The aim of the variable selection is threefold : to reduce model complexity , to promote diversity of committee networks , and to find a trade-off between the accuracy and diversity of the networks . To achieve the goal , the steps of neural network training , aggregation , and elimination of irrelevant input variables are integrated based on the negative correlation learning [ 1 ] error function . Experimental tests performed on three real world problems have shown that statistically significant improvements in classification performance can be achieved from neural network committees trained according to the technique proposed . Unsupervised pre-training method , which can greatly simplify the traditional manual extraction approach , was utilized to process the depth of the data automatically . Furthermore , the aggregation layer of stacked denoising auto-encoder ( SDA ) was proposed to get rid of gradient disappearance in deeper layers of network , mix superficial nodes ' expression with deeper layers , and avoid the insufficient express ability in deeper layers . Principal component analysis ( PCA ) was adopted to extract different features for classification . Connection s estimation . In the aggregation part , a final output is obtained by center of gravity method . Using machine learning data , performance index are shown and compared with other models . It allows to take complex objects collectively as a unit of high level queries and enables complex objects to be accessed at all levels of aggregation hierarchies without resorting to any kind of path expressions . Features of aggregation abstraction , such as acyclicity of aggregation hierarchies and aggregation inheritance , have played important roles in such a development . We also formally described the output type of each operator in order to support dynamic classification of query results in the IsA type/class semi-lattice . We argue that the influence of group members plays a particularly important role in group decision-making and it can better assist group profile modeling and perform more accurate group recommendation . To tackle the issue of preference aggregation for group recommendation , we propose a novel attentive aggregation representation learning method based on sociological theory for group recommendation , namely SIAGR ( short for " Social Influence-based Attentive Group Recommendation " ) , which takes attention mechanisms and the popular method ( BERT ) as the aggregation representation for group profile modeling . Specifically , we analyze the influence of group members based on social identity theory and two-step flow theory and exploit an attentive mavens mining method . This month we look at composition ( aggregation ) and containment . AGGREGATION Aggregation is also often known as composition ( refs . 4,6 ) . We study the moving average and exponential smoothing methods . Focusing on the aggregation aspect of the data smoothing problem allows us to draw upon the work done with the ordered weighted averaging aggregation operators to suggest new methods for developing smoothing techniques . A new class of smoothing operators based on the use of linearly decaying weights is introduced and is shown to have some better features than either exponential smoothing or the moving average . In discrete systems , the aggregation elements are some selected units , and the remaining units form the steady-state systems . In continuous systems , the aggregation elements are dynamic equations connecting the steady-state systems described by boundary value problems . In order to obtain models of lower complexity than the original models , the steady-state systems have to be eliminated from the reduced models . To reveal the relationship between smog and meteorological attributes , we design a Correlation Detection View that simultaneously visualizes the air quality change patterns across multiple cities and related meteorological attributes . Component Trend View is used to show the variation patterns of six air components , while Aggregation View can reveal the regional overall pollution situations . A case study has been conducted using the China Air Quality Observation Data and European Centre for Medium-Range Weather Forecasts re-analysis data to verify the effectiveness of the proposed approach . This compact representation requires minimal memory storage and enables efficient similarity computation . Second , we propose a novel GhostVLAD layer that includes ghost clusters , that do not contribute to the aggregation . We show that a quality weighting on the input faces emerges automatically such that informative images contribute more than those with low quality , and that the ghost clusters enhance the network 's ability to deal with poor quality images . Similar to bi - directional LSTM , there are four directions in coupled - LSTMs . subsubsection : Aggregation Layer The aggregation layer sums the outputs of four directional coupled - LSTMs into a vector . Specifically , to reduce the false detections of the adjacent overlapping pedestrians , we expect the proposals to be close and locate compactly to the corresponding objects . Thus , inspired by the herd behavior in psychology , we design a new loss function , called aggregation loss ( AggLoss ) , not only to enforce proposals to be close to the corresponding objects , but also to minimize the internal region distances of proposals associated with the same objects . Meanwhile , to effectively handle partial occlusion , we propose a new part occlusion - aware region of interest ( PORoI ) pooling unit to replace the original RoI pooling layer in the second stage Fast R - CNN module of the detector , which integrates the prior structure information of human body with visibility prediction into the network . We train each STM on its dataset for one epoch , and change the target STM as follows : where denotes concatenation and denotes an aggregation operation such as max pooling or concatenation . We used weighted max pooling for the aggregation operation . is the input sequences of - th dataset , and is output , defined by Equation [ reference ] . A probability distribution over the entities in is obtained by computing dot products between and the entity embeddings and taking a softmax . Then , an aggregation scheme named pointer - sum attention is further applied to sum the probabilities of the same entity , so that frequent entities the document will be favored compared to rare ones . Building on the AS Reader , the Attention - over - Attention ( AoA ) Reader cui2016attention introduces a two - way attention mechanism where the query and the document are mutually attentive to each other . , we employ averaging , and delay applying the final nonlinearity ( usually a softmax or logistic sigmoid for classification problems ) until then : The aggregation process of a multi - head graph attentional layer is illustrated by Figure 1 ( right ) . section : COMPARISONS TO RELATED WORK First , each node in a graph aggregates the forward representations of the nodes in its immediate neighborhood , { h⊢u - k1 , } , into a single vector , h⁢N⊢ ( v ) k ( line 5 ) . Note that this aggregation step depends on the representations generated at the previous