scholarly journals Galaxy Zoo: probabilistic morphology through Bayesian CNNs and active learning

2019 ◽  
Vol 491 (2) ◽  
pp. 1554-1574 ◽  
Author(s):  
Mike Walmsley ◽  
Lewis Smith ◽  
Chris Lintott ◽  
Yarin Gal ◽  
Steven Bamford ◽  
...  
Keyword(s):  
Neural Networks ◽  
Active Learning ◽  
Galaxy Evolution ◽  
Convolutional Neural Networks ◽  
Time Scale ◽  
Morphological Feature ◽  
Learning Strategy ◽  
Machine Intelligence ◽  
Active Learning Strategy ◽  
Support Research

ABSTRACT We use Bayesian convolutional neural networks and a novel generative model of Galaxy Zoo volunteer responses to infer posteriors for the visual morphology of galaxies. Bayesian CNN can learn from galaxy images with uncertain labels and then, for previously unlabelled galaxies, predict the probability of each possible label. Our posteriors are well-calibrated (e.g. for predicting bars, we achieve coverage errors of 11.8 per cent within a vote fraction deviation of 0.2) and hence are reliable for practical use. Further, using our posteriors, we apply the active learning strategy BALD to request volunteer responses for the subset of galaxies which, if labelled, would be most informative for training our network. We show that training our Bayesian CNNs using active learning requires up to 35–60 per cent fewer labelled galaxies, depending on the morphological feature being classified. By combining human and machine intelligence, Galaxy zoo will be able to classify surveys of any conceivable scale on a time-scale of weeks, providing massive and detailed morphology catalogues to support research into galaxy evolution.

scholarly journals A transferable active-learning strategy for reactive molecular force fields

2021 ◽  
Author(s):  
Tom Young ◽  
Tristan Johnston-Wood ◽  
Volker L. Deringer ◽  
Fernanda Duarte
Keyword(s):  
Machine Learning ◽  
Active Learning ◽  
Learning Strategy ◽  
Force Fields ◽  
Molecular Simulations ◽  
Quantum Mechanical ◽  
Interatomic Potentials ◽  
Molecular Force ◽  
High Level ◽  
Active Learning Strategy

Predictive molecular simulations require fast, accurate and reactive interatomic potentials. Machine learning offers a promising approach to construct such potentials by fitting energies and forces to high-level quantum-mechanical data, but...

scholarly journals Enhancing generic capabilities and metacognitive awareness of first-year nursing students using active learning strategy

BMC Nursing ◽  
2021 ◽  
Vol 20 (1) ◽  
Author(s):  
Carmen Wing Han Chan ◽  
Fiona Wing Ki Tang ◽  
Ka Ming Chow ◽  
Cho Lee Wong
Keyword(s):  
Nursing Education ◽  
Nursing Students ◽  
Active Learning ◽  
Flipped Classroom ◽  
Learning Strategy ◽  
Interactive Learning ◽  
Metacognitive Awareness ◽  
University Education ◽  
Strong Impact ◽  
Active Learning Strategy

Abstract Background Developing students’ generic capabilities is a major goal of university education as it can help to equip students with life-long learning skills and promote holistic personal development. However, traditional didactic teaching has not been very successful in achieving this aim. Kember and Leung’s Teaching and Learning Model suggests an interactive learning environment has a strong impact on developing students’ generic capabilities. Metacognitive awareness is also known to be related to generic capability development. This study aimed to assess changes on the development of generic capabilities and metacognitive awareness after the introduction of active learning strategy among nursing students. Methods This study adopted a quasi-experimental single group, matched pre- and posttest design. It was conducted in a school of nursing at a university in Hong Kong. Active learning approaches included the flipped classroom (an emphasis on pre-reading) and enhanced lectures (the breaking down of a long lecture into several mini-lectures and supplemented by interactive learning activities) were introduced in a foundational nursing course. The Capabilities Subscale of the Student Engagement Questionnaire and the Metacognitive Awareness Inventory were administered to two hundred students at the start (T0) and at the end of the course (T1). A paired t-test was performed to examine the changes in general capabilities and metacognitive awareness between T0 and T1. Results A total of 139 paired pre- and post-study responses (69.5 %) were received. Significant improvements were observed in the critical thinking (p < 0.001), creative thinking (p = 0.03), problem-solving (p < 0.001) and communication skills (p = 0.04) with the implementation of active learning. Significant changes were also observed in knowledge of cognition (p < 0.001) and regulation of cognition (p < 0.001) in the metacognitive awareness scales. Conclusions Active learning is a novel and effective teaching approach that can be applied in the nursing education field. It has great potential to enhance students’ development of generic capabilities and metacognitive awareness.

