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@@ -17,6 +17,6 @@ This trained model reproduces the results of Table 1 in [1].
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  | Model | Dataset | Metric | Obtained value | Reference value |
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  |:---:|:---:|:---:|:---:|:---:|
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- | SVAE | Dyn. Binarized MNIST | NLL (500 IS) | 93. (0.01) | 93.16 (0.31) |
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  [1] Tim R Davidson, Luca Falorsi, Nicola De Cao, Thomas Kipf, and Jakub M Tomczak. Hyperspherical variational auto-encoders. In 34th Conference on Uncertainty in Artificial Intelligence 2018, UAI 2018, pages 856–865. Association For Uncertainty in Artificial Intelligence (AUAI), 2018.
 
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  | Model | Dataset | Metric | Obtained value | Reference value |
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  |:---:|:---:|:---:|:---:|:---:|
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+ | SVAE | Dyn. Binarized MNIST | NLL (500 IS) | 93.27 (0.69) | 93.16 (0.31) |
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  [1] Tim R Davidson, Luca Falorsi, Nicola De Cao, Thomas Kipf, and Jakub M Tomczak. Hyperspherical variational auto-encoders. In 34th Conference on Uncertainty in Artificial Intelligence 2018, UAI 2018, pages 856–865. Association For Uncertainty in Artificial Intelligence (AUAI), 2018.