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Despite the success of diffusion models, the training and inference of diffusion models are notoriously expensive due to the long chain of the reverse process.
Measuring statistical dependence with hilbert-schmidt norms
A. Gretton, O. Bousquet, A. J. Smola, and B. Schölkopf · 2005
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Deep unsupervised learning using nonequilibrium thermodynamics
J. Sohl-Dickstein, E. A. Weiss, N. Maheswaranathan, and S. Ganguli · 2015
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The lottery ticket hypothesis: Finding sparse, trainable neural networks
J. Frankle and M. Carbin · 2019
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The lottery ticket hypothesis at scale
J. Frankle, G. K. Dziugaite, D. M. Roy, and M. Carbin · 2019
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Similarity of neural network representations revisited
S. Kornblith, M. Norouzi, H. Lee, and G. E. Hinton · 2019
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Deconstructing lottery tickets: Zeros, signs, and the supermask
H. Zhou, J. Lan, R. Liu, and J. Yosinski · 2019
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Losing heads in the lottery: Pruning transformer attention in neural machine translation
M. Behnke and K. Heafield · 2020
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Successfully applying the stabilized lottery ticket hypothesis to the transformer architecture
C. Brix, P. Bahar, and H. Ney · 2020
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The lottery ticket hypothesis for pre-trained BERT networks
T. Chen, J. Frankle, S. Chang, S. Liu, Y. Zhang, Z. Wang, and M. Carbin · 2020
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Denoising diffusion probabilistic models
J. Ho, A. Jain, and P. Abbeel · 2020
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Proving the lottery ticket hypothesis: Pruning is all you need
E. Malach, G. Yehudai, S. Shalev-Shwartz, and O. Shamir · 2020
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When BERT plays the lottery, all tickets are winning
S. Prasanna, A. Rogers, and A. Rumshisky · 2020
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Sanity-checking pruning methods: Random tickets can win the jackpot
J. Su, Y. Chen, T. Cai, T. Wu, R. Gao, L. Wang, and J. D. Lee · 2020
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The lottery tickets hypothesis for supervised and self-supervised pre-training in computer vision models
T. Chen, J. Frankle, S. Chang, S. Liu, Y. Zhang, M. Carbin, and Z. Wang · 2021
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A unified lottery ticket hypothesis for graph neural networks
T. Chen, Y. Sui, X. Chen, A. Zhang, and Z. Wang · 2021
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Gans can play lottery tickets too
X. Chen, Z. Zhang, Y. Sui, and T. Chen · 2021
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Diffusion models beat gans on image synthesis
P. Dhariwal and A. Q. Nichol · 2021
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Multi-prize lottery ticket hypothesis: Finding accurate binary neural networks by pruning A randomly weighted network
J. Diffenderfer and B. Kailkhura · 2021
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Winning lottery tickets in deep generative models
N. M. Kalibhat, Y. Balaji, and S. Feizi · 2021
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Diffwave: A versatile diffusion model for audio synthesis
Z. Kong, W. Ping, J. Huang, K. Zhao, and B. Catanzaro · 2021
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Sanity checks for lottery tickets: Does your winning ticket really win the jackpot?
X. Ma, G. Yuan, X. Shen, T. Chen, X. Chen, X. Chen, N. Liu, M. Qin, S. Liu, Z. Wang, and Y. Wang · 2021
Cited alongside, same era.
Improved denoising diffusion probabilistic models
A. Q. Nichol and P. Dhariwal · 2021
Elucidating the design space of diffusion-based generative models
T. Karras, M. Aittala, T. Aila, and S. Laine · 2022
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Dpm-solver: A fast ODE solver for diffusion probabilistic model sampling in around 10 steps
C. Lu, Y. Zhou, F. Bao, J. Chen, C. Li, and J. Zhu · 2022
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GLIDE: towards photorealistic image generation and editing with text-guided diffusion models
A. Q. Nichol, P. Dhariwal, A. Ramesh, P. Shyam, P. Mishkin, B. McGrew, I. Sutskever, and M. Chen · 2022
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Hierarchical text-conditional image generation with CLIP latents
A. Ramesh, P. Dhariwal, A. Nichol, C. Chu, and M. Chen · 2022
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High-resolution image synthesis with latent diffusion models
R. Rombach, A. Blattmann, D. Lorenz, P. Esser, and B. Ommer · 2022
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Cited alongside, same era.
Grad-tts: A diffusion probabilistic model for text-to-speech
V. Popov, I. Vovk, V. Gogoryan, T. Sadekova, and M. A. Kudinov · 2021
Cited alongside, same era.
Denoising diffusion implicit models
J. Song, C. Meng, and S. Ermon · 2021
Cited alongside, same era.
Score-based generative modeling through stochastic differential equations
Y. Song, J. Sohl-Dickstein, D. P. Kingma, A. Kumar, S. Ermon, and B. Poole · 2021
Cited alongside, same era.
Validating the lottery ticket hypothesis with inertial manifold theory
Z. Zhang, J. Jin, Z. Zhang, Y. Zhou, X. Zhao, J. Ren, J. Liu, L. Wu, R. Jin, and D. Dou · 2021
Cited alongside, same era.
On the existence of universal lottery tickets
R. Burkholz, N. Laha, R. Mukherjee, and A. Gotovos · 2022
Cited alongside, same era.
Coarsening the granularity: Towards structurally sparse lottery tickets
T. Chen, X. Chen, X. Ma, Y. Wang, and Z. Wang · 2022
Cited alongside, same era.
Photorealistic text-to-image diffusion models with deep language understanding
C. Saharia, W. Chan, S. Saxena, L. Li, J. Whang, E. L. Denton, S. K. S. Ghasemipour, R. G. Lopes, B. K. Ayan, T. Salimans, J. Ho, D. J. Fleet, and M. Norouzi · 2022
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Analyzing lottery ticket hypothesis from pac-bayesian theory perspective
K. Sakamoto and I. Sato · 2022
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Diffusion probabilistic modeling for video generation
R. Yang, P. Srivastava, and S. Mandt · 2022
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Robust lottery tickets for pre-trained language models
R. Zheng, B. Rong, Y. Zhou, D. Liang, S. Wang, W. Wu, T. Gui, Q. Zhang, and X. Huang · 2022
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Reliability of CKA as a similarity measure in deep learning
M. Davari, S. Horoi, A. Natik, G. Lajoie, G. Wolf, and E. Belilovsky · 2023
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A general framework for proving the equivariant strong lottery ticket hypothesis
D. Ferbach, C. Tsirigotis, G. Gidel, and A. J. Bose · 2023
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Rethinking graph lottery tickets: Graph sparsity matters
B. Hui, D. Yan, X. Ma, and W. Ku · 2023
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Diffusion probabilistic modeling of protein backbones in 3d for the motif-scaffolding problem
B. L. Trippe, J. Yim, D. Tischer, D. Baker, T. Broderick, R. Barzilay, and T. S. Jaakkola · 2023
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Diffusion-gan: Training gans with diffusion
Z. Wang, H. Zheng, P. He, W. Chen, and M. Zhou · 2023
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SE(3) diffusion model with application to protein backbone generation
J. Yim, B. L. Trippe, V. D. Bortoli, E. Mathieu, A. Doucet, R. Barzilay, and T. S. Jaakkola · 2023
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