Below are the abstract, introduction, and conclusion of a computer science research paper. Please summarize the main contribution of the work in a single sentence. Your response should include the summary and no additional text.

Paper text:

In this paper, we study deep diagonal circulant neural networks, that is deep neural networks in which weight matrices are the product of diagonal and circulant ones.

Besides making a theoretical analysis of their expressivity, we introduced principled techniques for training these models: we devise an initialization scheme and proposed a smart use of non-linearity functions in order to train deep diagonal circulant networks. 

Furthermore, we show that these networks outperform recently introduced deep networks with other types of structured layers.
We conduct a thorough experimental study to compare the performance of deep diagonal circulant networks with state of the art models based on structured matrices and with dense models.
We show that our models achieve better accuracy than other structured approaches while required 2x fewer weights as the next best approach.
Finally we train deep diagonal circulant networks to build a compact and accurate models on a real world video classification dataset with over 3.8 million training examples.
The deep learning revolution has yielded models of increasingly large size.
In recent years, designing compact and accurate neural networks with a small number of trainable parameters has been an active research topic, motivated by practical applications in embedded systems (to reduce memory footprint (Sainath & Parada, 2015) ), federated and distributed learning (to reduce communication (Konečný et al., 2016) ), derivative-free optimization in reinforcement learning (to simplify the computation of the approximated gradient (Choromanski et al., 2018) ).
Besides a number of practical applications, it is also an important research question whether or not models really need to be this big or if smaller results can achieve similar accuracy (Ba & Caruana, 2014) .
Structured matrices are at the very core of most of the work on compact networks.
In these models, dense weight matrices are replaced by matrices with a prescribed structure (e.g. low rank matrices, Toeplitz matrices, circulant matrices, LDR, etc.).
Despite substantial efforts (e.g. Cheng et al. (2015) ; ), the performance of compact models is still far from achieving an acceptable accuracy motivating their use in real-world scenarios.
This raises several questions about the effectiveness of such models and about our ability to train them.
In particular two main questions call for investigation: Q1 How to efficiently train deep neural networks with a large number of structured layers?
Q2 What is the expressive power of structured layers compared to dense layers?
In this paper, we provide principled answers to these questions for the particular case of deep neural networks based on diagonal and circulant matrices (a.k.a. Diagonal-circulant networks or DCNNs).
The idea of using diagonal and circulant matrices together comes from a series of results in linear algebra by Müller-Quade et al. (1998) and .
The most recent result from Huhtanen & Perämäki demonstrates that any matrix A in C n⇥n can be decomposed into the product of 2n 1 alternating diagonal and circulant matrices.
The diagonal-circulant decomposition inspired to design the AFDF structured layer, which is the building block of DCNNs.
However, were not able to train deep neural networks based on AFDF.
To answer Q1, we first describe a theoretically sound initialization procedure for DCNN which allows the signal to propagate through the network without vanishing or exploding.
Furthermore, we provide a number of empirical insights to explain the behaviour of DCNNs, and show the impact of the number of the non-linearities in the network on the convergence rate and the accuracy of the network.
By combining all these insights, we are able (for the first time) to train large and deep DCNNs.
We demonstrate the good performance of DCNNs on a large scale application (the YouTube-8M video classification problem) and obtain very competitive accuracy.
To answer Q2, we propose an analysis of the expressivity of DCNNs by extending the results by .
We introduce a new bound on the number of diagonal-circulant required to approximate a matrix that depends on its rank.
Building on this result, we demonstrate that a DCNN with bounded width and small depth can approximate any dense networks with ReLU activations.
Outline of the paper: We present in Section 2 the related work on structured neural networks and several compression techniques.
Section 3 introduces circulant matrices, our new result extending the one from .
Section 4 proposes an theoretical analysis on the expressivity on DCNNs.
Section 5 describes two efficient techniques for training deep diagonal circulant neural networks.
Finally, Section 6 presents extensive experiments to compare the performance of deep diagonal circulant neural networks in different settings w.r.t. other state of the art approaches.
Section 7 provides a discussion and concluding remarks.
This paper deals with the training of diagonal circulant neural networks.
To the best of our knowledge, training such networks with a large number of layers had not been done before.
We also endowed this kind of models with theoretical guarantees, hence enriching and refining previous theoretical work from the literature.
More importantly, we showed that DCNNs outperform their competing structured alternatives, including the very recent general approach based on LDR networks.
Our results suggest that stacking diagonal circulant layers with non linearities improves the convergence rate and the final accuracy of the network.
Formally proving these statements constitutes the future directions of this work.
As future work, we would like to generalize the good results of DCNNs to convolutions neural networks.
We also believe that circulant matrices deserve a particular attention in deep learning because of their strong ties with convolutions: a circulant matrix operator is equivalent to the convolution operator with circular paddings (as shown in [5]).
This fact makes any contribution to the area of circulant matrices particularly relevant to the field of deep learning with impacts beyond the problem of designing compact models.
As future work, we would like to generalize our results to deep convolutional neural networks.
We train deep neural networks based on diagonal and circulant matrices, and show that this type of networks are both compact and accurate on real world applications.