Original: A Review of Meta-Reinforcement Learning for Deep Neural Networks Architecture Search
Revised: A Review of Meta-Reinforcement Learning for Deep Neural Networks Architecture Search

Revised: Anonymous

Original: Abstract
Revised: Abstract

Original: Deep Neural networks are eﬃcient and ﬂexible models that perform well for a variety of tasks such as image, speech recognition and natural language understanding.
Revised: Deep Neural networks are eﬃcient and ﬂexible models that perform well for a variety of tasks such as image, speech recognition and natural language understanding.

Original: In particular, convolutional neural networks (CNN) generate a keen interest among researchers in computer vision and more speciﬁcally in classiﬁcation tasks.
Revised: In particular, convolutional neural networks (CNN) generate a keen interest among researchers in computer vision and more speciﬁcally in classiﬁcation tasks.

Original: CNN architecture and related hyperparameters are generally correlated to the nature of the processed task as the network extracts complex and relevant characteristics allowing the optimal convergence.
Revised: CNN architecture and related hyperparameters are generally correlated to the nature of the processed task as the network extracts complex and relevant characteristics allowing the optimal convergence.

Original: Designing such architectures requires signiﬁcant human expertise, substantial computation time and doesnt always lead to the optimal network.
Revised: Designing such architectures requires signiﬁcant human expertise, substantial computation time and doesn’t always lead to the optimal network.

Original: Model conﬁguration topic has been extensively studied in machine learning without leading to a standard automatic method.
Revised: Model conﬁguration topic has been extensively studied in machine learning without leading to a standard automatic method.

Original: This survey focuses on reviewing and discussing the current progress in automating CNN architecture search.
Revised: This survey focuses on reviewing and discussing the current progress in automating CNN architecture search.

Original: 1. Introduction
Revised: 1. Introduction

Original: ”A neuron is nothing more than a switch with information input and output.
Revised: ”A neuron is nothing more than a switch with information input and output.

Original: The switch will be activated if there are enough stimuli of other neurons hitting the information input.
Revised: The switch will be activated if there are enough stimuli of other neurons hitting the information input.

Original: Then, at the information output, a pulse is sent to, for example, other neurons ” [1].
Revised: Then, at the information output, a pulse is sent to, for example, other neurons ” [1].

Original: Brain-inspired machine learning imitates in a simpliﬁed manner the hierarchical operating mode Preprint submitted to Pattern Recognition December a r
Revised: Brain-inspired machine learning imitates in a simpliﬁed manner the hierarchical operating mode of biological neurons [2].

Original: X i v : .

Original: 07995v [ c s .

Original: L G ] D ec of biological neurons [2]. The concept of artiﬁcial neural networks (ANN) achieved a huge progress from its ﬁrst theoretical proposal in the 1950s until the recent considerable outcomes of deep learning.
Revised: The concept of artiﬁcial neural networks (ANN) achieved a huge progress from its ﬁrst theoretical proposal in the 1950s until the recent considerable outcomes of deep learning.

Original: In computer vision and more speciﬁcally in classiﬁcation tasks, CNN, which we will examine in this review, are among the most popular deep learning techniques since they are outperforming humans in some vision complex tasks [3].
Revised: In computer vision and more speciﬁcally in classiﬁcation tasks, CNN, which we will examine in this review, are among the most popular deep learning techniques since they are outperforming humans in some vision complex tasks [3].

Original: The origin of CNN that were initially established by [4] goes back to the 1950s with the advent of ”perceptron”, the ﬁrst neural network prototyped by Frank Rosenblatt.
Revised: The origin of CNN that were initially established by [4] goes back to the 1950s with the advent of ”perceptron”, the ﬁrst neural network prototyped by Frank Rosenblatt.

Original: However, neural network models were not extensively used until recently, after researchers overcame certain limits.
Revised: However, neural network models were not extensively used until recently, after researchers overcame certain limits.

Original: Among these advances we can mention the generalization of perceptrons to many layers [5], the emergence of backpropagation algorithm as an appropriate training method for such architectures [6] and, mainly, the availability of large training datasets and computational resources to learn millions of parameters.
Revised: Among these advances we can mention the generalization of perceptrons to many layers [5], the emergence of backpropagation algorithm as an appropriate training method for such architectures [6] and, mainly, the availability of large training datasets and computational resources to learn millions of parameters.

Original: CNN diﬀer from classical neural networks in the fact that the connectivity of a hidden layer neuron is limited to a subset of neurons in the previous layer.
Revised: CNN diﬀer from classical neural networks in the fact that the connectivity of a hidden layer neuron is limited to a subset of neurons in the previous layer.

Original: This selective connection endow the network with the ability to operate, implicitly, hierarchical features extraction.
Revised: This selective connection endow the network with the ability to operate, implicitly, hierarchical features extraction.

Original: For an image classiﬁcation case, the ﬁrst hidden layer can visualize edges, the second a speciﬁc shape and so on until the ﬁnal layer that will identify the object.
Revised: For an image classiﬁcation case, the ﬁrst hidden layer can visualize edges, the second a speciﬁc shape and so on until the ﬁnal layer that will identify the object.

Original: CNN architecture consists of several types of layers including convolution, pooling, and fully connected.
Revised: CNN architecture consists of several types of layers including convolution, pooling, and fully connected.

Original: The network expert has to make multiple choices while designing a CNN such as the number and ordering of layers, the hyperparameters for each type of layer (receptive ﬁeld size, stride, etc.).
Revised: The network expert has to make multiple choices while designing a CNN such as the number and ordering of layers, the hyperparameters for each type of layer (receptive ﬁeld size, stride, etc.).

Original: Thus, selecting the appropriate architecture and related hyperparameters requires a trial and error manual search process mainly directed by intuition and experience.
Revised: Thus, selecting the appropriate architecture and related hyperparameters requires a trial and error manual search process mainly directed by intuition and experience.

Original: Additionally, the number of available choices makes the selection space of CNN architectures extremely wide and impossible for an exhaustive manual exploration.
Revised: Additionally, the number of available choices makes the selection space of CNN architectures extremely wide and impossible for an exhaustive manual exploration.

Original: Many research eﬀort in meta-modeling tries to minimize human intervention in designing neural network architectures.
Revised: Many research eﬀort in meta-modeling tries to minimize human intervention in designing neural network architectures.

Original: In this paper, we ﬁrst give a general overview and deﬁne the ﬁeld of deep learning.
Revised: In this paper, we ﬁrst give a general overview and deﬁne the ﬁeld of deep learning.

Original: We then brieﬂy survey the history of CNN architectures.
Revised: We then brieﬂy survey the history of CNN architectures.

Original: In the following section we review several methods for automating CNN design according to three dimensions: search optimization, architecture design methods (plain or modular) and search acceleration techniques.
Revised: In the following section we review several methods for automating CNN design according to three dimensions: search optimization, architecture design methods (plain or modular) and search acceleration techniques.

Original: Finally, we conclude the article with a discussion of future works.
Revised: Finally, we conclude the article with a discussion of future works.

Original: 2. Background
Revised: 2. Background

Original: Before embarking with CNN, we will introduce in this section some basic generalities about artiﬁcial networks and deep learning.
Revised: Before embarking with CNN, we will introduce in this section some basic generalities about artiﬁcial networks and deep learning.

Original: 2.1. Artiﬁcial Neural Networks
Revised: 2.1. Artiﬁcial Neural Networks

Original: ANN are a major ﬁeld of artiﬁcial intelligence that attempts to replicate human brain processing.
Revised: ANN are a major ﬁeld of artiﬁcial intelligence that attempts to replicate human brain processing.

Original: Three types of neural layers distinguish an ANN: input, output and hidden layers.
Revised: Three types of neural layers distinguish an ANN: input, output and hidden layers.

Original: The latter operate transitional representations of the input data evolving from low level features (lines and edges) to higher ones (complex patterns) as far as deeper layers are reached.
Revised: The latter operate transitional representations of the input data evolving from low level features (lines and edges) to higher ones (complex patterns) as far as deeper layers are reached.

Original: Figureprovide an example of ANN involving classical fully-connected layers where every neuron is connected to all ones of the previous layer.
Revised: Figureprovide an example of ANN involving classical fully-connected layers where every neuron is connected to all ones of the previous layer.

Original: During training, an ANN aims at learning two types of parameters that will condition its predictive performance.
Revised: During training, an ANN aims at learning two types of parameters that will condition its predictive performance.

