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Denoising diffusion probabilistic models are currently becoming the leading paradigm of generative modeling for many important data modalities.
Scaling up the accuracy of naive-bayes classifiers: a decision-tree hybrid
Kohavi, R · 1996
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Sparse spatial autoregressions
Kelley Pace, R. and Barry, R · 1997
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Smote: synthetic minority over-sampling technique
Chawla, N. V., Bowyer, K. W., Hall, L. O., and Kegelmeyer, W. P · 2002
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Wavegrad: Estimating gradients for waveform generation
Chen, N., Zhang, Y., Zen, H., Weiss, R. J., Norouzi, M., and Chan, W · 2009
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Scikit-learn: Machine learning in Python
Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., Vanderplas, J., Passos, A., Cournapeau, D., Brucher, M., Perrot, M., and Duchesnay, E · 2011
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Gesture unit segmentation using support vector machines: segmenting gestures from rest positions
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Searching for exotic particles in high-energy physics with deep learning
Baldi, P., Sadowski, P., and Whiteson, D · 2014
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Openml: networked science in machine learning
Vanschoren, J., van Rijn, J. N., Bischl, B., and Torgo, L · 2014
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Comment volume prediction using neural networks and decision trees
Singh, K., Sandhu, R. K., and Kumar, D · 2015
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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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Pate-gan: Generating synthetic data with differential privacy guarantees
Jordon, J., Yoon, J., and Van Der Schaar, M · 2018
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Catboost: unbiased boosting with categorical features
Prokhorenkova, L., Gusev, G., Vorobev, A., Dorogush, A. V., and Gulin, A · 2018
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Optuna: A next-generation hyperparameter optimization framework
Akiba, T., Sano, S., Yanase, T., Ohta, T., and Koyama, M · 2019
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Generative modeling by estimating gradients of the data distribution
Song, Y. and Ermon, S · 2019
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Modeling tabular data using conditional gan
Xu, L., Skoularidou, M., Cuesta-Infante, A., and Veeramachaneni, K · 2019
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Oversampling tabular data with deep generative models: Is it worth the effort?
Camino, R. D., Hammerschmidt, C. A., et al · 2020
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Gan-leaks: A taxonomy of membership inference attacks against generative models
Chen, D., Yu, N., Zhang, Y., and Fritz, M · 2020
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Relational data synthesis using generative adversarial networks: A design space exploration
Fan, J., Liu, T., Li, G., Chen, J., Shen, Y., and Du, X · 2020
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Denoising diffusion probabilistic models
Ho, J., Jain, A., and Abbeel, P · 2020
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Diffwave: A versatile diffusion model for audio synthesis
Kong, Z., Ping, W., Huang, J., Zhao, K., and Catanzaro, B · 2020
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Reliable fidelity and diversity metrics for generative models
Naeem, M. F., Oh, S. J., Uh, Y., Choi, Y., and Yoo, J · 2020
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Handling incomplete heterogeneous data using vaes
Nazabal, A., Olmos, P. M., Ghahramani, Z., and Valera, I · 2020
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Improved techniques for training score-based generative models
Song, Y. and Ermon, S · 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
Score-based generative modeling through stochastic differential equations
Song, Y., Sohl-Dickstein, J., Kingma, D. P., Kumar, A., Ermon, S., and Poole, B · 2021
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Csdi: Conditional score-based diffusion models for probabilistic time series imputation
Tashiro, Y., Song, J., Song, Y., and Ermon, S · 2021
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Ganblr: a tabular data generation model
Zhang, Y., Zaidi, N. A., Zhou, J., and Li, G · 2021
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Ctab-gan: Effective table data synthesizing
Zhao, Z., Kunar, A., Birke, R., and Chen, L. Y · 2021
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A continuous time framework for discrete denoising models
Campbell, A., Benton, J., De Bortoli, V., Rainforth, T., Deligiannidis, G., and Doucet, A · 2022
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Analog bits: Generating discrete data using diffusion models with self-conditioning
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Label-efficient semantic segmentation with diffusion models
Baranchuk, D., Rubachev, I., Voynov, A., Khrulkov, V., and Babenko, A · 2021
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Diffusion models beat gans on image synthesis
Dhariwal, P. and Nichol, A · 2021
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Conditional wasserstein gan-based oversampling of tabular data for imbalanced learning
Engelmann, J. and Lessmann, S · 2021
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Revisiting deep learning models for tabular data
Gorishniy, Y., Rubachev, I., Khrulkov, V., and Babenko, A · 2021
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Argmax flows and multinomial diffusion: Learning categorical distributions
Hoogeboom, E., Nielsen, D., Jaini, P., Forré, P., and Welling, M · 2021
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Oct-gan: Neural ode-based conditional tabular gans
Kim, J., Jeon, J., Lee, J., Hyeong, J., and Park, N · 2021
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Chen, T., Zhang, R., and Hinton, G · 2022
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Equivariant diffusion for molecule generation in 3d
Hoogeboom, E., Satorras, V. G., Vignac, C., and Welling, M · 2022
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Torsional diffusion for molecular conformer generation
Jing, B., Corso, G., Chang, J., Barzilay, R., and Jaakkola, T · 2022
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Sos: Score-based oversampling for tabular data
Kim, J., Lee, C., Shin, Y., Park, S., Kim, M., Park, N., and Cho, J · 2022
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Diffusion-lm improves controllable text generation
Li, X. L., Thickstun, J., Gulrajani, I., Liang, P., and Hashimoto, T. B · 2022
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Generative trees: Adversarial and copycat
Nock, R. and Guillame-Bert, M · 2022
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High-resolution image synthesis with latent diffusion models
Rombach, R., Blattmann, A., Lorenz, D., Esser, P., and Ommer, B · 2022
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Photorealistic text-to-image diffusion models with deep language understanding
Saharia, C., Chan, W., Saxena, S., Li, L., Whang, J., Denton, E., Ghasemipour, S. K. S., Ayan, B. K., Mahdavi, S. S., Lopes, R. G., et al · 2022
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Differentially private synthetic medical data generation using convolutional gans
Torfi, A., Fox, E. A., and Reddy, C. K · 2022
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Causal-tgan: Modeling tabular data using causally-aware gan
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Ctab-gan+: Enhancing tabular data synthesis
Zhao, Z., Kunar, A., Birke, R., and Chen, L. Y · 2022
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Diffusion models for missing value imputation in tabular data
Zheng, S. and Charoenphakdee, N · 2022
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