Parameter learning - Open Access .click

Results 111 to 120 of about 7,967,373 (246)

This paper considers learning when the distinction between risk and ambiguity (Knightian uncertainty) matters. Working within the framework of recursive multiple-priors utility, the paper formulates a counterpart of the Bayesian model of learning about ...
Larry Epstein, Martin Schneider
core

A Q‐Learning Algorithm to Solve the Two‐Player Zero‐Sum Game Problem for Nonlinear Systems

International Journal of Adaptive Control and Signal Processing, Volume 39, Issue 3, Page 566-581, March 2025.
A Q‐learning algorithm to solve the two‐player zero‐sum game problem for nonlinear systems. ABSTRACT This paper deals with the two‐player zero‐sum game problem, which is a bounded L2$$ {L}_2 $$‐gain robust control problem. Finding an analytical solution to the complex Hamilton‐Jacobi‐Issacs (HJI) equation is a challenging task.
Afreen Islam, Anthony Siming Chen, Guido Herrmann +2 more
wiley +1 more source

SA-LoRA: Shared-A decoupled low-rank adaptation for class-incremental learning

Journal of King Saud University: Computer and Information Sciences
Parameter-efficient fine-tuning methods have shown promise for continual learning with pre-trained models, yet existing approaches either sacrifice performance or incur linear parameter growth with task count.
Xiaohuan Bing, Yuanhao Xi, Ramin Yahyapour +2 more
doaj +1 more source

dynoGP: Deep Gaussian Processes for Dynamic System Identification

International Journal of Adaptive Control and Signal Processing, EarlyView.
This work introduces a novel class of deep models for system identification, dynamical deep Gaussian processes, which combine the strengths of data‐driven methods, such as those based on neural network architectures, with the ability to output a probability distribution for uncertainty representation.
Alessio Benavoli, Marco Forgione, Dario Piga +2 more
wiley +1 more source

Gala Committee

, 1976
Meeting dates for the Learning About Learning Gala ...
Learning About Learning
core +1 more source

Learned Parameter Compression for Efficient and Privacy-Preserving Federated Learning

IEEE Open Journal of the Communications Society
Federated learning (FL) performs collaborative training of deep learning models among multiple clients, safeguarding data privacy, security, and legal adherence by preserving training data locally.
Yiming Chen +3 more
doaj +1 more source

Characterization of Defect Distribution in an Additively Manufactured AlSi10Mg as a Function of Processing Parameters and Correlations with Extreme Value Statistics

Advanced Engineering Materials, EarlyView.
Predicting extreme defects in additive manufacturing remains a key challenge limiting its structural reliability. This study proposes a statistical framework that integrates Extreme Value Theory with advanced process indicators to explore defect–process relationships and improve the estimation of critical defect sizes. The approach provides a basis for
Muhammad Muteeb Butt +8 more
wiley +1 more source

Targeted Active Learning for Bayesian Decision-Making

Active learning is usually applied to acquire labels of informative data points in supervised learning, to maximize accuracy in a sample-efficient way.
Kaski, Samuel +5 more
core +1 more source

What Do Large Language Models Know About Materials?

Advanced Engineering Materials, EarlyView.
If large language models (LLMs) are to be used inside the material discovery and engineering process, they must be benchmarked for the accurateness of intrinsic material knowledge. The current work introduces 1) a reasoning process through the processing–structure–property–performance chain and 2) a tool for benchmarking knowledge of LLMs concerning ...
Adrian Ehrenhofer +2 more
wiley +1 more source

The Academy Gazette

, 1974
A newsletter from Learning About Learning titled "The Academy Gazette.
Learning About Learning
core +1 more source

bayesian network
structure learning
bayesian networks

reinforcement learning
deep learning