Original: First, connection weights that assess to which extent a neuron result will impact the output of higher level neuron.
Revised: First, connection weights that assess to which extent a neuron result will impact the output of higher level neuron.

Original: Second, the bias which is a global estimator of a feature presence across all inputs.
Revised: Second, the bias which is a global estimator of a feature presence across all inputs.

Original: Hence, a neuron output can be formalized through a linear combination of weighted inputs and associated bias:
Revised: Hence, a neuron output can be formalized through a linear combination of weighted inputs and associated bias:

Original: [Equation]
Revised: [Equation]

Original: In order to allow the network operating non-linear transformations, an activation function is applied to the previous output.
Revised: In order to allow the network operating non-linear transformations, an activation function is applied to the previous output.

Original: Equation 1 presents an example of such transformation using one of the most common and eﬃcient activation function which is the Rectiﬁed Linear Unit (ReLU) [7]:
Revised: Equation 1 presents an example of such transformation using one of the most common and eﬃcient activation function which is the Rectiﬁed Linear Unit (ReLU) [7]:

Original: [Equation]
Revised: [Equation]

Original: 2.2. Deep learning
Revised: 2.2. Deep learning

Original: The concept of deep learning refers to machine learning processing within multi-layer ANN [8].
Revised: The concept of deep learning refers to machine learning processing within multi-layer ANN [8].

Original: The training of these networks relies on a loss function evaluation.
Revised: The training of these networks relies on a loss function evaluation.

Original: For example, in supervised learning the loss is assimilated to the matching accuracy between ANN predictions and real expected outputs.
Revised: For example, in supervised learning the loss is assimilated to the matching accuracy between ANN predictions and real expected outputs.

Original: An iterative update procedure is implemented to adjust network parameters according to loss function computed gradient.
Revised: An iterative update procedure is implemented to adjust network parameters according to loss function computed gradient.

Original: This procedure is called Backpropagation since parameters updates are spread from ﬁnal layers to initial ones.
Revised: This procedure is called Backpropagation since parameters updates are spread from ﬁnal layers to initial ones.

Original: Deep learning implies a certain number of challenges such as vanishing/exploding gradient and overﬁtting.
Revised: Deep learning implies a certain number of challenges such as vanishing/exploding gradient and overﬁtting.

Original: The solutions to these problems will be discussed when developing CNN design architectures in next sections.
Revised: The solutions to these problems will be discussed when developing CNN design architectures in next sections.

Original: 3. CNN Layers
Revised: 3. CNN Layers

Original: CNN are widely used in a great number of pattern and image recognition problems.
Revised: CNN are widely used in a great number of pattern and image recognition problems.

Original: Three main characteristics are making this deep learning technique successful and suitable to visual data.
Revised: Three main characteristics are making this deep learning technique successful and suitable to visual data.

Original: First, local receptive ﬁelds perfectly reﬂect image data speciﬁcity to be correlated locally and uncorrelated in global segments.
Revised: First, local receptive ﬁelds perfectly reﬂect image data speciﬁcity to be correlated locally and uncorrelated in global segments.

Original: Second, shared weights allows a substantial parameter reduction without altering image processing since the convolution is applicable to the whole image.
Revised: Second, shared weights allows a substantial parameter reduction without altering image processing since the convolution is applicable to the whole image.

Original: Last, grid-structured image enable pooling operations that simplify data without losing useful information [9].
Revised: Last, grid-structured image enable pooling operations that simplify data without losing useful information [9].

Original: 3.1. Convolutional Layer
Revised: 3.1. Convolutional Layer

Original: The convolutional layer is the basic CNN unit that has been inspired by physiological research evidence of hierarchical processing in the visual cortex of mammals [10].
Revised: The convolutional layer is the basic CNN unit that has been inspired by physiological research evidence of hierarchical processing in the visual cortex of mammals [10].

Original: Simple cells detect primitive attributes while more compound structures are subsequently extracted by complex cells.
Revised: Simple cells detect primitive attributes while more compound structures are subsequently extracted by complex cells.

Original: Thus, convolutional layer consists of a set of feature maps issued from convolving diﬀerent ﬁlters (kernels) with an input image or previous layer output [11].
Revised: Thus, convolutional layer consists of a set of feature maps issued from convolving diﬀerent ﬁlters (kernels) with an input image or previous layer output [11].

Original: The 2-dimensional maps are stacked together to produce the resulting volume of the convolutional layer.
Revised: The 2-dimensional maps are stacked together to produce the resulting volume of the convolutional layer.

Original: This process reduces drastically the network complexity since the neurons of a same feature map share the same weights and bias maintaining a low number of parameters to learn [12].
Revised: This process reduces drastically the network complexity since the neurons of a same feature map share the same weights and bias maintaining a low number of parameters to learn [12].

Original: The hyperparameters characterizing a convolutional layer are the depth
Revised: The hyperparameters characterizing a convolutional layer are the depth

Original: F (number of ﬁlters), the stride S (ﬁlter movement from a receptive ﬁeld to the next one) and the zero padding P to control input size [13].
Revised: F (number of ﬁlters), the stride S (ﬁlter movement from a receptive ﬁeld to the next one) and the zero padding P to control input size [13].

Original: Assuming that the ﬁlter size ( height, width, depth )
Revised: Assuming that the ﬁlter size ( height, width, depth )

Original: =
Revised: =

Original: ( h, w, D ), the dimensions of the feature maps generated can be obtained according to:
Revised: ( h, w, D ), the dimensions of the feature maps generated can be obtained according to:

Original: [Equation]
Revised: [Equation]

Original: Where ( H, W, D ) is the size (height, width, depth) of the input image.
Revised: Where ( H, W, D ) is the size (height, width, depth) of the input image.

Original: 3.2. Pooling Layer
Revised: 3.2. Pooling Layer

Original: CNN architectures generally alternate convolution and pooling layers.
Revised: CNN architectures generally alternate convolution and pooling layers.

Original: The latter have the purpose of reducing network complexity and avoid the problem of overﬁtting.
Revised: The latter have the purpose of reducing network complexity and avoid the problem of overﬁtting.

Original: At biological level, pooling is assimilated to the behavior of cortical complex cells that reveal a certain degree of position invariance.
Revised: At biological level, pooling is assimilated to the behavior of cortical complex cells that reveal a certain degree of position invariance.

Original: A pooling layer neuron is connected to a region of the previous layer by performing a non-parameterized function.
Revised: A pooling layer neuron is connected to a region of the previous layer by performing a non-parameterized function.

Original: Thus it diﬀers from convolution as it doesn’t have learnable weights or bias and additionally, it keeps the same depth of the previous layer.
Revised: Thus it diﬀers from convolution as it doesn’t have learnable weights or bias and additionally, it keeps the same depth of the previous layer.

Original: Max pooling [14] is one of the most common type of pooling that consists in retaining the maximum value of a neurons cluster.
Revised: Max pooling [14] is one of the most common type of pooling that consists in retaining the maximum value of a neurons cluster.

Original: It means that max pooling is detecting if a given feature has been identiﬁed in a receptive ﬁeld without recording the exact location [9].
Revised: It means that max pooling is detecting if a given feature has been identiﬁed in a receptive ﬁeld without recording the exact location [9].

Original: 3.3. Fully connected Layer
Revised: 3.3. Fully connected Layer

Original: The convolution layers identify local features in the input data such as edges and shapes.
Revised: The convolution layers identify local features in the input data such as edges and shapes.

Original: The Fully connected layer operates the high level reasoning (classiﬁcation for image case) by combining information from all the previous layers.
Revised: The Fully connected layer operates the high level reasoning (classiﬁcation for image case) by combining information from all the previous layers.

Original: As in a regular ANN, neurons at this level are fully connected to all ones in the previous layer.
Revised: As in a regular ANN, neurons at this level are fully connected to all ones in the previous layer.

Original: A softmax loss layer is then used to compute the probability distribution of the CNN ﬁnal outputs.
Revised: A softmax loss layer is then used to compute the probability distribution of the CNN ﬁnal outputs.

Original: 4. CNN Architecture History
Revised: 4. CNN Architecture History

Original: This section presents the most inﬂuential hand-crafted CNN architectures that have impacted the recent work on automatic architecture design.
Revised: This section presents the most inﬂuential hand-crafted CNN architectures that have impacted the recent work on automatic architecture design.

