Machine Learning Approaches For Motor Learning: A Short Review

• URL: http://arxiv.org/abs/2002.04317v4
• Date: Wed, 3 Jun 2020 15:00:42 GMT
• Title: Machine Learning Approaches For Motor Learning: A Short Review
• Authors: Baptiste Caramiaux, Jules Fran\c{c}oise, Wanyu Liu, T\'eo Sanchez and Fr\'ed\'eric Bevilacqua
• Abstract summary: We outline existing machine learning models for motor learning and their adaptation capabilities. We identify and describe three types of adaptation: Reinforcement in probabilistic models, Transfer and meta-learning in deep neural networks, and Planning adaptation by reinforcement learning.
• Score: 1.5736917233375265
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Machine learning approaches have seen considerable applications in human movement modeling, but remain limited for motor learning. Motor learning requires accounting for motor variability, and poses new challenges as the algorithms need to be able to differentiate between new movements and variation of known ones. In this short review, we outline existing machine learning models for motor learning and their adaptation capabilities. We identify and describe three types of adaptation: Parameter adaptation in probabilistic models, Transfer and meta-learning in deep neural networks, and Planning adaptation by reinforcement learning. To conclude, we discuss challenges for applying these models in the domain of motor learning support systems.

Related papers

• Machine Unlearning in Contrastive Learning [3.6218162133579694]
  We introduce a novel gradient constraint-based approach for training the model to effectively achieve machine unlearning. Our approach demonstrates proficient performance not only on contrastive learning models but also on supervised learning models.
  arXiv  Detail & Related papers  (2024-05-12T16:09:01Z)
• Enhancing Generative Class Incremental Learning Performance with Model Forgetting Approach [50.36650300087987]
  This study presents a novel approach to Generative Class Incremental Learning (GCIL) by introducing the forgetting mechanism. We have found that integrating the forgetting mechanisms significantly enhances the models' performance in acquiring new knowledge.
  arXiv  Detail & Related papers  (2024-03-27T05:10:38Z)
• Physics-Inspired Interpretability Of Machine Learning Models [0.0]
  The ability to explain decisions made by machine learning models remains one of the most significant hurdles towards widespread adoption of AI. We propose a novel approach to identify relevant features of the input data, inspired by methods from the energy landscapes field.
  arXiv  Detail & Related papers  (2023-04-05T11:35:17Z)
• Deep Active Learning for Computer Vision: Past and Future [50.19394935978135]
  Despite its indispensable role for developing AI models, research on active learning is not as intensive as other research directions. By addressing data automation challenges and coping with automated machine learning systems, active learning will facilitate democratization of AI technologies.
  arXiv  Detail & Related papers  (2022-11-27T13:07:14Z)
• Panoramic Learning with A Standardized Machine Learning Formalism [116.34627789412102]
  This paper presents a standardized equation of the learning objective, that offers a unifying understanding of diverse ML algorithms. It also provides guidance for mechanic design of new ML solutions, and serves as a promising vehicle towards panoramic learning with all experiences.
  arXiv  Detail & Related papers  (2021-08-17T17:44:38Z)
• Bidirectional Interaction between Visual and Motor Generative Models using Predictive Coding and Active Inference [68.8204255655161]
  We propose a neural architecture comprising a generative model for sensory prediction, and a distinct generative model for motor trajectories. We highlight how sequences of sensory predictions can act as rails guiding learning, control and online adaptation of motor trajectories.
  arXiv  Detail & Related papers  (2021-04-19T09:41:31Z)
• Measuring and modeling the motor system with machine learning [117.44028458220427]
  The utility of machine learning in understanding the motor system is promising a revolution in how to collect, measure, and analyze data. We discuss the growing use of machine learning: from pose estimation, kinematic analyses, dimensionality reduction, and closed-loop feedback, to its use in understanding neural correlates and untangling sensorimotor systems.
  arXiv  Detail & Related papers  (2021-03-22T12:42:16Z)
• Knowledge as Invariance -- History and Perspectives of Knowledge-augmented Machine Learning [69.99522650448213]
  Research in machine learning is at a turning point. Research interests are shifting away from increasing the performance of highly parameterized models to exceedingly specific tasks. This white paper provides an introduction and discussion of this emerging field in machine learning research.
  arXiv  Detail & Related papers  (2020-12-21T15:07:19Z)
• A Weighted Solution to SVM Actionability and Interpretability [0.0]
  Actionability is as important as interpretability or explainability of machine learning models, an ongoing and important research topic. This paper finds a solution to the question of actionability on both linear and non-linear SVM models.
  arXiv  Detail & Related papers  (2020-12-06T20:35:25Z)
• Differential Replication in Machine Learning [0.90238471756546]
  We propose a solution based on reusing the knowledge acquired by the already deployed machine learning models. This is the idea behind differential replication of machine learning models.
  arXiv  Detail & Related papers  (2020-07-15T20:26:49Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.