• MINDFUL

  • Referenced in 4 articles [sw08865]
  • framework for Meta-INDuctive neuro-FUzzy Learning. Common inductive learning strategies offer tools for knowledge ... research trend explores the potentialities of meta-learning, which is oriented to the development ... this paper, we present a meta-learning framework called Mindful (Meta INDuctive neuro-FUzzy Learning ... specific knowledge is gathered during the meta-learning activity and it is exploited to suggest...
  • Torchmeta

  • Referenced in 3 articles [sw30740]
  • Torchmeta: A Meta-Learning library for PyTorch. The constant introduction of standardized benchmarks ... helped accelerating the recent advances in meta-learning research. They offer ... enables seamless and consistent evaluation of meta-learning algorithms on multiple datasets, by providing data ... development of models compatible with meta-learning algorithms. The code is available here: https://github.com...
  • ProMP

  • Referenced in 2 articles [sw34914]
  • Search. Credit assignment in Meta-reinforcement learning (Meta-RL) is still poorly understood. Existing methods ... This leads to poor sample-efficiency during meta-training as well as ineffective task identification ... gained insights we develop a novel meta-learning algorithm that overcomes both the issue ... proposed algorithm endows efficient and stable meta-learning. Our approach leads to superior pre-adaptation...
  • learn2learn

  • Referenced in 1 article [sw34913]
  • learn2learn: A Library for Meta-Learning Research. Meta-learning researchers face two fundamental issues ... algorithms and tasks because modern meta-learning methods rely on unconventional functionalities of machine learning ... manuscript introduces learn2learn, a library for meta-learning research focused on solving those prototyping ... common across a wide-range of meta-learning techniques (e.g. meta-descent, meta-reinforcement learning...
  • BOML

  • Referenced in 1 article [sw35222]
  • Python for Meta Learning. Meta-learning (a.k.a. learning to learn) has recently emerged ... applications. There are now many meta-learning methods, each focusing on different modeling aspects ... modularized optimization library that unifies several meta-learning algorithms into a common bilevel optimization framework ... solve the mainstream categories of meta-learning methods, such as meta-feature-based and meta...
  • MetaFraud

  • Referenced in 1 article [sw35604]
  • Metafraud: a meta-learning framework for detecting financial fraud. Financial fraud can have serious ramifications ... approach to develop MetaFraud, a novel meta-learning framework for enhanced financial fraud detection ... experiments demonstrate the effectiveness of the meta-learning framework over state-of-the-art financial...
  • LinkCluE

  • Referenced in 2 articles [sw15839]
  • ensembles have emerged as a powerful meta-learning paradigm that provides improved accuracy and robustness...
  • Far-HO

  • Referenced in 1 article [sw25680]
  • Programming Package for Hyperparameter Optimization and Meta-Learning. In (Franceschi et al., 2018) we proposed ... encompasses gradient-based hyperparameter optimization and meta-learning. We formulated an approximate version...
  • METALA

  • Referenced in 1 article [sw02420]
  • Meta-learning has been accepted, in the last five years, as a proper machine learning ... more to do with inductive meta-learning. It is the process of learning from others...
  • mfe

  • Referenced in 1 article [sw35447]
  • design of recommendation systems based on Meta-Learning. The meta-features, also called characterization measures ... making available a large set of meta-feature extraction functions, tasks like comprehensive data characterization ... data exploration and large number of Meta-Learning based data analysis can be performed. These...
  • LEAF

  • Referenced in 1 article [sw34096]
  • research areas such as federated learning, meta-learning, and multi-task learning. As the machine...
  • SmartML

  • Referenced in 1 article [sw32867]
  • tuning for machine learning algorithms. Being meta-learning based, the framework is able to simulate...
  • CAZSL

  • Referenced in 1 article [sw38827]
  • Omnipush datatset that allows testing of meta-learning capabilities using low-dimensional data. Codes...
  • Autotext

  • Referenced in 1 article [sw40695]
  • Autotext: AutoML for Text Classification. Meta-learning of textual representations. Recent progress in AutoML...
  • RLDDE

  • Referenced in 3 articles [sw35866]
  • time delay. A novel method, called reinforcement learning-based dimension and delay estimator (RLDDE ... delay. RLDDE is a meta-learner that tries to learn the selection policy...
  • EnsemblePCReg

  • Referenced in 0 articles [sw15658]
  • Principal-Component-Regression-Based Heterogeneous Ensemble Meta-Learning. Extends the base classes and methods...
  • Paramils

  • Referenced in 76 articles [sw00678]
  • Paramils: an automatic algorithm configuration framework. The identification...
  • PYTHIA

  • Referenced in 29 articles [sw00747]
  • Often scientists need to locate appropriate software for...
  • QAPLIB

  • Referenced in 229 articles [sw00751]
  • A collection of electronically available data instances for...
  • R

  • Referenced in 9188 articles [sw00771]
  • R is a language and environment for statistical...