Original: Most of them won at least one of the ”ImageNet Large Scale Visual Recognition
Revised: Most of them won at least one of the ”ImageNet Large Scale Visual Recognition

Original: Competition” (ILSVRC) challenges [3].
Revised: Competition” (ILSVRC) challenges [3].

Original: 4.1. LeNet
Revised: 4.1. LeNet

Original: As mentioned previously, LeNet [7] was the innovative work that introduced convolutional networks.
Revised: As mentioned previously, LeNet [7] was the innovative work that introduced convolutional networks.

Original: The model was experimented successfully to classify handwritten digits without any preprocessing of the input image (of size 32 ∗ 32 pixels).
Revised: The model was experimented successfully to classify handwritten digits without any preprocessing of the input image (of size 32 ∗ 32 pixels).

Original: LeNet architecture is illustrated in ﬁgure 2.
Revised: LeNet architecture is illustrated in ﬁgure 2.

Original: It consists of an input and an output layers of respective sizes 32 ∗ 32 and 10 as well as 6 hidden Layers.
Revised: It consists of an input and an output layers of respective sizes 32 ∗ 32 and 10 as well as 6 hidden Layers.

Original: The basic idea of this design is to operate multiple convolutions (3) with pooling in-between (2) then transmitting the ﬁnal signal via a fully-connected layer toward the output layer.
Revised: The basic idea of this design is to operate multiple convolutions (3) with pooling in-between (2) then transmitting the ﬁnal signal via a fully-connected layer toward the output layer.

Original: Unfortunately, due to the lack of adequate training data and computing power, it wasn’t possible to extend this architecture to more complex applications.
Revised: Unfortunately, due to the lack of adequate training data and computing power, it wasn’t possible to extend this architecture to more complex applications.

Original: 4.2. AlexNet
Revised: 4.2. AlexNet

Original: AlexNet [11] is one of the most inﬂuential deep CNN that won the
Revised: AlexNet [11] is one of the most inﬂuential deep CNN that won the

Original: ILSVRC (Imagenet Large Scale Visual Recognition Challenge) competitions in 2012.
Revised: ILSVRC (Imagenet Large Scale Visual Recognition Challenge) competitions in 2012.

Original: As shown in ﬁgure 3, it is not much diﬀerent from LeNet.
Revised: As shown in ﬁgure 3, it is not much diﬀerent from LeNet.

Original: Nevertheless, the corresponding architecture is deeper with 8 layers in total, 5 convolutional and 3 fully connected.
Revised: Nevertheless, the corresponding architecture is deeper with 8 layers in total, 5 convolutional and 3 fully connected.

Original: The eﬀective contribution of AlexNet lies in several design and training speciﬁcities.
Revised: The eﬀective contribution of AlexNet lies in several design and training speciﬁcities.

Original: First, it introduced the Rectiﬁed
Revised: First, it introduced the Rectiﬁed

Original: Linear Unit (ReLU) nonlinearity which helped to overcome the problem of vanishing gradient and boosted a faster training.
Revised: Linear Unit (ReLU) nonlinearity which helped to overcome the problem of vanishing gradient and boosted a faster training.

Original: Furthermore, AlexNet implements a dropout step [15] that consists in setting to zero a predeﬁned percentage of layers’ parameters.
Revised: Furthermore, AlexNet implements a dropout step [15] that consists in setting to zero a predeﬁned percentage of layers’ parameters.

Original: This technique decreases learned parameters and controls neurons correlation in order to limit overﬁtting impact.
Revised: This technique decreases learned parameters and controls neurons correlation in order to limit overﬁtting impact.

Original: Third, training process convergence is accelerated with momentum and conditional learning rate decrease (e.g. when learning stagnates).
Revised: Third, training process convergence is accelerated with momentum and conditional learning rate decrease (e.g. when learning stagnates).

Original: Finally, training data volume is increased artiﬁcially by generating variations of the original images that are shifted randomly.
Revised: Finally, training data volume is increased artiﬁcially by generating variations of the original images that are shifted randomly.

Original: Thus the network learning is enhanced with the use of invariant representations of the data.
Revised: Thus the network learning is enhanced with the use of invariant representations of the data.

Original: 4.3. VGGNet
Revised: 4.3. VGGNet

Original: Submitted for the ILSVRC 2014, VGGNet [16] won the second place and demonstrated that deeper architectures achieve better results.
Revised: Submitted for the ILSVRC 2014, VGGNet [16] won the second place and demonstrated that deeper architectures achieve better results.

Original: Indeed, with its 19 hidden layers, it was much deeper than previous convolutional networks.
Revised: Indeed, with its 19 hidden layers, it was much deeper than previous convolutional networks.

Original: In order to allow an increase in depth without an exponential growth of the parameters number, smaller convolution ﬁlters (3 ∗ 3) were used in all layers (e.g. lower size than the 11 ∗ 11 ﬁlters adopted in AlexNet).
Revised: In order to allow an increase in depth without an exponential growth of the parameters number, smaller convolution ﬁlters (3 ∗ 3) were used in all layers (e.g. lower size than the 11 ∗ 11 ﬁlters adopted in AlexNet).

Original: An additional advantage of using smaller ﬁlters consists in reducing overlapping scanned pixels which results in feature maps with more local details [17].
Revised: An additional advantage of using smaller ﬁlters consists in reducing overlapping scanned pixels which results in feature maps with more local details [17].

Original: 4.4. GoogLeNet
Revised: 4.4. GoogLeNet

Original: Since it has been demonstrated that a CNN architecture size is positively correlated to its performance, recent eﬀorts focus on how to increase the depth of a CNN while keeping an acceptable number of parameters.
Revised: Since it has been demonstrated that a CNN architecture size is positively correlated to its performance, recent eﬀorts focus on how to increase the depth of a CNN while keeping an acceptable number of parameters.

Original: Winner of ILSVRC 2014, GoogLeNet [18] innovated network design by replacing the classical strategy of alternating convolutional and pooling layers with stacked
Revised: Winner of ILSVRC 2014, GoogLeNet [18] innovated network design by replacing the classical strategy of alternating convolutional and pooling layers with stacked

Original: Inception Modules depicted in ﬁgure 4.
Revised: Inception Modules depicted in ﬁgure 4.

Original: Despite being deeper than VGGNet with 22 hidden layers, GoogLeNet requires outstandingly fewer parameters due to this sparse connection technique.
Revised: Despite being deeper than VGGNet with 22 hidden layers, GoogLeNet requires outstandingly fewer parameters due to this sparse connection technique.

Original: Within an inception module, several convolutions with diﬀerent scales and pooling are performed in parallel then concatenated in one single layer.
Revised: Within an inception module, several convolutions with diﬀerent scales and pooling are performed in parallel then concatenated in one single layer.

Original: This enables the CNN to detect patterns of various sizes within the same layer and avoid heavy parameters redundancies [18].
Revised: This enables the CNN to detect patterns of various sizes within the same layer and avoid heavy parameters redundancies [18].

Original: GoogLeNet hidden layers consist of 3 convolutions, 9 inception blocks (2 layers deep each one) and one fully connected.
Revised: GoogLeNet hidden layers consist of 3 convolutions, 9 inception blocks (2 layers deep each one) and one fully connected.

Original: 4.5. ResNet
Revised: 4.5. ResNet

Original: Deep Residual Network (ResNet) [19], was the ﬁrst neural network to exceed human-level accuracy in ImageNet Challenge (ILSVRC 2015).
Revised: Deep Residual Network (ResNet) [19], was the ﬁrst neural network to exceed human-level accuracy in ImageNet Challenge (ILSVRC 2015).

Original: Thanks to residual connections, such kind of architecture went deeper and was implemented with multiple versions of 34, 50, 101 and 152 layers.
Revised: Thanks to residual connections, such kind of architecture went deeper and was implemented with multiple versions of 34, 50, 101 and 152 layers.

Original: Indeed, one of the diﬃculties with very deep networks training is the vanishing gradient during error backpropagation which penalizes the appropriate update of earlier layers weights.
Revised: Indeed, one of the diﬃculties with very deep networks training is the vanishing gradient during error backpropagation which penalizes the appropriate update of earlier layers weights.

Original: ResNet main contribution consists in dividing convolutional layers into residual blocks.
Revised: ResNet main contribution consists in dividing convolutional layers into residual blocks.