iteration of the outer loop , , and the forward representations are defined as the input node feature vector . After aggregating the neighboring feature vectors , we concatenate the node current forward representation , h⊢v - k1 , with the aggregated neighborhood vector , h⁢N⊢ ( v ) k . Thus , it introduces very little distortion . Then , we concatenate all six faces while utilizing the connectivity between faces on the cube for image padding ( i.e. , Cube Padding ) in convolution , pooling , convolutional LSTM layers . In this way , CP introduces no image boundary while being applicable to almost all Convolutional Neural Network ( CNN ) structures . Visual examination on the probability maps generated by PCAM pooling shows clear and sharp boundaries around lesion regions compared to the localization heatmaps generated by CAM . PCAM pooling is open sourced at https://github.com/jfhealthcare/Chexpert . Motivated by biological neural networks and distributed sensing networks , we study how pooling networks – or quantizers – with random thresholds can be used in detection tasks . We provide a brief overview of the use of deterministic quantizers in detection by presenting how quantizers can be optimally designed for detection purposes . Convolution and average pooling computation are commutative . In direct sum method ， switching convolution and average pooling computation reduces floating point operations for convolution and pooling computation . Experimental results using NVIDIA V100 show direct sum method attains a speed-up factor of up to 1.8 ( single precision ) and 4.4 ( half precision ) over cuDNN naive implementation . In the proposed scheme , a dictionary is first learned from paired low-resolution and high-resolution facial images , and the input low-resolution query face can then be modelled by a set of high-resolution visual words via a dictionary lookup . A random pooling strategy is then applied to select subsets of visual words , and kernel Fisher 's linear discriminant analysis ( k-LDA ) is introduced to find the discriminant metrics . The final decision is based on the average over different pooling results . The learned hyperplane is stored as the relative feature . Second , we propose an adaptive pooling technique to assemble multiple relative features generated by different RBs under the SVM framework , where the classifier and the pooling weights are jointly learned . Experiments based on three challenging datasets : Caltech-101 , Scene 15 and Willow-Actions , demonstrate the effectiveness and generality of our framework . Then effect of different pooling methods on the classification performance of these networks was investigated using the MNIST dataset . In this study , in addition to the average and maximum pooling methods commonly used in the literature , Mixed pooling , Stochastic pooling , random pooling , Gaussian pooling , median pooling , minimum pooling methods are also used . Experimental results show that the Mixed pooling method has highest accuracy and minimum pooling method has the lowest accuracy . Based on the sliding cycle time constraint , the proposed algorithm guarantees the maximum polling interval , an essential property for delay-sensitive applications and interactive services . We then introduce an iterative resource pooling that processes bursty best-effort traffic and achieves high throughput even under non-uniform upstream traffic distribution . We prove that greedy iterative resource pooling converges to equal resource allocation exponentially fast . Object pooling stores object in the memory buffer , lowers the systematic expenses produced by frequently creation and destruction of the object , avoids frequently data exchange with database and improves the performance of accessing object . By analyzing the design and implementation principle of object pooling , the object pooling framework based on J2EE with LRU algorithm is implemented . In this paper , beyond conventional spatial pooling that usually has a regular shape of NxN , we rethink the formulation of spatial pooling by introducing a new pooling strategy , called strip pooling , which considers a long but narrow kernel , i.e. , 1xN or Nx1 . Based on strip pooling , we further investigate spatial pooling architecture design by 1 ) introducing a new strip pooling module that enables backbone networks to efficiently model long-range dependencies ; 2 ) presenting a novel building block with diverse spatial pooling as a core ; and 3 ) systematically comparing the performance of the proposed strip pooling and conventional spatial pooling techniques . Both novel pooling-based designs are lightweight and can serve as an efficient plug-and-play modules in existing scene parsing networks . The relationship to other joint predictions as well as the general context and understanding of the full image is crucial . Applying supervision earlier in the pipeline before pooling is a possibility , but at this point the features at a given pixel are the result of processing a relatively local receptive field and are thus ignorant of critical global cues . Repeated bottom - up , top - down inference with stacked hourglasses alleviates these concerns . To address this problem , we put forward two improvements in this paper : 1 ) Instead of estimating instance weights through weighted sum pooling , we propose to add some blocks in the network for learning more discriminative instance classifiers by explicitly assigning binary instance labels ; 2 ) We propose to refine instance classifier online using spatial relation . Our motivation is that , though some detectors only capture objects partially , proposals having high spatial overlaps with detected parts may cover the whole object , or at least contain larger portion of the object . We replace the last max - pooling layer of the two models by SPP layer , and the last fc layer and softmax loss layer by the layers described in Section [ reference ] . To increase the feature map size from the last conv layer , we replace the penultimate max - pooling layer and its subsequent conv layers by the dilated conv layers . The new added layers are initialized using Gaussian distributions with - mean and standard deviations . ‘ middle - section ’ ’ of the table shows only small fluctuations in miss - rate ( specially when considering the variance ) . In addition to filter size and layer width , we also experimented with different types of pooling layers ( max - pooling versus mean - pooling ) , see figure 2 of main paper . Other than on the first layer , replacing mean - pooling with max - pooling hurts performance . The top - left corner pooling layer can be implemented very efficiently . We scan from right to left for the horizontal max - pooling and from bottom to top for the vertical max - pooling . We then add two max - pooled feature maps . section : Corner Pooling Corner pooling is a key component of CornerNet . To understand its contribution to performance , we train another network without corner pooling but with the same number of parameters . It also makes the max - pooling domain become independent from in the subsampled maps , as it exactly cancels out the effect of doubling at each iteration . We call the max - pooling operator with the