Copertina

Improving the classification of extreme classes by means of loss regularisation and generalised beta distributions

Fuente: arXiv

Salvato in:

Dettagli Bibliografici
Autori principali:	Vargas, Víctor Manuel, Gutiérrez, Pedro Antonio, Barbero-Gómez, Javier, Hervás-Martínez, César
Natura:	Preprint
Pubblicazione:	2024
Soggetti:	Machine Learning Artificial Intelligence
Accesso online:	Acceder al recurso
Tags:	Aggiungi Tag Nessun Tag, puoi essere il primo ad aggiungerne!!

Documenti analoghi

Affect and Effect: Limitations of regularisation-based continual learning in EEG-based emotion classification
di: Peire, Nina, et al.
Pubblicazione: (2026)

Out-of-distribution generalisation is hard: evidence from ARC-like tasks
di: Dimitriadis, George, et al.
Pubblicazione: (2025)

Accuracy on the wrong line: On the pitfalls of noisy data for out-of-distribution generalisation
di: Sanyal, Amartya, et al.
Pubblicazione: (2024)

Improving generalisability of 3D binding affinity models in low data regimes
di: Buhmann, Julia, et al.
Pubblicazione: (2024)

Specialty detection in the context of telemedicine in a highly imbalanced multi-class distribution
di: Alomari, Alaa, et al.
Pubblicazione: (2024)

Robust training of implicit generative models for multivariate and heavy-tailed distributions with an invariant statistical loss
di: de Frutos, José Manuel, et al.
Pubblicazione: (2024)

Data distribution impacts the performance and generalisability of contrastive learning-based foundation models of electrocardiograms
di: Khattak, Gul Rukh, et al.
Pubblicazione: (2025)

Extending confidence calibration to generalised measures of variation
di: Thompson, Andrew, et al.
Pubblicazione: (2026)

Soft $Q(λ)$: A multi-step off-policy method for entropy regularised reinforcement learning using eligibility traces
di: Mahajan, Pranav, et al.
Pubblicazione: (2026)

Studying and Improving Graph Neural Network-based Motif Estimation
di: Vieira, Pedro C., et al.
Pubblicazione: (2025)

Finite time analysis of temporal difference learning with linear function approximation: Tail averaging and regularisation
di: Patil, Gandharv, et al.
Pubblicazione: (2022)

Infant movement classification through pressure distribution analysis
di: Kulvicius, Tomas, et al.
Pubblicazione: (2022)

Improving Cardiac Risk Prediction Using Data Generation Techniques
di: Cabodevila, Alexandre, et al.
Pubblicazione: (2025)

Bayesian uncertainty-weighted loss for improved generalisability on polyp segmentation task
di: Stone, Rebecca S., et al.
Pubblicazione: (2023)

Probabilistic classification from possibilistic data: computing Kullback-Leibler projection with a possibility distribution
di: Baaj, Ismaïl, et al.
Pubblicazione: (2026)

K-means Derived Unsupervised Feature Selection using Improved ADMM
di: Sun, Ziheng, et al.
Pubblicazione: (2024)

An approach to automated videogame beta testing
di: Hernández-Bécares, Jennifer, et al.
Pubblicazione: (2024)

Improved deep learning of chaotic dynamical systems with multistep penalty losses
di: Chakraborty, Dibyajyoti, et al.
Pubblicazione: (2024)

Adaptive operator selection utilising generalised experience
di: Aydin, Mehmet Emin, et al.
Pubblicazione: (2023)

An exploration of features to improve the generalisability of fake news detection models
di: Hoy, Nathaniel, et al.
Pubblicazione: (2025)

The generalised distribution semantics and projective families of distributions
di: Weitkämper, Felix
Pubblicazione: (2022)

How does the optimizer implicitly bias the model merging loss landscape?
di: Zhang, Chenxiang, et al.
Pubblicazione: (2025)

dlordinal: a Python package for deep ordinal classification
di: Bérchez-Moreno, Francisco, et al.
Pubblicazione: (2024)

An Approach to Variable Clustering: K-means in Transposed Data and its Relationship with Principal Component Analysis
di: Saquicela, Victor, et al.
Pubblicazione: (2025)

Improving out-of-distribution generalization in graphs via hierarchical semantic environments
di: Piao, Yinhua, et al.
Pubblicazione: (2024)

Reliable Explainability of Deep Learning Spatial-Spectral Classifiers for Improved Semantic Segmentation in Autonomous Driving
di: Gutiérrez-Zaballa, Jon, et al.
Pubblicazione: (2025)

Transformer based super-resolution downscaling for regional reanalysis: Full domain vs tiling approaches
di: Pérez, Antonio, et al.
Pubblicazione: (2024)

Improving One-class Recommendation with Multi-tasking on Various Preference Intensities
di: Shao, Chu-Jen, et al.
Pubblicazione: (2024)

Out-of-distribution forgetting: vulnerability of continual learning to intra-class distribution shift
di: Guo, Liangxuan, et al.
Pubblicazione: (2023)

Experiential Reflective Learning for Self-Improving LLM Agents
di: Allard, Marc-Antoine, et al.
Pubblicazione: (2026)

Carré du champ flow matching: better quality-generalisation tradeoff in generative models
di: Bamberger, Jacob, et al.
Pubblicazione: (2025)

The foundations of cost-sensitive causal classification
di: Verbeke, Wouter, et al.
Pubblicazione: (2020)

Class-specific feature selection for classification explainability
di: Aguilar-Ruiz, Jesus S.
Pubblicazione: (2024)

Improving Subgraph-GNNs via Edge-Level Ego-Network Encodings
di: Alvarez-Gonzalez, Nurudin, et al.
Pubblicazione: (2023)

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

Cross Domain Adaptation using Adversarial networks with Cyclic loss
di: Kaur, Manpreet, et al.
Pubblicazione: (2024)

Longitudinal Ensemble Integration for sequential classification with multimodal data
di: Susman, Aviad, et al.
Pubblicazione: (2024)

Why pre-training is beneficial for downstream classification tasks?
di: Jiang, Xin, et al.
Pubblicazione: (2024)

Effects of term weighting approach with and without stop words removing on Arabic text classification
di: Alhenawi, Esra'a, et al.
Pubblicazione: (2024)

Softmax is not Enough (for Sharp Size Generalisation)
di: Veličković, Petar, et al.
Pubblicazione: (2024)