Results 91 to 100 of about 26,557 (311)
Contrastive Learning Using Spectral Methods [PDF]
, 2017 In many natural settings, the analysis goal is not to characterize a single data set in isolation, but rather to understand the difference between one set of observations and another. For example, given a background corpus of news articles together with
Parkes, David +3 more
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LIGHTGCL: SIMPLE YET EFFECTIVE GRAPH CONTRASTIVE LEARNING FOR RECOMMENDATION [PDF]
, 2023 Graph neural network (GNN) is a powerful learning approach for graph-based recommender systems. Recently, GNNs integrated with contrastive learning have shown superior performance in recommendation with their data augmentation schemes, aiming at dealing ...
Cai, Xuheng +3 more
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Applied Artificial IntelligenceUtilizing self-supervised learning to learn meaningful representations from unlabeled data can be a cost-effective strategy, particularly in medical domains where expert labeling incurs high costs.
Jihyo Kim +6 more
doaj +1 more source
Molecular Oncology, EarlyView.Single‐cell multi‐omics reveals epigenetic heterogeneity across therapy‐adaptive tumor states, including quiescent/dormant, drug‐tolerant persister, and EMT‐like phenotypes. By linking regulatory features with state‐associated biomarkers, these approaches inform biomarker‐guided therapeutic strategies for evolving tumors.
Hee Jung Kim +3 more
wiley +1 more source
Adversarial graph contrastive learning with information regularization [PDF]
, 2022 Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2022-11-11 without embargo termsThe student, Shengyu Feng, accepted the attached license on 2022-04-24 at 01:50.The student, Shengyu Feng ...
Feng, Shengyu
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Dual Contrastive Learning Model Based Background Debiasing in SAR ATR
Shuju Caiji Yu ChuliContrastive learning， as a self-supervised approach， enables the extraction of target representations from unlabeled SAR images， serving as a critical technique for automatic target recognition （ATR） in SAR.
ZHANG Wenqing +6 more
doaj +1 more source
Self-Supervised Contrastive Learning is Approximately Supervised Contrastive Learning
CoRRDespite its empirical success, the theoretical foundations of self-supervised contrastive learning (CL) are not yet fully established. In this work, we address this gap by showing that standard CL objectives implicitly approximate a supervised variant we call the negatives-only supervised contrastive loss (NSCL), which excludes same-class contrasts. We
Achleshwar Luthra +2 more
openaire +2 more sources
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
Parallel and comparable corpora: What are they up to? [PDF]
, 2007 With ever increasing international exchange and accelerated globalisation, translation and contrastive studies are more popular than ever. As part of this new wave of research on translation and contrastive studies, corpora, and multilingual corpora in ...
Xiao, R. Z., McEnery, A. M.
core
Contrastive Learning for Image Captioning
CoRR, 2017 Image captioning, a popular topic in computer vision, has achieved substantial progress in recent years. However, the distinctiveness of natural descriptions is often overlooked in previous work. It is closely related to the quality of captions, as distinctive captions are more likely to describe images with their unique aspects.
Bo Dai 0002, Dahua Lin
openaire +3 more sources