Multi-objective Deep Learning: Taxonomy and Survey of the State of the Art | Paper Citation Record

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This paper cites Dynamic weights in multi-objective deep reinforcement learning,.

Multi-objective Deep Learning: Taxonomy and Survey of the State of the Art Dynamic weights in multi-objective deep reinforcement learning,

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This paper cites Multi-objective reinforcement learning with non-linear scalarization,.

Multi-objective Deep Learning: Taxonomy and Survey of the State of the Art Multi-objective reinforcement learning with non-linear scalarization,

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This paper cites Common pitfalls to avoid while using multiobjective optimization in machine learning.

Multi-objective Deep Learning: Taxonomy and Survey of the State of the Art Common pitfalls to avoid while using multiobjective optimization in machine learning

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This paper cites Multi-objective unsupervised feature selection and cluster based on symbiotic organism search,.

Multi-objective Deep Learning: Taxonomy and Survey of the State of the Art Multi-objective unsupervised feature selection and cluster based on symbiotic organism search,

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Multi-objective Deep Learning: Taxonomy and Survey of the State of the Art Multi-objective training of generative adversarial net- works with multiple discriminators,

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This paper cites A new semi-supervised clustering technique using multi-objective optimization,.

Multi-objective Deep Learning: Taxonomy and Survey of the State of the Art A new semi-supervised clustering technique using multi-objective optimization,

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This paper cites A multiobjective continuation method to compute the regularization path of deep neural networks.

Multi-objective Deep Learning: Taxonomy and Survey of the State of the Art A multiobjective continuation method to compute the regularization path of deep neural networks

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This paper cites Hypervolume-based multiobjective optimization: Theoretical foundations and practical im- plications,.

Multi-objective Deep Learning: Taxonomy and Survey of the State of the Art Hypervolume-based multiobjective optimization: Theoretical foundations and practical im- plications,

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This paper cites SVM classification for imbalanced data sets using a multiobjective optimization framework,.

Multi-objective Deep Learning: Taxonomy and Survey of the State of the Art SVM classification for imbalanced data sets using a multiobjective optimization framework,

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Multi-objective Deep Learning: Taxonomy and Survey of the State of the Art Hype: An algorithm for fast hypervolume- based many-objective optimization,

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This paper cites Evolutionary multi-objective optimization of large language model prompts for balancing sentiments,.

Multi-objective Deep Learning: Taxonomy and Survey of the State of the Art Evolutionary multi-objective optimization of large language model prompts for balancing sentiments,

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Multi-objective Deep Learning: Taxonomy and Survey of the State of the Art The explicit linear quadratic regulator for constrained systems,

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This paper cites Autoen- coders and their applications in machine learning: a survey,.

Multi-objective Deep Learning: Taxonomy and Survey of the State of the Art Autoen- coders and their applications in machine learning: a survey,

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This paper cites Derivative-free multiobjective trust region descent method using radial basis function surrogate models,.

Multi-objective Deep Learning: Taxonomy and Survey of the State of the Art Derivative-free multiobjective trust region descent method using radial basis function surrogate models,

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Multi-objective Deep Learning: Taxonomy and Survey of the State of the Art Bertsekas, Reinforcement learning and optimal control

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This paper cites DeepSI: Interactive deep learning for semantic interaction,.

Multi-objective Deep Learning: Taxonomy and Survey of the State of the Art DeepSI: Interactive deep learning for semantic interaction,

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This paper cites On the Treatment of Optimization Problems with L1 Penalty Terms via Multiobjective Continuation,.

Multi-objective Deep Learning: Taxonomy and Survey of the State of the Art On the Treatment of Optimization Problems with L1 Penalty Terms via Multiobjective Continuation,

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This paper cites On gradients and hybrid evolutionary algorithms for real-valued multiobjective optimization,.

Multi-objective Deep Learning: Taxonomy and Survey of the State of the Art On gradients and hybrid evolutionary algorithms for real-valued multiobjective optimization,

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This paper cites Enhancing multi-objective optimisation through machine learning-supported multiphysics simulation,.

