Imagen de Portada

A survey and taxonomy of loss functions in machine learning

Fuente: arXiv

Guardado en:

Detalles Bibliográficos
Autores principales:	Ciampiconi, Lorenzo, Elwood, Adam, Leonardi, Marco, Mohamed, Ashraf, Rozza, Alessandro
Formato:	Preprint
Publicado:	2023
Materias:	Machine Learning
Acceso en línea:	Acceder al recurso
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

Ejemplares similares

A Primal-Dual Online Learning Approach for Dynamic Pricing of Sequentially Displayed Complementary Items under Sale Constraints
por: Stradi, Francesco Emanuele, et al.
Publicado: (2024)

Predicting loss-of-function impact of genetic mutations: a machine learning approach
por: Kaur, Arshmeet, et al.
Publicado: (2024)

A step towards the integration of machine learning and classic model-based survey methods
por: Żądło, Tomasz, et al.
Publicado: (2024)

Towards a machine learning pipeline in reduced order modelling for inverse problems: neural networks for boundary parametrization, dimensionality reduction and solution manifold approximation
por: Ivagnes, Anna, et al.
Publicado: (2022)

Optimal feature rescaling in machine learning based on neural networks
por: Vitrò, Federico Maria, et al.
Publicado: (2024)

The Nyström method for convex loss functions
por: Della Vecchia, Andrea, et al.
Publicado: (2020)

FastNet: Improving the physical consistency of machine-learning weather prediction models through loss function design
por: Dunstan, Tom, et al.
Publicado: (2025)

Latent Dynamics Graph Convolutional Networks for model order reduction of parameterized time-dependent PDEs
por: Tomada, Lorenzo, et al.
Publicado: (2026)

GL-TSVM: A robust and smooth twin support vector machine with guardian loss function
por: Akhtar, Mushir, et al.
Publicado: (2024)

A survey and taxonomy of methods interpreting random forest models
por: Haddouchi, Maissae, et al.
Publicado: (2024)

A local approach to parameter space reduction for regression and classification tasks
por: Romor, Francesco, et al.
Publicado: (2021)

Reinforcement learning-based architecture search for quantum machine learning
por: Rapp, Frederic, et al.
Publicado: (2024)

Assessment of machine learning methods for state-to-state approaches
por: Campoli, Lorenzo, et al.
Publicado: (2021)

Deep learning outperforms traditional machine learning methods in predicting childhood malnutrition: evidence from survey data
por: Bastola, Deepak, et al.
Publicado: (2026)

Multi-objective methods in Federated Learning: A survey and taxonomy
por: Hartmann, Maria, et al.
Publicado: (2025)

Self-test loss functions for learning weak-form operators and gradient flows
por: Gao, Yuan, et al.
Publicado: (2024)

Q-learning with temporal memory to navigate turbulence
por: Rando, Marco, et al.
Publicado: (2024)

A comparison between geostatistical and machine learning models for spatio-temporal prediction of PM2.5 data
por: Mohamed, Zeinab, et al.
Publicado: (2025)

Compositional learning of functions in humans and machines
por: Zhou, Yanli, et al.
Publicado: (2024)

Mislabeled examples detection viewed as probing machine learning models: concepts, survey and extensive benchmark
por: George, Thomas, et al.
Publicado: (2024)

Dynamical loss functions shape landscape topography and improve learning in artificial neural networks
por: Pallero, Eduardo Lavin, et al.
Publicado: (2024)

A No-Defense Defense Against Gradient-Based Adversarial Attacks on ML-NIDS: Is Less More?
por: elShehaby, Mohamed, et al.
Publicado: (2026)

Ultra-low latency quantum-inspired machine learning predictors implemented on FPGA
por: Borella, Lorenzo, et al.
Publicado: (2024)

DPG loss functions for learning parameter-to-solution maps by neural networks
por: Castillo, Pablo Cortés, et al.
Publicado: (2025)

Cross-functional transferability in universal machine learning interatomic potentials
por: Huang, Xu, et al.
Publicado: (2025)

Prospects for quantum advantage in machine learning from the representability of functions
por: Masot-Llima, Sergi, et al.
Publicado: (2025)

Exoplanetary atmospheres retrieval via a quantum extreme learning machine
por: Vetrano, Marco, et al.
Publicado: (2025)

Cardinality augmented loss functions
por: O'Malley, Miguel
Publicado: (2026)

Data Augmentation techniques in time series domain: A survey and taxonomy
por: Iglesias, Guillermo, et al.
Publicado: (2022)

Review of multimodal machine learning approaches in healthcare
por: Krones, Felix, et al.
Publicado: (2024)

Feasibility of machine learning-based rice yield prediction in India at the district level using climate reanalysis data
por: De Clercq, Djavan, et al.
Publicado: (2024)

Random functions as data compressors for machine learning of molecular processes
por: Debnath, Jayashrita, et al.
Publicado: (2025)

Exploring the Effect of DNN Depth on Adversarial Attacks in Network Intrusion Detection Systems
por: ElShehaby, Mohamed, et al.
Publicado: (2025)

Locating disparities in machine learning
por: von Zahn, Moritz, et al.
Publicado: (2022)

A survey on GANs for computer vision: Recent research, analysis and taxonomy
por: Iglesias, Guillermo, et al.
Publicado: (2022)

Variable transformations in consistent loss functions
por: Tyralis, Hristos, et al.
Publicado: (2025)

Achieving interpretable machine learning by functional decomposition of black-box models into explainable predictor effects
por: Köhler, David, et al.
Publicado: (2024)

A Bayesian explanation of machine learning models based on modes and functional ANOVA
por: Long, Quan
Publicado: (2024)

ASRL:A robust loss function with potential for development
por: Hui, Chenyu, et al.
Publicado: (2025)

A Novel Perturb-ability Score to Mitigate Evasion Adversarial Attacks on Flow-Based ML-NIDS
por: elShehaby, Mohamed, et al.
Publicado: (2024)