Score-based generative modeling via multi-noise-level score matching and annealed Langevin dynamics produces samples on par with GANs and sets a new inception score record on CIFAR-10.
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2 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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2019 2verdicts
UNVERDICTED 2representative citing papers
NECL uses neighborhood-similarity graph compression as a preprocessing step to accelerate random-walk network embedding algorithms without reducing their effectiveness on classification tasks.
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Generative Modeling by Estimating Gradients of the Data Distribution
Score-based generative modeling via multi-noise-level score matching and annealed Langevin dynamics produces samples on par with GANs and sets a new inception score record on CIFAR-10.
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Network Embedding: on Compression and Learning
NECL uses neighborhood-similarity graph compression as a preprocessing step to accelerate random-walk network embedding algorithms without reducing their effectiveness on classification tasks.