Neural Architecture Search ( NAS ) had produced highly competitive results in generating neural architectures for many deep learning applications , some of them achieving state of the art performances . Even though there are many Recurrent Neural Network ( RNN ) variations like Long Short-Term Memory ( LSTM ) , Gated Recurrent Unit ( GRU ) , etc . , available for electric load demand forecasting , finding an optimal internal structure of RNN is still of much interest . In this paper we present a novel Neural Architecture Search ( NAS ) framework to improve keyword spotting and spoken language identification models . Even with the huge success of deep neural networks ( DNNs ) in many different domains , finding the best network architecture is still a laborious task and very computationally expensive at best with existing searching approaches . Recently , neural architecture search ( NAS ) methods have attracted much attention and outperformed manually designed architectures on a few high-level vision tasks . In this paper , we propose HiNAS ( Hierarchical NAS ) , an effort towards employing NAS to automatically design effective neural network architectures for image denoising . Moreover , on ImageNet , our method outperforms MobileNetV2 full model by 1.47 % in classification accuracy with 25 % FLOPs reduction . With the same backbone architecture as baseline models , our method even outperforms some neural architecture search ( NAS ) based methods such as AMC [ 2 ] and MNasNet [ 3 ] . To quantify this alignment effect of data representations on the difficulty of a learning task , we make use of an existing task complexity score and show its connection to the representation-dependent information coding length of the input . Empirically we extract the necessary statistics from a linear regression approximation and show that these are sufficient to predict relative learning performance outcomes of different data representations and neural network types obtained when utilizing an extensive neural network architecture search . We conclude that to ensure better learning outcomes , representations may need to be tailored to both task and model to align with the implicit distribution of model and task . Parallel training of an ensemble of Deep Neural Networks ( DNN ) on a cluster of nodes is an effective approach to shorten the process of neural network architecture search and hyper-parameter tuning for a given learning task . Prior efforts have shown that data sharing , where the common preprocessing operation is shared across the DNN training pipelines , saves computational resources and improves pipeline efficiency . However , these backbones were originally proposed for image classification , and therefore may not be naturally fit for speaker recognition . Due to the prohibitive complexity of manually exploring the design space , we propose the first neural architecture search approach approach for the speaker recognition tasks , named as AutoSpeech . Our algorithm first identifies the optimal operation combination in a neural cell and then derives a CNN model by stacking the neural cell for multiple times . We adopt the progressive neural architecture search \cite{liu : pnas_google : DBLP : journals/corr/abs-1712 - 00559 } to find optimal architectures for meta-learners . The gradient based meta-learner whose architecture was automatically found achieved state-of-the-art results on the 5-shot 5-way Mini-ImageNet classification problem with $ 74.65\%$ accuracy , which is $ 11.54\%$ improvement over the result obtained by the first gradient-based meta-learner called MAML \cite{finn : maml : DBLP : conf/icml/FinnAL17}. To our best knowledge , this work is the first successful neural architecture search implementation in the context of meta learning . Then , based on the extracted network structure features , referring to the steps of SRMs , we give a new recipe for predicting the accuracy score of neural network models on the training set . Finally , through experiments and comparison , we prove the accuracy of this prediction model and the effectiveness of accelerating the neural architecture search process . Related to that is the line of research concerned with speeding up static networks by approximating expensive parts of them with cheaper alternatives , while propagating information from previous frames . In this work we attempt to come up with generalisation of those methods , and instead of manually designing contextual blocks that connect per-frame outputs , we propose a neural architecture search solution , where the choice of operations together with their sequential arrangement are being predicted by a separate neural network . We showcase that such generalisation leads to stable and accurate results across common benchmarks , such as CityScapes and CamVid datasets . Important examples include multiplex or multilayer networks ( 2 ) , where different types