Explicit intent enhanced contrastive learning with denoising networks for sequential recommendation by Jinfang Sheng, Xuhao Zhang, Bin Wang

“…To address this issue, we propose a model named Explicit Intent Enhanced Contrastive Learning with Denoising Networks for Sequential Recommendation (EICD-Rec). …”
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Explicit and Implicit Feature Contrastive Learning Model for Knowledge Graph Link Prediction by Xu Yuan, Weihe Wang, Buyun Gao, Liang Zhao, Ruixin Ma, Feng Ding

“…Recently, graph neural networks and contrastive learning have demonstrated superior performance compared with traditional translation-based models; they successfully extracted common features through explicit linking between entities. …”
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Implicit and explicit learning strategies and fatigue: an evaluation of throwing task performance by Reihaneh Banihosseini, Behrouz Abdoli, Maryam Kavyani

“…The explicit learning group began at a significant distance from the target and gradually moved closer. …”
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Hierarchical contrastive learning for multi-label text classification by Wei Zhang, Yun Jiang, Yun Fang, Shuai Pan

“…To address this shortcoming, we introduce a novel method called Hierarchical Contrastive Learning for Multi-label Text Classification (HCL-MTC). …”
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Enhancing aspect-based financial sentiment analysis through contrastive learning by Viacheslav Ivanenko

“…Secondly, it aims to architect a unified model that integrates state-of-the-art machine learning techniques, including DeBERTa v3,  contrast learning, and LoRa fine-tuning. …”
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Enhancing aspect-based financial sentiment analysis through contrastive learning by В’ячеслав Іваненко

“…Secondly, it aims to architect a unified model that integrates state-of-the-art machine learning techniques, including DeBERTa v3,  contrast learning, and LoRa fine-tuning. …”
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Enhancing chemical reaction search through contrastive representation learning and human-in-the-loop by Youngchun Kwon, Hyunjeong Jeon, Joonhyuk Choi, Youn-Suk Choi, Seokho Kang

“…To implement this system effectively, we incorporate and adapt contrastive representation learning, dimensionality reduction, and human-in-the-loop techniques. …”
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Semi-Supervised Burn Depth Segmentation Network with Contrast Learning and Uncertainty Correction by Dongxue Zhang, Jingmeng Xie

“…To improve segmentation in complex regions like burn edges, contrastive learning refines the outputs of the three-branch decoder, enabling more discriminative feature representation learning. …”
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Advancing Self-Supervised Learning for Building Change Detection and Damage Assessment: Unified Denoising Autoencoder and Contrastive Learning Framework by Songxi Yang, Bo Peng, Tang Sui, Meiliu Wu, Qunying Huang

“…In contrast, self-supervised learning enables the extraction of meaningful data representations without explicit training labels. …”
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Neighborhood Information Aggregation and Multi-View Feature Extraction-Based Contrastive Graph Clustering by Liulong Yao, Jinrong Cui, Yazi Xie, Chengli Sun

“…This module not only considers the explicit structures in the data, but also generates a new neighborhood relationship graph by combining the learned potential relationship structures. …”
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Disentangled Contrastive Learning From Synthetic Matching Pairs for Targeted Chest X-Ray Generation by Euyoung Kim, Soochahn Lee, Kyoung Mu Lee

“…The proposed method leverages contrastive learning to disentangle latent spaces for patient and disease attributes, modeling these spaces with multivariate Gaussians for precise and exclusive attribute sampling. …”
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Multi-Scale Contrastive Learning with Hierarchical Knowledge Synergy for Visible-Infrared Person Re-Identification by Yongheng Qian, Su-Kit Tang

“…MCLNet utilizes supervised contrastive learning (SCL) at each intermediate layer to strengthen visual representations and enhance cross-modality feature learning. …”
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Multi-modal representation learning in retinal imaging using self-supervised learning for enhanced clinical predictions by Emese Sükei, Elisabeth Rumetshofer, Niklas Schmidinger, Andreas Mayr, Ursula Schmidt-Erfurth, Günter Klambauer, Hrvoje Bogunović

“…In this context, we introduce a novel multi-modal contrastive learning-based pipeline to facilitate learning joint representations for the two retinal imaging modalities. …”
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Capturing User Preferences via Multi-Perspective Hypergraphs with Contrastive Learning for Next-Location Prediction by Fengyu Liu, Kexin Zhang, Chao Lian, Yunong Tian

“…To address these challenges, we propose a novel framework named Multi-Perspective Hypergraphs with Contrastive Learning (MPHCL), which explicitly captures and disentangles user preferences from three complementary perspectives. …”
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Activity cliff-aware reinforcement learning for de novo drug design by Xiuyuan Hu, Guoqing Liu, Yang Zhao, Hao Zhang

“…In response to the limitations of current models in capturing these critical discontinuities, we propose the Activity Cliff-Aware Reinforcement Learning (ACARL) framework. ACARL leverages a novel activity cliff index to identify and amplify activity cliff compounds, uniquely incorporating them into the reinforcement learning (RL) process through a tailored contrastive loss. …”
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Semi-supervised gearbox fault diagnosis under variable working conditions based on masked contrastive learning by ZHANG Huiyun, ZUO Fangjun, LI Hang, YU Xi

“…Secondly, a dynamic convolutional neural network was employed to dynamically weight and aggregate the masked instances, enabling discriminative feature modeling of different masked instances. Then, a contrastive learning framework was constructed with the optimization goal of maximizing the similarity between features of different masked instances. …”
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Maven: a multimodal foundation model for supernova science by Gemma Zhang, Thomas Helfer, Alexander T Gagliano, Siddharth Mishra-Sharma, V Ashley Villar

“…At the same time, no data-driven models exist to understand these photometric and spectroscopic observables in a common context. Contrastive learning objectives, which have grown in popularity for aligning distinct data modalities in a shared embedding space, provide a potential solution to extract information from these modalities. …”
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Multi-pattern time-aware sequential recommendation with data augmentation by LI Jiale, WANG Ruiqin, YU Yang

“…Therefore, the DMTiSASRec model was proposed, which not only efficiently extracted relevant orders beyond temporal information but also leveraged techniques like contrastive learning and multi-modal methods to mine different types of additional information. …”
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Block-Wise Domain Adaptation for Workload Prediction from fNIRS Data by Jiyang Wang, Ayse Altay, Leanne Hirshfield, Senem Velipasalar

“…Moreover, the experimental results show that our proposed contrastive learning method can also be leveraged to improve the performance of the baseline models. …”
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Implicit versus explicit Bayesian priors for epistemic uncertainty estimation in clinical decision support. by Malte Blattmann, Adrian Lindenmeyer, Stefan Franke, Thomas Neumuth, Daniel Schneider

“…We show that implicit functional-prior methods-specifically neural network ensembles and factorized weight prior variational Bayesian neural networks-exhibit reduced fidelity when approximating the posterior distribution and yield systematically biased estimates of epistemic uncertainty. By contrast, models employing explicitly defined, distance-aware priors-such as spectral-normalized neural Gaussian processes (SNGP)-provide more accurate posterior approximations and more reliable uncertainty quantification. …”
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