Relation-propagation meta-learning on an explicit preference graph for cold-start recommendation

H Liu, L Wang, P Li, C Qian, P Zhao, X Wu - Knowledge-Based Systems, 2023 - Elsevier
The cold-start problem has been of great concern in the recommendation domain. To
address this problem, meta-learning frameworks have been widely adopted due to their fast …

LaANIL: ANIL with Look-Ahead Meta-Optimization and Data Parallelism

V Tammisetti, K Bierzynski, G Stettinger… - Electronics, 2024 - mdpi.com
Meta-few-shot learning algorithms, such as Model-Agnostic Meta-Learning (MAML) and
Almost No Inner Loop (ANIL), enable machines to learn complex tasks quickly with limited …

Leveraging task variability in meta-learning

A Aimen, B Ladrecha, S Sidheekh, NC Krishnan - SN Computer Science, 2023 - Springer
Meta-learning (ML) utilizes extracted meta-knowledge from data to enable models to
perform well on unseen data that they have not encountered before. Typically, this meta …

Advances in MetaDL: AAAI 2021 challenge and workshop

A El Baz, I Guyon, Z Liu, JN Van Rijn… - AAAI Workshop on …, 2021 - proceedings.mlr.press
To stimulate advances in meta-learning using deep learning techniques (MetaDL), we
organized in 2021 a challenge and an associated workshop. This paper presents the design …

Adaptation: Blessing or Curse for Higher Way Meta-Learning

A Aimen, S Sidheekh, B Ladrecha… - IEEE Transactions …, 2023 - ieeexplore.ieee.org
The prevailing literature typically assesses the effectiveness of meta-learning (ML)
approaches on tasks that involve no more than 20 classes. However, we challenge this …

Advances in MetaDL

A El Baz, I Guyon, Z Liu, JN van Rijn… - AAAI 2021 challenge …, 2021 - hal.science
To stimulate advances in metalearning using deep learning techniques (MetaDL), we
organized in 2021 a challenge and an associated workshop. This paper presents the design …