Results 41 to 50 of about 120,140 (335)
Contrastive Fairness in Machine Learning [PDF]
IEEE Letters of the Computer Society, 2020 Was it fair that Harry was hired but not Barry? Was it fair that Pam was fired instead of Sam? How can one ensure fairness when an intelligent algorithm takes these decisions instead of a human? How can one ensure that the decisions were taken based on merit and not on protected attributes like race or sex? These are the questions that must be answered
Tapabrata Chakraborti +2 more
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Spectral Temporal Contrastive Learning
CoRR, 2023 Accepted to Self-Supervised Learning - Theory and Practice, NeurIPS Workshop ...
Sacha Morin +3 more
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MoCoUTRL: a momentum contrastive framework for unsupervised text representation learning
Connection Science, 2023 This paper presents MoCoUTRL: a Momentum Contrastive Framework for Unsupervised Text Representation Learning. This model improves two aspects of recently popular contrastive learning algorithms in natural language processing (NLP).
Ao Zou +4 more
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Multi-Modal 3D Shape Clustering with Dual Contrastive Learning
Applied Sciences, 2022 3D shape clustering is developing into an important research subject with the wide applications of 3D shapes in computer vision and multimedia fields. Since 3D shapes generally take on various modalities, how to comprehensively exploit the multi-modal ...
Guoting Lin +4 more
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Conditional Contrastive Learning with Kernel
CoRR, 2022 Conditional contrastive learning frameworks consider the conditional sampling procedure that constructs positive or negative data pairs conditioned on specific variables. Fair contrastive learning constructs negative pairs, for example, from the same gender (conditioning on sensitive information), which in turn reduces undesirable information from the ...
Yao-Hung Hubert Tsai +6 more
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Frontiers in Physics, 2023 An improved algorithm has been proposed to address the challenges encountered in object detection using visible and thermal infrared images. These challenges include the diversity of object detection perspectives, deformation of the object, occlusion ...
Xiaoguang Tu +7 more
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Short-Term Load Forecasting of Natural Gas with Deep Neural Network Regression [PDF]
, 2018 Deep neural networks are proposed for short-term natural gas load forecasting. Deep learning has proven to be a powerful tool for many classification problems seeing significant use in machine learning fields such as image recognition and speech ...
Brown, Ronald H. +2 more
core +2 more sources
SSCLNet: A Self-Supervised Contrastive Loss-Based Pre-Trained Network for Brain MRI Classification
IEEE Access, 2023 Brain magnetic resonance images (MRI) convey vital information for making diagnostic decisions and are widely used to detect brain tumors. This research proposes a self-supervised pre-training method based on feature representation learning through ...
Animesh Mishra +2 more
doaj +1 more source
Poisoning and Backdooring Contrastive Learning
CoRR, 2021 Multimodal contrastive learning methods like CLIP train on noisy and uncurated training datasets. This is cheaper than labeling datasets manually, and even improves out-of-distribution robustness. We show that this practice makes backdoor and poisoning attacks a significant threat.
Nicholas Carlini, Andreas Terzis
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