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As you suggest, the update rule for Adam is based only on the sign, not the magnitude of the gradient. Gradient clipping should not be needed to prevent exploding gradients when using Adam to optimize (though it might still be useful, depending on the circumstance).

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You could have a single network, feed both inputs separately, compute the distance/loss, then perform backpropagation. Or (as in the cited paper) you could initialize a network, and then create a parallel twin of that network. Because both networks see the same loss, they will remain identical after backpropagation. The paper is explained in the form of the ...

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Maximum Likelihood Maximum likelihood estimation involves defining a likelihood function for calculating the conditional probability of observing the data sample given probability distribution and distribution parameters. This approach can be used to search a space of possible distributions and parameters. The logistic model uses the sigmoid function (...

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In short, Maximum Likelihood estimation is used to find parameters given target values y and x. The Maximum likelhood estimation finds the parameters maximises the probability of y given x. It has been proved that MLE estimation problem caan be solved by finding the parametrs which gives least cross entropy in case of binary classification. Gradients descent ...

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I would look into what learning rate scheduler they are using. This seems like an effect of reducing lr based on a Cosine or ReduceOnPlateau strategy. See a figure from Loshchilov et al.

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MLE (Maximum Likelihood Estimation) sets up the optimization problem and gradient descent is a method for finding a specific solution to the optimization problem. MLE defines the optimization problem as finding the values of the model parameters that maximize the likelihood function over the parameter space, selecting the parameter values that make the ...

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Gradient descent is a numerical method used by a computer to calculate the minimum of a loss function. If that loss function is related to the likelihood function (such as negative log likelihood in logistic regression or a neural network), then the gradient descent is finding a maximum likelihood estimator of a parameter (the regression coefficients). For ...

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At high level, you can think of vanishing gradients in the way Chinese whispers work: Part of the original information is being lost every time it is being passed backwards to another person. In a similar way, RNN architecture "looses" part of the original information of a gradient as it is being propagated from the very last time step backwards to ...

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It can always happen , You see if the Weights are really tiny numbers close to zero, gradients are just the same if the dot product per neuron is positive then the gradients are just equal to the weights of that layer which can be small or if its negative , then the gradients are exactly equal to zero , small enough , so the answer to your question is Yes I ...

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