Title: A MEMORY-AUGMENTED NEURAL NETWORK BY RE-SEMBLING HUMAN COGNITIVE PROCESS OF MEMO-RIZATION

Abstract: Memorization of long-term information is a core task in sequence learning of neural networks, and inspired by human cognitive process, we propose a sparse memory-augmented neural network (SMANN) to deal with it in this paper, which is composed of a two-layer neural controller and an external memory. In the first layer of the network, the information is divided into segments according to a sparse mask, which preserve immediate memory, and then in the second layer, the segmented information are collected and processed as short-term memory. For alleviation of gradient vanishing problem, constrained LSTM structures are utilized in both of the layers to make the chrono-initializer more reasonable. Lastly, the external memory is used to store long-term information, and its access rate is reduced sharply owing to the sparse mask. In experiments, we evaluate the network and its components like constrained LSTM and the neural controller independently, results on different tasks has demonstrated the superiorities of our networks compared with their counterparts.

1. INTRODUCTION:

Capture of long-term dependencies is a core task in sequence learning, and recurrent neural networks (RNNs) have become the standard approach for addressing this learning problem. In one aspect, some RNNs models integrate historical information in the state parameters and propagate them along the cells in a serial order, such as the standard Long Short-Term Memory (LSTM) Lipton et al. (2015) , the Gated Recurrent Unit (GRU) Cho et al. (2014) and the JANET Westhuizen & Lasenby (2018) , which differ in cell structures and all employ information latching to alleviate gradient vanishing problem; In the other aspect, some adopt distinct network architectures, such as the hierarchical recurrent neural network El Hihi & Bengio (1996) which captures long-term dependencies in multiple time scales, the skip RNN Campos et al. (2017) which learns to skip redundant state updates and shortens the valid path length in long-term memory and the dilated recurrent neural networks Chang et al. (2017) which employs dilated skip connections to capture complex dependencies. Nevertheless, it is still difficult for these methods to learn and retain memory over long distance, and then models simulating human cognitive architecture have been developed recently and provided a promising potential in long-term sequential learning.

Memory-augmented neural networks (MANNs) consist of a neural controller and an external memory, which resemble human cognitive process roughly as we perceive information sequentially and process them in the brain by consulting memory Le et al. (2019) . The MANNs models have been demonstrated to have superior performance over recurrent neural networks by many literatures Graves et al. (2016) ; Le et al. (2018) . Despite the promising results, there remains some problems to be solved: (i) the network architecture should be refined so as to simulate human cognitive process more precisely; (ii) as access of external memory is expensive, the steps of reading and writing should be reduced while maintaining high performance.

Richard et.al Atkinson & Shiffrin (1968) proposed a modal model, in which human perceive information by sensory memory (immediate memory) firstly, and then the information selected by human attention will be transferred into short-term memory, lastly it can be stored into the cerebral cortex as semipermanent memory (long-term memory) if repeated for certain times. Besides, long-term memory can be retrieved and reused in short-term memory Eichenbaum & Cohen (2004); Kandel et al. (2000) . In this paper, we propose a sparse memory-augmented neural network (abbreviated as SMANN) to simulate this cognitive process. The network comprises a neural controller with two layers and a external memory: the first layer of the controller is divided into segments according to a sparse mask, in which each segment stores immediate memory; the second layer of the controller collects information from the segments, it integrates short-term memory into the state of the network and transfers processed information to external memory as long-term memory; reading and writing on the external memory are performed via attention mechanism on the mask, as it is sparse, it can reduce the memory access rate sharply. Besides, we propose to use a constrained cell structure in the neural controller to make chrono-initialization on the parameters more reasonable. The experiments are divided into three parts: improved LSTM with chrono-initializer, independent neural controller and the memory-augmented neural network. The results demonstrate the superiority of our models over other recurrent networks or the neural turing machine.

2. MODEL ARCHITECTURE:

The architecture of our proposed neural network is depicted in Fig. 1 , where the colored circles represent network cells in the neural controller and the squares represents binary elements in the sparse mask, M indicates the external memory. The first layer of the controller is broken by the mask and the segments store immediate memory; the second layer preserves short-term memory and its states are only updated at certain time steps according to the mask, meanwhile the external memory (long-term memory) is accessed simultaneously. In the following parts of this section, we present the cell structure and the initialization method firstly, and then the neural controller is illustrated in detail, lastly the access mechanism of the external memory is explained briefly. 

