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Title: Gating Revisited: Deep Multi-layer RNNs That Can Be Trained

Paper content:
Recurrent Neural Networks (RNNs) are widely used models for sequence data.
Just like for feedforward networks, it has become common to build "deep" RNNs, i.e., stack multiple recurrent layers to obtain higher-level abstractions of the data.
However, this works only for a handful of layers.
Unlike feedforward networks, stacking more than a few recurrent units (e.g., LSTM cells) usually hurts model performance, the reason being vanishing or exploding gradients during training.
We investigate the training of multi-layer RNNs and examine the magnitude of the gradients as they propagate through the network.
We show that, depending on the structure of the basic recurrent unit, the gradients are systematically attenuated or amplified, so that with an increasing depth they tend to vanish, respectively explode.
Based on our analysis we design a new type of gated cell that better preserves gradient magnitude, and therefore makes it possible to train deeper RNNs.
We experimentally validate our design with five different sequence modelling tasks on three different datasets.
The proposed stackable recurrent (STAR) cell allows for substantially deeper recurrent architectures, with improved performance.
Recurrent Neural Networks (RNN) have established themselves as a powerful tool for modelling sequential data.
They have significantly advanced a number of applications, notably language processing and speech recognition (Sutskever et al., 2014; Graves et al., 2013; Vinyals & Le, 2015) .
The basic building block of an RNN is a computational unit (or cell) that combines two inputs: the data of the current time step in the sequence and the unit's own output from the previous time step.
While RNNs are an effective approach that can in principle handle sequences of arbitrary and varying length, they are (in their basic form) challenged by long-term dependencies, since learning those would require the propagation of gradients over many time steps.
To alleviate this limitation, gated architectures have been proposed, most prominently Long Short-Term Memory (LSTM) cells (Hochreiter & Schmidhuber, 1997) and Gated Recurrent Units (GRU, Chung et al., 2014) .
They use a gating mechanism to store and propagate information over longer time intervals, thus mitigating the vanishing gradient problem.
Although such networks can, in principle, capture long-term dependencies, it is known that more abstract and longer-term features are often represented better by deeper architectures (Bengio et al., 2009) .
To that end, multiple recurrent cells are stacked on top of each other in a feedforward manner, i.e., the output (or the hidden state) of the lower cell is connected to the input gate of the next-higher cell.
Many works have used such deep recurrent architectures, e.g., (Chung et al., 2015; Zilly et al., 2017) , and have shown their ability to extract more complex features from the input and make better predictions.
The need for multi-layer RNNs is particularly apparent for image-like input data, where multiple convolutional layers are required to extract a good representation, while the recurrence captures the evolution of each layer over time.
Since recurrent architectures are trained by propagating gradients across time, it is convenient to "unwrap" them into a lattice with two axes for depth (abstraction level) and time, see Fig. 1 .
This view makes it apparent that gradients flow in two directions, namely backwards in time and downwards from deeper to shallower layers.
In this paper we ask the question how the basic recurrent unit must be designed to ensure the "vertical" gradient flow across layers is stable and not impaired by vanishing or exploding gradients.
We show that stacking several layers of common RNN cells, by their construction, leads to instabilities (e.g., for deep LSTMs the gradients tend to vanish; for deep vanilla RNNs they tend to explode).
Our study makes three contributions:
(i) We analyse how the magnitude of the gradient changes as it propagates through a cell of the two-dimensional deep RNN lattice.
We show that, depending on the inner architecture of the employed RNN cell, gradients tend to be either amplified or attenuated.
As the depth increases, the repeated amplification (resp., attenuation) increases the risk of exploding (resp., vanishing) gradients.
(ii) We then leverage our analysis to design a new form of gated cell, termed the STAR (stackable recurrent) unit, which better preserves the gradient magnitude inside the RNN lattice.
It can therefore be stacked to much greater depth and still remains trainable.
(iii) Finally, we compare deep recurrent architectures built from different basic cells in an extensive set of experiments with three popular datasets.
The results confirm our analysis: training deep recurrent nets fail with most conventional units, whereas the proposed STAR unit allows for significantly deeper architectures.
In several cases, the ability to go deeper also leads to improved performance.
We have investigated the problem of vanishing/exploding gradient in deep RNNs.
In a first step, we analyse how the derivatives of the non-linear activation functions rescale the gradients as they propagate through the temporally unrolled network.
From both, the theoretical analysis, and associated numerical simulations, we find that standard RNN cells do not preserve the gradient magnitudes during backpropagation, and therefore, as the depth of the network grows, the risk that the gradients vanish or explode increases.
In a second step, we have proposed a new RNN cell, termed the STAckable Recurrent unit, which better preserves gradients through deep architectures and facilitates their training.
An extensive evaluation on three popular datasets confirms that STAR units can be stacked into deeper architectures than other RNN cells.
We see two main directions for future work.
On the one hand, it would be worthwhile to develop a more formal and thorough mathematical analysis of the gradient flow, and perhaps even derive rigorous bounds for specific cell types, that could, in turn, inform the network design.
On the other hand, it appears promising to investigate whether the analysis of the gradient flows could serve as a basis for better initialisation schemes to compensate the systematic influences of the cells structure, e.g., gating functions, in the training of deep RNNs.
C TRAINING DETAILS C.1
PIXEL-BY-PIXEL MNIST Following Tallec & Ollivier, chrono initialisation is applied for the bias term of k, b k .
The basic idea is that k should not be too large; such that the memory h can be retained over longer time intervals.
The same initialisation is used for the input and forget bias of the LSTM and the RHN and for the forget bias of LSTMw/f.
For the final prediction, a feedforward layer with softmax activation converts the hidden state to a class label.
The numbers of hidden units in the RNN layers are set to 128.
All networks are trained for 100 epochs with batch size 100, using the Adam optimizer (Kingma & Ba, 2014) with learning rate 0.001, β 1 = 0.9 and β 2 = 0.999.

assistant: text: We analyze the gradient propagation in deep RNNs and from our analysis, we propose a new multi-layer deep RNN.