of interactions are accounted for , higher-order networks ( 3 ⇓ –5 ) , focusing on pathways instead of dyadic interactions , and temporal networks ( 6 , 7 ) , where nodes and links become dynamical entities . In PNAS , Sekara et al. ( 8) make two important contributions to the latter approach , first by studying a longitudinal , high-resolution dataset on human interactions over an extended time window and second by showing that significant structural patterns naturally emerge from the system when considered at an appropriate time scale ( Fig. 1 ) . In a recent Letter in PNAS ( 1 ) , Pluss et al. compare the speed and accuracy of the Burrows – Wheeler aligner ( BWA ) (2)/Genome Analysis Toolkit ( GATK ) ( 3 ) best-practices pipeline ( 4 ) , against our PEMapper/PECaller pipeline ( 5 ) , as well as against a commercially available , but un – peer-reviewed method called GENALICEMAP ( genalice.com ) . This test was conducted in an interesting fashion , limiting the tested region to the high-confidence coding regions from GIAB 3.3 ( https://github.com/genome-in-a-bottle ) , in only four individuals . Using “ divide and conquer ” strategy , the dete cted difference is split into groups of connected elemen ts . Those groups are used for update of PNAS to make it consi stent with GIS . The paper also explains an algorithm for detection of the difference between two states and a procedur e for formation of groups , as well as the conditions and limitations of automation procedure for incremental integration . The Department of Health and Human Services ( DHHS ) found out about the article prior to its publication in Proceedings of the National Academy of Sciences ( PNAS ) and asked the journal to not publish it . PNAS editors met with DHHS officials and heard their concerns about the article . They decided to publish the article despite its national security risks because , in their judgment , the benefits of publication far outweighed its risks , since the article contained information that would help public health agencies prevent or mitigate a terrorist attack ( Alberts 2005 ) . So it stands to reason that the journal will not accept work that has already been published at a level of detail that compromises its originality . The PNAS Editorial Board exercises discretion in judging the originality of submissions . Inder M. Verma . In this paper , DNAS , a system for utilizing computer resources that exist in distributed manner is proposed . DNAS uses tree structure for communication topoogy between nodes . The obXML schema is used for the practical implementation of the DNAS framework into building simulation tools . The topology of the DNAS framework implemented in the obXML schema has a main root element OccupantBehavior , linking three main elements representing Buildings , Occupants and Behaviors . Using the schema structure , the actions of turning on an air conditioner and closing blinds provide two examples of how the schema standardizes these actions using XML . In the process of streaming , a music file will sound when the computer starts receiving the music file data . Shoutcast DNAS is a freeware that is used for streaming radio technology . Shoutcast help the user to provide an Internet Radio Private servers using software that has been provided . Also , previous work focuses primarily on reducing FLOPs , but FLOP count does not always reflect actual latency . To address these , we propose a differentiable neural architecture search ( DNAS ) framework that uses gradient-based methods to optimize ConvNet architectures , avoiding enumerating and training individual architectures separately as in previous methods . FBNets ( Facebook-Berkeley-Nets ) , a family of models discovered by DNAS surpass state-of-the-art models both designed manually and generated automatically . In this paper , we explore a new dimension of the design space : quantizing different layers with different bit-widths . We formulate this problem as a neural architecture search problem and propose a novel differentiable neural architecture search ( DNAS ) framework to efficiently explore its exponential search space with gradient-based optimization . Experiments show we surpass the state-of-the-art compression of ResNet on CIFAR-10 and ImageNet . The use of streaming technology requires a server that is used to distribute the content to the client , in addition to it is also responsible for recording the data statistics . This study will try to build an application that can monitor the activity of a server machine that is used as an audio streaming using SHOUTcast DNAS server . SHOUTcast Distributed Network Audio Streaming ( SHOUTcast DNAS ) is a multiplatform software audio transmission over the Internet with http protocol created by Nullsoft , and use MP3 or AAC for audio encoding . SHOUTcast monitoring applications can be used to manage and monitor the activity running SHOUTcast Server