iteration - independent domain : For the same reason , the shift applied to the correlation maps in ’s definition becomes simply after subsampling . Let be the shift ( or translation ) operator on the correlation map : Finally , we incorporate an additional non - linear mapping at each iteration on top of Eq . As such , the objective of getting a higher reward from D φ is equivalent to finding a higher reward region in this extracted feature space F ( S ; φ f ) = { F ( s ; φ f ) } s∈S . Specifically , our feature extractor F ( · ; φ f ) in D φ is implemented by a CNN [ reference ] ; thus F ( s ; φ f ) outputs the CNN feature map vector as f after its convolution - pooling - activation layer . Other neural network models such as LSTM ( Hochreiter and Schmidhuber 1997 ) can also be used to implement clarifies that bilinear pooling is closely related to the second - order polynomial kernel , and presents two compact representations via low - dimensional feature maps for kernel approximation . Kernel pooling approximates Gaussian RBF kernel to a given order through compact explicit feature maps , aiming to characterize higher order feature interactions . Cai et al . introduce a polynomial kernel based predictor to model higher - order statistics of convolutional features across multiple layers . Comparison with Other Covariance Pooling Methods We compare iSQRT - COV with other covariance pooling methods , as shown in Tab . [ reference ] . It is the primary reason why various pooling can not be applied in the dynamic and multidimensional dataset . To deal with the limitations , it needs to construct an optimal pooling method as a better option than max pooling and average pooling . Therefore , we introduce a dynamic pooling layer called RunPool to train the convolutional neural network ( CNN ) architecture . In this paper , we present a deep architecture to match two Chinese sentences , which only relies on alignment instead of long short-term memory network after attention mechanism is employed to get interaction information between sentence-pairs , the model becomes more lightweight and simple . Meanwhile , in order to capture semantic features enough , in addition to using max pooling and average pooling operation , we also employ a pooling operation named attention-pooling to aggregate information from the whole sentence , the final matching score is obtained after a multilayer perceptron classifier . Experiments are carried out on ATEC-NLP dataset and outline the effectiveness of our approach . 1 ) We demonstrate the advantage of using images with a properly large size as input to CNN instead of the conventionally resized one . 2 ) We benchmark the performance of different CNN layers improved by average/max pooling on the feature maps . Our observation suggests that the Conv5 feature yields very competitive accuracy under such pooling step . Nevertheless , state-of-the-art CNNs appear to incur huge computational cost and require the storage of a large number of parameters especially in fully connected layers , which is difficult to deploy such networks into alternative hardware devices with limited computation capacity . To solve the storage problem , we propose a novel technique called weighted average pooling for reducing the parameters in fully connected layer without loss in accuracy . Besides , we implement a cascaded model in single CNN by adding mid output to complete recognition as early as possible , which reduces average inference time significantly . To effectively extract the complementary information concealed within these predictions , we develop a novel pooling mechanism which accounts for competition among contestants : a stacking paradigm integrating conditional logit regression and log-likelihood-ratio-based forecast selection . Empirical results using data related to horseracing events demonstrate that : ( i ) base model strength and diversity are important when combining model-based predictions for CEs ; ( ii ) average-based pooling , commonly employed elsewhere , may not be appropriate for CEs ( because average-based pooling exclusively focuses on strength ) ; and ( iii ) the proposed stacking ensemble provides statistically and economically accurate forecasts . These results have important implications for regulators of betting markets associated with CEs and in particular for the accurate assessment of market efficiency . At present , the vast majority of GCNs use a neighborhood aggregation framework to learn a continuous and compact vector , then performing a pooling operation to generalize graph embedding for the classification task . These approaches have two disadvantages in the graph classification task : (1)when only the largest sub-graph structure ( k-hop neighbor ) is used for neighborhood aggregation , a large amount of early-stage information is lost during the graph convolution step ; ( 2 ) simple average/sum pooling or max pooling utilized , which loses the characteristics of each node and the topology between nodes . In this paper , we propose a novel framework called , dual attention graph convolutional networks ( DAGCN ) to address these problems . We evaluate our method on various benchmarks for generic action , fine-grained action and gesture recognition . Results show that rank pooling brings an absolute improvement of 7 - 10 average pooling baseline . At the same time , rank pooling is compatible with and complementary to several appearance and local motion based methods and features , such as improved trajectories and deep learning features . This paper extends the algorithm to the vocoder-free TTS and propose a GAN-based training algorithm using low-frequency-resolution amplitude spectra to overcome the difficulty in modeling complicated distribution of the high-dimensional spectra . In the proposed algorithm , amplitude spectra are transformed into low-frequency-resolution amplitude spectra by applying an average pooling function along with a frequency axis ; then the GAN-based distribution compensation is performed in the low-frequency-resolution domain . Because the low-frequency-resolution amplitude spectra approximately emulate filter banks , the proposed algorithm is expected to improve synthetic speech quality by reducing differences in spectral envelopes of natural and synthetic speech . This paper describes a model that we submitted to W-NUT 2016 Shared task # 1 : Geolocation Prediction in Twitter . Our model classifies a tweet or a user to a city using a simple neural networks structure with fully-connected layers and average pooling processes . From the findings of previous geolocation prediction approaches , we integrated various user metadata along with message texts and trained the model with them . To address those issues , we propose a two-stage partial mean pooling ( PMP ) approach to construct compact and discriminative global feature representations . The proposed PMP is meant to tackle the limits of traditional max pooling and mean ( or average ) pooling . By injecting the PMP pooling strategy into the CNN based patch-level mid-level feature extraction and representation , we have significantly improved the state-of-the-art retrieval performance over several common benchmark datasets . The Eqn. ( 9 ) has