STORYTELLING AND STORYACTING AS AN ACTIVE LEARNING STRATEGY

1999 ◽  
Vol 20 (3) ◽  
pp. 347-352 ◽  
Author(s):  
Karen Cachevki Williams ◽  
Margaret Cooney ◽  
Jane Nelson
Keyword(s):  
Active Learning ◽  
Learning Strategy ◽  
Active Learning Strategy

scholarly journals PENGGUNAAN STRATEGI PEMBELAJARAN AKTIF EVERYONE IS A TEACHER HERE (ETH) TERHADAP KEMAMPUAN PEMAHAMAN KONSEP MATEMATIKA SISWA KELAS VII

2018 ◽  
Vol 4 (1) ◽  
pp. 95-100
Author(s):  
Sri Yunita Ningsih ◽  
Gustimalasari Gustimalasari
Keyword(s):  
Active Learning ◽  
Learning Strategy ◽  
Control Group ◽  
Control Group Design ◽  
Concept Understanding ◽  
Group Design ◽  
Quasi Experimental ◽  
Active Learning Strategy ◽  
And Control ◽  
Better Than

Abstract. This research has been made to know skill of student’s concept by using active learning strategy everyone is teacher here (ETH). Beside that this study aims to measure student’s concept understanding with statistical test between Experimental Class (Active Learning Strategy Everyone Is Teacher Here) and control class (Conventional Learning ). The population was seventh grade of SMPN 3 Lirik consist 94 students in three classes. Sample was took randomly, experiment class ( VII.2 ) and control class ( VII.I ) This research was experiment, the form of this research was Quasi Experimental Design with randomized subject posttest only control group design. based on statistic data processing has been retrieved - t hitung -3,159 smaller than - t table was -2,000 and based on t test has been retrieved -thitung < -t table so Ho rejected and Ha received. So that the writer conclude that skill of math student’s concept understanding by using active learning Strategy Everyone Is Teacher Here (ETH) is better than conventional concept understanding.Keywords: Everyone Is A Teacher Here, Concept Understanding

scholarly journals Batch Decorrelation for Active Metric Learning

2020 ◽  
Author(s):  
Priyadarshini Kumari ◽  
Ritesh Goru ◽  
Siddhartha Chaudhuri ◽  
Subhasis Chaudhuri
Keyword(s):  
Active Learning ◽  
Learning Strategy ◽  
Metric Learning ◽  
Parametric Models ◽  
Distance Metrics ◽  
Learning Approaches ◽  
Fundamental Goal ◽  
Novel Method ◽  
Active Learning Strategy ◽  
Perceptual Metrics

We present an active learning strategy for training parametric models of distance metrics, given triplet-based similarity assessments: object $x_i$ is more similar to object $x_j$ than to $x_k$. In contrast to prior work on class-based learning, where the fundamental goal is classification and any implicit or explicit metric is binary, we focus on perceptual metrics that express the degree of (dis)similarity between objects. We find that standard active learning approaches degrade when annotations are requested for batches of triplets at a time: our studies suggest that correlation among triplets is responsible. In this work, we propose a novel method to decorrelate batches of triplets, that jointly balances informativeness and diversity while decoupling the choice of heuristic for each criterion. Experiments indicate our method is general, adaptable, and outperforms the state-of-the-art.

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