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Title: VARIATIONAL SGD: DROPOUT , GENERALIZATION AND CRITICAL POINT AT THE END OF CONVEXITY

Paper content:
The goal of the paper is to propose an algorithm for learning the most generalizable solution from given training data.
It is shown that Bayesian approach leads to a solution that dependent on statistics of training data and not on particular
samples.
The solution is stable under perturbations of training data because it is defined by an integral contribution of multiple maxima of the likelihood and not by a single global maximum.
Specifically, the Bayesian probability distribution
of parameters (weights) of a probabilistic model given by a neural network is estimated via recurrent variational approximations.
Derived recurrent update rules correspond to SGD-type rules for finding a minimum of an effective loss that is an average of an original negative log-likelihood over the Gaussian distributions of weights, which makes it a function of means and variances.
The effective loss is convex for large variances and non-convex in the limit of small variances.
Among stationary solutions of the update rules there are trivial solutions with zero variances at local minima of the original loss and a single non-trivial solution with finite variances that is a critical point at the end of convexity of the effective loss
in the mean-variance space.
At the critical point both first- and second-order gradients of the effective loss w.r.t. means are zero.
The empirical study confirms that the critical point represents the most generalizable solution.
While the location of
the critical point in the weight space depends on specifics of the used probabilistic model some properties at the critical point are universal and model independent.
Finding a generalizable solution is a critical problem for any machine learning task.
The ultimate goal of learning from the available ground truths is to make a good prediction for new data.
The Bayesian method is a very powerful approach that gives a probabilistic measure of the ability of a proposed model to predict by estimating how well the model predicts known data.The accuracy of the predictions depends on how the found solution is able to overcome a sampling bias to avoid overfitting for given particular samples of training data.Specifically, in Bayesian method predictions of labels y for an input x are made by using a probabilistic model, for certainty a neural network, which defines a function parametrized by weights w that allows computing probabilities P (y|x, w) for each weight point.
Each weight point contributes to predicted probabilities of labels P rob(y|x) in accordance with probability distribution of weights.
The distribution of weights is learned from a known training data set {x n , y n ; n = 1..
N } and its prior probability distribution P 0 (w) in the following way: P rob(y|x) = w P (y|x, w)P 0 (w) N n=1 P (y n |x n , w)/ w P 0 (w) N n=1 P (y n |x n , w)Here the predicted probability P rob(y|x) is an average of the model probability P (y|x, w) at a weight w over the learned weight distribution.
To make predictions we are only interested in a method that allows to find averages in eq. FORMULA0 and not absolute values of the integrals.
According to mean value theorem (Cauchy (1813) , also in Encyclopedia of Mathematics Hazewinkel (1994) ) values of the averages can be represented by a single point, which in our case means that there is a single point in the weight space w 0 that represents a result of computing the integrals, so P rob(y|x) = P (y|x, w 0 ).
That point w 0 is a solution of the training of the neural network.A standard approach to get the solution is a maximum likelihood method that finds a maximum of the integrand.
However, there are some cases when the maximum likelihood fails to represent main contribution to the integral by weights.
Consider this example: if log-likelihood for N data samples has a maximum at some weight point w 1 , then in general its first derivative by weights is zero, second derivative is negative and proportional to N , so corresponding Gaussian integral by the weights is proportional to N −d/2 , where d is number of weights.
This will change if there is a flat maximum, which has not only first but also second and third derivatives equal to zero.
In this case the integral is proportional to N −d/4 .
For large number of samples the flat maximum makes the most significant contribution to the integral by weights: DISPLAYFORM0 and DISPLAYFORM1 .
For a typical case when the number of weights d ∼ N and average sample probabilities at maxima are comparable O(P 1 ) ∼ O(P 2 ) the integral around flat maximum I 2 is always bigger than the integral around narrow maximum I 1 , unless P 2 is zero.While in general a likelihood has a number of regular local maxima and no flat maximum the effect of integration over multiple frequent local maxima can result in an effective flat maximum that defines a solution.We argue that any local or global maximum of likelihood gives a wrong solution that is not generalizable and so makes inaccurate predictions, because the locations for the global maximum and local maxima depend on specific samples in the training data and any modification of it by adding or removing samples will change the solution BID6 .
Instead we will show that there is another solution that more associated with properties of the distribution of training data and less with particular samples.The purpose of this paper is to show that the effective flat maximum always exists for specific parameters of prior weight distribution P 0 (w) (regularization parameters) and corresponding solution is the most generalizable solution that can be found in training.
We show that the solution is a critical point in an effective loss that represents the result of integration over the weights.In the next sections we derive the algorithm for the optimizer for finding the critical point solution and analyze properties of the solutions.
The empirical study is outside of the scope of the paper and will be presented separately.For simplicity we use same notations for a vector of weights and its components, as well as corresponding parameters of distributions of weights because all weight components are independent in our consideration and it is clear from context when it is a vector or its component.
In the paper we consider a learning of a predictive model from training data by approximately computing Bayesian integral over weights -the parameters of the model.By using recurrent variational approximations with Gaussian weight distributions we are able to find a solution -a single point in weight space that represents an effect of averaging over distribution of weights in the Bayesian integrals.We show that this approach leads to SGD-type optimization problem for an effective loss in meanvariance space.
For each mean-variance point the effective loss is defined by average of the loglikelihood over Gaussian distribution at same mean-variance point.
Due to averaging the effective loss and its gradients of any order are continuous function of means even for ReLU based neural networks.The recurrent update rules define trajectories in mean-variance space.
Starting points of the trajectories are defined by regularization parameters, which are parameters of the Gaussian weight prior in Bayesian integrals.It is shown that there are two types of stationary solutions of the update rules.
First solution type corresponds to local minima of the original loss or maxima of the log-likelihood.
Second solution type is a critical point in mean-variance space that is a result of the integration over multiple maxima of the log-likelihood.At the critical point both first and second gradient of the effective loss are zero.
That leads to stability of the solution against perturbations of the training data set due to addition or removal data samples or via creation of adversarial examples.

assistant: text: Proposed method for finding the most generalizable solution that is stable w.r.t. perturbations of trainig data.