Continuous Semi-Implicit Models | Paper Citation Record

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This paper cites write newline.

Continuous Semi-Implicit Models write newline

Reference 1

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This paper cites Functional inequalities for gaussian convolutions of compactly supported measures: explicit bounds and dimension dependence.

Continuous Semi-Implicit Models Functional inequalities for gaussian convolutions of compactly supported measures: explicit bounds and dimension dependence

Reference 2

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This paper cites and Guillin, A.

Continuous Semi-Implicit Models and Guillin, A

Reference 3

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This paper cites Improved Analysis of Score-based Generative Modeling: User-Friendly Bounds under Minimal Smoothness Assumptions.

Continuous Semi-Implicit Models Improved Analysis of Score-based Generative Modeling: User-Friendly Bounds under Minimal Smoothness Assumptions

Reference 4

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This paper cites Dimension-free log-Sobolev inequalities for mixture distributions.

Continuous Semi-Implicit Models Dimension-free log-Sobolev inequalities for mixture distributions

Reference 5

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This paper cites Particle-based variational inference with generalized wasserstein gradient flow.

Continuous Semi-Implicit Models Particle-based variational inference with generalized wasserstein gradient flow

Reference 6

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This paper cites Kernel semi-implicit variational inference.

Continuous Semi-Implicit Models Kernel semi-implicit variational inference

Reference 7

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This paper cites Imagenet: A large-scale hierarchical image database.

Continuous Semi-Implicit Models Imagenet: A large-scale hierarchical image database

Reference 8

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This paper cites Generating Images with Perceptual Similarity Metrics based on Deep Networks.

Continuous Semi-Implicit Models Generating Images with Perceptual Similarity Metrics based on Deep Networks

Reference 9

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Observation b0e3fa15-b29d-4da6-aa39-c8343e1fa494 · outbound

This paper cites Consistency Models Made Easy.

Continuous Semi-Implicit Models Consistency Models Made Easy

Reference 10

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This paper cites DRAW: A Recurrent Neural Network For Image Generation.

Continuous Semi-Implicit Models DRAW: A Recurrent Neural Network For Image Generation

Reference 11

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Continuous Semi-Implicit Models Unresolved cited work

Reference 12

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This paper cites Multistep Consistency Models.

Continuous Semi-Implicit Models Multistep Consistency Models

Reference 13

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This paper cites GAN s trained by a two time-scale update rule converge to a local N ash equilibrium.

Continuous Semi-Implicit Models GAN s trained by a two time-scale update rule converge to a local N ash equilibrium

Reference 14

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This paper cites Denoising diffusion probabilistic models.

Continuous Semi-Implicit Models Denoising diffusion probabilistic models

Reference 15

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This paper cites Elucidating the design space of diffusion-based generative models.

Continuous Semi-Implicit Models Elucidating the design space of diffusion-based generative models

Reference 16

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This paper cites Analyzing and improving the training dynamics of diffusion models.

Continuous Semi-Implicit Models Analyzing and improving the training dynamics of diffusion models

Reference 17

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This paper cites Consistency Trajectory Models: Learning Probability Flow ODE Trajectory of Diffusion.

Continuous Semi-Implicit Models Consistency Trajectory Models: Learning Probability Flow ODE Trajectory of Diffusion

Reference 18

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Continuous Semi-Implicit Models Unresolved cited work

Reference 19

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Observation e7c10b9d-b8b5-427b-b20e-a5ec23b49888 · outbound

This paper cites Improving Variational Inference with Inverse Autoregressive Flow.

Continuous Semi-Implicit Models Improving Variational Inference with Inverse Autoregressive Flow

Reference 20

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This paper cites and Hinton, G.

Continuous Semi-Implicit Models and Hinton, G

Reference 21

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Observation a7f7910c-733b-4172-bfa6-57ef1fbc504f · outbound

This paper cites Convergence for score-based generative modeling with polynomial complexity.

Continuous Semi-Implicit Models Convergence for score-based generative modeling with polynomial complexity

Reference 22

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This paper cites Towards a mathematical theory for consistency training in diffusion models.

Continuous Semi-Implicit Models Towards a mathematical theory for consistency training in diffusion models

Reference 23

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This paper cites Simplifying, Stabilizing and Scaling Continuous-Time Consistency Models.

Continuous Semi-Implicit Models Simplifying, Stabilizing and Scaling Continuous-Time Consistency Models

Reference 24

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This paper cites DPM -solver: A fast ODE solver for diffusion probabilistic model sampling in around 10 steps.

Continuous Semi-Implicit Models DPM -solver: A fast ODE solver for diffusion probabilistic model sampling in around 10 steps

Reference 25

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This paper cites Diff-instruct: A universal approach for transferring knowledge from pre-trained diffusion models.

Continuous Semi-Implicit Models Diff-instruct: A universal approach for transferring knowledge from pre-trained diffusion models

Reference 26

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This paper cites Sampling is as easy as keeping the consistency: convergence guarantee for consistency models.

Continuous Semi-Implicit Models Sampling is as easy as keeping the consistency: convergence guarantee for consistency models

Reference 27

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This paper cites Efficient Semi-Implicit Variational Inference.

Continuous Semi-Implicit Models Efficient Semi-Implicit Variational Inference

Reference 28

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Continuous Semi-Implicit Models Unresolved cited work

Reference 29

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This paper cites and Villani, C.

