Effective Non-Random Extreme Learning Machine
Fuente:
arXiv
Saved in:
| Main Authors: | De Canditiis, Daniela, Veglianti, Fabiano |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
Counterfactual Explanations for Hypergraph Neural Networks
by: Veglianti, Fabiano, et al.
Published: (2026)
by: Veglianti, Fabiano, et al.
Published: (2026)
Generalizability vs. Counterfactual Explainability Trade-Off
by: Veglianti, Fabiano, et al.
Published: (2025)
by: Veglianti, Fabiano, et al.
Published: (2025)
Countering Overfitting with Counterfactual Examples
by: Giorgi, Flavio, et al.
Published: (2025)
by: Giorgi, Flavio, et al.
Published: (2025)
A network-constrain Weibull AFT model for biomarkers discovery
by: Angelini, Claudia, et al.
Published: (2024)
by: Angelini, Claudia, et al.
Published: (2024)
Learning binary undirected graph in low dimensional regime
by: De Canditiis, Daniela
Published: (2019)
by: De Canditiis, Daniela
Published: (2019)
Solving Partial Differential Equations with Equivariant Extreme Learning Machines
by: Harder, Hans, et al.
Published: (2024)
by: Harder, Hans, et al.
Published: (2024)
Physics-Informed Extreme Learning Machine (PIELM): Opportunities and Challenges
by: Yang, He, et al.
Published: (2025)
by: Yang, He, et al.
Published: (2025)
Non-Determinism and the Lawlessness of Machine Learning Code
by: Cooper, A. Feder, et al.
Published: (2022)
by: Cooper, A. Feder, et al.
Published: (2022)
A Rapid Physics-Informed Machine Learning Framework Based on Extreme Learning Machine for Inverse Stefan Problems
by: Zhuang, Pei-Zhi, et al.
Published: (2025)
by: Zhuang, Pei-Zhi, et al.
Published: (2025)
Fast Cerebral Blood Flow Analysis via Extreme Learning Machine
by: Chen, Xi, et al.
Published: (2024)
by: Chen, Xi, et al.
Published: (2024)
The Unreasonable Effectiveness of Randomized Representations in Online Continual Graph Learning
by: Donghi, Giovanni, et al.
Published: (2025)
by: Donghi, Giovanni, et al.
Published: (2025)
The Unreasonable Effectiveness of Random Target Embeddings for Continuous-Output Neural Machine Translation
by: Tokarchuk, Evgeniia, et al.
Published: (2023)
by: Tokarchuk, Evgeniia, et al.
Published: (2023)
A Critical Analysis of the Theoretical Framework of the Extreme Learning Machine
by: Perfilievaa, Irina, et al.
Published: (2024)
by: Perfilievaa, Irina, et al.
Published: (2024)
Bayesian Physics-Informed Extreme Learning Machine for Forward and Inverse PDE Problems with Noisy Data
by: Liu, Xu, et al.
Published: (2022)
by: Liu, Xu, et al.
Published: (2022)
Causal Machine Learning for Cost-Effective Allocation of Development Aid
by: Kuzmanovic, Milan, et al.
Published: (2024)
by: Kuzmanovic, Milan, et al.
Published: (2024)
Trans-Bifurcation Prediction of Dynamics in terms of Extreme Learning Machines with Control Inputs
by: Tadokoro, Satoru, et al.
Published: (2024)
by: Tadokoro, Satoru, et al.
Published: (2024)
A Federated Random Forest Solution for Secure Distributed Machine Learning
by: Cotorobai, Alexandre, et al.
Published: (2025)
by: Cotorobai, Alexandre, et al.
Published: (2025)
SELM: Siamese Extreme Learning Machine with Application to Face Biometrics
by: Kudisthalert, Wasu, et al.
Published: (2021)
by: Kudisthalert, Wasu, et al.
Published: (2021)
Federated Active Learning Under Extreme Non-IID and Global Class Imbalance
by: Zong, Chen-Chen, et al.
Published: (2026)
by: Zong, Chen-Chen, et al.
Published: (2026)
On the Effectiveness of Random Weights in Graph Neural Networks
by: Bui, Thu, et al.
Published: (2025)
by: Bui, Thu, et al.
Published: (2025)
Theory and interpretability of Quantum Extreme Learning Machines: a Pauli-transfer matrix approach
by: Gross, Markus, et al.
Published: (2026)
by: Gross, Markus, et al.
Published: (2026)
Classification of Deceased Patients from Non-Deceased Patients using Random Forest and Support Vector Machine Classifiers
by: Saha, Dheeman, et al.
