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Score-based diffusion models synthesize samples by reversing a stochastic process that diffuses data to noise, and are trained by minimizing a weighted combination of score matching losses.
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GANs trained by a two time-scale update rule converge to a local Nash equilibrium
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Neural Ordinary Differential Equations
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Generating long sequences with sparse transformers
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Distribution augmentation for generative modeling
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DiffWave: A Versatile Diffusion Model for Audio Synthesis
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Scalable Gradients for Stochastic Differential Equations
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Interacting particle solutions of Fokker–Planck equations through gradient-log-density estimation
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Improved Techniques for Training Score-Based Generative Models
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Very deep VAEs generalize autoregressive models and can outperform them on images
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