an equivalent form as following : It is worth noting that is exactly the interaction feature between word and class . Therefore , the FastText is a special case of EXAM with an average pooling as the aggregation layer . In EXAM , we use a non - linear MLP to be the aggregation layer , and it will generalize FastText to a non - linear setting which might be more expressive than the original one . Our inference model converts the output hidden vectors of BiLSTMs to the fixed - length vector with pooling operations and puts it into the final classifier to determine the overall inference class . Particularly , in addition to using mean pooling and max pooling similarly to ESIM DBLP : conf / acl / ChenZLWJI17 , we propose to use weighted pooling based on external knowledge to obtain a fixed - length vector as in Equation ( [ reference ] ) ( [ reference ] ) . In our experiments , we regard the function as a 1 - layer feed - forward neural network with ReLU activation function . After the enhancement of local inference , another Bi - LSTM layer is used to capture local inference information and their context for inference composition . Instead of summation adopted by Parikh et al . , ESIM proposes to compute both max and average pooling and feeds the concatenate fixed length vector to the final classifier : a fully connected multi - layer perceptron . Figure [ reference ] shows a high - level view of the ESIM architecture , where the bottom LSTM1 layer of Figure [ reference ] is the input encoding layer , For example , [ reference ] aggregates local descriptors ( RootSIFT [ reference ] ) extracted from face crops using Fisher Vector [ reference ] ( FV ) encoding to obtain a single descriptor per face track . Since the success of deep learning in image - based face recognition [ reference ][ reference ][ reference ][ reference ][ reference ][ reference ][ reference ] , simple strategies for face descriptor aggregation prevailed , such as average - and max - pooling [ reference ][ reference ] . On IJB - A dataset , QAN significantly outperforms the state - of - the - art algorithm NAN by 10.81 % at 0.001 FPR , 4.5 % at 0.01 FPR and 2.12 % at FPR=0.1 , as shown in Table [ reference ] . Compared with average pooling baseline , QAN reduces false negative rate at above three FPRs by 29.32 % , 6.45 % and 7.91 % . Our experiments on the two tasks show that QAN is robust for set - to - set recognition . Possible applications of automatic facial expression recognition include better transcription of videos , movie or advertisement recommendations , detection of pain in telemedicine etc . Traditional convolutional neural networks ( CNNs ) that use convolutional layers , max or average pooling and fully connected layers are considered to capture only first - order statistics . Second - order statistics such as covariance are considered to be better regional descriptors than first - order statistics such as mean or maximum . ( see Figure [ reference ] ) . We can use two parallel stride 2 blocks : and . is a pooling layer ( either average or maximum pooling ) the activation , both of them are stride the filter banks of which are concatenated as in figure [ reference ] . section : Inception - v2 7 ( b ) show that our NAN significantly reduces the error of the baseline CNN + AvePool . This again suggests that in the presence of large face variances , the widely used strategies such as average - pooling aggregation and the pairwise distance computation are far from optimal . In such cases , our learned NAN model is clearly more powerful , and the aggregated feature representation by it is more favorable for the video face recognition task . appendix : Evaluation of off - the - shelf classifiers at higher resolutions In this section , we present some additional classification results using off - the - shelf pretrained classification networks trained with standard average pooling ( ) . As outlined in sec : expand - pooling , sec : expanding - resolution , one of our contributions is a strategy for evaluating classifier networks trained with GeM pooling at scale and exponent at a higher resolution and adapted exponent . However in applications with rich user behaviors , features are often contained with variable - length list of ids , e.g. , searched terms or watched videos in YouTube recommender system . These models often transform corresponding list of embedding vectors into a fixed - length vector via sum / average pooling , which causes loss of information . The proposed DIN tackles it by adaptively learning the representation vector w.r.t .
The aim of the variable selection is threefold : to reduce model complexity , to promote diversity of committee networks , and to find a trade-off between the accuracy and diversity of the networks . To achieve the goal , the steps of neural network training , aggregation , and elimination of irrelevant input variables are integrated based on the negative correlation learning [ 1 ] error function . Experimental tests performed on three real world problems have shown that statistically significant improvements in classification performance can be achieved from neural network committees trained according to the technique proposed .
Unsupervised pre-training method , which can greatly simplify the traditional manual extraction approach , was utilized to process the depth of the data automatically . Furthermore , the aggregation layer of stacked denoising auto-encoder ( SDA ) was proposed to get rid of gradient disappearance in deeper layers of network , mix superficial nodes ' expression with deeper layers , and avoid the insufficient express ability in deeper layers . Principal component analysis ( PCA ) was adopted to extract different features for classification .
Connection s estimation . In the aggregation part , a final output is obtained by center of gravity method . Using machine learning data , performance index are shown and compared with other models .
It allows to take complex objects collectively as a unit of high level queries and enables complex objects to be accessed at all levels of aggregation hierarchies without resorting to any kind of path expressions . Features of aggregation abstraction , such as acyclicity of aggregation hierarchies and aggregation inheritance , have played important roles in such a development . We also formally described the output type of each operator in order to support dynamic classification of query results in the IsA type/class semi-lattice .
We argue that the influence of group members plays a particularly important role in group decision-making and it can better assist group profile modeling and perform more accurate group recommendation . To tackle the issue of preference aggregation for group recommendation , we propose a novel attentive aggregation representation learning method based on sociological theory for group recommendation , namely SIAGR ( short for " Social Influence-based Attentive Group Recommendation " ) , which takes attention mechanisms and the popular method ( BERT ) as the aggregation representation for group profile modeling . Specifically , we analyze the influence of group members based on social identity theory and two-step flow theory and exploit an attentive mavens mining method .
