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Diversity is an important criterion for many areas of machine learning (ML), including generative modeling and dataset curation.
Prescribed generative adversarial networks
Dieng, A. B., Ruiz, F. J., Blei, D. M., and Titsias, M. K. (2019) · 1910
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Machine learning for scent: Learning generalizable perceptual representations of small molecules
Sanchez-Lengeling, B., Wei, J. N., Lee, B. K., Gerkin, R. C., Aspuru-Guzik, A., and Wiltschko, A. B. (2019) · 1910
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Huggingface’s transformers: State-of-the-art natural language processing
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Comment on the "H" concentration measure as a numbers-equivalent
Adelman, M. A. (1969) · 1969
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Diversity and Evenness: A Unifying Notation and Its Consequences
Hill, M. O. (1973) · 1973
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Diversity as a Concept and Its Measurement
Patil, G. and Taillie, C. (1982) · 1982
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Using the nyström method to speed up kernel machines
Williams, C. and Seeger, M. (2000) · 2000
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BLEU: A method for automatic evaluation of machine translation
Papineni, K., Roukos, S., Ward, T., and Zhu, W.-J. (2002) · 2002
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Probability product kernels
Jebara, T., Kondor, R., and Howard, A. (2004) · 2004
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Nltk: The natural language toolkit
Bird, S. (2006) · 2006
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Entropy and Diversity
Jost, L. (2006) · 2006
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The effective rank: A measure of effective dimensionality
Roy, O. and Vetterli, M. (2007) · 2007
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A tutorial on spectral clustering
Von Luxburg, U. (2007) · 2007
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Learning multiple layers of features from tiny images
Krizhevsky, A. (2009) · 2009
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Very deep VAEs generalize autoregressive models and can outperform them on images
Child, R. (2020) · 2011
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Determinantal point processes for machine learning
Kulesza, A., Taskar, B., et al. (2012) · 2012
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Measuring Diversity: The Importance of Species Similarity
Leinster, T. and Cobbold, C. A. (2012) · 2012
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Quantum information theory
Wilde, M. M. (2013) · 2013
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Microsoft COCO: Common objects in context
Lin, T.-Y., Maire, M., Belongie, S., Hays, J., Perona, P., Ramanan, D., Dollár, P., and Zitnick, C. L. (2014) · 2014
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Deep learning face attributes in the wild
Liu, Z., Luo, P., Wang, X., and Tang, X. (2015) · 2015
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Confronting the challenge of quality diversity
Pugh, J. K., Soros, L. B., Szerlip, P. A., and Stanley, K. O. (2015) · 2015
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Unsupervised representation learning with deep convolutional generative adversarial networks
Radford, A., Metz, L., and Chintala, S. (2015) · 2015
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ImageNet large scale visual recognition challenge
Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., et al. (2015) · 2015
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LSUN: Construction of a large-scale image dataset using deep learning with humans in the loop
Yu, F., Seff, A., Zhang, Y., Song, S., Funkhouser, T., and Xiao, J. (2015) · 2015
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A diversity-promoting objective function for neural conversation models
Li, J., Galley, M., Brockett, C., Gao, J., and Dolan, W. B. (2016) · 2016
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Improved Techniques for Training GANs
Salimans, T., Goodfellow, I., Zaremba, W., Cheung, V., Radford, A., Chen, X., and Chen, X. (2016) · 2016
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Rethinking the Inception architecture for computer vision
Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., and Wojna, Z. (2016) · 2016
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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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Revisiting precision and recall definition for generative model evaluation
Simon, L., Webster, R., and Rabin, J. (2019) · 2019
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Unsupervised quality estimation for neural machine translation
Fomicheva, M., Sun, S., Yankovskaya, L., Blain, F., Guzmán, F., Fishel, M., Aletras, N., Chaudhary, V., and Specia, L. (2020) · 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) · 2020
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The multilingual Amazon reviews corpus
Keung, P., Lu, Y., Szarvas, G., and Smith, N. A. (2020) · 2020
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Diverse image generation via self-conditioned gans
Liu, S., Wang, T., Bau, D., Zhu, J.-Y., and Torralba, A. (2020) · 2020
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ChemGAN challenge for drug discovery: can AI reproduce natural chemical diversity?
Benhenda, M. (2017) · 2017
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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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Unrolled generative adversarial networks
Metz, L., Poole, B., Pfau, D., and Sohl-Dickstein, J. (2017) · 2017
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VEEGAN: reducing mode collapse in GANs using implicit variational learning
Srivastava, A., Valkov, L., Russell, C., Gutmann, M. U., and Sutton, C. (2017) · 2017
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Fashion-MNIST: A novel image dataset for benchmarking machine learning algorithms
Xiao, H., Rasul, K., and Vollgraf, R. (2017) · 2017
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GILBO: one metric to measure them all
Alemi, A. A. and Fischer, I. (2018) · 2018
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Diversity and inclusion metrics in subset selection
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Reliable fidelity and diversity metrics for generative models
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Molecular Sets (MOSES): A Benchmarking Platform for Molecular Generation Models
Polykovskiy, D., Zhebrak, A., Sanchez-Lengeling, B., Golovanov, S., Tatanov, O., Belyaev, S., Kurbanov, R., Artamonov, A., Aladinskiy, V., Veselov, M., Kadurin, A., Johansson, S., Chen, H., Nikolenko, S., Aspuru-Guzik, A., and Zhavoronkov, A. (2020) · 2020
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Gait: A geometric approach to information theory
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On the correlation of word embedding evaluation metrics
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Diffusion models beat GANs on image synthesis
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SimCSE: Simple contrastive learning of sentence embeddings
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Densely connected normalizing flows
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Entropy and Diversity: The Axiomatic Approach
Leinster, T. (2021) · 2021
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Improved denoising diffusion probabilistic models
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Denoising diffusion implicit models
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Information theory with kernel methods
Bach, F. (2022) · 2022
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Multiple importance sampling elbo and deep ensembles of variational approximations
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Autoregressive Image Generation using Residual Quantization
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On aliased resizing and surprising subtleties in gan evaluation
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How much of the chemical space has been explored? selecting the right exploration measure for drug discovery
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