on Ubuntu Server operating system with a web-based interface . Neural architecture search ( NAS ) aims to discover network architectures with desired properties such as high accuracy or low latency . Recently , differentiable NAS ( DNAS ) has demonstrated promising results while maintaining a search cost orders of magnitude lower than reinforcement learning ( RL ) based NAS . However , DNAS models can only optimize differentiable loss functions in search , and they require an accurate differentiable approximation of non-differentiable criteria . In this work , we present UNAS , a unified framework for NAS , that encapsulates recent DNAS and RL-based approaches under one framework . To address this problem , an ontology was developed using the drivers-needs-actions-systems ( DNAS ) framework . Recent work has been carried out to implement the theoretical DNAS framework into an eXtensible Markup Language ( XML ) schema , titled ‘ occupant behaviour XML ’ ( obXML ) which is a practical implementation of OB models that can be integrated into building performance simulation ( BPS ) programs . This paper presents a newly developed library of OB models represented in the standardised obXML schema format . Differentiable Neural Architecture Search ( DNAS ) has demonstrated great success in designing state-of-the-art , efficient neural networks . However , DARTS-based DNAS 's search space is small when compared to other search methods ' , since all candidate network layers must be explicitly instantiated in memory . To address this bottleneck , we propose a memory and computationally efficient DNAS variant : DMaskingNAS . Differentiable architecture search ( DARTS ) provided a fast solution in finding effective network architectures , but suffered from large memory and computing overheads in jointly training a super-network and searching for an optimal architecture . In this paper , we present a novel approach , namely , Partially-Connected DARTS , by sampling a small part of super-network to reduce the redundancy in exploring the network space , thereby performing a more efficient search without comprising the performance . Differentiable neural architecture search methods became popular in automated machine learning , mainly due to their low search costs and flexibility in designing the search space . However , these methods suffer the difficulty in optimizing network , so that the searched network is often unfriendly to hardware . In this paper , we explore the application of neural architecture search ( NAS ) in PolSAR area for the first time . Different from the utilization of existing NAS methods , we propose a differentiable architecture search ( DAS ) method which is customized for PolSAR classification . The proposed DAS is equipped with a PolSAR tailored search space and an improved one-shot search strategy . We present the first differentiable Network Architecture Search ( NAS ) for Graph Neural Networks ( GNNs ) . GNNs show promising performance on a wide range of tasks , but require a large amount of architecture engineering . However , little attention has been given to explicitly optimizing the architectures for FSL , nor to an adaptation of the architecture at test time to particular novel tasks . In this work , we propose to employ tools borrowed from the Differentiable Neural Architecture Search ( D-NAS ) literature in order to optimize the architecture for FSL without over-fitting . Additionally , to make the architecture task adaptive , we propose the concept of ` MetAdapt Controller ' modules . This enables not only the most informative reviews to be utilized for prediction but also a deeper word-level interaction . Our pointer-based method operates with a gumbel-softmax based pointer mechanism that enables the incorporation of discrete vectors within differentiable neural architectures . Our pointer mechanism is co-attentive in nature , learning pointers which are co-dependent on user-item relationships . In this work , the problem of optimal mixed-precision network search ( MPS ) is considered . To circumvent its difficulties of discrete search space and combinatorial optimization , a new differentiable search architecture is proposed , with several novel contributions to advance the efficiency by leveraging the unique properties of the MPS problem . The resulting Efficient differentiable MIxed-Precision network Search ( EdMIPS ) method is effective at finding the optimal bit allocation for multiple popular networks , and can search a large model , e.g. Inception-V3 , directly on ImageNet without proxy task in a reasonable amount of time . We introduce Convolutional Pose Machines ( CPMs ) for the task of articulated pose estimation . CPMs inherit the benefits of the pose machine [ reference ] architecture - the implicit learning of long - range dependencies between image and multi - part cues , tight integration between learning and inference , a