Multi-objective Deep Learning: Taxonomy and Survey of the State of the Art Enhancing multi-objective optimisation through machine learning-supported multiphysics simulation,

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This paper cites A survey on active learning and human-in-the-loop deep learning for medical image analysis,.

Multi-objective Deep Learning: Taxonomy and Survey of the State of the Art A survey on active learning and human-in-the-loop deep learning for medical image analysis,

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This paper cites MoMAC: Multi-objective optimization to combine multiple association rules into an interpretable classification,.

Multi-objective Deep Learning: Taxonomy and Survey of the State of the Art MoMAC: Multi-objective optimization to combine multiple association rules into an interpretable classification,

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Multi-objective Deep Learning: Taxonomy and Survey of the State of the Art A Bregman learning framework for sparse neural networks,

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Multi-objective Deep Learning: Taxonomy and Survey of the State of the Art Ad-dmkde: Anomaly detection through density matrices and fourier features,

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Multi-objective Deep Learning: Taxonomy and Survey of the State of the Art Deep clustering for unsupervised learning of visual features,

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Multi-objective Deep Learning: Taxonomy and Survey of the State of the Art EMORL: Effective multi-objective reinforcement learning method for hyperparameter optimization,

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Multi-objective Deep Learning: Taxonomy and Survey of the State of the Art Semi-supervised and unsupervised deep visual learning: A survey,

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Multi-objective Deep Learning: Taxonomy and Survey of the State of the Art A tutorial on kernel density estimation and recent ad- vances,

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Multi-objective Deep Learning: Taxonomy and Survey of the State of the Art Pareto self- supervised training for few-shot learning,

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This paper cites Handling computationally expensive multiobjective optimization problems with evolutionary algorithms-a survey,.

Multi-objective Deep Learning: Taxonomy and Survey of the State of the Art Handling computationally expensive multiobjective optimization problems with evolutionary algorithms-a survey,

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Multi-objective Deep Learning: Taxonomy and Survey of the State of the Art A fast and elitist multiobjective genetic algorithm: NSGA-II,

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Multi-objective Deep Learning: Taxonomy and Survey of the State of the Art Surrogate modeling approaches for multiobjective optimization: Methods, taxonomy, and results,

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Multi-objective Deep Learning: Taxonomy and Survey of the State of the Art Magnetic control of tokamak plasmas through deep reinforcement learning,

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Multi-objective Deep Learning: Taxonomy and Survey of the State of the Art Covering pareto sets by multilevel subdivision techniques,

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Multi-objective Deep Learning: Taxonomy and Survey of the State of the Art Static video summarization with multi-objective constrained optimization,

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Multi-objective Deep Learning: Taxonomy and Survey of the State of the Art Survey on unsupervised learning methods for optical flow estimation,

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Multi-objective Deep Learning: Taxonomy and Survey of the State of the Art Generative multi-adversarial networks,

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Multi-objective Deep Learning: Taxonomy and Survey of the State of the Art Multiple-gradient descent algorithm (MGDA) for mul- tiobjective optimization,

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Multi-objective Deep Learning: Taxonomy and Survey of the State of the Art Ehrgott, Multicriteria optimization, 2nd ed

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Multi-objective Deep Learning: Taxonomy and Survey of the State of the Art An adaptive scalarization method in multiobjective optimization,

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Multi-objective Deep Learning: Taxonomy and Survey of the State of the Art Efficient multi-objective neural architecture search via Lamarckian evolution,

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Multi-objective Deep Learning: Taxonomy and Survey of the State of the Art Neural architecture search: A survey,

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Multi-objective Deep Learning: Taxonomy and Survey of the State of the Art Pareto navigator for interactive nonlinear multiobjective optimization,

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Multi-objective Deep Learning: Taxonomy and Survey of the State of the Art Multi-objective reinforcement learning based on decomposition: A taxonomy and framework,

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Multi-objective Deep Learning: Taxonomy and Survey of the State of the Art Multi-objective adversarial gesture generation,

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Multi-objective Deep Learning: Taxonomy and Survey of the State of the Art Newton’s method for multiobjective optimization,

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Multi-objective Deep Learning: Taxonomy and Survey of the State of the Art Steepest descent methods for multicriteria optimization,