2.1. CONSTRAINED LSTM WITH CHRONO-INITIALIZER:

In the neural controller, we adopt a LSTM cell with constrained output, which is accommodated to the chrono-initializer. Our proposed LSTM cell structure is formulated as below:

EQUATION

where the symbols take the standard meaning as in Lipton et al. (2015) , the variable u is a trainable vector with elements initialized as 0.5, and D is a trainable diagonal matrix with all of the diagonal elements initialized as 1. The standard procedure for LSTMs is to initialize the weights with uniform distribution, to initialize the forget gate bias as one and the rest biases as zero Jozefowicz et al. (2015) . Nevertheless, as mentioned in Westhuizen & Lasenby (2018), the problem is that little information would be propagated through a long range of the sequence by this scheme. Thus, Tallec et.al Tallec & Ollivier (2018) proposed a chrono-initializer, the motivation is to force the model's memory lying approximately in the same range with the sequence's temporal dependencies.

Assuming the sequential data have temporal dependencies in a range [T l , T r ], we start by a leaky RNN, whose time-discretized equation is given by:

EQUATION

which can be transformed to continuous time version by using first order Taylor expansion h(t

EQUATION

As stated in Tallec & Ollivier (2018) , the solution of Eqn.3 is h(t) = e -α(t-t0) h(t 0 ) with x(t) = 0 for t > t 0 , b = 0 and U = 0, So h(t) decreases to e -1 of h(t 0 ) over a time proportional to T = 1/α, which can be called characteristic forgetting time of the leaky RNN. It is sensible to have T lying in the same range with the sequential data's temporal dependencies. Nevertheless, T is an unknown prior in LSTM networks, thus we use the gates f t and i t to learn the dominating parameters 1 -α and α respectively. With the values of both inputs and hidden layers zero-centred over time,

f t will typically center around σ(b f ). Values of σ(b f ) in the desired range [1 -1 T l , 1 -1 Tr ] can be obtained by initializing b f as distributed as log(U([T l , T r ]))

where U is the uniform distribution. For the same reason, i t will fall into the range of [ 1 Tr , 1 T l ] when b i = -b f . Usually T l is set to the shortest length of 1 and T r set to the maximum length in the sequence, then we get:

EQUATION

we can see that each component of b f corresponds to one forgetting time of the network, it means that each component of c t in Eqn.1 preserves information for a length of the forgetting time. However, the temporal dependencies of the sequence data will not distribute uniformly in the range of sequence length, thus we use a double tanh function to make constraint on the hidden state originated from c t , which can fitter out useless components and assign weights on the rest ones. Experiments are carried out to provide insights and understanding of this improvement in subsection 4.1.

2.2. NEURAL CONTROLLER WITH SPARSE MASK:

The mask (denoted as m) in the neural controller is a one-dimensional vector with binary elements, and it is used to segment the first layer of the controller and determine the state update of the second layer and access of the external memory. If the mask element at time step t equals to 1, the cell connection in the first layer is forced to be broken, and the last two vectors in Eqn.1 change into this formula:

c t = f t c t-1 + i t tanh(W c x t + U c h t-1 + b c ) h t = D 2 (tanh(c t + u) + tanh(c t -u)) c t = (1 -m t ) c t .

(5)

Simultaneously, the cell state in the second layer is updated, c t and h t evolve as:

EQUATION

where the states are responsible for short-term memory as the information is difficult to be propagated along extreme long distance on account of the vanishing gradient. The mask is generated by the formula as below:

EQUATION

where σ symbolizes sigmoid function, and f binary is a deterministic step function which rounds a vector with elements in (0, 1) to a vector in {0, 1}. As f binary is a non-differentiable step function, we use the straight through (ST) estimator to compute the gradients, which is a biased estimator firstly proposed by Hinton et al. Hinton (2012) ; Bengio et al. (2013) . When m is initialized as zero vector, it will result in a initial mask with all elements equal to 1, which means that the first layer of the controller is broken into segments at every time step and the second layer collects information from below and updates its cell states at the same time steps. Next, in the training process, cells in the first layer will group into lots of clusters as zeros arise in the mask. Finally, if the mask is sparse, several clusters appear and the number of update operation in the second layer along with the access number to the external memory will be reduced sharply. The mask is forced to be sparse by adding a term of Lasso regularization Zhao & Yu (2006) ; Donoho & Elad (2003) to the loss of the model.

