user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Kronecker Recurrent Units

Paper content:
Our work addresses two important issues with recurrent neural networks: (1) they are over-parameterized, and (2) the recurrent weight matrix is ill-conditioned.
The former increases the sample complexity of learning and the training time.
The latter causes the vanishing and exploding gradient problem.
We present a flexible recurrent neural network model called Kronecker Recurrent Units (KRU).
KRU achieves parameter efficiency in RNNs through a Kronecker factored recurrent matrix.
It overcomes the ill-conditioning of the recurrent matrix by enforcing soft unitary constraints on the factors.
Thanks to the small dimensionality of the factors, maintaining these constraints is computationally efficient.
Our experimental results on seven standard data-sets reveal that KRU can reduce the number of parameters by three orders of magnitude in the recurrent weight matrix compared to the existing recurrent models, without trading the statistical performance.
These results in particular show that while there are advantages in having a high dimensional recurrent space, the capacity of the recurrent part of the model can be dramatically reduced.
Deep neural networks have defined the state-of-the-art in a wide range of problems in computer vision, speech analysis, and natural language processing BID28 BID36 .
However, these models suffer from two key issues.
(1) They are over-parametrized; thus it takes a very long time for training and inference.
(2) Learning deep models is difficult because of the poor conditioning of the matrices that parameterize the model.
These difficulties are especially relevant to recurrent neural networks.
Indeed, the number of distinct parameters in RNNs grows as the square of the size of the hidden state conversely to convolutional networks which enjoy weight sharing.
Moreover, poor conditioning of the recurrent matrices results in the gradients to explode or vanish exponentially fast along the time horizon.
This problem prevents RNN from capturing long-term dependencies BID22 BID5 .There
exists an extensive body of literature addressing over-parametrization in neural networks. BID31
first studied the problem and proposed to remove unimportant weights in neural networks by exploiting the second order information. Several
techniques which followed include low-rank decomposition BID13 , training a small network on the soft-targets predicted by a big pre-trained network BID2 , low bit precision training BID12 , hashing BID8 , etc. A notable
exception is the deep fried convnets BID44 which explicitly parameterizes the fully connected layers in a convnet with a computationally cheap and parameter-efficient structured linear operator, the Fastfood transform BID29 . These techniques
are primarily aimed at feed-forward fully connected networks and very few studies have focused on the particular case of recurrent networks BID1 .The problem of vanishing
and exploding gradients has also received significant attention. BID23 proposed an effective
gating mechanism in their seminal work on LSTMs. Later, this technique was adopted
by other models such as the Gated Recurrent Units (GRU) BID10 and the Highway networks BID39 for recurrent and feed-forward neural networks respectively. Other popular strategies include
gradient clipping BID37 , and orthogonal initialization of the recurrent weights . More recently BID1 proposed to use
a unitary recurrent weight matrix. The use of norm preserving unitary
maps prevent the gradients from exploding or vanishing, and thus help to capture long-term dependencies. The resulting model called unitary
RNN (uRNN) is computationally efficient since it only explores a small subset of general unitary matrices. Unfortunately, since uRNNs can only
span a reduced subset of unitary matrices their expressive power is limited BID42 . We denote this restricted capacity
unitary RNN as RC uRNN. Full capacity unitary RNN (FC uRNN
) BID42 proposed to overcome this issue by parameterizing the recurrent matrix with a full dimensional unitary matrix, hence sacrificing computational efficiency. Indeed, FC uRNN requires a computationally
expensive projection step which takes O(N 3 ) time (N being the size of the hidden state) at each step of the stochastic optimization to maintain the unitary constraint on the recurrent matrix. BID35 in their orthogonal RNN (oRNN) avoided
the expensive projection step in FC uRNN by parametrizing the orthogonal matrices using Householder reflection vectors, it allows a fine-grained control over the number of parameters by choosing the number of Householder reflection vectors. When the number of Householder reflection vector
approaches N this parametrization spans the full reflection set, which is one of the disconnected subset of the full orthogonal set. BID25 also presented a way of parametrizing unitary
matrices which allows fine-grained control on the number of parameters. This work called as Efficient Unitary RNN (EURNN),
