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Measuring diversity accurately is important for many scientific fields, including machine learning (ML), ecology, and chemistry.
Prescribed generative adversarial networks
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Logan: Latent optimisation for generative adversarial networks
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Evolution and measurement of species diversity
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Diversity and evenness: a unifying notation and its consequences
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Imagenet: A large-scale hierarchical image database
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Learning multiple layers of features from tiny images
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Entropy and diversity: The axiomatic approach
Leinster, T. (2020) · 2012
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Measuring diversity: the importance of species similarity
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Illuminating search spaces by mapping elites
Mouret, J.-B. and Clune, J. (2015) · 2015
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Lsun: Construction of a large-scale image dataset using deep learning with humans in the loop
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Quality diversity: A new frontier for evolutionary computation
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Chemgan challenge for drug discovery: can ai reproduce natural chemical diversity?
Benhenda, M. (2017) · 2017
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Openmm 7: Rapid development of high performance algorithms for molecular dynamics
Eastman, P., Swails, J., Chodera, J. D., McGibbon, R. T., Zhao, Y., Beauchamp, K. A., Wang, L.-P., Simmonett, A. C., Harrigan, M. P., Stern, C. D., et al. (2017) · 2017
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Improved training of wasserstein gans
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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) · 2017
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Conditional image synthesis with auxiliary classifier gans
Odena, A., Olah, C., and Shlens, J. (2017) · 2017
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Using centroidal voronoi tessellations to scale up the multidimensional archive of phenotypic elites algorithm
Vassiliades, V., Chatzilygeroudis, K., and Mouret, J.-B. (2017) · 2017
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Large scale gan training for high fidelity natural image synthesis
Brock, A., Donahue, J., and Simonyan, K. (2018) · 2018
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Diversity is all you need: Learning skills without a reward function
Eysenbach, B., Gupta, A., Ibarz, J., and Levine, S. (2018) · 2018
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Assessing generative models via precision and recall
Sajjadi, M. S., Bachem, O., Lucic, M., Bousquet, O., and Gelly, S. (2018) · 2018
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Residual flows for invertible generative modeling
Chen, R. T., Behrmann, J., Duvenaud, D. K., and Jacobsen, J.-H. (2019) · 2019
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A style-based generator architecture for generative adversarial networks
Karras, T., Laine, S., and Aila, T. (2019) · 2019
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Boltzmann generators: Sampling equilibrium states of many-body systems with deep learning
Noé, F., Olsson, S., Köhler, J., and Wu, H. (2019) · 2019
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Mixture models for diverse machine translation: Tricks of the trade
Shen, T., Ott, M., Auli, M., and Ranzato, M. (2019) · 2019
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Metropolis-hastings generative adversarial networks
Turner, R., Hung, J., Frank, E., Saatchi, Y., and Yosinski, J. (2019) · 2019
Data-efficient instance generation from instance discrimination
Yang, C., Shen, Y., Xu, Y., and Zhou, B. (2021) · 2021
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Unleashing transformers: Parallel token prediction with discrete absorbing diffusion for fast high-resolution image generation from vector-quantized codes
Bond-Taylor, S., Hessey, P., Sasaki, H., Breckon, T. P., and Willcocks, C. G. (2022) · 2022
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The vendi score: A diversity evaluation metric for machine learning
Friedman, D. and Dieng, A. B. (2022) · 2022
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Hazami, L., Mama, R., and Thurairatnam, R. (2022) · 2022
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Autoregressive image generation using residual quantization
Lee, D., Kim, C., Kim, S., Cho, M., and Han, W.-S. (2022) · 2022
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Unsupervised quality estimation for neural machine translation
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Denoising diffusion probabilistic models
Ho, J., Jain, A., and Abbeel, P. (2020) · 2020
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Training generative adversarial networks with limited data
Karras, T., Aittala, M., Hellsten, J., Laine, S., Lehtinen, J., and Aila, T. (2020) · 2020
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A non-parametric test to detect data-copying in generative models
Meehan, C., Chaudhuri, K., and Dasgupta, S. (2020) · 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) · 2020
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Nvae: A deep hierarchical variational autoencoder
Vahdat, A. and Kautz, J. (2020) · 2020
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Discovering many diverse solutions with bayesian optimization
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Molecular dynamics simulations and diversity selection by extended continuous similarity indices
Rácz, A., Mihalovits, L. M., Bajusz, D., Héberger, K., and Miranda-Quintana, R. A. (2022) · 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) · 2022
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Stylegan-xl: Scaling stylegan to large diverse datasets
Sauer, A., Schwarz, K., and Geiger, A. (2022) · 2022
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Stylenat: Giving each head a new perspective
Walton, S., Hassani, A., Xu, X., Wang, Z., and Shi, H. (2022) · 2022
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Diffusion-gan: Training gans with diffusion
Wang, Z., Zheng, H., He, P., Chen, W., and Zhou, M. (2022) · 2022
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Styleswin: Transformer-based gan for high-resolution image generation
Zhang, B., Gu, S., Zhang, B., Bao, J., Chen, D., Wen, F., Wang, Y., and Guo, B. (2022) · 2022
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Towards mode balancing of generative models via diversity weights
Berns, S., Colton, S., and Guckelsberger, C. (2023) · 2023
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Diamantis, D. E., Gatoula, P., Koulaouzidis, A., and Iakovidis, D. K. (2023) · 2023
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Nonmyopic multiclass active search with diminishing returns for diverse discovery
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Dinov2: Learning robust visual features without supervision
Oquab, M., Darcet, T., Moutakanni, T., Vo, H., Szafraniec, M., Khalidov, V., Fernandez, P., Haziza, D., Massa, F., El-Nouby, A., et al. (2023) · 2023
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Vendi sampling for molecular simulations: Diversity as a force for faster convergence and better exploration
Pasarkar, A. P., Bencomo, G. M., Olsson, S., and Dieng, A. B. (2023) · 2023
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Exposing flaws of generative model evaluation metrics and their unfair treatment of diffusion models
Stein, G., Cresswell, J. C., Hosseinzadeh, R., Sui, Y., Ross, B. L., Villecroze, V., Liu, Z., Caterini, A. L., Taylor, J. E. T., and Loaiza-Ganem, G. (2023) · 2023
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Pfgm++: Unlocking the potential of physics-inspired generative models
Xu, Y., Liu, Z., Tian, Y., Tong, S., Tegmark, M., and Jaakkola, T. (2023) · 2023
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