EQUATION

where L symbolizes the total loss, L 1 represents error between outputs and the ground truth, such as cross entropy in classification applications, 1 denotes 1 -norm, and ε is a scalar set to 1e -6.

In subsection 4.2, the experimental results demonstrate that this simple segmented neural network outperforms other state-of-the-art models, and for convenience, it is abbreviated as SNN.

2.3. EXTERNAL MEMORY:

The external memory is used for preserving long-term memory in our works. As mentioned before, access of the external memory happens at the time steps corresponding to non-zero elements in the sparse mask. The read vector is a convex combination of the row-vectors in the memory, and write comprises two parts: erase and add Graves et al. (2016) . The addressing mechanism adopted in our implementation is content-addressing, in which the content-based system produces a normalised weighting based on cosine similarity. At an access time step t, the read vector is concatenated with the output of the neural controller (h t of the second layer), and then passes through a full connected layer to get a final output; in the other case that external memory is not accessed, the outputs are copied just form the previous time steps. Experimental results in subsection 4.3 indicate that the access rate has been reduced sharply by our designed neural controller.

3. THEORETICAL ANALYSIS:



3.1. ADVANTAGE IN MEMORY CAPACITY:

The recurrent length is proposed and used to measure the memory capacity of recurrent neural networks Zhang et al. (2016) ; Chang et al. (2017) . The network is better in memory capacity when the mean path length spanning all time steps is shorter. We use d i (n) to denote the path length, where i is the time index of input node and n means that the output node is located at time step i + n. In a regular two-layer recurrent neural network, where the cells are connected in sequence without break, we have [d i (n)] RNN = n apparently. However, in our network (SMANN), [d i (n)] SMANN may be smaller than n if one or more time steps for updating state and accessing external memory (the red nodes in Fig. 1 ) locate between i and i + n. Then, we adopt a mean value as below:

EQUATION

where is the sequence length, V is a set comprising all time steps. For the regular recurrent neural network, we have [d] RNN = 1+ 2 , and for SMANN [d] SMANN ≤ 1+ 2 , it means that the memory capacity of our network has been improved compared with the RNN network.

In another aspect, for RNN the gradient of the objective function is given by:

EQUATION

)

For SMANN, in the second layer, we have ∂c k+1 ∂c k = 1 if the state is not updated according to zero element of the mask, then the gradient can be formulated as:

EQUATION

As pointed out by Bengio et al. Bengio et al. (1994) ; Hochreiter et al. (2001) , the norm of each partial derivative must be less than 1 in order to latch information robustly. Thus, we have:

EQUATION

As we know, the trained mask m is sparse. When T -τ increases, Eqn.12 approaches 0, which means that the gradients of SMANN vanish slower than that of RNN and can be propagated along a further distance. Eventually, the memory capacity can be improved by this neural architecture.

3.2. SPARSITY OF THE MASK:

As mentioned before, the mask is used to divide long-term memory into immediate memory fragments. Assuming that the non-zero elements distribute averagely in the mask, the distance among them is denoted as x, then we have:

EQUATION

where . is a floor function. When x = +1 2 , d is minimized, which means that the network achieves the best ability of memorization by this setting of the mask. As for the MNIST dataset with period of = 784, the optimal value of x approximates 20. The experimental results in subsection 4.2 and 4.3 show that the non-zero elements in the resulted mask is approximately uniform (will be influenced by the content of sequence data) and the number equals to a near-optimal value.

