Neural network, Control flow, Probability distribution, Mathematics, Unsupervised learning, Continuation

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On Nov 19, 2020
@weballergy shared
RT @mblondel_ml: Excited to share our new work "Differentiable Divergences Between Time Series", in which we propose improved non-negative divergences based on soft-DTW! PDF: https://t.co/7vsooICTEU Code: https://t.co/1DiIASYeS1 https://t.co/SEXdIXw2G9
Open

FaceAll 71.36 80.77 74.38 82.31 76.27 82.78 25.33 81.89 FaceFour 78.41 82.95 82.95 89.77 87.50 89.77 62.50 84.09 FacesUCR 76.93 90.49 92.34 94.78 92.34 94.54 45.90 80.44 FordA 65.90 56.21 NA NA NA NA 51.26 51.26 FordB 55.78 59.41 58.55 NA 58.83 NA 48.84 48.84 Gun Point 91.33 90.67 97.33 ...

mblondel.org
On Nov 19, 2020
@weballergy shared
RT @mblondel_ml: Excited to share our new work "Differentiable Divergences Between Time Series", in which we propose improved non-negative divergences based on soft-DTW! PDF: https://t.co/7vsooICTEU Code: https://t.co/1DiIASYeS1 https://t.co/SEXdIXw2G9
Open

Click here to read the article

Click here to read the article

FaceAll 71.36 80.77 74.38 82.31 76.27 82.78 25.33 81.89 FaceFour 78.41 82.95 82.95 89.77 87.50 89.77 62.50 84.09 FacesUCR 76.93 90.49 92.34 94.78 92.34 94.54 45.90 80.44 FordA 65.90 56.21 ...

Click here to read the article

Click here to read the article

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Backpropagation with Callbacks: Foundations for Efficient and Expressive Differentiable Programming

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Microsoft Word - HelmholtzTutorialKoeln.doc

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pG(x) = ∑yd pG(xyd) pG(y|x) = pG(xy) / pG(x) = ∑d pG(xyd) / ∑yd pG(xyd) pG(d|y) = pG(yd) / pG(y) = ∑x pG(xyd) / ∑xd pG(xyd) – log pG (d) = – log pG (d) 1 = – log pG ...