This month we look at composition ( aggregation ) and containment . AGGREGATION Aggregation is also often known as composition ( refs . 4,6 ) .
We study the moving average and exponential smoothing methods . Focusing on the aggregation aspect of the data smoothing problem allows us to draw upon the work done with the ordered weighted averaging aggregation operators to suggest new methods for developing smoothing techniques . A new class of smoothing operators based on the use of linearly decaying weights is introduced and is shown to have some better features than either exponential smoothing or the moving average .
In discrete systems , the aggregation elements are some selected units , and the remaining units form the steady-state systems . In continuous systems , the aggregation elements are dynamic equations connecting the steady-state systems described by boundary value problems . In order to obtain models of lower complexity than the original models , the steady-state systems have to be eliminated from the reduced models .
To reveal the relationship between smog and meteorological attributes , we design a Correlation Detection View that simultaneously visualizes the air quality change patterns across multiple cities and related meteorological attributes . Component Trend View is used to show the variation patterns of six air components , while Aggregation View can reveal the regional overall pollution situations . A case study has been conducted using the China Air Quality Observation Data and European Centre for Medium-Range Weather Forecasts re-analysis data to verify the effectiveness of the proposed approach .
This compact representation requires minimal memory storage and enables efficient similarity computation . Second , we propose a novel GhostVLAD layer that includes ghost clusters , that do not contribute to the aggregation . We show that a quality weighting on the input faces emerges automatically such that informative images contribute more than those with low quality , and that the ghost clusters enhance the network 's ability to deal with poor quality images .
Similar to bi - directional LSTM , there are four directions in coupled - LSTMs . subsubsection : Aggregation Layer The aggregation layer sums the outputs of four directional coupled - LSTMs into a vector .
Specifically , to reduce the false detections of the adjacent overlapping pedestrians , we expect the proposals to be close and locate compactly to the corresponding objects . Thus , inspired by the herd behavior in psychology , we design a new loss function , called aggregation loss ( AggLoss ) , not only to enforce proposals to be close to the corresponding objects , but also to minimize the internal region distances of proposals associated with the same objects . Meanwhile , to effectively handle partial occlusion , we propose a new part occlusion - aware region of interest ( PORoI ) pooling unit to replace the original RoI pooling layer in the second stage Fast R - CNN module of the detector , which integrates the prior structure information of human body with visibility prediction into the network .
We train each STM on its dataset for one epoch , and change the target STM as follows : where denotes concatenation and denotes an aggregation operation such as max pooling or concatenation . We used weighted max pooling for the aggregation operation . is the input sequences of - th dataset , and is output , defined by Equation [ reference ] .
A probability distribution over the entities in is obtained by computing dot products between and the entity embeddings and taking a softmax . Then , an aggregation scheme named pointer - sum attention is further applied to sum the probabilities of the same entity , so that frequent entities the document will be favored compared to rare ones . Building on the AS Reader , the Attention - over - Attention ( AoA ) Reader cui2016attention introduces a two - way attention mechanism where the query and the document are mutually attentive to each other .
, we employ averaging , and delay applying the final nonlinearity ( usually a softmax or logistic sigmoid for classification problems ) until then : The aggregation process of a multi - head graph attentional layer is illustrated by Figure 1 ( right ) . section : COMPARISONS TO RELATED WORK
First , each node in a graph aggregates the forward representations of the nodes in its immediate neighborhood , { h⊢u - k1 , } , into a single vector , h⁢N⊢ ( v ) k ( line 5 ) . Note that this aggregation step depends on the representations generated at the previous iteration of the outer loop , , and the forward representations are defined as the input node feature vector . After aggregating the neighboring feature vectors , we concatenate the node current forward representation , h⊢v - k1 , with the aggregated neighborhood vector , h⁢N⊢ ( v ) k .
Thus , it introduces very little distortion . Then , we concatenate all six faces while utilizing the connectivity between faces on the cube for image padding ( i.e. , Cube Padding ) in convolution , pooling , convolutional LSTM layers . In this way , CP introduces no image boundary while being applicable to almost all Convolutional Neural Network ( CNN ) structures .
Visual examination on the probability maps generated by PCAM pooling shows clear and sharp boundaries around lesion regions compared to the localization heatmaps generated by CAM . PCAM pooling is open sourced at https://github.com/jfhealthcare/Chexpert .
Motivated by biological neural networks and distributed sensing networks , we study how pooling networks – or quantizers – with random thresholds can be used in detection tasks . We provide a brief overview of the use of deterministic quantizers in detection by presenting how quantizers can be optimally designed for detection purposes .
Convolution and average pooling computation are commutative . In direct sum method ， switching convolution and average pooling computation reduces floating point operations for convolution and pooling computation . Experimental results using NVIDIA V100 show direct sum method attains a speed-up factor of up to 1.8 ( single precision ) and 4.4 ( half precision ) over cuDNN naive implementation .
In the proposed scheme , a dictionary is first learned from paired low-resolution and high-resolution facial images , and the input low-resolution query face can then be modelled by a set of high-resolution visual words via a dictionary lookup . A random pooling strategy is then applied to select subsets of visual words , and kernel Fisher 's linear discriminant analysis ( k-LDA ) is introduced to find the discriminant metrics . The final decision is based on the average over different pooling results .
The learned hyperplane is stored as the relative feature . Second , we propose an adaptive pooling technique to assemble multiple relative features generated by different RBs under the SVM framework , where the classifier and the pooling weights are jointly learned . Experiments based on three challenging datasets : Caltech-101 , Scene 15 and Willow-Actions , demonstrate the effectiveness and generality of our framework .
