Results 41 to 50 of about 98,849 (283)

Contrastive Hebbian Learning with Random Feedback Weights

, 2018
Neural networks are commonly trained to make predictions through learning algorithms. Contrastive Hebbian learning, which is a powerful rule inspired by gradient backpropagation, is based on Hebb's rule and the contrastive divergence algorithm.
Bartley, Travis, Detorakis, Georgios, Neftci, Emre   +2 more
core   +1 more source

Prototypical Graph Contrastive Learning

IEEE Transactions on Neural Networks and Learning Systems
Graph-level representations are critical in various real-world applications, such as predicting the properties of molecules. However, in practice, precise graph annotations are generally very expensive and time-consuming. To address this issue, graph contrastive learning constructs an instance discrimination task, which pulls together positive pairs ...
Shuai Lin, Chen Liu, Pan Zhou, Zi-Yuan Hu, Shuojia Wang, Ruihui Zhao, Yefeng Zheng, Liang Lin, Eric Xing, Xiaodan Liang   +9 more
openaire   +3 more sources

Supervised Contrastive Learning

, 2020
Contrastive learning applied to self-supervised representation learning has seen a resurgence in recent years, leading to state of the art performance in the unsupervised training of deep image models. Modern batch contrastive approaches subsume or significantly outperform traditional contrastive losses such as triplet, max-margin and the N-pairs loss.
Khosla, Prannay, Teterwak, Piotr, Wang, Chen, Sarna, Aaron, Tian, Yonglong, Isola, Phillip, Maschinot, Aaron, Liu, Ce, Krishnan, Dilip   +8 more
openaire   +2 more sources

Unsupervised learning with contrastive latent variable models

, 2018
In unsupervised learning, dimensionality reduction is an important tool for data exploration and visualization. Because these aims are typically open-ended, it can be useful to frame the problem as looking for patterns that are enriched in one dataset ...
Ghosh, Soumya, Ng, Kenney, Severson, Kristen   +2 more
core   +1 more source

Contrastive Mask Learning for Self-Supervised 3D Skeleton-Based Action Recognition

Sensors
In this paper, we propose a contrastive mask learning (CML) method for self-supervised 3D skeleton-based action recognition. Specifically, the mask modeling mechanism is integrated into multi-level contrastive learning with the aim of forming a mutually ...
Haoyuan Zhang
doaj   +1 more source

Graph Clustering with High-Order Contrastive Learning

Entropy, 2023
Graph clustering is a fundamental and challenging task in unsupervised learning. It has achieved great progress due to contrastive learning. However, we find that there are two problems that need to be addressed: (1) The augmentations in most graph ...
Wang Li, En Zhu, Siwei Wang, Xifeng Guo
doaj   +1 more source

Imaging of High‐Risk Neuroblastoma: Recommendations From SIOPEN Radiology and Nuclear Medicine Specialty Committees

Pediatric Blood &Cancer, EarlyView.
ABSTRACT Neuroblastoma is the most common extracranial solid tumor in early childhood. Its clinical behavior is highly variable, ranging from spontaneous regression to fatal outcome despite intensive treatment. The International Society of Pediatric Oncology Europe Neuroblastoma Group (SIOPEN) Radiology and Nuclear Medicine Specialty Committees ...
Annemieke Littooij, Catia Olianti, Herve Brisse, Pier Luigi Di Paolo, Maria Garganese, Tejas Kapadia, Jurgen Schaefer, Hugh Wallace, Simon Wan, Bart de Keizer, Irmina Sefic Pasic, Julian Rogasch   +11 more
wiley   +1 more source

Cognitive Functioning in Vorinostat‐Treated Pediatric and Young Adult Patients Over the First 180 Days After Hematopoietic Stem Cell Transplant

Pediatric Blood &Cancer, EarlyView.
ABSTRACT Purpose Cognitive and psychological difficulties could negatively interfere with treatment adherence and quality of life before and after hematopoietic stem cell transplant (HSCT). Methods to mitigate these changes may have positive effects on treatment success.
Kristen L. Votruba, Michelle Rozwadowski, Thomas Braun, April Rahrig, Edward Peres, Kirsten M. Williams, Vanessa Fabrizio, Julie‐An Talano, Carrie L. Kitko, Nicolas Parnell, Pavan Reddy, Sung Won Choi   +11 more
wiley   +1 more source

Study on Graph Collaborative Filtering Model Based on FeatureNet Contrastive Learning [PDF]

Jisuanji kexue
Graph-based collaborative filtering recommendation techniques have gained significant attention for their ability to efficiently process large-scale interaction data.However,the effectiveness of these techniques is limited by the sparsity of data in real-
WU Pengyuan, FANG Wei
doaj   +1 more source

Deep Metric Learning with Angular Loss

, 2017
The modern image search system requires semantic understanding of image, and a key yet under-addressed problem is to learn a good metric for measuring the similarity between images.
Lin, Yuanqing, Liu, Xiao, Wang, Jian, Wen, Shilei, Zhou, Feng   +4 more
core   +1 more source