Zobrazeno 1 - 10
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pro vyhledávání: '"Learning challenges"'
Group fairness in machine learning is a critical area of research focused on achieving equitable outcomes across different groups defined by sensitive attributes such as race or gender. Federated learning, a decentralized approach to training machine
Externí odkaz:
http://arxiv.org/abs/2410.03855
Federated learning (FL) has emerged as a promising approach to training machine learning models across decentralized data sources while preserving data privacy, particularly in manufacturing and shared production environments. However, the presence o
Externí odkaz:
http://arxiv.org/abs/2408.09556
Autor:
Bruintjes, Robert-Jan, Lengyel, Attila, Rios, Marcos Baptista, Kayhan, Osman Semih, Zambrano, Davide, Tomen, Nergis, van Gemert, Jan
The fourth edition of the "VIPriors: Visual Inductive Priors for Data-Efficient Deep Learning" workshop features two data-impaired challenges. These challenges address the problem of training deep learning models for computer vision tasks with limite
Externí odkaz:
http://arxiv.org/abs/2406.18176
Autor:
Romero, Margarida
Publikováno v:
IRMBAM, Ipag, Jul 2024, Nice, France
The rapid advancement of artificial intelligence (AI) has brought significant challenges to the education and workforce skills required to take advantage of AI for human-AI collaboration in the workplace. As AI continues to reshape industries and job
Externí odkaz:
http://arxiv.org/abs/2405.19837
Autor:
Srikanth, Hari
Neural Network based approximations of the Value function make up the core of leading Policy Based methods such as Trust Regional Policy Optimization (TRPO) and Proximal Policy Optimization (PPO). While this adds significant value when dealing with v
Externí odkaz:
http://arxiv.org/abs/2405.20350
Federated learning (FL) is a novel distributed machine learning paradigm that enables participants to collaboratively train a centralized model with privacy preservation by eliminating the requirement of data sharing. In practice, FL often involves m
Externí odkaz:
http://arxiv.org/abs/2403.01387
One of the main motivations of MTL is to develop neural networks capable of inferring multiple tasks simultaneously. While countless methods have been proposed in the past decade investigating robust model architectures and efficient training algorit
Externí odkaz:
http://arxiv.org/abs/2402.03557
Autor:
Tina Byrom, Editor
Universal design has traditionally focused on learning spaces—that is, the physical buildings and areas that support teaching and learning. This book takes a broad interpretation of this concept to include a specific focus on teaching and learning