Then effect of different pooling methods on the classification performance of these networks was investigated using the MNIST dataset . In this study , in addition to the average and maximum pooling methods commonly used in the literature , Mixed pooling , Stochastic pooling , random pooling , Gaussian pooling , median pooling , minimum pooling methods are also used . Experimental results show that the Mixed pooling method has highest accuracy and minimum pooling method has the lowest accuracy .
Based on the sliding cycle time constraint , the proposed algorithm guarantees the maximum polling interval , an essential property for delay-sensitive applications and interactive services . We then introduce an iterative resource pooling that processes bursty best-effort traffic and achieves high throughput even under non-uniform upstream traffic distribution . We prove that greedy iterative resource pooling converges to equal resource allocation exponentially fast .
Object pooling stores object in the memory buffer , lowers the systematic expenses produced by frequently creation and destruction of the object , avoids frequently data exchange with database and improves the performance of accessing object . By analyzing the design and implementation principle of object pooling , the object pooling framework based on J2EE with LRU algorithm is implemented .
In this paper , beyond conventional spatial pooling that usually has a regular shape of NxN , we rethink the formulation of spatial pooling by introducing a new pooling strategy , called strip pooling , which considers a long but narrow kernel , i.e. , 1xN or Nx1 . Based on strip pooling , we further investigate spatial pooling architecture design by 1 ) introducing a new strip pooling module that enables backbone networks to efficiently model long-range dependencies ; 2 ) presenting a novel building block with diverse spatial pooling as a core ; and 3 ) systematically comparing the performance of the proposed strip pooling and conventional spatial pooling techniques . Both novel pooling-based designs are lightweight and can serve as an efficient plug-and-play modules in existing scene parsing networks .
The relationship to other joint predictions as well as the general context and understanding of the full image is crucial . Applying supervision earlier in the pipeline before pooling is a possibility , but at this point the features at a given pixel are the result of processing a relatively local receptive field and are thus ignorant of critical global cues . Repeated bottom - up , top - down inference with stacked hourglasses alleviates these concerns .
To address this problem , we put forward two improvements in this paper : 1 ) Instead of estimating instance weights through weighted sum pooling , we propose to add some blocks in the network for learning more discriminative instance classifiers by explicitly assigning binary instance labels ; 2 ) We propose to refine instance classifier online using spatial relation . Our motivation is that , though some detectors only capture objects partially , proposals having high spatial overlaps with detected parts may cover the whole object , or at least contain larger portion of the object .
We replace the last max - pooling layer of the two models by SPP layer , and the last fc layer and softmax loss layer by the layers described in Section [ reference ] . To increase the feature map size from the last conv layer , we replace the penultimate max - pooling layer and its subsequent conv layers by the dilated conv layers . The new added layers are initialized using Gaussian distributions with - mean and standard deviations .
‘ middle - section ’ ’ of the table shows only small fluctuations in miss - rate ( specially when considering the variance ) . In addition to filter size and layer width , we also experimented with different types of pooling layers ( max - pooling versus mean - pooling ) , see figure 2 of main paper . Other than on the first layer , replacing mean - pooling with max - pooling hurts performance .
The top - left corner pooling layer can be implemented very efficiently . We scan from right to left for the horizontal max - pooling and from bottom to top for the vertical max - pooling . We then add two max - pooled feature maps .
section : Corner Pooling Corner pooling is a key component of CornerNet . To understand its contribution to performance , we train another network without corner pooling but with the same number of parameters .
It also makes the max - pooling domain become independent from in the subsampled maps , as it exactly cancels out the effect of doubling at each iteration . We call the max - pooling operator with the iteration - independent domain : For the same reason , the shift applied to the correlation maps in ’s definition becomes simply after subsampling . Let be the shift ( or translation ) operator on the correlation map : Finally , we incorporate an additional non - linear mapping at each iteration on top of Eq .
As such , the objective of getting a higher reward from D φ is equivalent to finding a higher reward region in this extracted feature space F ( S ; φ f ) = { F ( s ; φ f ) } s∈S . Specifically , our feature extractor F ( · ; φ f ) in D φ is implemented by a CNN [ reference ] ; thus F ( s ; φ f ) outputs the CNN feature map vector as f after its convolution - pooling - activation layer . Other neural network models such as LSTM ( Hochreiter and Schmidhuber 1997 ) can also be used to implement
clarifies that bilinear pooling is closely related to the second - order polynomial kernel , and presents two compact representations via low - dimensional feature maps for kernel approximation . Kernel pooling approximates Gaussian RBF kernel to a given order through compact explicit feature maps , aiming to characterize higher order feature interactions . Cai et al . introduce a polynomial kernel based predictor to model higher - order statistics of convolutional features across multiple layers .
Comparison with Other Covariance Pooling Methods We compare iSQRT - COV with other covariance pooling methods , as shown in Tab . [ reference ] .
It is the primary reason why various pooling can not be applied in the dynamic and multidimensional dataset . To deal with the limitations , it needs to construct an optimal pooling method as a better option than max pooling and average pooling . Therefore , we introduce a dynamic pooling layer called RunPool to train the convolutional neural network ( CNN ) architecture .
In this paper , we present a deep architecture to match two Chinese sentences , which only relies on alignment instead of long short-term memory network after attention mechanism is employed to get interaction information between sentence-pairs , the model becomes more lightweight and simple . Meanwhile , in order to capture semantic features enough , in addition to using max pooling and average pooling operation , we also employ a pooling operation named attention-pooling to aggregate information from the whole sentence , the final matching score is obtained after a multilayer perceptron classifier . Experiments are carried out on ATEC-NLP dataset and outline the effectiveness of our approach .
