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Self-supervised representation learning methods have achieved significant success in computer vision and natural language processing, where data samples exhibit explicit spatial or semantic dependencies.
Contrastive Variational Autoencoder Enhances Salient Features
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Multi-state reliability demonstration tests
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Generalized linear models
Hastie, T. J.; and Pregibon, D. 2017 · 2017
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Self-normalizing neural networks
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Relational autoencoder for feature extraction
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TabNN: A universal neural network solution for tabular data
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Telco Customer Churn
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Self-supervised representation learning: Introduction, advances, and challenges
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Masked autoencoders as spatiotemporal learners
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Why do tree-based models still outperform deep learning on typical tabular data?
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Masked autoencoders are scalable vision learners
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Fastshap: Real-time shapley value estimation
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Masked autoencoders in 3D point cloud representation learning
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Executive orders or public fear: What caused transit ridership to drop in Chicago during COVID-19?
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Scale-mae: A scale-aware masked autoencoder for multiscale geospatial representation learning
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Relation-Aware Network with Attention-Based Loss for Few-Shot Knowledge Graph Completion
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SRDA: Mobile Sensing based Fluid Overload Detection for End Stage Kidney Disease Patients using Sensor Relation Dual Autoencoder
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Hallucination improves the performance of unsupervised visual representation learning
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Accel-gcn: High-performance gpu accelerator design for graph convolution networks
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Multiplexed OAM beams classification via Fourier optical convolutional neural network
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TabCBM: Concept-based Interpretable Neural Networks for Tabular Data
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