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Contrastive Learning for Inference in Dialogue
Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, 2023 Accepted to ...
Etsuko Ishii +6 more
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2025 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)In recent times, contrastive learning based loss functions have become increasingly popular for visual self-supervised representation learning owing to their state-of-the-art (SOTA) performance. Most of the modern contrastive learning methods generalize only to one positive and multiple negatives per anchor.
Chaitanya Animesh, Manmohan Chandraker
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Graph Communal Contrastive Learning
Proceedings of the ACM Web Conference 2022, 2022 Graph representation learning is crucial for many real-world applications (e.g. social relation analysis). A fundamental problem for graph representation learning is how to effectively learn representations without human labeling, which is usually costly and time-consuming. Graph contrastive learning (GCL) addresses this problem by pulling the positive
Bolian Li, Baoyu Jing, Hanghang Tong
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Study on Graph Collaborative Filtering Model Based on FeatureNet Contrastive Learning [PDF]
Jisuanji kexueGraph-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
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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
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Time-Contrastive Networks: Self-Supervised Learning from Video
, 2018 We propose a self-supervised approach for learning representations and robotic behaviors entirely from unlabeled videos recorded from multiple viewpoints, and study how this representation can be used in two robotic imitation settings: imitating object ...
Chebotar, Yevgen +6 more
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Graph Contrastive Learning for Materials
CoRR, 2022 Recent work has shown the potential of graph neural networks to efficiently predict material properties, enabling high-throughput screening of materials. Training these models, however, often requires large quantities of labelled data, obtained via costly methods such as ab initio calculations or experimental evaluation.
Teddy Koker +4 more
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IEEE Journal of Selected Topics in Applied Earth Observations and Remote SensingPolarimetric synthetic aperture radar (PolSAR) has rich polarization information, offering an efficient and reliable means of collecting information. However, how to effectively leverage these complex data to extract polarization features remains a key ...
Bo Ren +6 more
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An Unsupervised Band Selection Method via Contrastive Learning for Hyperspectral Images
Remote Sensing, 2023 Band selection (BS) is an efficacious approach to reduce hyperspectral information redundancy while preserving the physical meaning of hyperspectral images (HSIs).
Xiaorun Li +3 more
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Learning in Markov Random Fields with Contrastive Free Energies [PDF]
, 2005 Learning Markov random field (MRF) models is notoriously hard due to the presence of a global normalization factor. In this paper we present a new framework for learning MRF models based on the contrastive free energy (CF) objective function.
Sutton, Charles, Welling, Max
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