4. EXPERIMENTS:

The performance evaluation is conducted mainly on four kinds of tasks: sequential classification, copy, add and frequency discrimination. The dataset of MNIST comprising 60, 000 training samples and 10, 000 testing ones, in which each sample is reshaped as an 784-dimensional row vector. Also a permuted MNIST (pMNIST) is adopted, as stated by Arjovsky et al. Arjovsky et al. (2016) , the pixels are permuted to lengthen temporal dependencies and increase difficulty for recurrent neural networks to learn.

In the add task, the networks are feed with sequences of (value, marker) tuples, and aim to generate addition of the values marked with 1 while ignoring others marked with 0. The synthetic dataset is composed of 50, 000 sequences in which 70 percents are used for training, 10 percents are for validation and the rest is for testing. The values of each sequence are drawn from U[-0.5, 0.5] with U denotes uniform distribution, and the first marker is randomly placed among the first 10% of the length and the second one is placed among the last 10%, thus the useful information for adding has interval of at least 80% of the total length, which is beneficial for testing of long-term memory. Mean Squared Error (MSE) between the output and the ground truth is adopted as loss function.

In the copy task, 50, 000 sequences are generated and divided as training, validation and testing sets by the same proportion as in the add task. The sequence includes I + 21 items in which each indicates a category selected from {a i } 9 i=0 . Specifically, the first 10 items are used for copy and sampled uniformly from {a i } 8 i=1 , and then the following I items are filled with zeros, next, the item located at I + 11 is set to 9, which acts as a delimiter telling the network starts to copy the first ten items, finally, the last 10 items are also filled with zeros. Consequently, the ground truth contains I + 11 items of zeros and ten ones copied from the head of the input sequence. The networks aim to minimize the average cross entropy of the category predictions.

The task of frequency discrimination is to discriminate the sinusoids with two kinds of periods. Different from the experimental setting in Campos et al. (2017) , where the data is generated on the fly, our constructed dataset has 30, 000 sequences drawn from sinusoids with length of certain milliseconds, in which half of the sinusoids have periods P in range of U(1, 2) milliseconds and the other half have periods in range of {U(0, 1) ∪ U(2, 3)} milliseconds Neil et al. (2016) ; Campos et al. (2017) . Moreover, each sequence has a random phase shift drawn from U(0, P ). As sinusoid is continuous signal, the amplitudes are sampled and saved at intervals of 1.0 milliseconds.

In the following experiments, all the networks are trained by Adam Kingma & Ba (2014) optimizer with a learning rate of 0.001 for 100 epoches, dropout rate of 0.1 is used on the output of networks, and the batch size is set to 200. Moreover, the model parameters with the minimum validation loss are saved and used for testing.

4.1. SINGLE-LAYER CONSTRAINED LSTM:

Here the constrained LSTM is compared with the standard LSTM Lipton et al. ( 2015) to verify its improvement in performance, and the two networks are both composed of a single layer of 128 units. The comparison is performed in sequential classification, add and copy tasks. The mean values and standard deviations of testing accuracies [%] on MNIST and pMNIST are exhibited in Table .1. For the add and copy tasks, comparisons are conducted on sequences with length of 120, and losses of MSE and cross entropy are also displayed as form of mean ± std. All the results are generated by five different runs. The resulted curves on the MNIST and pMNIST datasets are depicted in Fig. 2 , where the different results of the constrained LSTM over five runs are drawn with dashed lines and the averaging results of two compared networks are drawn with solid lines. Apparently, our proposed LSTM works better than the standard version. 