Original: Each block is bypassed by a residual (skip) connection that forwards the block input using an identity mapping.
Revised: Each block is bypassed by a residual (skip) connection that forwards the block input using an identity mapping.

Original: The ﬁnal output is the summation of the block output and the mapped input as illustrated in ﬁgure 5.
Revised: The ﬁnal output is the summation of the block output and the mapped input as illustrated in ﬁgure 5.

Original: By adding skip connections, backpropagation can be operated without any interference with previous layers which allows to prevent vanishing gradient and train very deep architectures.
Revised: By adding skip connections, backpropagation can be operated without any interference with previous layers which allows to prevent vanishing gradient and train very deep architectures.

Original: ResNet-101 consists of one convolutional layer followed by 33 residual blocks (3 layers deep each one), and one fully connected layer.
Revised: ResNet-101 consists of one convolutional layer followed by 33 residual blocks (3 layers deep each one), and one fully connected layer.

Original: 4.6. More Networks
Revised: More Networks

Original: After ResNet [19] success, which exceeded human-level accuracy in (ILSVRC 2015), the so-called modern hand-crafted CNN are still being designed on the basis of previous models looking for more eﬃciency and lower training time.
Revised: After ResNet [19] success, which exceeded human-level accuracy in (ILSVRC 2015), the so-called modern hand-crafted CNN are still being designed on the basis of previous models looking for more eﬃciency and lower training time.

Original: Inception-v4 [20] is a new release of GoogLeNet that involved many more layers than the initial version.
Revised: Inception-v4 [20] is a new release of GoogLeNet that involved many more layers than the initial version.

Original: Inception-ResNet [20] is built as a combination of an Inception network and a ResNet, joining inception blocks and residual connections.
Revised: Inception-ResNet [20] is built as a combination of an Inception network and a ResNet, joining inception blocks and residual connections.

Original: The last example of this section is DenseNet (Dense
Revised: The last example of this section is DenseNet (Dense

Original: Convolutional Networks)
Revised: Convolutional Networks)

Original: [21] where each dense block layer is connected via skip connections to all subsequent ones allowing the learning of new features.
Revised: [21] where each dense block layer is connected via skip connections to all subsequent ones allowing the learning of new features.

Original: 5. Meta-modeling for CNN automatic architecture design
Revised: 5. Meta-modeling for CNN automatic architecture design

Original: Meta-modeling for neural network architectures design aims at reducing the intervention of human expertise in this process.
Revised: Meta-modeling for neural network architectures design aims at reducing the intervention of human expertise in this process.

Original: The earliest metamodeling methods were based on genetic algorithms and Bayesian optimization then more recently, reinforcement learning became among the most implemented approaches [22].
Revised: The earliest metamodeling methods were based on genetic algorithms and Bayesian optimization then more recently, reinforcement learning became among the most implemented approaches [22].

Original: 5.1. Context of automation
Revised: 5.1. Context of automation

Original: The performance of a neural network and particularly a CNN mainly depends on the setting of the model structure, the training process, and the data representation.
Revised: The performance of a neural network and particularly a CNN mainly depends on the setting of the model structure, the training process, and the data representation.

Original: All of these variables are controlled through a number of hyperparameters and impact the learning process to a large extent.
Revised: All of these variables are controlled through a number of hyperparameters and impact the learning process to a large extent.

Original: In order to achieve an optimal performance of CNN, these hyperparameters including the depth of the network, learning rates, layer type, number of units per layer, dropout rates, etc., should be then carefully tuned.
Revised: In order to achieve an optimal performance of CNN, these hyperparameters including the depth of the network, learning rates, layer type, number of units per layer, dropout rates, etc., should be then carefully tuned.

Original: On the other hand, the advent of deeper and more complex modern architectures (see section 4) is increasing the number and the types of hyperparameters.
Revised: On the other hand, the advent of deeper and more complex modern architectures (see section 4) is increasing the number and the types of hyperparameters.

Original: Hence, tuning step and more generally CNN architectures search become very expensive and heavy for an expert trial-and-error procedure.
Revised: Hence, tuning step and more generally CNN architectures search become very expensive and heavy for an expert trial-and-error procedure.

Original: Additionally, CNN parameters setting is considered as a black-box [23] optimization problem because of the unknown nature of the mapping between the architecture, the performance, and the learning task.
Revised: Additionally, CNN parameters setting is considered as a black-box [23] optimization problem because of the unknown nature of the mapping between the architecture, the performance, and the learning task.

Original: In this context, automatic design solutions are highly required and instigates a large volume of research.
Revised: In this context, automatic design solutions are highly required and instigates a large volume of research.

Original: The task of CNN hyperparameters tuning has been handled through meta-modeling that consists in applying machine learning models for designing CNN architectures.
Revised: The task of CNN hyperparameters tuning has been handled through meta-modeling that consists in applying machine learning models for designing CNN architectures.

Original: Three meta-modeling approaches are generally used in the literature of architecture search and will be described in the next paragraph: bayesian optimization, evolutionary algorithms and reinforcement learning.
Revised: Three meta-modeling approaches are generally used in the literature of architecture search and will be described in the next paragraph: bayesian optimization, evolutionary algorithms and reinforcement learning.

Original: 5.2. Meta-controllers
Revised: 5.2. Meta-controllers

Original: Meta-modeling approaches perform iterative selection from the hyperparameters space and build associated architectures that are then trained and evaluated.
Revised: Meta-modeling approaches perform iterative selection from the hyperparameters space and build associated architectures that are then trained and evaluated.

Original: Accuracies records are fed to meta-modeling controllers (metacontrollers) to guide next architectures sampling.
Revised: Accuracies records are fed to meta-modeling controllers (metacontrollers) to guide next architectures sampling.

Original: Meta-controllers for CNN design are mainly based on bayesian optimization ([24], [25]), evolutionary algorithms ([26], [27]) or more recently on reinforcement learning ([28], [29]).
Revised: Meta-controllers for CNN design are mainly based on bayesian optimization ([24], [25]), evolutionary algorithms ([26], [27]) or more recently on reinforcement learning ([28], [29]).

Original: Bayesian optimization is an eﬃcient way to optimize black-box objective functions f : X → R that are slow to evaluate [30].
Revised: Bayesian optimization is an eﬃcient way to optimize black-box objective functions f : X → R that are slow to evaluate [30].

Original: It aims at ﬁnding an input x = arg min x ∈ X f ( x ) that globally minimizes f where in the context of a machine learning algorithm, x refers to the set of hyperparameters to optimize.
Revised: It aims at ﬁnding an input x = arg min x ∈ X f ( x ) that globally minimizes f where in the context of a machine learning algorithm, x refers to the set of hyperparameters to optimize.

Original: The problem with this kind of optimization is that evaluating the objective function is very costly due to the great number of hyperparameters and the complex nature of models like deep neural networks.
Revised: The problem with this kind of optimization is that evaluating the objective function is very costly due to the great number of hyperparameters and the complex nature of models like deep neural networks.

Original: In order to overcome this problem, bayesian approaches propose probabilistic surrogate reconstruction of the objective function p ( f | D ) where D is a set of past observations.
Revised: In order to overcome this problem, bayesian approaches propose probabilistic surrogate reconstruction of the objective function p ( f | D ) where D is a set of past observations.

Original: The evaluation of the empirical function is much cheaper than the true objective function [31].
Revised: The evaluation of the empirical function is much cheaper than the true objective function [31].

Original: Some of the most used probabilistic surrogate (regression) models are gaussian processes [32], random forests [33] and treestructured Parzen estimator [24].
Revised: Some of the most used probabilistic surrogate (regression) models are gaussian processes [32], random forests [33] and treestructured Parzen estimator [24].

Original: Brieﬂy, the processing of a bayesian optimization consists in building an empirical (probabilistic) model of the objective function.
Revised: Brieﬂy, the processing of a bayesian optimization consists in building an empirical (probabilistic) model of the objective function.

Original: Then, iteratively, the model identiﬁes a set of optimal hyperparameters for which the objective function returns corresponding results (e.g. loss values).
Revised: Then, iteratively, the model identiﬁes a set of optimal hyperparameters for which the objective function returns corresponding results (e.g. loss values).

Original: Each feedback allows the update of the surrogate model and the guidance of hyperparameters predictions until the process reaches a termination condition.
Revised: Each feedback allows the update of the surrogate model and the guidance of hyperparameters predictions until the process reaches a termination condition.