1 ) We demonstrate the advantage of using images with a properly large size as input to CNN instead of the conventionally resized one . 2 ) We benchmark the performance of different CNN layers improved by average/max pooling on the feature maps . Our observation suggests that the Conv5 feature yields very competitive accuracy under such pooling step .
Nevertheless , state-of-the-art CNNs appear to incur huge computational cost and require the storage of a large number of parameters especially in fully connected layers , which is difficult to deploy such networks into alternative hardware devices with limited computation capacity . To solve the storage problem , we propose a novel technique called weighted average pooling for reducing the parameters in fully connected layer without loss in accuracy . Besides , we implement a cascaded model in single CNN by adding mid output to complete recognition as early as possible , which reduces average inference time significantly .
To effectively extract the complementary information concealed within these predictions , we develop a novel pooling mechanism which accounts for competition among contestants : a stacking paradigm integrating conditional logit regression and log-likelihood-ratio-based forecast selection . Empirical results using data related to horseracing events demonstrate that : ( i ) base model strength and diversity are important when combining model-based predictions for CEs ; ( ii ) average-based pooling , commonly employed elsewhere , may not be appropriate for CEs ( because average-based pooling exclusively focuses on strength ) ; and ( iii ) the proposed stacking ensemble provides statistically and economically accurate forecasts . These results have important implications for regulators of betting markets associated with CEs and in particular for the accurate assessment of market efficiency .
At present , the vast majority of GCNs use a neighborhood aggregation framework to learn a continuous and compact vector , then performing a pooling operation to generalize graph embedding for the classification task . These approaches have two disadvantages in the graph classification task : (1)when only the largest sub-graph structure ( k-hop neighbor ) is used for neighborhood aggregation , a large amount of early-stage information is lost during the graph convolution step ; ( 2 ) simple average/sum pooling or max pooling utilized , which loses the characteristics of each node and the topology between nodes . In this paper , we propose a novel framework called , dual attention graph convolutional networks ( DAGCN ) to address these problems .
We evaluate our method on various benchmarks for generic action , fine-grained action and gesture recognition . Results show that rank pooling brings an absolute improvement of 7 - 10 average pooling baseline . At the same time , rank pooling is compatible with and complementary to several appearance and local motion based methods and features , such as improved trajectories and deep learning features .
This paper extends the algorithm to the vocoder-free TTS and propose a GAN-based training algorithm using low-frequency-resolution amplitude spectra to overcome the difficulty in modeling complicated distribution of the high-dimensional spectra . In the proposed algorithm , amplitude spectra are transformed into low-frequency-resolution amplitude spectra by applying an average pooling function along with a frequency axis ; then the GAN-based distribution compensation is performed in the low-frequency-resolution domain . Because the low-frequency-resolution amplitude spectra approximately emulate filter banks , the proposed algorithm is expected to improve synthetic speech quality by reducing differences in spectral envelopes of natural and synthetic speech .
This paper describes a model that we submitted to W-NUT 2016 Shared task # 1 : Geolocation Prediction in Twitter . Our model classifies a tweet or a user to a city using a simple neural networks structure with fully-connected layers and average pooling processes . From the findings of previous geolocation prediction approaches , we integrated various user metadata along with message texts and trained the model with them .
To address those issues , we propose a two-stage partial mean pooling ( PMP ) approach to construct compact and discriminative global feature representations . The proposed PMP is meant to tackle the limits of traditional max pooling and mean ( or average ) pooling . By injecting the PMP pooling strategy into the CNN based patch-level mid-level feature extraction and representation , we have significantly improved the state-of-the-art retrieval performance over several common benchmark datasets .
The Eqn. ( 9 ) has an equivalent form as following : It is worth noting that is exactly the interaction feature between word and class . Therefore , the FastText is a special case of EXAM with an average pooling as the aggregation layer . In EXAM , we use a non - linear MLP to be the aggregation layer , and it will generalize FastText to a non - linear setting which might be more expressive than the original one .
Our inference model converts the output hidden vectors of BiLSTMs to the fixed - length vector with pooling operations and puts it into the final classifier to determine the overall inference class . Particularly , in addition to using mean pooling and max pooling similarly to ESIM DBLP : conf / acl / ChenZLWJI17 , we propose to use weighted pooling based on external knowledge to obtain a fixed - length vector as in Equation ( [ reference ] ) ( [ reference ] ) . In our experiments , we regard the function as a 1 - layer feed - forward neural network with ReLU activation function .
After the enhancement of local inference , another Bi - LSTM layer is used to capture local inference information and their context for inference composition . Instead of summation adopted by Parikh et al . , ESIM proposes to compute both max and average pooling and feeds the concatenate fixed length vector to the final classifier : a fully connected multi - layer perceptron . Figure [ reference ] shows a high - level view of the ESIM architecture , where the bottom LSTM1 layer of Figure [ reference ] is the input encoding layer ,
For example , [ reference ] aggregates local descriptors ( RootSIFT [ reference ] ) extracted from face crops using Fisher Vector [ reference ] ( FV ) encoding to obtain a single descriptor per face track . Since the success of deep learning in image - based face recognition [ reference ][ reference ][ reference ][ reference ][ reference ][ reference ][ reference ] , simple strategies for face descriptor aggregation prevailed , such as average - and max - pooling [ reference ][ reference ] .
