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Title: Exponentially Decaying Flows for Optimization in Deep Learning

Paper content:
The field of deep learning has been craving for an optimization method that shows outstanding property for both optimization and generalization.
We propose a method for mathematical optimization based on flows along geodesics, that is, the shortest paths between two points, with respect to the Riemannian metric induced by a non-linear function.
In our method, the flows refer to Exponentially Decaying Flows (EDF), as they can be designed to converge on the local solutions exponentially.
In this paper, we conduct experiments to show its high performance on optimization benchmarks (i.e., convergence properties), as well as its potential for producing good machine learning benchmarks (i.e., generalization properties).
Due to recent progress in the field of machine learning, it becomes more and more important to develop and sophisticate methods of solving hard optimization problems.
At the same time, in this field, such methods are additionally required to elicit decent generalization performance from statistical models.
An efficient method of mathematical optimization, however, does not always produce sufficient generalization properties, since these are involved with two distinct mathematical problems; The former is to find one of the solutions which minimize a given (possibly non-convex) objective function, and the latter is to adjust parameters so that a statistical estimator achieves its best result.
To address such a hard issue, we introduce a new mathematical perspective on optimization, and develop a method for machine learning based on this perspective.
We then empirically show its rapid convergence rate and high compatibility with deep learning techniques, as well as good statistical properties.In this field, many optimization methods have been proposed and modified so that they fit specific problems or models.
One of the current standard methods is the gradient descent method.
The method tends to converge slowly in general optimization problems.
However, with various specific techniques, such as mini-batch training and batch normalization BID9 ), it has been found to be efficient for state-of-the-art purposes in the field of deep learning.
Another class of methods that are now becoming popular and standard is adaptive methods, such as AdaGrad BID6 ) and Adam (Kingma & Ba (2015) ).
Compared to the gradient descent method, these methods have been shown to improve convergence rates with almost the same computational cost as the gradient descent method, but are reported to result in poor statistical outcomes in some cases of machine learning BID16 ).Other
class of methods that have been thoroughly studied in the theory of mathematical optimization is second-order methods, such as the Newton method and the Gauss-Newton method. These
methods possess great convergence properties, and in particular, have a potential to overcome plateau's problems BID5 ). Furthermore
, when it comes to applications in stochastic settings, the method based on the Gauss-Newton Matrix (or Fisher information Matrix) is shown to asymptotically attain the best statistical result, which is called Fisher efficiency (see BID0 ). Despite these
attractive characteristics, the methods have not yet been spotlighted in the field of machine learning due to several severe drawbacks; They suffer from high computational cost in general and their useful properties are no longer guaranteed in practical settings (see Section 12 in BID12 ). One of the continuously
developing second-order methods in this field, K-FAC BID1 , BID7 ), successfully produced high convergence rate empirically with relatively low computational cost. However, it still requires
much effort to become compatible with some deep learning techniques. In addition, it is unclear
whether the method has advantages in generalization performance.In our approach, by introducing a Riemannian metric induced by non-linear functions, we constitute dynamical systems which describe motions along the shortest route from arbitrary initial points to the zeros of non-linear functions on the corresponding Riemannian manifold, that is, geodesic with respect to the Riemannian metric. One of the remarkable characteristics
of our approach is that it enables us to flexibly design flows of such dynamical systems to control convergence rates. The results for the flows are then applicable
to mathematical optimization problems, in particular, with deep neural network (DNN) models. In this paper, after providing mathematical ground
of our methods, we experimentally demonstrate their performance in various aspects, from convergence rates to statistical properties.
Obtaining good statistical results from limited available data is a critical goal in machine learning.
To reach this goal, while developing an effective model is an essential approach, eliciting the best performance from the fixed model through optimization is important as well.
In our study, to examine the performance of our optimization methods, Exponentially Decaying Flows (EDF) based methods, we explored their generalization properties pertaining to results of optimization.
Our experiments showed that EDF-based methods are more likely to achieve optimal solutions which generalize the test data well than other standard optimizers are.
Therefore, EDF-based methods are considered to be optimization methods that have a high potential in their application to various tasks, and thus, are worthwhile to be sophisticated through future studies.
In terms of computation of the EDF-based methods with GPU, the Jacobian-vector-product can be carried out at almost the same cost as the gradient of loss function.
In fact, multiplying a vector by Jacobian and its transposed matrix (written as R-op and L-op, respectively) are implemented in combination with gradients of scholar functions.
For the psuedo-code for update scheme of the EDF-G with L/R-op, refer to Algorithm 1, and Algorithm 2 in particular case that k = 1.Algorithm 1: Update scheme for EDF-G with non-preconditioned MINRES Input : FIG3 shows the results of experiments using EDF for simple examples that compare a full-batch training with stochastic trainings.
In this example, the convolutional network similar to that used in Section 6.1 was employed on MNIST.
The curve labeled as "EDF F" depicts the result of Full-batch training per step, and those labeled as "EDF S" illustrate the results of stochastic trainings per epoch with a mini-batch of size 500.
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assistant: text: Introduction of a new optimization method and its application to deep learning.