modular sequential design - and combine them with the advantages afforded by convolutional architectures : the ability to learn feature representations for both image and spatial context directly from data ; a differentiable architecture that allows for globally joint training with backpropagation ; and the ability to efficiently handle large training datasets . CPMs consist of a sequence of convolutional networks that repeatedly produce 2D belief maps [ reference ] for the location [ reference ] This task is important since many natural language processing ( NLP ) problems , such as information extraction , relation extraction , text summarization or machine translation , rely on it explicitly or implicitly and could benefit from more accurate RTE systems [ reference ] . State - of - the - art systems for RTE so far relied heavily on engineered NLP pipelines , extensive manual creation of features , as well as various external resources and specialized subcomponents such as negation detection ( e.g. [ reference ][ reference ][ reference ][ reference ] . Despite the success of neural networks for paraphrase detection ( e.g. [ reference ][ reference ][ reference ] , end - to - end differentiable neural architectures failed to get close to acceptable performance for RTE due to the lack of large high - quality datasets . An end - to - end differentiable solution to RTE is desirable , since it avoids specific assumptions about the underlying language . We propose to attenuate the greediness of the original SMBO method by relaxing the role of the surrogate function so it predicts architecture sampling probability instead . We demonstrate with experiments on the CIFAR-10 dataset that our method , denominated Efficient progressive neural architecture search ( EPNAS ) , leads to increased search efficiency , while retaining competitiveness of found architectures . In this paper , we propose Efficient Progressive Neural Architecture Search ( EPNAS ) , a neural architecture search ( NAS ) that efficiently handles large search space through a novel progressive search policy with performance prediction based on REINFORCE~\cite{this http URL}. EPNAS is designed to search target networks in parallel , which is more scalable on parallel systems such as GPU/TPU clusters . More importantly , EPNAS can be generalized to architecture search with multiple resource constraints , \eg , model size , compute complexity or intensity , which is crucial for deployment in widespread platforms such as mobile and cloud . For a stricter hardware constraint , the architecture of a specialized network can be very efficiently determined based on this table by picking the best candidate blocks that yield the least accuracy loss . To accomplish this idea , we propose Progressive One-shot Neural Architecture Search ( PONAS ) that combines advantages of progressive NAS and one-shot methods . In PONAS , we propose a two-stage training scheme , including the meta training stage and the fine-tuning stage , to make the search process efficient and stable . We developed an effective and efficient parameter pruning technique for backoff language models that allowed us to cope with ever increasing amounts of training data and expanded N -gram scopes . Finally , we improved our progressive search architecture with more efficient algorithms for lattice generation , compaction , and incorporation of higher-order language models .
Neural Architecture Search ( NAS ) had produced highly competitive results in generating neural architectures for many deep learning applications , some of them achieving state of the art performances . Even though there are many Recurrent Neural Network ( RNN ) variations like Long Short-Term Memory ( LSTM ) , Gated Recurrent Unit ( GRU ) , etc . , available for electric load demand forecasting , finding an optimal internal structure of RNN is still of much interest .
In this paper we present a novel Neural Architecture Search ( NAS ) framework to improve keyword spotting and spoken language identification models . Even with the huge success of deep neural networks ( DNNs ) in many different domains , finding the best network architecture is still a laborious task and very computationally expensive at best with existing searching approaches .
Recently , neural architecture search ( NAS ) methods have attracted much attention and outperformed manually designed architectures on a few high-level vision tasks . In this paper , we propose HiNAS ( Hierarchical NAS ) , an effort towards employing NAS to automatically design effective neural network architectures for image denoising .
Moreover , on ImageNet , our method outperforms MobileNetV2 full model by 1.47 % in classification accuracy with 25 % FLOPs reduction . With the same backbone architecture as baseline models , our method even outperforms some neural architecture search ( NAS ) based methods such as AMC [ 2 ] and MNasNet [ 3 ] .