Original: Evolutionary algorithms present another strategy of hyperparameters optimization that modiﬁes a set of candidate solutions (population) on the basis of a number of rules (operators).
Revised: Evolutionary algorithms present another strategy of hyperparameters optimization that modiﬁes a set of candidate solutions (population) on the basis of a number of rules (operators).

Original: Following an iterative procedure of mutation, crossover and selection [34], an evolutionary algorithm initializes, in a ﬁrst step, a set of N random networks to create a primary population.
Revised: Following an iterative procedure of mutation, crossover and selection [34], an evolutionary algorithm initializes, in a ﬁrst step, a set of N random networks to create a primary population.

Original: The second step consists in introducing a ﬁtness function to score each network through its classiﬁcation accuracy and keep the top ranked networks to construct the next generation.
Revised: The second step consists in introducing a ﬁtness function to score each network through its classiﬁcation accuracy and keep the top ranked networks to construct the next generation.

Original: The evolutionary process continues until a termination criteria is met, which is generally deﬁned as the maximum number of allowed generations.
Revised: The evolutionary process continues until a termination criteria is met, which is generally deﬁned as the maximum number of allowed generations.

Original: One of the advantage of evolutionary algorithms is the adaptation to complex combination of discrete (layer type) and continuous (learning rate) hyperparameters which is suitable to neuronal network optimization models [35].
Revised: One of the advantage of evolutionary algorithms is the adaptation to complex combination of discrete (layer type) and continuous (learning rate) hyperparameters which is suitable to neuronal network optimization models [35].

Original: An important approach for goal-oriented optimization is reinforcement learning (RL) inspired from behaviorist psychology [36].
Revised: An important approach for goal-oriented optimization is reinforcement learning (RL) inspired from behaviorist psychology [36].

Original: The frame of RL is an agent learning through interaction with its environment (ﬁgure 6).
Revised: The frame of RL is an agent learning through interaction with its environment (ﬁgure 6).

Original: Thus the agent adapts its behavior (transition to a state s t +1 ) on the basis of observed consequences (rewards) of an action a t taken in state s t .
Revised: Thus the agent adapts its behavior (transition to a state s t +1 ) on the basis of observed consequences (rewards) of an action a t taken in state s t .

Original: The agent purpose is to learn a policy π that is able to identify the optimal sequence of actions maximizing the expected cumulative rewards.
Revised: The agent purpose is to learn a policy π that is able to identify the optimal sequence of actions maximizing the expected cumulative rewards.

Original: The environment return reinforces the agent to select new actions to improve learning process, hence the name of reinforcement learning.
Revised: The environment return reinforces the agent to select new actions to improve learning process, hence the name of reinforcement learning.

Original: The methods developed to resolve reinforcement tasks are based on value functions, policy search or a combination of both strategies (actor-critic methods) [37].
Revised: The methods developed to resolve reinforcement tasks are based on value functions, policy search or a combination of both strategies (actor-critic methods) [37].

Original: Value function methods consist in estimating the expected reward value R when reaching a given state s and following a policy π :
Revised: Value function methods consist in estimating the expected reward value R when reaching a given state s and following a policy π :

Original: [Equation]
Revised: [Equation]

Original: A recursive form of this function is particularly used in recent Q-learning [38] models assigned to CNN architecture design ([39], [40]):
Revised: A recursive form of this function is particularly used in recent Q-learning [38] models assigned to CNN architecture design ([39], [40]):

Original: [Equation]
Revised: [Equation]

Original: Where s t is a current state, a t is a current action, α is the learning rate, r t +1 is the reward earned when transitioning from time t to the next and γ is the discount rate.
Revised: Where s t is a current state, a t is a current action, α is the learning rate, r t +1 is the reward earned when transitioning from time t to the next and γ is the discount rate.

Original: In contrast to value function methods, policy search methods do not implement a value function and apply, instead, a gradient-based procedure to identify directly an optimal policy π ∗ .
Revised: In contrast to value function methods, policy search methods do not implement a value function and apply, instead, a gradient-based procedure to identify directly an optimal policy π ∗ .

Original: In this context, deep reinforcement learning is achieved when deep neural networks are used to approximate one of the reinforcement learning components : value function, policy or reward function [41].
Revised: In this context, deep reinforcement learning is achieved when deep neural networks are used to approximate one of the reinforcement learning components : value function, policy or reward function [41].

Original: Among the active ﬁelds of designing CNN architectures through deep reinforcement learning, recurrent neural networks (RNN) arise as a valuable model that handles a set of tasks such as hyperparameters prediction ([28], [29]).
Revised: Among the active ﬁelds of designing CNN architectures through deep reinforcement learning, recurrent neural networks (RNN) arise as a valuable model that handles a set of tasks such as hyperparameters prediction ([28], [29]).

Original: In fact, a RNN operates sequentially involving hidden units to store processing history, which allows the reinforcement learning to proﬁt from past observations.
Revised: In fact, a RNN operates sequentially involving hidden units to store processing history, which allows the reinforcement learning to proﬁt from past observations.

Original: Long short term memory networks (LSTM), a variant of
Revised: Long short term memory networks (LSTM), a variant of

Original: RNN, oﬀers a more eﬃcient way of evolving conditionally on the basis of previous elements.
Revised: RNN, oﬀers a more eﬃcient way of evolving conditionally on the basis of previous elements.

Original: 6. Neural Architecture Search
Revised: 6. Neural Architecture Search

Original: Various strategies have been developed to operate CNN architectures design for the majority of which reinforcement learning has been selected as meta-controller.
Revised: Various strategies have been developed to operate CNN architectures design for the majority of which reinforcement learning has been selected as meta-controller.

Original: This section is assigned to review in detail most recent promising automatic search approaches diﬀerentiated according to search spaces speciﬁcities and complexity level.
Revised: This section is assigned to review in detail most recent promising automatic search approaches diﬀerentiated according to search spaces speciﬁcities and complexity level.

Original: 6.1. Plain Architecture Design
Revised: 6.1. Plain Architecture Design

Original: Some architecture search approaches focus on designing plain CNN which consists exclusively of conventional layers, mainly convolution, pooling and fully-connected.
Revised: Some architecture search approaches focus on designing plain CNN which consists exclusively of conventional layers, mainly convolution, pooling and fully-connected.

Original: The resulting research space is relatively simple and the approaches contribution lies almost entirely in the design strategy.
Revised: The resulting research space is relatively simple and the approaches contribution lies almost entirely in the design strategy.

Original: 6.1.1. MetaQNN
Revised: 6.1.1. MetaQNN

Original: MetaQNN model [39] relies on Q-learning, a type of reinforcement learning (refer to previous section for more details), to sequentially select network layers and their parameters among a ﬁnite space.
Revised: MetaQNN model [39] relies on Q-learning, a type of reinforcement learning (refer to previous section for more details), to sequentially select network layers and their parameters among a ﬁnite space.

Original: This method implies, ﬁrst, the deﬁnition of each learning agent state as a layer with all associated relevant parameters.
Revised: This method implies, ﬁrst, the deﬁnition of each learning agent state as a layer with all associated relevant parameters.

Original: As an example, 5 layers are depicted in ﬁgure 7: convolution (C), pooling (P), fully connected (FC), global average pooling (GAP), and softmax (SM).
Revised: As an example, 5 layers are depicted in ﬁgure 7: convolution (C), pooling (P), fully connected (FC), global average pooling (GAP), and softmax (SM).

Original: Second, the agent action space is assimilated to the possible layers the agent may move to given a certain number of constraints set intentionally, for the majority, to enable faster convergence.
Revised: Second, the agent action space is assimilated to the possible layers the agent may move to given a certain number of constraints set intentionally, for the majority, to enable faster convergence.

Original: Figure 8 illustrates a set of state and action spaces and an eventual agent path to design a CNN architecture.
Revised: Figure 8 illustrates a set of state and action spaces and an eventual agent path to design a CNN architecture.

Original: MetaQNN was evaluated competitive with similar and diﬀerent hand-crafted
Revised: MetaQNN was evaluated competitive with similar and diﬀerent hand-crafted

Original: CNN architectures as with existing automated network design methods.
Revised: CNN architectures as with existing automated network design methods.

