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Diffusion models (DMs) have revolutionized generative learning.
Glide: a new approach for rapid, accurate docking and scoring. 2. enrichment factors in database screening
Halgren, T. A., Murphy, R. B., Friesner, R. A., Beard, H. S., Frye, L. L., Pollard, W. T., and Banks, J. L · 2004
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Estimation of non-normalized statistical models by score matching
Hyvärinen, A · 2005
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ImageNet: A large-scale hierarchical image database
Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., and Fei-Fei, L · 2009
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Interpretation and generalization of score matching
Lyu, S · 2009
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Deep boltzmann machines
Salakhutdinov, R. and Hinton, G · 2009
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Autodock vina: improving the speed and accuracy of docking with a new scoring function, efficient optimization, and multithreading
Trott, O. and Olson, A. J · 2010
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A connection between score matching and denoising autoencoders
Vincent, P · 2011
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A practical guide to training restricted boltzmann machines
Hinton, G. E · 2012
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Lessons learned in empirical scoring with smina from the csar 2011 benchmarking exercise
Koes, D. R., Baumgartner, M. P., and Camacho, C. J · 2013
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Rdkit documentation
Landrum, G · 2013
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Auto-encoding variational bayes
Kingma, D. P. and Welling, M · 2014
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Stochastic backpropagation and approximate inference in deep generative models
Rezende, D. J., Mohamed, S., and Wierstra, D · 2014
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Deep Unsupervised Learning using Nonequilibrium Thermodynamics
Sohl-Dickstein, J., Weiss, E., Maheswaranathan, N., and Ganguli, S · 2015
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Categorical reparameterization with gumbel-softmax
Jang, E., Gu, S. S., and Poole, B · 2016
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Gans trained by a two time-scale update rule converge to a local nash equilibrium
Heusel, M., Ramsauer, H., Unterthiner, T., Nessler, B., and Hochreiter, S · 2017
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beta-VAE: Learning basic visual concepts with a constrained variational framework
Higgins, I., Matthey, L., Pal, A., Burgess, C., Glorot, X., Botvinick, M., Mohamed, S., and Lerchner, A · 2017
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Forging the basis for developing protein–ligand interaction scoring functions
Liu, Z., Su, M., Han, L., Liu, J., Yang, Q., Li, Y., and Wang, R · 2017
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Discrete variational autoencoders
Rolfe, J. T · 2017
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Schnet: A continuous-filter convolutional neural network for modeling quantum interactions
Schütt, K., Kindermans, P.-J., Sauceda Felix, H. E., Chmiela, S., Tkatchenko, A., and Müller, K.-R · 2017
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Neural discrete representation learning
van den Oord, A., Vinyals, O., and kavukcuoglu, k · 2017
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Attention is all you need
Vaswani, A., Shazeer, N. M., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, L., and Polosukhin, I · 2017
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Large scale gan training for high fidelity natural image synthesis
Brock, A., Donahue, J., and Simonyan, K · 2018
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P2rank: machine learning based tool for rapid and accurate prediction of ligand binding sites from protein structure
Krivák, R. and Hoksza, D · 2018
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A style-based generator architecture for generative adversarial networks
Karras, T., Laine, S., and Aila, T · 2019
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Generative modeling by estimating gradients of the data distribution
Song, Y. and Ermon, S · 2019
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Momentum contrast for unsupervised visual representation learning
He, K., Fan, H., Wu, Y., Xie, S., and Girshick, R · 2020
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Denoising Diffusion Probabilistic Models
Ho, J., Jain, A., and Abbeel, P · 2020
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Analyzing and improving the image quality of StyleGAN
Karras, T., Laine, S., Aittala, M., Hellsten, J., Lehtinen, J., and Aila, T · 2020
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Structured denoising diffusion models in discrete state-spaces
Austin, J., Johnson, D. D., Ho, J., Tarlow, D., and van den Berg, R · 2021
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Emerging properties in self-supervised vision transformers
Caron, M., Touvron, H., Misra, I., Jegou, H., Mairal, J., Bojanowski, P., and Joulin, A · 2021
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Instance-conditioned gan
Casanova, A., Careil, M., Verbeek, J., Drozdzal, M., and Romero-Soriano, A · 2021
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Diffusion Models Beat GANs on Image Synthesis
Dhariwal, P. and Nichol, A. Q · 2021
Cited alongside, same era.