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Multi-objective Deep Learning: Taxonomy and Survey of the State of the Art A method for constrained multiobjective optimization based on sqp techniques,

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Multi-objective Deep Learning: Taxonomy and Survey of the State of the Art An overview of evolutionary algorithms in multiobjective optimization,

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Multi-objective Deep Learning: Taxonomy and Survey of the State of the Art DessiLBI: Exploring structural sparsity of deep networks via differential inclusion paths,

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Multi-objective Deep Learning: Taxonomy and Survey of the State of the Art Exploring structural sparsity of deep networks via inverse scale spaces,

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This paper cites Mpgan: Multi pareto generative adversarial network for the denoising and quantitative analysis of low-dose pet images of human brain,.

Multi-objective Deep Learning: Taxonomy and Survey of the State of the Art Mpgan: Multi pareto generative adversarial network for the denoising and quantitative analysis of low-dose pet images of human brain,

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This paper cites An Efficient Descent Method for Locally Lip- schitz Multiobjective Optimization Problems,.

Multi-objective Deep Learning: Taxonomy and Survey of the State of the Art An Efficient Descent Method for Locally Lip- schitz Multiobjective Optimization Problems,

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This paper cites A Descent Method for Equality and Inequality Constrained Multiobjective Optimization Problems,.

Multi-objective Deep Learning: Taxonomy and Survey of the State of the Art A Descent Method for Equality and Inequality Constrained Multiobjective Optimization Problems,

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This paper cites On the hierarchical structure of Pareto critical sets,.

Multi-objective Deep Learning: Taxonomy and Survey of the State of the Art On the hierarchical structure of Pareto critical sets,

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This paper cites Unsupervised band selection based on evolutionary multiobjective optimization for hyperspectral images,.

Multi-objective Deep Learning: Taxonomy and Survey of the State of the Art Unsupervised band selection based on evolutionary multiobjective optimization for hyperspectral images,

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Multi-objective Deep Learning: Taxonomy and Survey of the State of the Art Improving constrained clustering via decomposition-based multiobjective optimization with memetic elitism,

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Multi-objective Deep Learning: Taxonomy and Survey of the State of the Art Goodfellow, Y

Reference 62

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Multi-objective Deep Learning: Taxonomy and Survey of the State of the Art Generative adversarial nets,

Reference 63

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Multi-objective Deep Learning: Taxonomy and Survey of the State of the Art A Survey on Self-supervised Learning: Algorithms, Applications, and Future Trends

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Multi-objective Deep Learning: Taxonomy and Survey of the State of the Art Constructing Multiple Tasks for Augmentation: Improving Neural Image Classification With K-means Features

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Multi-objective Deep Learning: Taxonomy and Survey of the State of the Art Soft actor-critic: Off- policy maximum entropy deep reinforcement learning with a stochastic actor,

Reference 66

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Multi-objective Deep Learning: Taxonomy and Survey of the State of the Art Kill two birds with one stone: A multi-view multi-adversarial learning approach for joint air quality and weather prediction,

Reference 67

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Multi-objective Deep Learning: Taxonomy and Survey of the State of the Art Feature subset selection in unsupervised learning via multiobjective optimization,

Reference 68

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Multi-objective Deep Learning: Taxonomy and Survey of the State of the Art Hastie, R

Reference 69

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Multi-objective Deep Learning: Taxonomy and Survey of the State of the Art A practical guide to multi- objective reinforcement learning and planning,

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Multi-objective Deep Learning: Taxonomy and Survey of the State of the Art Evolutionary Multiobjective Optimization Driven by Generative Adversarial Networks (GANs)

Reference 71

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Multi-objective Deep Learning: Taxonomy and Survey of the State of the Art Masked autoencoders are scalable vision learners,

Reference 72

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Multi-objective Deep Learning: Taxonomy and Survey of the State of the Art Hillermeier, Nonlinear Multiobjective Optimization: A Generalized Homotopy Approach

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Multi-objective Deep Learning: Taxonomy and Survey of the State of the Art Multi-objective optimiza- tion for sparse deep multi-task learning,