To quantify this alignment effect of data representations on the difficulty of a learning task , we make use of an existing task complexity score and show its connection to the representation-dependent information coding length of the input . Empirically we extract the necessary statistics from a linear regression approximation and show that these are sufficient to predict relative learning performance outcomes of different data representations and neural network types obtained when utilizing an extensive neural network architecture search . We conclude that to ensure better learning outcomes , representations may need to be tailored to both task and model to align with the implicit distribution of model and task .
Parallel training of an ensemble of Deep Neural Networks ( DNN ) on a cluster of nodes is an effective approach to shorten the process of neural network architecture search and hyper-parameter tuning for a given learning task . Prior efforts have shown that data sharing , where the common preprocessing operation is shared across the DNN training pipelines , saves computational resources and improves pipeline efficiency .
However , these backbones were originally proposed for image classification , and therefore may not be naturally fit for speaker recognition . Due to the prohibitive complexity of manually exploring the design space , we propose the first neural architecture search approach approach for the speaker recognition tasks , named as AutoSpeech . Our algorithm first identifies the optimal operation combination in a neural cell and then derives a CNN model by stacking the neural cell for multiple times .
We adopt the progressive neural architecture search \cite{liu : pnas_google : DBLP : journals/corr/abs-1712 - 00559 } to find optimal architectures for meta-learners . The gradient based meta-learner whose architecture was automatically found achieved state-of-the-art results on the 5-shot 5-way Mini-ImageNet classification problem with $ 74.65\%$ accuracy , which is $ 11.54\%$ improvement over the result obtained by the first gradient-based meta-learner called MAML \cite{finn : maml : DBLP : conf/icml/FinnAL17}. To our best knowledge , this work is the first successful neural architecture search implementation in the context of meta learning .
Then , based on the extracted network structure features , referring to the steps of SRMs , we give a new recipe for predicting the accuracy score of neural network models on the training set . Finally , through experiments and comparison , we prove the accuracy of this prediction model and the effectiveness of accelerating the neural architecture search process .
Related to that is the line of research concerned with speeding up static networks by approximating expensive parts of them with cheaper alternatives , while propagating information from previous frames . In this work we attempt to come up with generalisation of those methods , and instead of manually designing contextual blocks that connect per-frame outputs , we propose a neural architecture search solution , where the choice of operations together with their sequential arrangement are being predicted by a separate neural network . We showcase that such generalisation leads to stable and accurate results across common benchmarks , such as CityScapes and CamVid datasets .
Important examples include multiplex or multilayer networks ( 2 ) , where different types of interactions are accounted for , higher-order networks ( 3 ⇓ –5 ) , focusing on pathways instead of dyadic interactions , and temporal networks ( 6 , 7 ) , where nodes and links become dynamical entities . In PNAS , Sekara et al. ( 8) make two important contributions to the latter approach , first by studying a longitudinal , high-resolution dataset on human interactions over an extended time window and second by showing that significant structural patterns naturally emerge from the system when considered at an appropriate time scale ( Fig. 1 ) .
In a recent Letter in PNAS ( 1 ) , Pluss et al. compare the speed and accuracy of the Burrows – Wheeler aligner ( BWA ) (2)/Genome Analysis Toolkit ( GATK ) ( 3 ) best-practices pipeline ( 4 ) , against our PEMapper/PECaller pipeline ( 5 ) , as well as against a commercially available , but un – peer-reviewed method called GENALICEMAP ( genalice.com ) . This test was conducted in an interesting fashion , limiting the tested region to the high-confidence coding regions from GIAB 3.3 ( https://github.com/genome-in-a-bottle ) , in only four individuals .
Using “ divide and conquer ” strategy , the dete cted difference is split into groups of connected elemen ts . Those groups are used for update of PNAS to make it consi stent with GIS . The paper also explains an algorithm for detection of the difference between two states and a procedur e for formation of groups , as well as the conditions and limitations of automation procedure for incremental integration .
