks
par: Das, Sreetama, et autres
Publié: (2024)

Early evidence of how LLMs outperform traditional systems on OCR/HTR tasks for historical records
par: Kim, Seorin, et autres
Publié: (2025)

Graph neural network for colliding particles with an application to sea ice floe modeling
par: Zhu, Ruibiao
Publié: (2026)

Joint Embedding vs Reconstruction: Provable Benefits of Latent Space Prediction for Self Supervised Learning
par: Van Assel, Hugues, et autres
Publié: (2025)

From 100,000+ images to winning the first brain MRI foundation model challenges: Sharing lessons and models
par: Gordaliza, Pedro M., et autres
Publié: (2026)

Comprehensive framework for evaluation of deep neural networks in detection and quantification of lymphoma from PET/CT images: clinical insights, pitfalls, and observer agreement analyses
par: Ahamed, Shadab, et autres
Publié: (2023)

Updating the standard neuron model in artificial neural networks
par: Mohedano, Raul, et autres
Publié: (2026)

Conformal uncertainty quantification to evaluate predictive fairness of foundation AI model for skin lesion classes across patient demographics
par: Bhattacharyya, Swarnava, et autres
Publié: (2025)

Dimensions underlying the representational alignment of deep neural networks with humans
par: Mahner, Florian P., et autres
Publié: (2024)

GenRL: Multimodal-foundation world models for generalization in embodied agents
par: Mazzaglia, Pietro, et autres
Publié: (2024)

OPFormer: Object Pose Estimation leveraging foundation model with geometric encoding
par: Moroz, Artem, et autres
Publié: (2025)

PiLocNet: Physics-informed neural network on 3D localization with rotating point spread function
par: Lu, Mingda, et autres
Publié: (2024)

Image space formalism of convolutional neural networks for k-space interpolation
par: Dawood, Peter, et autres
Publié: (2024)

GeONet: a neural operator for learning the Wasserstein geodesic
par: Gracyk, Andrew, et autres
Publié: (2022)

Rolling the dice for better deep learning performance: A study of randomness techniques in deep neural networks
par: Altarabichi, Mohammed Ghaith, et autres
Publié: (2024)

TelcoAI: Advancing 3GPP Technical Specification Search through Agentic Multi-Modal Retrieval-Augmented Generation
par: Ghosh, Rahul, et autres
Publié: (2025)

Implicit neural representation of textures
par: Kwok, Albert, et autres
Publié: (2026)

Neuron-based explanations of neural networks sacrifice completeness and interpretability
par: Dey, Nolan, et autres
Publié: (2020)

ProtoS-ViT: Visual foundation models for sparse self-explainable classifications
par: Turbé, Hugues, et autres
Publié: (2024)

Learning advisor networks for noisy image classification
par: Ricci, Simone, et autres
Publié: (2022)

Forward-Cooperation-Backward (FCB) learning in a Multi-Encoding Uni-Decoding neural network architecture
par: Dutta, Prasun, et autres
Publié: (2025)

Robustness to distribution shifts of compressed networks for edge devices
par: Shen, Lulan, et autres
Publié: (2024)

HCR-Net: A deep learning based script independent handwritten character recognition network
par: Chauhan, Vinod Kumar, et autres
Publié: (2021)

Training morphological neural networks with gradient descent: some theoretical insights
par: Blusseau, Samy
Publié: (2024)

Revisiting FunnyBirds evaluation framework for prototypical parts networks
par: Opłatek, Szymon, et autres
Publié: (2024)

Comparing supervised learning dynamics: Deep neural networks match human data efficiency but show a generalisation lag
par: Huber, Lukas S., et autres
Publié: (2024)

On the Difficulty of Learning a Meta-network for Training Data Selection
par: Du, Zilin, et autres
Publié: (2026)