An image is worth 16x16 words: Transformers for image recognition at scale
Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., Uszkoreit, J., and Houlsby, N · 2021
Cited alongside, same era.
Taming transformers for high-resolution image synthesis
Esser, P., Rombach, R., and Ommer, B · 2021
Cited alongside, same era.
Classifier-Free Diffusion Guidance
Ho, J. and Salimans, T · 2021
Cited alongside, same era.
Cascaded diffusion models for high fidelity image generation
Ho, J., Saharia, C., Chan, W., Fleet, D. J., Norouzi, M., and Salimans, T · 2021
Cited alongside, same era.
A variational perspective on diffusion-based generative models and score matching
Huang, C.-W., Lim, J. H., and Courville, A · 2021
Poisson flow generative models
Xu, Y., Liu, Z., Tegmark, M., and Jaakkola, T · 2022
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Scaling autoregressive models for content-rich text-to-image generation
Yu, J., Xu, Y., Koh, J. Y., Luong, T., Baid, G., Wang, Z., Vasudevan, V., Ku, A., Yang, Y., Ayan, B. K., Hutchinson, B., Han, W., Parekh, Z., Li, X., Zhang, H., Baldridge, J., and Wu, Y · 2022
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LION: Latent Point Diffusion Models for 3D Shape Generation
Zeng, X., Vahdat, A., Williams, F., Gojcic, Z., Litany, O., Fidler, S., and Kreis, K · 2022
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A unified approach for text- and image-guided 4d scene generation
Zheng, Y., Li, X., Nagano, K., Liu, S., Kreis, K., Hilliges, O., and Mello, S. D · 2022
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4d-fy: Text-to-4d generation using hybrid score distillation sampling
Bahmani, S., Skorokhodov, I., Rong, V., Wetzstein, G., Guibas, L., Wonka, P., Tulyakov, S., Park, J. J., Tagliasacchi, A., and Lindell, D. B · 2023
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Cited alongside, same era.
Variational diffusion models
Kingma, D. P., Salimans, T., Poole, B., and Ho, J · 2021
Cited alongside, same era.
Gnina 1.0: molecular docking with deep learning
McNutt, A. T., Francoeur, P., Aggarwal, R., Masuda, T., Meli, R., Ragoza, M., Sunseri, J., and Koes, D. R · 2021
Cited alongside, same era.
Improved denoising diffusion probabilistic models
Nichol, A. and Dhariwal, P · 2021
Cited alongside, same era.
Learning Transferable Visual Models From Natural Language Supervision
Radford, A., Kim, J. W., Hallacy, C., Ramesh, A., Goh, G., Agarwal, S., Sastry, G., Askell, A., Mishkin, P., Clark, J., Krueger, G., and Sutskever, I · 2021
Cited alongside, same era.
Zero-shot text-to-image generation
Ramesh, A., Pavlov, M., Goh, G., Gray, S., Voss, C., Radford, A., Chen, M., and Sutskever, I · 2021
Cited alongside, same era.
Score-Based Generative Modeling through Stochastic Differential Equations
Song, Y., Sohl-Dickstein, J., Kingma, D. P., Kumar, A., Ermon, S., and Poole, B · 2021
Cited alongside, same era.