The Department of Health and Human Services ( DHHS ) found out about the article prior to its publication in Proceedings of the National Academy of Sciences ( PNAS ) and asked the journal to not publish it . PNAS editors met with DHHS officials and heard their concerns about the article . They decided to publish the article despite its national security risks because , in their judgment , the benefits of publication far outweighed its risks , since the article contained information that would help public health agencies prevent or mitigate a terrorist attack ( Alberts 2005 ) .
So it stands to reason that the journal will not accept work that has already been published at a level of detail that compromises its originality . The PNAS Editorial Board exercises discretion in judging the originality of submissions . Inder M. Verma .
In this paper , DNAS , a system for utilizing computer resources that exist in distributed manner is proposed . DNAS uses tree structure for communication topoogy between nodes .
The obXML schema is used for the practical implementation of the DNAS framework into building simulation tools . The topology of the DNAS framework implemented in the obXML schema has a main root element OccupantBehavior , linking three main elements representing Buildings , Occupants and Behaviors . Using the schema structure , the actions of turning on an air conditioner and closing blinds provide two examples of how the schema standardizes these actions using XML .
In the process of streaming , a music file will sound when the computer starts receiving the music file data . Shoutcast DNAS is a freeware that is used for streaming radio technology . Shoutcast help the user to provide an Internet Radio Private servers using software that has been provided .
Also , previous work focuses primarily on reducing FLOPs , but FLOP count does not always reflect actual latency . To address these , we propose a differentiable neural architecture search ( DNAS ) framework that uses gradient-based methods to optimize ConvNet architectures , avoiding enumerating and training individual architectures separately as in previous methods . FBNets ( Facebook-Berkeley-Nets ) , a family of models discovered by DNAS surpass state-of-the-art models both designed manually and generated automatically .
In this paper , we explore a new dimension of the design space : quantizing different layers with different bit-widths . We formulate this problem as a neural architecture search problem and propose a novel differentiable neural architecture search ( DNAS ) framework to efficiently explore its exponential search space with gradient-based optimization . Experiments show we surpass the state-of-the-art compression of ResNet on CIFAR-10 and ImageNet .
The use of streaming technology requires a server that is used to distribute the content to the client , in addition to it is also responsible for recording the data statistics . This study will try to build an application that can monitor the activity of a server machine that is used as an audio streaming using SHOUTcast DNAS server . SHOUTcast Distributed Network Audio Streaming ( SHOUTcast DNAS ) is a multiplatform software audio transmission over the Internet with http protocol created by Nullsoft , and use MP3 or AAC for audio encoding . SHOUTcast monitoring applications can be used to manage and monitor the activity running SHOUTcast Server on Ubuntu Server operating system with a web-based interface .
Neural architecture search ( NAS ) aims to discover network architectures with desired properties such as high accuracy or low latency . Recently , differentiable NAS ( DNAS ) has demonstrated promising results while maintaining a search cost orders of magnitude lower than reinforcement learning ( RL ) based NAS . However , DNAS models can only optimize differentiable loss functions in search , and they require an accurate differentiable approximation of non-differentiable criteria . In this work , we present UNAS , a unified framework for NAS , that encapsulates recent DNAS and RL-based approaches under one framework .
To address this problem , an ontology was developed using the drivers-needs-actions-systems ( DNAS ) framework . Recent work has been carried out to implement the theoretical DNAS framework into an eXtensible Markup Language ( XML ) schema , titled ‘ occupant behaviour XML ’ ( obXML ) which is a practical implementation of OB models that can be integrated into building performance simulation ( BPS ) programs . This paper presents a newly developed library of OB models represented in the standardised obXML schema format .
Differentiable Neural Architecture Search ( DNAS ) has demonstrated great success in designing state-of-the-art , efficient neural networks . However , DARTS-based DNAS 's search space is small when compared to other search methods ' , since all candidate network layers must be explicitly instantiated in memory . To address this bottleneck , we propose a memory and computationally efficient DNAS variant : DMaskingNAS .