On IJB - A dataset , QAN significantly outperforms the state - of - the - art algorithm NAN by 10.81 % at 0.001 FPR , 4.5 % at 0.01 FPR and 2.12 % at FPR=0.1 , as shown in Table [ reference ] . Compared with average pooling baseline , QAN reduces false negative rate at above three FPRs by 29.32 % , 6.45 % and 7.91 % . Our experiments on the two tasks show that QAN is robust for set - to - set recognition .
Possible applications of automatic facial expression recognition include better transcription of videos , movie or advertisement recommendations , detection of pain in telemedicine etc . Traditional convolutional neural networks ( CNNs ) that use convolutional layers , max or average pooling and fully connected layers are considered to capture only first - order statistics . Second - order statistics such as covariance are considered to be better regional descriptors than first - order statistics such as mean or maximum .
( see Figure [ reference ] ) . We can use two parallel stride 2 blocks : and . is a pooling layer ( either average or maximum pooling ) the activation , both of them are stride the filter banks of which are concatenated as in figure [ reference ] . section : Inception - v2
7 ( b ) show that our NAN significantly reduces the error of the baseline CNN + AvePool . This again suggests that in the presence of large face variances , the widely used strategies such as average - pooling aggregation and the pairwise distance computation are far from optimal . In such cases , our learned NAN model is clearly more powerful , and the aggregated feature representation by it is more favorable for the video face recognition task .
appendix : Evaluation of off - the - shelf classifiers at higher resolutions In this section , we present some additional classification results using off - the - shelf pretrained classification networks trained with standard average pooling ( ) . As outlined in sec : expand - pooling , sec : expanding - resolution , one of our contributions is a strategy for evaluating classifier networks trained with GeM pooling at scale and exponent at a higher resolution and adapted exponent .
However in applications with rich user behaviors , features are often contained with variable - length list of ids , e.g. , searched terms or watched videos in YouTube recommender system . These models often transform corresponding list of embedding vectors into a fixed - length vector via sum / average pooling , which causes loss of information . The proposed DIN tackles it by adaptively learning the representation vector w.r.t .
Selecting Variables for Neural Network Committees Fault Diagnosis for Rolling Bearings with Stacked Denoising Auto-encoder of Information Aggregation 증분형 K-means 클러스터링 기반 방사형 기저함수 신경회로망 모델 설계 A Recursive Object Algebra Based on Aggregation Abstraction for Manipulating Complex Object Social Influence-based Attentive Mavens Mining and Aggregative Representation Learning for Group Recommendation OPEN Relationships - Compositions and Containments Time Series Smoothing and OWA Aggregation An aggregation model reduction method for one-dimensional distributed systems Visual analytics of smogs in China GhostVLAD for set-based face recognition Modelling Interaction of Sentence Pair with Coupled-LSTMs Occlusion-aware R-CNN: Detecting Pedestrians in a Crowd CollaboNet: collaboration of deep neural networks for biomedical named entity recognition Gated-Attention Readers for Text Comprehension Graph Attention Networks Graph2Seq: Graph to Sequence Learning with Attention-based Neural Networks Cube Padding for Weakly-Supervised Saliency Prediction in 360° Videos Weakly Supervised Lesion Localization With Probabilistic-CAM Pooling On pooling networks and fluctuation in suboptimal detection framework Fast Algorithm of Unified Layer Performing Convolution and Average Pooling on the GPU Low-quality facial biometric verification via dictionary-based random pooling Learning relative features through adaptive pooling for image classification Comparison of Pooling Methods for Handwritten Digit Recognition Problem Iterative resource pooling for bandwidth allocation in TDM-PON: algorithm, convergence and experimental evaluation Java Object Pooling Based on LRU Algorithm Strip Pooling: Rethinking Spatial Pooling for Scene Parsing Stacked Hourglass Networks for Human Pose Estimation Multiple Instance Detection Network with Online Instance Classifier Refinement Taking a deeper look at pedestrians CornerNet: Detecting Objects as Paired Keypoints DeepMatching: Hierarchical Deformable Dense Matching Long Text Generation via Adversarial Training with Leaked Information Towards Faster Training of Global Covariance Pooling Networks by Iterative Matrix Square Root Normalization RunPool: A Dynamic Pooling Layer for Convolution Neural Network Bi-directional attention comparison for semantic sentence matching Good Practice in CNN Feature Transfer Building efficient CNN architecture for offline handwritten Chinese character recognition A new methodology for generating and combining statistical forecasting models to enhance competitive event prediction DAGCN: Dual Attention Graph Convolutional Networks Rank Pooling for Action Recognition Vocoder-free text-to-speech synthesis incorporating generative adversarial networks using low-/multi-frequency STFT amplitude spectra A Simple Scalable Neural Networks based Model for Geolocation Prediction in Twitter Two-stage pooling of deep convolutional features for image retrieval Explicit Interaction Model towards Text Classification Neural Natural Language Inference Models Enhanced with External Knowledge Attention Boosted Sequential Inference Model GhostVLAD for set-based face recognition Quality Aware Network for Set to Set Recognition Covariance Pooling for Facial Expression Recognition Rethinking the Inception Architecture for Computer Vision Neural Aggregation Network for Video Face Recognition MultiGrain: a unified image embedding for classes and instances Deep Interest Network for Click-Through Rate Prediction
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ISNN (1) ACCV EMNLP ECCV BMC Bioinformatics ACL ICLR ArXiv CVPR ICME ECCV 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) ECCV International Journal of Computer Vision AAAI 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition NUT@COLING ICIP AAAI ACL ArXiv ACCV 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) ArXiv KDD
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