Continuous Semi-Implicit Models and Villani, C

Reference 30

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This paper cites Hierarchical Variational Models.

Continuous Semi-Implicit Models Hierarchical Variational Models

Reference 31

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This paper cites Generating Diverse High-Fidelity Images with VQ-VAE-2.

Continuous Semi-Implicit Models Generating Diverse High-Fidelity Images with VQ-VAE-2

Reference 32

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This paper cites Variational Inference with Normalizing Flows.

Continuous Semi-Implicit Models Variational Inference with Normalizing Flows

Reference 33

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This paper cites Stochastic Backpropagation and Approximate Inference in Deep Generative Models.

Continuous Semi-Implicit Models Stochastic Backpropagation and Approximate Inference in Deep Generative Models

Reference 34

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This paper cites Improved techniques for training gans.

Continuous Semi-Implicit Models Improved techniques for training gans

Reference 35

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This paper cites Multistep distillation of diffusion models via moment matching.

Continuous Semi-Implicit Models Multistep distillation of diffusion models via moment matching

Reference 36

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This paper cites Deep Unsupervised Learning using Nonequilibrium Thermodynamics.

Continuous Semi-Implicit Models Deep Unsupervised Learning using Nonequilibrium Thermodynamics

Reference 37

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This paper cites Denoising diffusion implicit models.

Continuous Semi-Implicit Models Denoising diffusion implicit models

Reference 38

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This paper cites and Dhariwal, P.

Continuous Semi-Implicit Models and Dhariwal, P

Reference 39

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This paper cites and Ermon, S.

Continuous Semi-Implicit Models and Ermon, S

Reference 40

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This paper cites Score-Based Generative Modeling through Stochastic Differential Equations.

Continuous Semi-Implicit Models Score-Based Generative Modeling through Stochastic Differential Equations

Reference 41

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This paper cites Consistency Models.

Continuous Semi-Implicit Models Consistency Models

Reference 42

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This paper cites L., Taylor, E., and Loaiza-Ganem, G.

Continuous Semi-Implicit Models L., Taylor, E., and Loaiza-Ganem, G

Reference 43

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This paper cites Rethinking the inception architecture for computer vision.

Continuous Semi-Implicit Models Rethinking the inception architecture for computer vision

Reference 44

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This paper cites Ladder Variational Autoencoders.

Continuous Semi-Implicit Models Ladder Variational Autoencoders

Reference 45

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Continuous Semi-Implicit Models Unresolved cited work

Reference 46

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This paper cites NVAE: A Deep Hierarchical Variational Autoencoder.

Continuous Semi-Implicit Models NVAE: A Deep Hierarchical Variational Autoencoder

Reference 47

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This paper cites A connection between score matching and denoising autoencoders.

Continuous Semi-Implicit Models A connection between score matching and denoising autoencoders

Reference 48

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This paper cites Prolificdreamer: High-fidelity and diverse text-to-3d generation with variational score distillation.

Continuous Semi-Implicit Models Prolificdreamer: High-fidelity and diverse text-to-3d generation with variational score distillation

Reference 49

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This paper cites Diffusion- GAN : Training GAN s with diffusion.

Continuous Semi-Implicit Models Diffusion- GAN : Training GAN s with diffusion

Reference 50

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This paper cites Pfgm++: Unlocking the potential of physics-inspired generative models.

Continuous Semi-Implicit Models Pfgm++: Unlocking the potential of physics-inspired generative models

Reference 51

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This paper cites and Zhou, M.

Continuous Semi-Implicit Models and Zhou, M

Reference 52

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This paper cites One-step Diffusion with Distribution Matching Distillation.

Continuous Semi-Implicit Models One-step Diffusion with Distribution Matching Distillation

Reference 53

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This paper cites and Zhang, C.

Continuous Semi-Implicit Models and Zhang, C

Reference 54

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This paper cites Hierarchical semi-implicit variational inference with application to diffusion model acceleration.

Continuous Semi-Implicit Models Hierarchical semi-implicit variational inference with application to diffusion model acceleration

Reference 55

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This paper cites Truncated diffusion probabilistic models and diffusion-based adversarial auto-encoders.

Continuous Semi-Implicit Models Truncated diffusion probabilistic models and diffusion-based adversarial auto-encoders

Reference 56

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This paper cites Learning stackable and skippable LEGO bricks for efficient, reconfigurable, and variable-resolution diffusion modeling, 2023 b.

Continuous Semi-Implicit Models Learning stackable and skippable LEGO bricks for efficient, reconfigurable, and variable-resolution diffusion modeling, 2023 b

Reference 57

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Observation 5734c103-22fa-4315-9760-84842d852df1 · outbound

This paper cites Guided Score identity Distillation for Data-Free One-Step Text-to-Image Generation.

Continuous Semi-Implicit Models Guided Score identity Distillation for Data-Free One-Step Text-to-Image Generation

Reference 58

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This paper cites Score identity distillation: Exponentially fast distillation of pretrained diffusion models for one-step generation.

Continuous Semi-Implicit Models Score identity distillation: Exponentially fast distillation of pretrained diffusion models for one-step generation

Reference 59

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This paper cites Adversarial score identity distillation: Rapidly surpassing the teacher in one step.

Continuous Semi-Implicit Models Adversarial score identity distillation: Rapidly surpassing the teacher in one step

Reference 60

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