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Align your Latents: High-Resolution Video Synthesis with Latent Diffusion Models
Blattmann, A., Rombach, R., Ling, H., Dockhorn, T., Kim, S. W., Fidler, S., and Kreis, K · 2023
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Muse: Text-to-image generation via masked generative transformers
Chang, H., Zhang, H., Barber, J., Maschinot, A., Lezama, J., Jiang, L., Yang, M.-H., Murphy, K. P., Freeman, W. T., Rubinstein, M., Li, Y., and Krishnan, D · 2023
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Diffdock: Diffusion steps, twists, and turns for molecular docking
Corso, G., Stärk, H., Jing, B., Barzilay, R., and Jaakkola, T · 2023
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Preserve Your Own Correlation: A Noise Prior for Video Diffusion Models
Ge, S., Nah, S., Liu, G., Poon, T., Tao, A., Catanzaro, B., Jacobs, D., Huang, J.-B., Liu, M.-Y., and Balaji, Y · 2023
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Diffdock-site: A novel paradigm for enhanced protein-ligand predictions through binding site identification
Guo, H., Liu, S., Mingdi, H., Lou, Y., and Jing, B · 2023
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Visual chain-of-thought diffusion models
Harvey, W. and Wood, F · 2023
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Simple Diffusion: End-to-End Diffusion for High Resolution Images
Hoogeboom, E., Heek, J., and Salimans, T · 2023
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Self-guided diffusion models
Hu, V. T., Zhang, D. W., Asano, Y. M., Burghouts, G. J., and Snoek, C. G. M · 2023
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Illuminating protein space with a programmable generative model
Ingraham, J., Baranov, M., Costello, Z., Frappier, V., Ismail, A., Tie, S., Wang, W., Xue, V., Obermeyer, F., Beam, A., and Grigoryan, G · 2023
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Diffdock-pp: Rigid protein-protein docking with diffusion models
Ketata, M. A., Laue, C., Mammadov, R., Stärk, H., Wu, M., Corso, G., Marquet, C., Barzilay, R., and Jaakkola, T. S · 2023
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Understanding diffusion objectives as the ELBO with simple data augmentation
Kingma, D. P. and Gao, R · 2023
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Self-conditioned image generation via generating representations
Li, T., Katabi, D., and He, K · 2023
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Align your gaussians: Text-to-4d with dynamic 3d gaussians and composed diffusion models
Ling, H., Kim, S. W., Torralba, A., Fidler, S., and Kreis, K · 2023
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Zero-1-to-3: Zero-shot One Image to 3D Object
Liu, R., Wu, R., Van Hoorick, B., Tokmakov, P., Zakharov, S., and Vondrick, C · 2023
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Fusiondock: Physics-informed diffusion model for molecular docking
Masters, M. R., Mahmoud, A. H., and Lill, M. A · 2023
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Scalable diffusion models with transformers
Peebles, W. and Xie, S · 2023
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Wuerstchen: An efficient architecture for large-scale text-to-image diffusion models
Pernias, P., Rampas, D., Richter, M. L., Pal, C. J., and Aubreville, M · 2023
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Diffdock-pocket: Diffusion for pocket-level docking with sidechain flexibility
Plainer, M., Toth, M., Dobers, S., Stärk, H., Corso, G., Marquet, C., and Barzilay, R · 2023
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SDXL: Improving Latent Diffusion Models for High-Resolution Image Synthesis
Podell, D., English, Z., Lacey, K., Blattmann, A., Dockhorn, T., Müller, J., Penna, J., and Rombach, R · 2023
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DreamFusion: Text-to-3D using 2D Diffusion
Poole, B., Jain, A., Barron, J. T., and Mildenhall, B · 2023
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WildFusion: Learning 3D-Aware Latent Diffusion Models in View Space
Schwarz, K., Kim, S. W., Gao, J., Fidler, S., Geiger, A., and Kreis, K · 2023
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InfoDiffusion: Representation learning using information maximizing diffusion models
Wang, Y., Schiff, Y., Gokaslan, A., Pan, W., Wang, F., De Sa, C., and Kuleshov, V · 2023
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De novo design of protein structure and function with rfdiffusion
Watson, J. L., Juergens, D., Bennett, N. R., Trippe, B. L., Yim, J., Eisenach, H. E., Ahern, W., Borst, A. J., Ragotte, R. J., Milles, L. F., Wicky, B. I. M., Hanikel, N., Pellock, S. J., Courbet, A., Sheffler, W., Wang, J., Venkatesh, P., Sappington, I., Torres, S. V., Lauko, A., Bortoli, V. D., Mathieu, E., Barzilay, R., Jaakkola, T. S., DiMaio, F., Baek, M., and Baker, D · 2023
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Se(3) diffusion model with application to protein backbone generation
Yim, J., Trippe, B. L., Bortoli, V. D., Mathieu, E., Doucet, A., Barzilay, R., and Jaakkola, T · 2023
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The discovery of binding modes requires rethinking docking generalization
Corso, G., Deng, A., Polizzi, N., Barzilay, R., and Jaakkola, T · 2024
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