exploits the continuity of unitary set to have a tunable parametrization ranging from a subset to the full unitary set.Although the idea of parametrizing recurrent weight matrices with strict unitary linear operator is appealing, it suffers from several issues: (1) Strict unitary constraints severely restrict the search space of the model, thus making the learning process unstable. (2) Strict unitary constraints make forgetting irrelevant
information difficult. While this may not be an issue for problems with non-vanishing
long term influence, it causes failure when dealing with real world problems that have vanishing long term influence 4.7. BID20 have previously pointed out that the good performance of
strict unitary models on certain synthetic problems is because it exploits the biases in these data-sets which favors a unitary recurrent map and these models may not generalize well to real world data-sets. More recently BID41 have also studied this problem of unitary
RNNs and the authors found out that relaxing the strict unitary constraint on the recurrent matrix to a soft unitary constraint improved the convergence speed as well as the generalization performance.Our motivation is to address the problems of existing recurrent networks mentioned above. We present a new model called Kronecker Recurrent Units (KRU)
. At the heart of KRU is the use of Kronecker factored recurrent
matrix which provide an elegant way to adjust the number of parameters to the problem at hand. This factorization allows us to finely modulate the number of
parameters required to encode N × N matrices, from O(log(N )) when using factors of size 2 × 2, to O(N 2 ) parameters when using a single factor of the size of the matrix itself. We tackle the vanishing and exploding gradient problem through
a soft unitary constraint BID26 BID20 BID11 BID41 . Thanks to the properties of Kronecker matrices BID40 , this constraint
can be enforced efficiently. Please note that KRU can readily be plugged into vanilla real space RNN
, LSTM and other variants in place of standard recurrent matrices. However in case of LSTMs we do not need to explicitly enforce the approximate
orthogonality constraints as the gating mechanism is designed to prevent vanishing and exploding gradients. Our experimental results on seven standard data-sets reveal that KRU and KRU
variants of real space RNN and LSTM can reduce the number of parameters drastically (hence the training and inference time) without trading the statistical performance. Our core contribution in this work is a flexible, parameter efficient and expressive
recurrent neural network model which is robust to vanishing and exploding gradient problem.The paper is organized as follows, in section 2 we restate the formalism of RNN and detail the core motivations for KRU. In section 3 we present the Kronecker recurrent units (KRU). We present our experimental
findings in section 4 and section 5 concludes our work. DISPLAYFORM0
We have presented a new recurrent neural network model based on its core a Kronecker factored recurrent matrix.
Our core reason for using a Kronecker factored recurrent matrix stems from it's elegant algebraic and spectral properties.
Kronecker matrices are neither low-rank nor block-diagonal but it is multi-scale like the FFT matrix.
Kronecker factorization provides a fine control over the model capacity and it's algebraic properties enable us to design fast matrix multiplication algorithms.
It's spectral properties allow us to efficiently enforce constraints like positive semi-definitivity, unitarity and stochasticity.
As we have shown, we used the spectral properties to efficiently enforce a soft unitary constraint.Experimental results show that our approach out-perform classical methods which uses O(N 2 ) parameters in the recurrent matrix.
Maybe as important, these experiments show that both on toy problems ( § 4.1 and 4.2), and on real ones ( § 4.3, 4.4, , and § 4.6) , while existing methods require tens of thousands of parameters in the recurrent matrix, competitive or better than state-of-the-art performance can be achieved with far less parameters in the recurrent weight matrix.
These surprising results provide a new and counter-intuitive perspective on desirable memory-capable architectures: the state should remain of high dimension to allow the use of high-capacity networks to encode the input into the internal state, and to extract the predicted value, but the recurrent dynamic itself can, and should, be implemented with a low-capacity model.From a practical standpoint, the core idea in our method is applicable not only to vanilla recurrent neural networks and LSTMS as we showed, but also to a variety of machine learning models such as feed-forward networks BID46 , random projections and boosting weak learners.
Our future work encompasses exploring other machine learning models and on dynamically increasing the capacity of the models on the fly during training to have a perfect balance between computational efficiency and sample complexity.

assistant: text: Out work presents a Kronecker factorization of recurrent weight matrices for parameter efficient and well conditioned recurrent neural networks.