4.2. INDEPENDENT NEURAL CONTROLLER:

In this part, the network of neural controller is evaluated independently from the assistance of external memory. The comparative networks of the RNN, standard LSTM, JANET, skip LSTM and skip GRU all have two hidden layers of 32 units, and their results are obtained by five independent runs. For other networks, we cited the stated results in the papers directly, and all of them have one single layer of 128 units unless specially stated. The testing accuracies on the MNISTs are presented in Table .2, where results in the upper part are displayed in the form of mean ± std, and others are cited mean values. Our neural controller (NC 1 ) with two hidden layers of 32 units behaves better than the comparative models except for the tLSTM and BN-LSTM, which both have much more amount of parameters than NC 1 . Assume that a LSTM layer has n 1 inputs and n 2 hidden units, then we Lipton et al. (2015) 96.1 ± 0.99 80.3 ± 1.14 JANET Westhuizen & Lasenby (2018) 97.9 ± 0.24 89.9 ± 0.66 Skip LSTM Campos et al. (2017) 96.8 ± 0.39 85.8 ± 0.86 Skip GRU Campos et al. (2017) 97.6 ± 0.33 89.2 ± 0.73 NC 1 98.6 ± 0.21 94.4 ± 0.45 NC 2 (32-64) 99.0 ± 0.12 95.6 ± 0.28 tLSTM a He et al. (2017) 99.2 94.9 BN-LSTM b Cooijmans et al. (2016) 99.0 95.4 sTANH- RNN Zhang et al. (2016) 98.1 94.0 uRNN Arjovsky et al. (2016) 95.1 91.4 iRNN Le et al. (2015) 97.0 82.0 TANH-RNN Le et al. (2015) 35.0 35.0 a actually three layers with 100 hidden units are used.

b one single layer with 100 hidden units.

have

x t ∈ R n1 , {c t , h t } ∈ R n2 , W j ∈ R n1×n2 , U j ∈ R n2×n2 and b j ∈ R n2 .

As for a single layer standard LSTM, we have j = {i, o, f, c} corresponding to four fully connected layers and the total number of parameters is 4(n 1 n 2 + n 2 2 + n 2 ); in our neural controller, j = {i, f, c} as the output gate is omitted. Then it is not too hard to calculate that the three-layer tLSTM with 100 hidden units has roughly 2 × 10 5 parameters, the BN-LSTM with the same hidden units approximately has 4 × 10 4 and the two-layer NC 1 has no more than 1 × 10 4 . Additionally, the neural controller can perform better with larger layer sizes, the two-layer NC 2 ranks the same with BN-LSTM on the MNIST and performs much better than others on the pMNIST while its parameter number is roughly 2 × 10 4 . The resulted curves on the MNISTs are depicted in the left and middle parts of Fig. 3 , apparently we can see that the accuracy rates of our neural controller climb more shapely than others and achieve the highest values eventually. Experimental results on another three tasks are presented in Table .3, and also the curve results of copy and frequency discrimination tasks are provided in Fig. 3(c, d ). Our neural controller achieves the best in add and copy, and behaves similarly with some of others on the frequency data.

4.3. SPARSE MEMORY-AUGMENTED NEURAL NETWORK:

We evaluate our designed neural network with external memory (SMANN) here. The neural controller (NC) and a neural turing machine (NTM) are adopted for comparison, which both comprise neural networks of two layers with 32 hidden units. The results on the MNISTs are depicted in Fig. 4 , where SMANN has a faster ascent curves and achieves better accuracies than others. Fig. 4 (c) is a sparse mask (with 23 nonzero elements) generated on the pMNIST, which indicates that the access rate to the external memory is reduced sharply to nearly 3 percents compared with a neural turing machine, in which the memory access is needed in every time step. Fig. 4 (d) is a resulted curves on copy task, and SMANN has a significant improvement with others. The evaluation between NTM and SMANN are presented in Table .4, SMANN behaves better than NTM apparently, but they both perform not very well in add task compared with models without external memory, we will explore it deeply in the future. 98.9 ± 0.14 95.9 ± 0.23 6.89e-5 ± 4.02e-6 --

Copy (len = 120) Frequency (len = 120) Frequency (len = 500) NTM(32-32) 0.0468 ± 3.98e-2 90.7 ± 0.32 86.9 ± 0.33 SMANN(32-32) 5.27e-4 ± 1.02e-3 91.0 ± 0.27 89.3 ± 0.31

5. CONCLUSION:

In this paper, we propose a sparse memory-augmented neural network to simulate human memory process. In the bottom layer of the neural controller, information is divided into segments via a mask, and then short-term information are collected and memorized in the top layer, constrained LSTMs are utilized in both of the layers in consideration of chrono-initializer. The external memory is accessed according to the sparse mask and the access rate is reduced to about 3 percents compared to conversional neural turing machine, which can accelerate the speed of training and running the whole network as access to external memory is time-consuming. Experiments are conducted as three parts: the constrained LSTM, the neural controller and the complete model. The two components are demonstrated to behave better than their counterparts, and the model with an external memory improves further and outperforms the neural turing machine with less memory access.