Differentiable architecture search ( DARTS ) provided a fast solution in finding effective network architectures , but suffered from large memory and computing overheads in jointly training a super-network and searching for an optimal architecture . In this paper , we present a novel approach , namely , Partially-Connected DARTS , by sampling a small part of super-network to reduce the redundancy in exploring the network space , thereby performing a more efficient search without comprising the performance .
Differentiable neural architecture search methods became popular in automated machine learning , mainly due to their low search costs and flexibility in designing the search space . However , these methods suffer the difficulty in optimizing network , so that the searched network is often unfriendly to hardware .
In this paper , we explore the application of neural architecture search ( NAS ) in PolSAR area for the first time . Different from the utilization of existing NAS methods , we propose a differentiable architecture search ( DAS ) method which is customized for PolSAR classification . The proposed DAS is equipped with a PolSAR tailored search space and an improved one-shot search strategy .
We present the first differentiable Network Architecture Search ( NAS ) for Graph Neural Networks ( GNNs ) . GNNs show promising performance on a wide range of tasks , but require a large amount of architecture engineering .
However , little attention has been given to explicitly optimizing the architectures for FSL , nor to an adaptation of the architecture at test time to particular novel tasks . In this work , we propose to employ tools borrowed from the Differentiable Neural Architecture Search ( D-NAS ) literature in order to optimize the architecture for FSL without over-fitting . Additionally , to make the architecture task adaptive , we propose the concept of ` MetAdapt Controller ' modules .
This enables not only the most informative reviews to be utilized for prediction but also a deeper word-level interaction . Our pointer-based method operates with a gumbel-softmax based pointer mechanism that enables the incorporation of discrete vectors within differentiable neural architectures . Our pointer mechanism is co-attentive in nature , learning pointers which are co-dependent on user-item relationships .
In this work , the problem of optimal mixed-precision network search ( MPS ) is considered . To circumvent its difficulties of discrete search space and combinatorial optimization , a new differentiable search architecture is proposed , with several novel contributions to advance the efficiency by leveraging the unique properties of the MPS problem . The resulting Efficient differentiable MIxed-Precision network Search ( EdMIPS ) method is effective at finding the optimal bit allocation for multiple popular networks , and can search a large model , e.g. Inception-V3 , directly on ImageNet without proxy task in a reasonable amount of time .
We introduce Convolutional Pose Machines ( CPMs ) for the task of articulated pose estimation . CPMs inherit the benefits of the pose machine [ reference ] architecture - the implicit learning of long - range dependencies between image and multi - part cues , tight integration between learning and inference , a modular sequential design - and combine them with the advantages afforded by convolutional architectures : the ability to learn feature representations for both image and spatial context directly from data ; a differentiable architecture that allows for globally joint training with backpropagation ; and the ability to efficiently handle large training datasets . CPMs consist of a sequence of convolutional networks that repeatedly produce 2D belief maps [ reference ] for the location [ reference ]
This task is important since many natural language processing ( NLP ) problems , such as information extraction , relation extraction , text summarization or machine translation , rely on it explicitly or implicitly and could benefit from more accurate RTE systems [ reference ] . State - of - the - art systems for RTE so far relied heavily on engineered NLP pipelines , extensive manual creation of features , as well as various external resources and specialized subcomponents such as negation detection ( e.g. [ reference ][ reference ][ reference ][ reference ] . Despite the success of neural networks for paraphrase detection ( e.g. [ reference ][ reference ][ reference ] , end - to - end differentiable neural architectures failed to get close to acceptable performance for RTE due to the lack of large high - quality datasets . An end - to - end differentiable solution to RTE is desirable , since it avoids specific assumptions about the underlying language .
We propose to attenuate the greediness of the original SMBO method by relaxing the role of the surrogate function so it predicts architecture sampling probability instead . We demonstrate with experiments on the CIFAR-10 dataset that our method , denominated Efficient progressive neural architecture search ( EPNAS ) , leads to increased search efficiency , while retaining competitiveness of found architectures .