Original: 6.1.2. NAS
Revised: 6.1.2. NAS

Original: Using reinforcement learning, [28] train a recurrent neural network to generate convolutional architectures.
Revised: Using reinforcement learning, [28] train a recurrent neural network to generate convolutional architectures.

Original: Figure 9 shows a RNN controller generating sequentially CNN parameters associated to convolutional layers.
Revised: Figure 9 shows a RNN controller generating sequentially CNN parameters associated to convolutional layers.

Original: Every sequence output is predicted by a softmax classiﬁer then used as input of the next sequence.
Revised: Every sequence output is predicted by a softmax classiﬁer then used as input of the next sequence.

Original: The parameters set consists of ﬁlter height and width, stride height and width and the number of ﬁlters per layer.
Revised: The parameters set consists of ﬁlter height and width, stride height and width and the number of ﬁlters per layer.

Original: The design of an architecture takes an end once the number of layers reaches a predeﬁned value that increases all along training.
Revised: The design of an architecture takes an end once the number of layers reaches a predeﬁned value that increases all along training.

Original: The accuracy of the designed architecture is fed as a reward to train the RNN controller through reinforcement learning in order to maximize the expected validation accuracy of the next architectures.
Revised: The accuracy of the designed architecture is fed as a reward to train the RNN controller through reinforcement learning in order to maximize the expected validation accuracy of the next architectures.

Original: The experimentation of the global approach achieved competitive results on
Revised: The experimentation of the global approach achieved competitive results on

Original: CIFAR-10 and Penn Treebank datasets.
Revised: CIFAR-10 and Penn Treebank datasets.

Original: 6.1.3. EAS
Revised: 6.1.3. EAS

Original: In their very recent work Eﬃcient Architecture Search, [42] implement network transformation techniques that allow reusing pre-existing models and eﬃciently exploring search space for automatic architecture design.
Revised: In their very recent work Eﬃcient Architecture Search, [42] implement network transformation techniques that allow reusing pre-existing models and eﬃciently exploring search space for automatic architecture design.

Original: This novel approach diﬀers from the previous ones in the deﬁnition of reinforcement learning states and actions.
Revised: This novel approach diﬀers from the previous ones in the deﬁnition of reinforcement learning states and actions.

Original: The state is the current network architecture while the action involves network transformation operations such as adding, enlarging and deleting layers.
Revised: The state is the current network architecture while the action involves network transformation operations such as adding, enlarging and deleting layers.

Original: Starting point architectures used in experiments are plain CNN which only consist of convolutional, fully-connected and pooling layers.
Revised: Starting point architectures used in experiments are plain CNN which only consist of convolutional, fully-connected and pooling layers.

Original: EAS approach is inspired from Net2Net technique introduced in [43] and based on the idea of building deeper student network to reproduce the same processing of an associated teacher network.
Revised: EAS approach is inspired from Net2Net technique introduced in [43] and based on the idea of building deeper student network to reproduce the same processing of an associated teacher network.

Original: As shown in ﬁgure 10, an encoder network implemented with bidirectional recurrent neural network [44] feeds actors network with given architectures.
Revised: As shown in ﬁgure 10, an encoder network implemented with bidirectional recurrent neural network [44] feeds actors network with given architectures.

Original: The selected actor networks performs 2 types of transformation: widening layers in terms of units and ﬁlters and inserting new layers.
Revised: The selected actor networks performs 2 types of transformation: widening layers in terms of units and ﬁlters and inserting new layers.

Original: EAS outperforms similar state-of-the-art models designed either manually or automatically with the attractive advantage of using relatively much smaller computational resources.
Revised: EAS outperforms similar state-of-the-art models designed either manually or automatically with the attractive advantage of using relatively much smaller computational resources.

Original: 6.2. Modular Architecture Design
Revised: 6.2. Modular Architecture Design

Original: Most of recent work on neural architecture search is based on more complex modular (multi-branch) structures inspired by modern architectures presented in section 4.
Revised: Most of recent work on neural architecture search is based on more complex modular (multi-branch) structures inspired by modern architectures presented in section 4.

Original: Rather than operating the tedious search over entire networks, this second set of approaches focus on ﬁnding building blocks similarly to the ones used in, e.g. GoogLeNet and ResNet models.
Revised: Rather than operating the tedious search over entire networks, this second set of approaches focus on ﬁnding building blocks similarly to the ones used in, e.g. GoogLeNet and ResNet models.

Original: These multibranch elements are then stacked repetitively involving skip connections to build the ﬁnal deep architecture.
Revised: These multibranch elements are then stacked repetitively involving skip connections to build the ﬁnal deep architecture.

Original: As we will see through the models detailed in this section, ”block-wise” architecture design reduces drastically search space speeding up search process, enhances generated networks performance and gives them more transferable ability through minor adaptation.
Revised: As we will see through the models detailed in this section, ”block-wise” architecture design reduces drastically search space speeding up search process, enhances generated networks performance and gives them more transferable ability through minor adaptation.

Original: 6.2.1. BlockQNN
Revised: 6.2.1. BlockQNN

Original: One of the ﬁrst approaches implementing block-wise architecture search,
Revised: One of the ﬁrst approaches implementing block-wise architecture search,

Original: BlockQNN [40] automatically builds convolutional neworks using Q-Learning reinforcement technique [45] with epsilon-greedy as exploration strategy [46].
Revised: BlockQNN [40] automatically builds convolutional neworks using Q-Learning reinforcement technique [45] with epsilon-greedy as exploration strategy [46].

Original: The block structure is similar to ResNet and Inception (GoogLeNet) modern networks since it contains shortcut connections and multi-branch layer combinations.
Revised: The block structure is similar to ResNet and Inception (GoogLeNet) modern networks since it contains shortcut connections and multi-branch layer combinations.

Original: The search space of the approach is reduced given that the focus is switched to explore network blocks rather than designing the entire network.
Revised: The search space of the approach is reduced given that the focus is switched to explore network blocks rather than designing the entire network.

Original: The block search space is detailed in ﬁgure 11 and consists of 5 parameters: a layer index (its position in the block), an operation type (selected amongtypes commonly used), a kernel size and 2 predecessors layers indexes.
Revised: The block search space is detailed in ﬁgure 11 and consists of 5 parameters: a layer index (its position in the block), an operation type (selected amongtypes commonly used), a kernel size and 2 predecessors layers indexes.

Original: Figure 12 depicts 2 diﬀerent samples of blocks, one with multi-branch structure and the second showing a skip connection.
Revised: Figure 12 depicts 2 diﬀerent samples of blocks, one with multi-branch structure and the second showing a skip connection.

Original: As described in previous sections, the Q-learning model includes an agent, states and actions, where the state represents the current layer of the agent and the action refers to the transition to the next layer.
Revised: As described in previous sections, the Q-learning model includes an agent, states and actions, where the state represents the current layer of the agent and the action refers to the transition to the next layer.

Original: On the basis of deﬁned blocks, the complete network is constructed by stacking them sequentially N times.
Revised: On the basis of deﬁned blocks, the complete network is constructed by stacking them sequentially N times.

Original: 6.2.2. PNAS
Revised: 6.2.2. PNAS

Original: Progressive neural architecture search [47] proposes to explore the space of modular structures starting from simple models then evolving to more complex ones, discarding underperforming structures as learning progresses.
Revised: Progressive neural architecture search [47] proposes to explore the space of modular structures starting from simple models then evolving to more complex ones, discarding underperforming structures as learning progresses.

Original: The modular structure in this approach is called a cell and consists of a ﬁxed number of blocks.
Revised: The modular structure in this approach is called a cell and consists of a ﬁxed number of blocks.

Original: Each block is a combination of 2 operators among 8 selected ones such as identity, pooling and convolution.
Revised: Each block is a combination of 2 operators among 8 selected ones such as identity, pooling and convolution.

Original: A cell structure is learned ﬁrst then it’s stacked N times in order to build the resulting CNN.
Revised: A cell structure is learned ﬁrst then it’s stacked N times in order to build the resulting CNN.

Original: The main contribution of PNAS lies in the optimization of the search process by avoiding direct search in the entire space of cells.
Revised: The main contribution of PNAS lies in the optimization of the search process by avoiding direct search in the entire space of cells.

Original: This was made possible with the use of a sequential model-based optimization (SMBO) strategy.
Revised: This was made possible with the use of a sequential model-based optimization (SMBO) strategy.

