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Title: Gram-Gauss-Newton Method: Learning Overparameterized Neural Networks for Regression Problems

Paper content:
First-order methods such as stochastic gradient descent (SGD) are currently the standard algorithm for training deep neural networks.
Second-order methods, despite their better convergence rate, are rarely used in practice due to the pro- hibitive computational cost in calculating the second-order information.
In this paper, we propose a novel Gram-Gauss-Newton (GGN) algorithm to train deep neural networks for regression problems with square loss.
Our method draws inspiration from the connection between neural network optimization and kernel regression of neural tangent kernel (NTK).
Different from typical second-order methods that have heavy computational cost in each iteration, GGN only has minor overhead compared to first-order methods such as SGD.
We also give theoretical results to show that for sufficiently wide neural networks, the convergence rate of GGN is quadratic.
Furthermore, we provide convergence guarantee for mini-batch GGN algorithm, which is, to our knowledge, the first convergence result for the mini-batch version of a second-order method on overparameterized neural net- works.
Preliminary experiments on regression tasks demonstrate that for training standard networks, our GGN algorithm converges much faster and achieves better performance than SGD.
First-order methods such as Stochastic Gradient Descent (SGD) are currently the standard choice for training deep neural networks.
The merit of first-order methods is obvious: they only calculate the gradient and therefore are computationally efficient.
In addition to better computational efficiency, SGD has even more advantages among the first-order methods.
At each iteration, SGD computes the gradient only on a mini-batch instead of all training data.
Such randomness introduced by sampling the mini-batch can lead to better generalization (Hardt et al., 2015; Keskar et al., 2016; Masters & Luschi, 2018; Mou et al., 2017; Zhu et al., 2018) and better convergence (Ge et al., 2015; Jin et al., 2017a; b) , which is crucial when the function class is highly overparameterized deep neural networks.
Recently there is a huge body of works trying to develop more efficient first-order methods beyond SGD (Duchi et al., 2011; Kingma & Ba, 2014; Luo et al., 2019; Liu et al., 2019) .
Second-order methods, despite their better convergence rate, are rarely used to train deep neural networks.
At each iteration, the algorithm has to compute second order information, for example, the Hessian or its approximation, which is typically an m by m matrix where m is the number of parameters of the neural network.
Moreover, the algorithm needs to compute the inverse of this matrix.
The computational cost is prohibitive and usually it is not even possible to store such a matrix.
Formula and require subtle implementation tricks to use backpropagation.
In contrast, GGN has simpler update rule and better guarantee for neural networks.
In a concurrent and independent work, Zhang et al. (2019a) showed that natural gradient method and K-FAC have a linear convergence rate for sufficiently wide networks in full-batch setting.
In contrast, our method enjoys a higher-order (quadratic) convergence rate guarantee for overparameterized networks, and we focus on developing a practical and theoretically sound optimization method.
We also reveal the relation between our method and NTK kernel regression, so using results based on NTK (Arora et al., 2019b) , one can easily give generalization guarantee of our method.
Another independent work (Achiam et al., 2019) proposed a preconditioned Q-learning algorithm which has similar form of our update rule.
Unlike the methods considered in Zhang et al. (2019a) ; Achiam et al. (2019) which contain the learning rate that needed to be tuned, our derivation of GGN does not introduce a learning rate term (or understood as suggesting that the learning rate can be fixed to be 1 to get good performance which is verified in Figure 2 (c)).
We propose a novel Gram-Gauss-Newton (GGN) method for solving regression problems with square loss using overparameterized neural networks.
Despite being a second-order method, the computation overhead of the GGN algorithm at each iteration is small compared to SGD.
We also prove that if the neural network is sufficiently wide, GGN algorithm enjoys a quadratic convergence rate.
Experimental results on two regression tasks demonstrate that GGN compares favorably to SGD on these data sets with standard network architectures.
Our work illustrates that second-order methods have the potential to compete with first-order methods for learning deep neural networks with huge number of parameters.
In this paper, we mainly focus on the regression task, but our method can be easily generalized to other tasks such as classification as well.
Consider the k-category classification problem, the neural network outputs a vector with k entries.
Although this will increase the computational complexity of getting the Jacobian whose size increases k times, i.e., J ∈ R (bk)×m , each row of J can be still computed in parallel, which means the extra cost only comes from parallel computation overhead when we calculate in a fully parallel setting.
While most first-order methods for training neural networks can hardly make use of the computational resource in parallel or distributed settings to accelerate training, our GGN method can exploit this ability.
For first-order methods, basically extra computational resource can only be used to calculate more gradients at a time by increasing batch size, which harms generalization a lot.
But for GGN, more resource can be used to refine the gradients and achieve accelerated convergence speed with the help of second-order information.
It is an important future work to study the application of GGN to classification problems.

assistant: text: A novel Gram-Gauss-Newton method to train neural networks, inspired by neural tangent kernel and Gauss-Newton method, with fast convergence speed both theoretically and experimentally.