Learning a Policy for Opportunistic Active Learning [PDF]
EMNLP 2018, 2018 Active learning identifies data points to label that are expected to be the most useful in improving a supervised model. Opportunistic active learning incorporates active learning into interactive tasks that constrain possible queries during interactions.
arxiv
Claustrum Volume Is Reduced in Multiple Sclerosis and Predicts Disability
Annals of Clinical and Translational Neurology, EarlyView.ABSTRACT Objective The claustrum is a small, thin structure of predominantly gray matter with broad connectivity and enigmatic function. Little is known regarding the impact of claustrum pathology in multiple sclerosis (MS). Methods This study assessed whether claustrum volume was reduced in MS and whether reductions were associated with specific ...
Nicole Shelley +5 more
wiley +1 more source
Deep Bayesian Active Learning, A Brief Survey on Recent Advances [PDF]
arXiv, 2020 Active learning frameworks offer efficient data annotation without remarkable accuracy degradation. In other words, active learning starts training the model with a small size of labeled data while exploring the space of unlabeled data in order to select most informative samples to be labeled. Generally speaking, representing the uncertainty is crucial
arxiv
Reverse chemical ecology in a moth: machine learning on odorant receptors identifies new behaviorally active agonists [PDF]
, 2021 Gabriela Caballero-Vidal +9 more
openalex +1 more source
EEG Response to Sedation Interruption Complements Behavioral Assessment After Severe Brain Injury
Annals of Clinical and Translational Neurology, EarlyView.ABSTRACT Objective Accurate assessment of the level of consciousness and potential to recover in patients with severe brain injury underpins crucial decisions in the intensive care unit but remains a major challenge for the clinical team. The neurological wake‐up test is a widely used assessment tool. However, many patients' behavioral responses during
Charlotte Maschke +12 more
wiley +1 more source
npj Computational MaterialsUnderstanding structural flexibility of metal-organic frameworks (MOFs) via molecular dynamics simulations is crucial to design better MOFs. Density functional theory (DFT) and quantum-chemistry methods provide highly accurate molecular dynamics, but the
Abhishek Sharma, Stefano Sanvito
doaj +1 more source
Active2 Learning: Actively reducing redundancies in Active Learning methods for Sequence Tagging and Machine Translation Learning: Actively reducing redundancies in Active Learning methods for Sequence Tagging and Machine Translation [PDF]
, 2021 Rishi Hazra +4 more
openalex +1 more source
Whole‐Blood RNA Sequencing Profiling of Patients With Rheumatoid Arthritis Treated With Tofacitinib
ACR Open Rheumatology, EarlyView.Objective Patients with rheumatoid arthritis (RA) often fail to respond to therapies, including JAK inhibitors (JAKi), and treatment allocation is made via a trial‐and‐error strategy. A comprehensive analysis of responses to JAKi, including tofacitinib, by RNA sequencing (RNAseq) would allow the discovery of transcriptomic markers with a two‐fold ...
Chiara Bellocchi +11 more
wiley +1 more source
Learning to Sample: an Active Learning Framework [PDF]
arXiv, 2019 Meta-learning algorithms for active learning are emerging as a promising paradigm for learning the ``best'' active learning strategy. However, current learning-based active learning approaches still require sufficient training data so as to generalize meta-learning models for active learning.
arxiv
Partitioning soil organic carbon into its centennially stable and active fractions with machine-learning models based on Rock-Eval® thermal analysis (PARTY<sub>SOC</sub>v2.0 and PARTY<sub>SOC</sub>v2.0<sub>EU</sub>) [PDF]
, 2021 Lauric Cécillon +14 more
openalex +1 more source