Published: (2024)
by: Saha, Dheeman, et al.
Published: (2024)
Research on Effectiveness Evaluation and Optimization of Baseball Teaching Method Based on Machine Learning
by: Sun, Shaoxuan, et al.
Published: (2024)
by: Sun, Shaoxuan, et al.
Published: (2024)
FederatedFactory: Generative One-Shot Learning for Extremely Non-IID Distributed Scenarios
by: Moleri, Andrea, et al.
Published: (2026)
by: Moleri, Andrea, et al.
Published: (2026)
Extreme Learning Machine-based Channel Estimation in IRS-Assisted Multi-User ISAC System
by: Liu, Yu, et al.
Published: (2024)
by: Liu, Yu, et al.
Published: (2024)
Escaping Spectral Bias without Backpropagation: Fast Implicit Neural Representations with Extreme Learning Machines
by: Cho, Woojin, et al.
Published: (2026)
by: Cho, Woojin, et al.
Published: (2026)
Rapidly Varying Completely Random Measures for Modeling Extremely Sparse Networks
by: Kilian, Valentin, et al.
Published: (2025)
by: Kilian, Valentin, et al.
Published: (2025)
Leveraging Non-linear Dimension Reduction and Random Walk Co-occurrence for Node Embedding
by: DeWolfe, Ryan
Published: (2026)
by: DeWolfe, Ryan
Published: (2026)
Representational Alignment Supports Effective Machine Teaching
by: Sucholutsky, Ilia, et al.
Published: (2024)
by: Sucholutsky, Ilia, et al.
Published: (2024)
A Hybrid Multilayer Extreme Learning Machine for Image Classification with an Application to Quadcopters
by: Hernandez-Hernandez, Rolando A., et al.
Published: (2025)
by: Hernandez-Hernandez, Rolando A., et al.
Published: (2025)
Generalizing Beyond Suboptimality: Offline Reinforcement Learning Learns Effective Scheduling through Random Data
by: van Remmerden, Jesse, et al.
Published: (2025)
by: van Remmerden, Jesse, et al.
Published: (2025)
Machine Learning: Progress and Prospects
by: Gammerman, Alexander
Published: (2025)
by: Gammerman, Alexander
Published: (2025)
Extreme Learning Machines for Attention-based Multiple Instance Learning in Whole-Slide Image Classification
by: Krishnakumar, Rajiv, et al.
Published: (2025)
by: Krishnakumar, Rajiv, et al.
Published: (2025)
Exact Constraint Enforcement in Physics-Informed Extreme Learning Machines using Null-Space Projection Framework
by: Mishra, Rishi, et al.
Published: (2026)
by: Mishra, Rishi, et al.
Published: (2026)
Recent and Upcoming Developments in Randomized Numerical Linear Algebra for Machine Learning
by: Dereziński, Michał, et al.
Published: (2024)
by: Dereziński, Michał, et al.
Published: (2024)
Machine Learning vs. Randomness: Challenges in Predicting Binary Options Movements
by: Arantes, Gabriel M., et al.
Published: (2025)
by: Arantes, Gabriel M., et al.
Published: (2025)
Quantum Spectral Reasoning: A Non-Neural Architecture for Interpretable Machine Learning
by: Kiruluta, Andrew
Published: (2025)
by: Kiruluta, Andrew
Published: (2025)
Review Non-convex Optimization Method for Machine Learning
by: Fotopoulos, Greg B, et al.
Published: (2024)
by: Fotopoulos, Greg B, et al.
Published: (2024)
A Simple but Effective Closed-form Solution for Extreme Multi-label Learning
by: Onishi, Kazuma, et al.
Published: (2025)
by: Onishi, Kazuma, et al.
Published: (2025)
Applications of Random Matrix Theory in Machine Learning and Brain Mapping
by: Lawrence, Katrina
Published: (2025)
by: Lawrence, Katrina
Published: (2025)
Similar Items
-
Counterfactual Explanations for Hypergraph Neural Networks
by: Veglianti, Fabiano, et al.
Published: (2026) -
Generalizability vs. Counterfactual Explainability Trade-Off
by: Veglianti, Fabiano, et al.
Published: (2025) -
Countering Overfitting with Counterfactual Examples
by: Giorgi, Flavio, et al.
Published: (2025) -
A network-constrain Weibull AFT model for biomarkers discovery
by: Angelini, Claudia, et al.
Published: (2024) -
Learning binary undirected graph in low dimensional regime
by: De Canditiis, Daniela
Published: (2019)