Original: The initial step consists in building, training and evaluating all possible 1block cells.
Revised: The initial step consists in building, training and evaluating all possible 1block cells.

Original: Then the cell is expanded to 2-block size exploding the number of total combinations.
Revised: Then the cell is expanded to 2-block size exploding the number of total combinations.

Original: The innovation brought by PNAS is to predict the performance of the second level cells by training a RNN (predictor) on the performance of previous level ones.
Revised: The innovation brought by PNAS is to predict the performance of the second level cells by training a RNN (predictor) on the performance of previous level ones.

Original: Only the K best cells (i.e. most promising ones) are transferred to the next step of cell size expansion.
Revised: Only the K best cells (i.e. most promising ones) are transferred to the next step of cell size expansion.

Original: This process is repeated until the maximum allowed blocks number is reached.
Revised: This process is repeated until the maximum allowed blocks number is reached.

Original: With an accuracy comparable to NAS [28] approach, PNAS is up to 5 times faster using a cell maximum size of 5 blocks and K equal to 256.
Revised: With an accuracy comparable to NAS [28] approach, PNAS is up to 5 times faster using a cell maximum size of 5 blocks and K equal to 256.

Original: This result is due to the fact that performance prediction takes much less time than full training of designed cells.
Revised: This result is due to the fact that performance prediction takes much less time than full training of designed cells.

Original: The best cell architecture is shown in ﬁgure 13.
Revised: The best cell architecture is shown in ﬁgure 13.

Original: 6.2.3. ENAS
Revised: 6.2.3. ENAS

Original: Eﬃcient neural architecture search [29] comes in the continuity of previous work NAS [28] and PNAS [47].
Revised: Eﬃcient neural architecture search [29] comes in the continuity of previous work NAS [28] and PNAS [47].

Original: It explores a cell-based search space through a controller RNN trained with reinforcement learning.
Revised: It explores a cell-based search space through a controller RNN trained with reinforcement learning.

Original: The cell structure is similar to PNAS model where block concept is replaced with a node that consists of 2 operations and two skip connections.
Revised: The cell structure is similar to PNAS model where block concept is replaced with a node that consists of 2 operations and two skip connections.

Original: The controller RNN manages thus 2 types of decisions at each node.
Revised: The controller RNN manages thus 2 types of decisions at each node.

Original: First it identiﬁes 2 previous nodes to connect to, allowing the cell to set skip connections.
Revised: First it identiﬁes 2 previous nodes to connect to, allowing the cell to set skip connections.

Original: Second, the controller selects 2 operations to implement among a set of 1 identity, 2 depth wise-separable convolutions of ﬁlter sizes 3 ∗ 3 and 5 ∗ 5 [48], max pooling and average pooling both of size 3 ∗ 3.
Revised: Second, the controller selects 2 operations to implement among a set of 1 identity, 2 depth wise-separable convolutions of ﬁlter sizes 3 ∗ 3 and 5 ∗ 5 [48], max pooling and average pooling both of size 3 ∗ 3.

Original: Within each node, the operations results are added in order to constitute an input for the next node.
Revised: Within each node, the operations results are added in order to constitute an input for the next node.

Original: Figure 14 illustrates the design of a 4-node cell.
Revised: Figure 14 illustrates the design of a 4-node cell.

Original: At the end, the entire CNN is built by stacking N times convolutional cells.
Revised: At the end, the entire CNN is built by stacking N times convolutional cells.

Original: Another contribution of ENAS consists in sampling mini-batches from validation dataset to train designed models.
Revised: Another contribution of ENAS consists in sampling mini-batches from validation dataset to train designed models.

Original: The models with the best accuracy are then trained on the entire validation dataset.
Revised: The models with the best accuracy are then trained on the entire validation dataset.

Original: Additionally, the approach eﬃciency is greatly improved by implementing a weight sharing strategy.
Revised: Additionally, the approach eﬃciency is greatly improved by implementing a weight sharing strategy.

Original: Each node has its own parameters (used when involved operations are activated) that are shared through inheritance by the generated child models.
Revised: Each node has its own parameters (used when involved operations are activated) that are shared through inheritance by the generated child models.

Original: The latter are hence not trained from scratch saving a considerable processing time.
Revised: The latter are hence not trained from scratch saving a considerable processing time.

Original: ENAS provides competitive results on CIFAR-10 and Penn
Revised: ENAS provides competitive results on CIFAR-10 and Penn

Original: Treebank datasets.
Revised: Treebank datasets.

Original: It speciﬁcally takes much less time to build the convolution cells than previous approaches that adopt the same strategy of designing modular structures then stack them to obtain a ﬁnal CNN.
Revised: It speciﬁcally takes much less time to build the convolution cells than previous approaches that adopt the same strategy of designing modular structures then stack them to obtain a ﬁnal CNN.

Original: 6.2.4. EAS With Path Level Transformation
Revised: 6.2.4. EAS With Path Level Transformation

Original: A developed version of EAS [42] which adopts network transformation for eﬃcient CNN architecture search is presented in [49].
Revised: A developed version of EAS [42] which adopts network transformation for eﬃcient CNN architecture search is presented in [49].

Original: The new approach tackle the constraint of only performing plain architecture modiﬁcation (layer-level), e.g. adding (removing) units, ﬁlters and layers, by using path-level transformation operations.
Revised: The new approach tackle the constraint of only performing plain architecture modiﬁcation (layer-level), e.g. adding (removing) units, ﬁlters and layers, by using path-level transformation operations.

Original: The proposed model is similar to ([42]) where the reinforcement learning meta-controller samples network transformation actions to build new architectures.
Revised: The proposed model is similar to ([42]) where the reinforcement learning meta-controller samples network transformation actions to build new architectures.

Original: The latter are then trained and resulting accuracies are used as reward to update the meta-controller.
Revised: The latter are then trained and resulting accuracies are used as reward to update the meta-controller.

Original: However, certain changes have been implemented in order to adapt search methods to the tree-structured architecture space: using a tree-structured LSTM, ([50]) as meta-controller, deﬁning a new action space consisting of feature maps allocation schemes (replication, skip), merge schemes (add, concatenation, none) and primitive operations (convolution, identity, depthwise-separable convolution, etc.).
Revised: However, certain changes have been implemented in order to adapt search methods to the tree-structured architecture space: using a tree-structured LSTM, ([50]) as meta-controller, deﬁning a new action space consisting of feature maps allocation schemes (replication, skip), merge schemes (add, concatenation, none) and primitive operations (convolution, identity, depthwise-separable convolution, etc.).

Original: Figure 15 presents an example of transformation decisions operated by the meta-controller.
Revised: Figure 15 presents an example of transformation decisions operated by the meta-controller.

Original: Experimenting with ResNet and DenseNet architectures as base input, the path level transformation approach achieves competitive performance with state-of-the-art models maintaining low computational resources comparable to EAS approach ones.
Revised: Experimenting with ResNet and DenseNet architectures as base input, the path level transformation approach achieves competitive performance with state-of-the-art models maintaining low computational resources comparable to EAS approach ones.

Original: 6.3. Architecture search accelerators
Revised: 6.3. Architecture search accelerators

Original: Reinforcement learning methods have been applied successfully to design neural networks.
Revised: Reinforcement learning methods have been applied successfully to design neural networks.

Original: Although multi-branch structures and skip connections improves the eﬃciency of architectures automatic search, the latter is still computationally expensive (hundreds of GPU hours), time consuming and requires further acceleration of learning process.
Revised: Although multi-branch structures and skip connections improves the eﬃciency of architectures automatic search, the latter is still computationally expensive (hundreds of GPU hours), time consuming and requires further acceleration of learning process.

Original: Thus, in addition to the methods assigned to architectural search optimization and complex component building, some techniques are developed to speed up learning and are depicted in the current section.
Revised: Thus, in addition to the methods assigned to architectural search optimization and complex component building, some techniques are developed to speed up learning and are depicted in the current section.

Original: Early stopping strategy proposed in [40] enables fast convergence of the learning agent while maintaining an acceptable level of eﬃciency.
Revised: Early stopping strategy proposed in [40] enables fast convergence of the learning agent while maintaining an acceptable level of eﬃciency.

Original: This is possible by taking into account intermediate rewards ignored in previous works (set to zero delaying reinforcement learning convergence [36].
Revised: This is possible by taking into account intermediate rewards ignored in previous works (set to zero delaying reinforcement learning convergence [36].