Reference 74

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Multi-objective Deep Learning: Taxonomy and Survey of the State of the Art Enhancing Adversarial Robustness through Multi-Objective Representation Learning

Reference 75

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Multi-objective Deep Learning: Taxonomy and Survey of the State of the Art A generative Kriging surrogate model for constrained and unconstrained multi-objective optimization,

Reference 76

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Multi-objective Deep Learning: Taxonomy and Survey of the State of the Art Multi-objective GFlowNets,

Reference 77

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Multi-objective Deep Learning: Taxonomy and Survey of the State of the Art Visual Representation Learning with Stochastic Frame Prediction

Reference 78

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Multi-objective Deep Learning: Taxonomy and Survey of the State of the Art Feature dimensionality reduction: a review,

Reference 79

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Multi-objective Deep Learning: Taxonomy and Survey of the State of the Art Jin, Ed., Multi-Objective Machine Learning

Reference 80

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Multi-objective Deep Learning: Taxonomy and Survey of the State of the Art Pareto-based multiobjective machine learning: An overview and case studies,

Reference 81

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Multi-objective Deep Learning: Taxonomy and Survey of the State of the Art Evolutionary multi-objective opti- mization of spiking neural networks,

Reference 82

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Multi-objective Deep Learning: Taxonomy and Survey of the State of the Art Self-Supervised Spatiotemporal Feature Learning via Video Rotation Prediction

Reference 83

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Multi-objective Deep Learning: Taxonomy and Survey of the State of the Art Multi-objective hyperparameter optimization in machine learning—an overview,

Reference 84

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Multi-objective Deep Learning: Taxonomy and Survey of the State of the Art Physics-informed machine learning,

Reference 85

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Multi-objective Deep Learning: Taxonomy and Survey of the State of the Art A conic scalarization method in multi-objective opti- mization,

Reference 86

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Multi-objective Deep Learning: Taxonomy and Survey of the State of the Art A Survey of the Self Supervised Learning Mechanisms for Vision Transformers

Reference 87

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Multi-objective Deep Learning: Taxonomy and Survey of the State of the Art Conflict-Averse Gradient Aggregation for Constrained Multi-Objective Reinforcement Learning

Reference 88

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Multi-objective Deep Learning: Taxonomy and Survey of the State of the Art MDARTS: Multi-objective differentiable neural architecture search,

Reference 89

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Multi-objective Deep Learning: Taxonomy and Survey of the State of the Art Adam: A Method for Stochastic Optimization

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Multi-objective Deep Learning: Taxonomy and Survey of the State of the Art Auto-Encoding Variational Bayes

Reference 91

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Multi-objective Deep Learning: Taxonomy and Survey of the State of the Art An introduction to variational autoencoders,

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Multi-objective Deep Learning: Taxonomy and Survey of the State of the Art Reinforcement learning in robotics: A survey,

Reference 93

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This paper cites Intensity-modulated radiotherapy - a large scale multi- criteria programming problem,.

Multi-objective Deep Learning: Taxonomy and Survey of the State of the Art Intensity-modulated radiotherapy - a large scale multi- criteria programming problem,

Reference 94

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This paper cites Contrastive self-supervised learning: review, progress, challenges and future research directions,.

Multi-objective Deep Learning: Taxonomy and Survey of the State of the Art Contrastive self-supervised learning: review, progress, challenges and future research directions,

Reference 95

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Multi-objective Deep Learning: Taxonomy and Survey of the State of the Art Deep learning,

Reference 96

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Multi-objective Deep Learning: Taxonomy and Survey of the State of the Art Lassonet: A neural network with feature sparsity,

Reference 97

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Multi-objective Deep Learning: Taxonomy and Survey of the State of the Art It's Morphing Time: Unleashing the Potential of Multiple LLMs via Multi-objective Optimization

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Multi-objective Deep Learning: Taxonomy and Survey of the State of the Art Deep reinforcement learning for mul- tiobjective optimization,

Reference 99

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This paper cites Multi-augmentation contrastive learning as multi- objective optimization for graph neural networks,.

Multi-objective Deep Learning: Taxonomy and Survey of the State of the Art Multi-augmentation contrastive learning as multi- objective optimization for graph neural networks,

Reference 100

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