Results 71 to 80 of about 120,140 (335)

NormFace: L2 Hypersphere Embedding for Face Verification

, 2017
Thanks to the recent developments of Convolutional Neural Networks, the performance of face verification methods has increased rapidly. In a typical face verification method, feature normalization is a critical step for boosting performance.
Cheng, Jian, Wang, Feng, Xiang, Xiang, Yuille, Alan L.   +3 more
core   +1 more source

Disjoint Contrastive Regression Learning for Multi-Sourced Annotations [PDF]

, 2021
Xiaoqian Ruan, Gaoang Wang
openalex   +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, Veronika Hovanová, Jiri Damborsky, Erik Sedlák   +3 more
wiley   +1 more source

Node classification in complex networks based on multi-view debiased contrastive learning

Complex & Intelligent Systems
In complex networks, contrastive learning has emerged as a crucial technique for acquiring discriminative representations from graph data. Maximizing the similarity among relevant sample pairs while minimizing that among irrelevant pairs is pivotal in ...
Zhe Li, Lei Zhou, Yandong Hou, Min Ji, Zhuanzheng Hang, Bolun Chen   +5 more
doaj   +1 more source

Contrastive Self-Supervised Learning for Sensor-Based Human Activity Recognition: A Review

IEEE Access
Deep learning models have achieved significant success in human activity recognition, particularly in assisted living and telemonitoring. However, training these models requires substantial amounts of labeled training data, which is time-consuming and ...
Hui Chen, Charles Gouin-Vallerand, Kevin Bouchard, Sebastien Gaboury, Melanie Couture, Nathalie Bier, Sylvain Giroux   +6 more
doaj   +1 more source

ResumeNet: A Learning-based Framework for Automatic Resume Quality Assessment

, 2018
Recruitment of appropriate people for certain positions is critical for any companies or organizations. Manually screening to select appropriate candidates from large amounts of resumes can be exhausted and time-consuming.
Luo, Yong, Wang, Yongjie, We, Yonggang, Zhang, Huaizheng, Zhang, Xinwen   +4 more
core   +1 more source

Single-Pass Contrastive Learning Can Work for Both Homophilic and Heterophilic Graph [PDF]

, 2022
Haonan Wang, Jieyu Zhang, Qi Zhu, Wei Huang, Kawaguchi, Kenji, Xiao, Xiaokui   +5 more
openalex   +1 more source

Large‐scale bidirectional arrayed genetic screens identify OXR1 and EMC4 as modifiers of αSynuclein aggregation

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, Lea Nikolić, Lorenzo Maraio, Thomas Goiran, Nathan Karpilovsky, Stefano Sellitto, Vangelis Bouris, Jiang‐An Yin, Ronald Melki, Edward A Fon, Adriano Aguzzi, Elena De Cecco   +11 more
wiley   +1 more source

Positive Unlabeled Contrastive Learning

CoRR, 2022
Self-supervised pretraining on unlabeled data followed by supervised fine-tuning on labeled data is a popular paradigm for learning from limited labeled examples. We extend this paradigm to the classical positive unlabeled (PU) setting, where the task is to learn a binary classifier given only a few labeled positive samples, and (often) a large amount ...
Anish Acharya, Sujay Sanghavi, Li Jing 0001, Bhargav Bhushanam, Dhruv Choudhary, Michael G. Rabbat, Inderjit S. Dhillon   +6 more
openaire   +2 more sources

Contrastive Adaptive Propagation Graph Neural Networks for Efficient Graph Learning [PDF]

, 2021
Jun Hu, Shengsheng Qian, Quan Fang, Changsheng Xu   +3 more
openalex   +1 more source