:



Figures and Tables:

Figure 1: Architecture of SMANN.

Figure 2: Testing accuracies of single-layer LSTMs over training epochs.

Figure 3: Testing accuracies on MNISTs and frequency data over training epoches are depicted in (a,b) and (d) respectively, and curves of cross entropy in copy task are drawn in (c).

Figure 4: Testing accuracies on MNISTs and cross entropy in the copy task.

Results of testing accuracies and MSE a . In the tables of this paper, 1e-n indicates 1 × 10 -n . b a single layer of 128 units is used.

Table content: <table><tr><td>Model</td><td>Task</td><td>MNIST</td><td>pMNIST</td><td>Add</td><td>Copy</td></tr><tr><td colspan="2">Standard LSTM b</td><td colspan="4">97.4 ± 0.49 91.3 ± 0.55 2.34e-5±5.34e-6 0.1246±0.0172</td></tr><tr><td colspan="6">Constrained LSTM b 98.2 ± 0.33 92.4 ± 0.50 2.21e-5±6.55e-6 0.1138±0.0125</td></tr></table>

Results of testing accuracies.

Table content: <table><tr><td>Model</td><td>Dataset</td><td colspan="2">Accuracy on MNIST Accuracy on pMNIST</td></tr><tr><td>RNN</td><td/><td>54.9 ± 5.56</td><td>60.6 ± 22.76</td></tr><tr><td>Standard LSTM</td><td/><td/></tr></table>

The experimental results of add, copy and frequency discrimination tasks.

Table content: <table><tr><td>Model</td><td colspan="3">Task Add (len a = 120) Copy (len = 120) Frequency (len = 120)</td></tr><tr><td>RNN</td><td/><td>7.40e-4 ± 3.35e-4 0.1475 ± 0</td><td>86.4 ± 1.42</td></tr><tr><td colspan="2">sLSTM</td><td>3.29e-5 ± 1.02e-5 0.1263 ± 7.81e-4</td><td>81.2 ± 6.65</td></tr><tr><td colspan="2">JANET</td><td>1.08e-5 ± 1.10e-6 0.0886 ± 1.66e-3</td><td>89.8 ± 0.39</td></tr><tr><td colspan="2">Skip LSTM</td><td>2.88e-5 ± 2.71e-6 0.1296 ± 1.27e-3</td><td>90.1 ± 0.36</td></tr><tr><td colspan="2">Skip GRU</td><td>1.76e-5 ± 1.05e-5 0.1147 ± 1.51e-2</td><td>90.4 ± 0.26</td></tr><tr><td>NC 1</td><td/><td>9.32e-6 ± 2.26e-6 0.0873 ± 3.61e-3</td><td>90.4 ± 0.23</td></tr></table>

Comparison of NTM and SMANN.

Table content: <table><tr><td>Model</td><td>Task</td><td>MNIST</td><td>pMNIST</td><td>Add (len = 120)</td></tr><tr><td colspan="2">NTM(32-32)</td><td>98.6 ± 0.17</td><td>93.6 ± 0.26</td><td>7.04e-5 ± 5.49e-6</td></tr></table>