Original: In such case, the agent stops searching in an early training phase as the accuracy rewards reach higher levels in fewer iterations.
Revised: In such case, the agent stops searching in an early training phase as the accuracy rewards reach higher levels in fewer iterations.

Original: The reward function is redeﬁned in order to include designed block complexity and density and avoid possible poor accuracy resulting from training early stopping.
Revised: The reward function is redeﬁned in order to include designed block complexity and density and avoid possible poor accuracy resulting from training early stopping.

Original: A second technique is presented in [40] which consists of a distributed asynchronous framework assembling 3 nodes with diﬀerent functions.
Revised: A second technique is presented in [40] which consists of a distributed asynchronous framework assembling 3 nodes with diﬀerent functions.

Original: The master node is the place where block structures are sampled by agent.
Revised: The master node is the place where block structures are sampled by agent.

Original: Then, in the controller node, the entire network is built from generated blocks and transmitted to multiple compute nodes for training.
Revised: Then, in the controller node, the entire network is built from generated blocks and transmitted to multiple compute nodes for training.

Original: The framework is a kind of simpliﬁed parameter-server [51] and allows the parallel training of designed networks in each compute nodes.
Revised: The framework is a kind of simpliﬁed parameter-server [51] and allows the parallel training of designed networks in each compute nodes.

Original: Hence, the whole design and learning processing is operated in multiple machines and GPUs.
Revised: Hence, the whole design and learning processing is operated in multiple machines and GPUs.

Original: [28] uses the same parameter server scheme with replication of controllers in order to train various architectures in parallel.
Revised: [28] uses the same parameter server scheme with replication of controllers in order to train various architectures in parallel.

Original: As seen previously, reinforcement learning policies use explored architectures performance as a guiding reward for controllers updates.
Revised: As seen previously, reinforcement learning policies use explored architectures performance as a guiding reward for controllers updates.

Original: Training and evaluating every sampled architecture (among hundreds) on validation data is responsible for most of computational load.
Revised: Training and evaluating every sampled architecture (among hundreds) on validation data is responsible for most of computational load.

Original: Extracting architecture performance was consequently subject to several estimation attempts.
Revised: Extracting architecture performance was consequently subject to several estimation attempts.

Original: A number of approaches focus on performance prediction on the basis of past observations.
Revised: A number of approaches focus on performance prediction on the basis of past observations.

Original: Most of such techniques are based on learning curve extrapolation [25] and surrogate models using RNN predictor [52] that aim at predicting and eliminating poor architectures before full training.
Revised: Most of such techniques are based on learning curve extrapolation [25] and surrogate models using RNN predictor [52] that aim at predicting and eliminating poor architectures before full training.

Original: Another idea to estimate performance and rank designed architectures is to use simpliﬁed (proxy) metrics for training such as data subsets (mini-batches)
Revised: Another idea to estimate performance and rank designed architectures is to use simpliﬁed (proxy) metrics for training such as data subsets (mini-batches)

Original: [29] and down-sampled data (like images with lower resolution) [53].
Revised: [29] and down-sampled data (like images with lower resolution) [53].

Original: Network transformation is one of the more recent techniques assigned to accelerate neural architecture search ([49], [54]).
Revised: Network transformation is one of the more recent techniques assigned to accelerate neural architecture search ([49], [54]).

Original: It consists in training explored architectures reusing previously trained or existing networks.
Revised: It consists in training explored architectures reusing previously trained or existing networks.

Original: This modeling feature allows to address a limitation of reinforcement learning approaches where training is performed with a random initialization of weights.
Revised: This modeling feature allows to address a limitation of reinforcement learning approaches where training is performed with a random initialization of weights.

Original: Thus, extending network morphisms [55] to initiate architecture search through the transfer of experience and knowledge reﬂected by reused weights enables the framework to scrutinize the search space eﬃciently.
Revised: Thus, extending network morphisms [55] to initiate architecture search through the transfer of experience and knowledge reﬂected by reused weights enables the framework to scrutinize the search space eﬃciently.

Original: Although the techniques presented above have saved substantial computational resources for neural architecture search, there is still more eﬀort needed to examine the extent of bias impact of such techniques on the search process.
Revised: Although the techniques presented above have saved substantial computational resources for neural architecture search, there is still more eﬀort needed to examine the extent of bias impact of such techniques on the search process.

Original: Indeed, it’s crucial to assure that modiﬁcations brought through re-sampled data, discarded cases and early convergence do not inﬂuence the models original predictions.
Revised: Indeed, it’s crucial to assure that modiﬁcations brought through re-sampled data, discarded cases and early convergence do not inﬂuence the models original predictions.

Original: Further studies are thus required to verify that learning accelerators do not have ampliﬁed eﬀect on approaches predictions and validation accuracies.
Revised: Further studies are thus required to verify that learning accelerators do not have ampliﬁed eﬀect on approaches predictions and validation accuracies.

Original: 7. Conclusion
Revised: 7. Conclusion

Original: The review of recent work trend on automatic design of CNN architectures raised some methodological options that are adopted by the majority of built approaches.
Revised: The review of recent work trend on automatic design of CNN architectures raised some methodological options that are adopted by the majority of built approaches.

Original: Despite some attempts to use design meta-controllers based on evolutionary algorithms ([26], [27]) and Bayesian optimization ([25], [56]), reinforcement learning has shown promising empirical results and stands as the preferred strategy to train design controllers [57].
Revised: Despite some attempts to use design meta-controllers based on evolutionary algorithms ([26], [27]) and Bayesian optimization ([25], [56]), reinforcement learning has shown promising empirical results and stands as the preferred strategy to train design controllers [57].

Original: Another common conception option is the introduction of multi-branch (modular) structures as an elementary component of the entire network which restricts the search space to block/cell level.
Revised: Another common conception option is the introduction of multi-branch (modular) structures as an elementary component of the entire network which restricts the search space to block/cell level.

Original: The plain network design is generally kept as a ﬁrst step of proposed approaches application ([28], [42]) given that it leads to simple networks and allows to focus on the method itself before switching to more complex structures with modular design ([29], [49]).
Revised: The plain network design is generally kept as a ﬁrst step of proposed approaches application ([28], [42]) given that it leads to simple networks and allows to focus on the method itself before switching to more complex structures with modular design ([29], [49]).

Original: A third option used in design approaches at a lower scale is the prediction of explored architectures rewards before full training the most promising ones ([25], [29]).
Revised: A third option used in design approaches at a lower scale is the prediction of explored architectures rewards before full training the most promising ones ([25], [29]).

Original: This training acceleration technique is implemented for performance improvement purpose and requires further attention to control possible bias impact on the models behavior.
Revised: This training acceleration technique is implemented for performance improvement purpose and requires further attention to control possible bias impact on the models behavior.

Original: The success of current reinforcement-learning-based approaches to design
Revised: The success of current reinforcement-learning-based approaches to design

Original: CNN architectures is widely proven especially for image classiﬁcation tasks.
Revised: CNN architectures is widely proven especially for image classiﬁcation tasks.

Original: However, it is achieved at the cost of high computational resources despite the acceleration attempts of most of recent models.
Revised: However, it is achieved at the cost of high computational resources despite the acceleration attempts of most of recent models.

Original: Such fact is preventing individual researchers and small research entities (companies and laboratories) from fully access to this innovative technology [42].
Revised: Such fact is preventing individual researchers and small research entities (companies and laboratories) from fully access to this innovative technology [42].

Original: Hence, deeper and more revolutionary optimizing methods are required to practically operate CNN automatic design.
Revised: Hence, deeper and more revolutionary optimizing methods are required to practically operate CNN automatic design.

Original: Transformation approaches based on extended network morphisms [49] are among the ﬁrst attempts in this direction that achieved drastic decrease in computational cost and demonstrated generalization capacity.
Revised: Transformation approaches based on extended network morphisms [49] are among the ﬁrst attempts in this direction that achieved drastic decrease in computational cost and demonstrated generalization capacity.

Original: Additional future directions to control automatic design complexity is to develop methods for multi-task problems [58] and weights sharing [59] in order to beneﬁt from knowledge transfer contributions.
Revised: Additional future directions to control automatic design complexity is to develop methods for multi-task problems [58] and weights sharing [59] in order to beneﬁt from knowledge transfer contributions.