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Generative modeling over discrete data has recently seen numerous success stories, with applications spanning language modeling, biological sequence design, and graph-structured molecular data.
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Polar factorization of maps on riemannian manifolds
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α \alpha -divergence is unique, belonging to both f f -divergence and bregman divergence classes
S.-I. Amari · 2009
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Optimal transport: old and new , volume 338
C. Villani · 2009
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Information geometry and its applications , volume 194
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Wavenet: A generative model for raw audio
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Image labeling by assignment
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Information geometry , volume 64
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An atlas of human long non-coding rnas with accurate 5’ ends
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Image labeling by assignment
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Learning generative models with sinkhorn divergences
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Eukaryotic core promoters and the functional basis of transcription initiation
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Latent variable modelling with hyperbolic normalizing flows
J. Bose, A. Smofsky, R. Liao, P. Panangaden, and W. Hamilton · 2020
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Denoising diffusion probabilistic models, 2020
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Geoopt: Riemannian optimization in pytorch, 2020
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Riemannian continuous normalizing flows
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Riemannian flow matching on general geometries
R. T. Chen and Y. Lipman · 2023
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Learning joint 2d & 3d diffusion models for complete molecule generation, 2023
H. Huang, L. Sun, B. Du, and W. Lv · 2023
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Flow matching for generative modeling
Y. Lipman, R. T. Q. Chen, H. Ben-Hamu, M. Nickel, and M. Le · 2023
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Discrete diffusion language modeling by estimating the ratios of the data distribution
A. Lou, C. Meng, and S. Ermon · 2023
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Tess: Text-to-text self-conditioned simplex diffusion
R. K. Mahabadi, H. Ivison, J. Tae, J. Henderson, I. Beltagy, M. E. Peters, and A. Cohan · 2023
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Interpretation of allele-specific chromatin accessibility using cell state–aware deep learning
Z. K. Atak, I. I. Taskiran, J. Demeulemeester, C. Flerin, D. Mauduit, L. Minnoye, G. Hulselmans, V. Christiaens, G.-E. Ghanem, J. Wouters, et al · 2021
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Structured denoising diffusion models in discrete state-spaces
J. Austin, D. D. Johnson, J. Ho, D. Tarlow, and R. Van Den Berg · 2021
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Pot: Python optimal transport
R. Flamary, N. Courty, A. Gramfort, M. Z. Alaya, A. Boisbunon, S. Chambon, L. Chapel, A. Corenflos, K. Fatras, N. Fournier, L. Gautheron, N. T. Gayraud, H. Janati, A. Rakotomamonjy, I. Redko, A. Rolet, A. Schutz, V. Seguy, D. J. Sutherland, R. Tavenard, A. Tong, and T. Vayer · 2021
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Argmax flows and multinomial diffusion: Learning categorical distributions, 2021
E. Hoogeboom, D. Nielsen, P. Jaini, P. Forré, and M. Welling · 2021
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Score-based generative modeling through stochastic differential equations
Y. Song, J. Sohl-Dickstein, D. P. Kingma, A. Kumar, S. Ermon, and B. Poole · 2021
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Matching normalizing flows and probability paths on manifolds
H. Ben-Hamu, S. Cohen, J. Bose, B. Amos, A. Grover, M. Nickel, R. T. Chen, and Y. Lipman · 2022
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https://www.midjourney.com/home/ , 2023
Midjourney · 2023
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Robust speech recognition via large-scale weak supervision
A. Radford, J. W. Kim, T. Xu, G. Brockman, C. McLeavey, and I. Sutskever · 2023
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On kinetic optimal probability paths for generative models
N. Shaul, R. T. Chen, M. Nickel, M. Le, and Y. Lipman · 2023
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Equivariant flow matching with hybrid probability transport, 2023
Y. Song, J. Gong, M. Xu, Z. Cao, Y. Lan, S. Ermon, H. Zhou, and W.-Y. Ma · 2023
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AI Forecasting: One Year In
J. Steinhardt · 2023
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Gemini: a family of highly capable multimodal models
G. Team, R. Anil, S. Borgeaud, Y. Wu, J.-B. Alayrac, J. Yu, R. Soricut, J. Schalkwyk, A. M. Dai, A. Hauth, et al · 2023
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Improving and generalizing flow-based generative models with minibatch optimal transport
A. Tong, N. Malkin, G. Huguet, Y. Zhang, J. Rector-Brooks, K. Fatras, G. Wolf, and Y. Bengio · 2023
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De novo design of protein structure and function with rfdiffusion
J. L. Watson, D. Juergens, N. R. Bennett, B. L. Trippe, J. Yim, H. E. Eisenach, W. Ahern, A. J. Borst, R. J. Ragotte, L. F. Milles, et al · 2023
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Lumiere: A space-time diffusion model for video generation, 2024
O. Bar-Tal, H. Chefer, O. Tov, C. Herrmann, R. Paiss, S. Zada, A. Ephrat, J. Hur, G. Liu, A. Raj, Y. Li, M. Rubinstein, T. Michaeli, O. Wang, D. Sun, T. Dekel, and I. Mosseri · 2024
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B. Boll, D. Gonzalez-Alvarado, and C. Schnörr · 2024
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Se(3)-stochastic flow matching for protein backbone generation
A. J. Bose, T. Akhound-Sadegh, G. Huguet, K. Fatras, J. Rector-Brooks, C.-H. Liu, A. C. Nica, M. Korablyov, M. Bronstein, and A. Tong · 2024
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Video generation models as world simulators
T. Brooks, B. Peebles, C. Holmes, W. DePue, Y. Guo, L. Jing, D. Schnurr, J. Taylor, T. Luhman, E. Luhman, C. Ng, R. Wang, and A. Ramesh · 2024
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A. Campbell, J. Yim, R. Barzilay, T. Rainforth, and T. Jaakkola · 2024
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Mixed continuous and categorical flow matching for 3d de novo molecule generation
I. Dunn and D. R. Koes · 2024
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Scaling rectified flow transformers for high-resolution image synthesis
P. Esser, S. Kulal, A. Blattmann, R. Entezari, J. Müller, H. Saini, Y. Levi, D. Lorenz, A. Sauer, F. Boesel, et al · 2024
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Likelihood-based diffusion language models
I. Gulrajani and T. B. Hashimoto · 2024
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Simple and effective masked diffusion language models
S. S. Sahoo, M. Arriola, Y. Schiff, A. Gokaslan, E. Marroquin, J. T. Chiu, A. Rush, and V. Kuleshov · 2024
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Simplified and generalized masked diffusion for discrete data, 2024
J. Shi, K. Han, Z. Wang, A. Doucet, and M. K. Titsias · 2024
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Dirichlet flow matching with applications to dna sequence design
H. Stark, B. Jing, C. Wang, G. Corso, B. Berger, R. Barzilay, and T. Jaakkola · 2024
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Cell-type-directed design of synthetic enhancers
I. I. Taskiran, K. I. Spanier, H. Dickmänken, N. Kempynck, A. Pančíková, E. C. Ekşi, G. Hulselmans, J. N. Ismail, K. Theunis, R. Vandepoel, et al · 2024
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Improving and unifying discrete&continuous-time discrete denoising diffusion
L. Zhao, X. Ding, L. Yu, and L. Akoglu · 2024
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