Peer review (iclr2020):
Contributions: This paper describes modifications to the MANN setup for sequence learning. The modifications include a variant of the LSTM controller which includes constrained outputs, and a trainable mask which is use to compute a boolean for each timestep indicating whether or not to perform costly updates to an external memory at that timestep. When trained on a certain problem, the mask becomes sparse (encouraged by L1 regularisation) to mean that far fewer memory updates are performed, with corresponding runtime benefits and keeping approximately the same performance. Motivation: I find the entire idea of learning an (apparently) non-input dependent mask of which timesteps to do memory access on very questionable - a) this seems to impose a fixed sequence length, which means the many domains on which RNNs are applied to arbitrary sequences (language, reinforcement learning, ...) are effectively excluded. b) even if this was not the case - why does it make sense to learn this as part of the trainable weights? Surely the sensible thing, which would match human cognition, would be that depending on the current input, plus previous state, we decide whether to perform an expensive memory update. If you decided ahead of time *It*s good to perform memory updates on timesteps 1, 5, 7 and 11* you have surely done nothing except overfit to the specific frequency characteristics of your data - in my view this severely limits the applicability of RNNs, whereas the paper reads like this is an across the board improvement - *the two components are demonstrated to behave better than their counterparts, and the model with an external memory improves further and outperforms the neural turing machine with less memory access.* I strongly feel the paper needs to address this limitation. If the entire argument was that *if you run MANNs on fixed sequence length problems, this technique could be good* that would be quite different. The authors do cite a good variety of recent relevant work, although some papers which would be good baselines are cited but not actually compared against. In particular I feel Cached Uniform Writing (Le et al 2019) is doing something very similar to this, but without aspects of this paper that I feel are problematic (see below). Decision: reject. The paper is not clear or easy to read, and the underlying technique does not have obvious utility to me (see above about fixed length mask). The experiments on Add, MNIST and pMNIST do not sufficiently convince me that this is practically useful - the easiest things to add for a resubmission would be something like Penn Treebank, even if RNNs are no longer SOTA on that dataset it would still be good to see the uplift of this method compared to regular LSTMs. I suspect the fact that PTB is inherently an ongong sequence of characters instead of fixed size chunks (like MNIST etc) would mean this method no longer makes much sense. Discussion: The access to the external memory in section 3.2 is too concise - the DNC is cited but the description sounds like a cut-down DNC, without the link matrix or any kind of location based addressing, meaning the memory used is possibly even less capable than the original NTM. The section on theoretical advantages of SMANN is quite unclear. To take one example, *The network is better in memory capacity when the mean path length spanning all time steps is shorter* - I feel the memory capacity of a MANN is just how many scalars it can store (ie a function of memory size / word size, to use NTM terminology). It feels like the argument here is that without the SMANN variants, the memory capacity _you can learn to make use of with BPTT_ is better when the mean path length is shorter - but that*s a totally different point. A diagram would really help to illustrate the concepts of path length and the other forms introduced - in particular the notation is a bit strange. I assume this just means *evaluate the term inside the brackets in the context of an RNN* but this is not clearly explained. *network architecture should be refined so as to simulate human cognitive process more precisely* - this is highly debateable, it would be fine to try and argue this but the paper presents it as if it is a fact that all practitioners would agree on. Equation 1 - It wasn*t clear to me on first reading that this constrained output LSTM (differing in the equation for ) is one of the contributions of the paper - from reading the abstract it could be interpreted as the authors are using this existing model variant. Either way the description right at the end of section 2.1 which starts and ends with *thus we use a double tanh function ... assign weights on the rest ones* is very unclear to me. Skipping to section 4.1 which is advertised to contain insights about this variant - the section contains some results which are unclear (are you doing 5 runs *each* for regular LSTM and your variant? If so, it is not clear which dotted line corresponds to which model. If not, then are there different numbers of runs per model? It should be simple to just run 5 for each, and only plot 2 lines with error bars), but there is no insight as to _why_ this is sensible variation to the model. Ideas like initializing the forget gate bias in a certain way are backed up with explanations about letting gradients flow through time easier - why should constrained the output of an LSTM (by introducing more trainable variables & computation) be a good thing? If the results in figure 2 showed a huge gap between the models that could mean no explanation is needed, but on the right hand side of the graphs the difference between the bold lines is marginal. Minor points: Figure 1: I think it would be more consistent with other work to show external memory as containing rows, not columns. Do the red/green/purple colouring of the circular nodes have any meaning that could not already be gathered from the diagram? It*s not super obvious why when memory access is enabled, both the controller cores are red, but when it*s not enabled they are different colours. It might also make thngs clearer to have read/write arrows coming from the later timestep where access is enabled. Top of page 5: *Bengio et al. Bengio et al. (1994)* - as far as I can tell there is only one Bengio paper from 1994 in your references. *In the add task, the networks are feed with* -> *In the add task, the networks are fed with*