In this paper , we propose Efficient Progressive Neural Architecture Search ( EPNAS ) , a neural architecture search ( NAS ) that efficiently handles large search space through a novel progressive search policy with performance prediction based on REINFORCE~\cite{this http URL}. EPNAS is designed to search target networks in parallel , which is more scalable on parallel systems such as GPU/TPU clusters . More importantly , EPNAS can be generalized to architecture search with multiple resource constraints , \eg , model size , compute complexity or intensity , which is crucial for deployment in widespread platforms such as mobile and cloud .
For a stricter hardware constraint , the architecture of a specialized network can be very efficiently determined based on this table by picking the best candidate blocks that yield the least accuracy loss . To accomplish this idea , we propose Progressive One-shot Neural Architecture Search ( PONAS ) that combines advantages of progressive NAS and one-shot methods . In PONAS , we propose a two-stage training scheme , including the meta training stage and the fine-tuning stage , to make the search process efficient and stable .
We developed an effective and efficient parameter pruning technique for backoff language models that allowed us to cope with ever increasing amounts of training data and expanded N -gram scopes . Finally , we improved our progressive search architecture with more efficient algorithms for lattice generation , compaction , and incorporation of higher-order language models .
Electric load demand forecasting with RNN cell generated by DARTS Improving Keyword Spotting and Language Identification via Neural Architecture Search at Scale Memory-Efficient Hierarchical Neural Architecture Search for Image Denoising Deep Model Compression via Filter Auto-sampling Quantifying the effect of representations on task complexity FLEET: Flexible Efficient Ensemble Training for Heterogeneous Deep Neural Networks AutoSpeech: Neural Architecture Search for Speaker Recognition Auto-Meta: Automated Gradient Based Meta Learner Search Performance Predict Method Based on Neural Architecture Search Architecture Search of Dynamic Cells for Semantic Video Segmentation Rich gets simpler Reply to Plüss et al.: The strength of PEMapper/PECaller lies in unbiased calling using large sample sizes Software development for incremental integration of GIS and power network analysis system What is “Dual Use” Research? A Response to Miller and Selgelid Preprint servers facilitate scientific discourse Optimization Computation in Dynamic Hierarchical System An ontology to represent energy-related occupant behavior in buildings. Part II: Implementation of the DNAS framework using an XML schema RANCANG BANGUN APLIKASI RADIO STREAMING DENGAN MENGGUNAKAN SHOUTCAST DISTRIBUTED NETWORK AUDIO SERVER (DNAS) FBNet: Hardware-Aware Efficient ConvNet Design via Differentiable Neural Architecture Search Mixed Precision Quantization of ConvNets via Differentiable Neural Architecture Search RANCANG BANGUN SISTEM MONITORING AUDIO STREAMING PADA JARINGAN MULTI RADIO UNAS: Differentiable Architecture Search Meets Reinforcement Learning A library of building occupant behaviour models represented in a standardised schema FBNetV2: Differentiable Neural Architecture Search for Spatial and Channel Dimensions PC-DARTS: Partial Channel Connections for Memory-Efficient Differentiable Architecture Search Latency-Aware Differentiable Neural Architecture Search Optimized CNN for PolSAR Image Classification via Differentiable Neural Architecture Search Probabilistic Dual Network Architecture Search on Graphs MetAdapt: Meta-Learned Task-Adaptive Architecture for Few-Shot Classification Multi-Pointer Co-Attention Networks for Recommendation Rethinking Differentiable Search for Mixed-Precision Neural Networks Convolutional Pose Machines Reasoning about Entailment with Neural Attention Efficient Progressive Neural Architecture Search EPNAS: Efficient Progressive Neural Architecture Search PONAS: Progressive One-shot Neural Architecture Search for Very Efficient Deployment Improved modeling and efficiency for automatic transcription of Broadcast News
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TENCON INTERSPEECH MLSys KDD 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) ICLR BMVC
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