Results 61 to 70 of about 2,126,246 (333)
The Development of Reading Comprehension Ability of Pratomsuksa 3 Students using SQ4R Integrated with Mind Mapping Learning Management [PDF]
Journal of Humanities and Social Sciences Mahasarakham University, 2022 The purposes of the study were. -1) to develop an SQ4R integrated with mind mapping learning management with an efficiency of 75/75 for Pratomsuksa 3 students, 2) to study reading comprehension ability of students learning with developed learning ...
Warawan Nansathit, Autthapon Inthasena
doaj
Curriculum Learning with a Progression Function [PDF]
arXiv, 2020 Curriculum Learning for Reinforcement Learning is an increasingly popular technique that involves training an agent on a sequence of intermediate tasks, called a Curriculum, to increase the agent's performance and learning speed. This paper introduces a novel paradigm for curriculum generation based on progression and mapping functions.
arxiv
PCC: Paraphrasing with Bottom-k Sampling and Cyclic Learning for Curriculum Data Augmentation [PDF]
arXiv, 2022 Curriculum Data Augmentation (CDA) improves neural models by presenting synthetic data with increasing difficulties from easy to hard. However, traditional CDA simply treats the ratio of word perturbation as the difficulty measure and goes through the curriculums only once.
arxiv
, 2017 Робоча навчальна програма дисципліни «Фізіологія рухової активності» для студентів за галуззю знань 22– «Охорона здоров’я», за спеціальністю 227 – «Фізична реабілітація», за освітньо-кваліфікаційним рівнем «Бакалавр», 2017 рік, 28 с.Рабочая учебная ...
Рижковський, Володимир Олегович
core
Molecular Oncology, EarlyView.This article advocates integrating temporal dynamics into cancer research. Rather than relying on static snapshots, researchers should increasingly consider adopting dynamic methods—such as live imaging, temporal omics, and liquid biopsies—to track how tumors evolve over time.
Gautier Follain +3 more
wiley +1 more source
Outcome-directed Reinforcement Learning by Uncertainty & Temporal Distance-Aware Curriculum Goal Generation [PDF]
arXiv, 2023 Current reinforcement learning (RL) often suffers when solving a challenging exploration problem where the desired outcomes or high rewards are rarely observed. Even though curriculum RL, a framework that solves complex tasks by proposing a sequence of surrogate tasks, shows reasonable results, most of the previous works still have difficulty in ...
arxiv
FEBS Open Bio, EarlyView.The C. elegans tetraspanin‐7 (tsp‐7) is a homologue of human CD63, which is a negative regulator of autophagy. The C. elegans strain, tm5761, has a dysfunctional (knockout) tsp‐7 gene. When compared to the wild‐type strain, the tm5761 strain shows increased: life‐ and health‐span; thermotolerance, and stress‐induced locomotion.
Brogan Jones +2 more
wiley +1 more source
Human not in the loop: objective sample difficulty measures for Curriculum Learning [PDF]
arXiv, 2023 Curriculum learning is a learning method that trains models in a meaningful order from easier to harder samples. A key here is to devise automatic and objective difficulty measures of samples. In the medical domain, previous work applied domain knowledge from human experts to qualitatively assess classification difficulty of medical images to guide ...
arxiv
Cogent Education, 2023 This study investigated how teacher educators utilize feedback to enhance reflective practices among pre-service teachers during microteaching sessions.
Demekash Asregid +2 more
doaj +1 more source
Improving Environment Robustness of Deep Reinforcement Learning Approaches for Autonomous Racing Using Bayesian Optimization-based Curriculum Learning [PDF]
arXiv, 2023 Deep reinforcement learning (RL) approaches have been broadly applied to a large number of robotics tasks, such as robot manipulation and autonomous driving. However, an open problem in deep RL is learning policies that are robust to variations in the environment, which is an important condition for such systems to be deployed into real-world ...
arxiv