Results 81 to 90 of about 476,790 (313)
Structural Hierarchy-Enhanced Network Representation Learning
Applied Sciences, 2020 Network representation learning (NRL) is crucial in generating effective node features for downstream tasks, such as node classification (NC) and link prediction (LP).
Cheng-Te Li, Hong-Yu Lin
doaj +1 more source
FEBS Open Bio, EarlyView.In this study, we developed a deep learning method for mitotic figure counting in H&E‐stained whole‐slide images and evaluated its prognostic impact in 13 external validation cohorts from seven different cancer types. Patients with more mitotic figures per mm2 had significantly worse patient outcome in all the studied cancer types except colorectal ...
Joakim Kalsnes +32 more
wiley +1 more source
Contrastive sentence representation learning with adaptive false negative cancellation [PDF]
Contrastive sentence representation learning has made great progress thanks to a range of text augmentation strategies and hard negative sampling techniques.
Lingling Xu +11 more
core +1 more source
Ambiguity and multiplicity in music representation. [PDF]
, 2009 Proper representation of music must often be ambiguous and/or multiple.
Marsden, Alan
core +1 more source
Graph Tree Networks: a graph representation learning framework
, 2023 Fang, XiaoGraph Neural Networks (GNNs) have been successfully applied in many areas to solve real-world problems. Among various architectures of GNNs, the class of spatial-based convolutional GNNs (Conv-GNNs) has gained particular attention due to its ...
Wu, Nan
core +1 more source
Rapid screening of staphylokinase protein variants using an unpurified cell‐free expression system
FEBS Open Bio, EarlyView.An unpurified cell‐free protein synthesis (CFPS) platform enables rapid functional screening of staphylokinase variants. Direct plasminogen‐activation assays performed in microplate format provide real‐time activity readouts, allowing rapid identification and ranking of variants with improved or reduced fibrinolytic activity without protein ...
Maria Tomková +3 more
wiley +1 more source
Learning Representations in Reinforcement Learning
, 2019 Reinforcement Learning (RL) algorithms allow artificial agents to improve their action selection policy to increase rewarding experiences in their environments. Temporal Difference (TD) learning algorithm, a model-free RL method, attempts to find an optimal policy through learning the values of agent's actions at any state by computing the expected ...
openaire +2 more sources
Geodesics of learned representations
, 2015 Published as a conference paper at ICLR ...
Olivier J. Hénaff, Eero P. Simoncelli
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FEBS Open Bio, EarlyView.Activation of the mitochondrial protein OXR1 increases pSyn129 αSynuclein aggregation by lowering ATP levels and altering mitochondrial membrane potential, particularly in response to MSA‐derived fibrils. In contrast, ablation of the ER protein EMC4 enhances autophagic flux and lysosomal clearance, broadly reducing α‐synuclein aggregates.
Sandesh Neupane +11 more
wiley +1 more source
Law, learning and representation
Artificial Intelligence, 2003 zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Ashley, KD, Rissland, EL
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