To model the responses of neurons in the early visual system , at least three basic components are required : a receptive field , a normalization term , and a specification of encoding noise . Here , we examine how the receptive field , the normalization factor , and the encoding noise affect the drive to model-neuron responses when stimulated with natural images . To model the responses of neurons in the early visual system , at least three basic components are required : a receptive field , a normalization term , and a specification of encoding noise . Here , we examine how the receptive field , the normalization factor , and the encoding noise impact the model neuron responses to natural images and the signal-to-noise ratio for natural image discrimination . The discussion applies to spotted as well as in-situ synthesized microarray data . Existing normalization methods such as curve-fit ( " lowess " ) normalization , parallel and perpendicular translation normalization , and quantile normalization , but also dye-swap normalization are revisited in the light of the affine model and their strengths and weaknesses are investigated in this context . As a direct result from this study , we propose a robust non-parametric multi-dimensional affine normalization method , which can be applied to any number of microarrays with any number of channels either individually or all at once . It describes a protocol to interconnect workflow management systems based on a simple , reliable and standard messaging protocol . This protocol integrates some WfMC normalization work , especially e-mail Internet MIME Binding and Wf-XML . An experiment of this protocol to interconnect two commercial workflow products is then described and discussed . Complicated illumination variation is still one key problem in face recognition technique . In view of illumination variation in face recognition , a model-based Local Normalization technique is adopted for the faces illumination treatment , and extracting illumination invariant feature . The contrastive experiments are done with different methods . The calibration of raw intensity data is therefore a fundamental prerequisite for the analysis of oligonucleotide microarray data . The field of array normalization tools can be separated into tools that use only the perfect match probes ( PMs ) of one chip , tools that use PMs and mismatch probes ( MMs ) of one chip and tools that rely on multichip normalization techniques and reference arrays . The best results are yielded by multichip techniques like RMA or GCRMA . RESULTS This paper describes a model-based simulation paradigm that compares the classification accuracy provided by these methods over a variety of noise types and presents the results of a study modeled on noise typical of cDNA microarray data . The model consists of four parts : ( 1 ) the measurement equation for genes in the reference state ; ( 2 ) the measurement equation for genes in the test state ; ( 3 ) the ratio and normalization procedure for a dual-channel system ; and ( 4 ) the intensity and normalization procedure for a single-channel system . In the reference state , the mean intensities are modeled as a shifted exponential distribution , and the intensity for a particular gene is modeled via a normal distribution , Normal(I , alphaI ) , about its mean intensity I , with alpha being the coefficient of variation of the cell system . The test results presented are false match rates and false non-match rates over a range of decision thresholds on a per-test-subject basis . The results reveal considerable differences in the comparison-score frequency distribution among test subjects , which necessitates the setting of user-dependent decision thresholds or comparison-score normalization . We also propose three new normalization techniques , @D- , @DT- and TC-norm , which also use SMCs to estimate the normalization parameters . Our results show that we can lower the equal error rate and minimum decision cost function with fewer cohort models using SMC-based score normalization approaches . A new unification method for some equational theories is given . It is a generalization of the Martelli and Montanari ’s algorithm and is based on a decomposition-merging-normalization process . We prove that for the class of decomposable theories this method gives a complete set of unifiers for any equation . The technique involves normalizing the nerve-to-electrode transfer functions ( NETFs ) at a plurality of closely spaced recording sites . CVD 's are estimated from short-segment nerve recordings before and after NETF normalization and the results are compared with a long-distance CVD estimated from the same nerve . Normalization is found to dramatically improve short-distance CVD and to substantially duplicate the long-distance estimate . One is dropout optionally applied to the input to the top layer . The other is response normalization as in , which in our case scales the output of the pooling layer at each location by multiplying . subsection : Baseline methods The ordering of the kernel maps is of course arbitrary and determined before training begins . This sort of response normalization implements a form of lateral inhibition inspired by the type found in real neurons , creating competition for big activities amongst neuron outputs computed using different kernels . The constants k , n , ↵ , and are hyper - parameters whose values are determined using a validation set ; we used k = 2 , n = 5 , ↵ = 10 4 , and = 0.75 . We take a first step towards answering this question by studying the most simple architecture we could conceive : a homogeneous network solely consisting of convolutional layers , with occasional dimensionality reduction by using a stride of 2 . Surprisingly , we find that this basic architecture – trained using vanilla stochastic gradient descent with momentum – reaches state of the art performance without the need for complicated activation functions , any response normalization or max - pooling . We empirically study the effect of transitioning from a more standard architecture to our simplified CNN by performing an ablation study on CIFAR - 10 and compare our model to the state of the art on CIFAR - 10 , CIFAR - 100 and the ILSVRC - 2012 ImageNet dataset . With SNIPER , we show that the image resolution bottleneck can be alleviated for instance level recognition tasks . As long as we can cover negatives and use appropriate scale normalization methods , we can train with a large batch size of resampled low resolution chips , even on challenging datasets like COCO . Our results suggest that context beyond a certain field of view may not be beneficial during training . These cells are known to exhibit similar phase - invariance properties . subsection : Local response normalization Our networks also include response normalization layers . AlexNet is the eight - layer that won the ImageNet Large Scale Visual Recognition Challenge ( ILSVRC ) in 2012 by a large margin . It employs five convolutional layers with optional pooling and local response normalization . These are then followed by three fully - connected layers and a softmax classifier ( cf . Therefore , our PointNet variant has all batch normalization layers removed . paragraph : Input normalization As described in the setup , the corresponding 3D point cloud of an image bounding box is obtained by finding all points in the scene that can be projected onto the box . paragraph : QUAD We extend the smoothed - L1 loss proposed in by adding an extra normalization term designed for word quadrangles , which is typically longer in one direction . Let all coordinate values of be an ordered set then the loss can be written as where the normalization term is the shorted edge length of the quadrangle , given by and is the set of all equivalent quadrangles of with different vertices ordering . We do not update the word vectors during training . Performance was highest when adding ELMo without layer normalization to both the input and output of the contextual GRU layer and leaving the ELMo weights unregularized ( ) . Table [ reference ] compares test set results from the SQuAD leaderboard as of November 17 , 2017 when we submitted our system . It is empirically set to . The offset normalization is necessary to make the offset learning invariant to RoI size . The fc layer is learned by back - propagation , as detailed in appendix [ reference ] . In this paper , we propose a light-weight integration between , and thus harness the best of , the two schema . We present Attentive Normalization ( AN ) which generalizes the common affine transformation component in the vanilla feature normalization . Instead of learning a single affine transformation , AN learns a mixture of affine transformations and utilizes their weighted-sum as the final affine transformation applied to re-calibrate features in an instance-specific way . Batch normalization ( BN ) is an effective method to accelerate model training and improve the generalization performance of neural networks . In this paper , we propose an improved batch normalization technique called attentive batch normalization ( ABN ) in Long Short Term Memory ( LSTM ) based acoustic modeling for automatic speech recognition ( ASR ) . In the proposed method , an auxiliary network is used to dynamically generate the scaling and shifting parameters in batch normalization , and attention mechanisms are introduced to improve their regularized performance . Moreover , the GP-based classifier can not well distinguish less separable activities in the presence of multiple activities . Our work aims to address these issues by improving the GP-based classifier via : ( 1 ) using the area under the receiver operating characteristic curve ( AUC ) as fitness function , ( 2 ) using an online local time series normalization procedure to pre-smooth undesirable features , and ( 3 ) using a binary tree based classification framework to force GP to learn key discriminating features that can better distinguish less separable activities . We test the proposed method on a sensor data set collected from a smartphone , consisting of four common human activities , sitting , standing , walking and running . Like Batch Normalization , it normalizes the sample dimension . While Online Normalization does not use batches , it is as accurate as Batch Normalization . We resolve a theoretical limitation of Batch Normalization by introducing an unbiased technique for computing the gradient of normalized activations . Regardless of the level of the class hierarchy and the number of classes for each level , ARTMAP-HC is able to incrementally learn sequentially added input data belonging to new classes . By using a novel online normalization process , ARTMAP-HC can classify the new data without prior knowledge of the maximum value of the dataset . By adopting the prior labels appending process , the class dependency between class hierarchy levels is reflected in ARTMAP-HC . Specifically , we first extend the segmentation network , U-Net with a Self-Attention module , named SAU-Net , for cell counting . Second , we design an online version of Batch Normalization to mitigate the generalization gap caused by data augmentation in small datasets . We evaluate the proposed method on four public cell counting benchmarks - synthetic fluorescence microscopy ( VGG ) dataset , Modified Bone Marrow ( MBM ) dataset , human subcutaneous adipose tissue ( ADI ) dataset , and Dublin Cell Counting ( DCC ) dataset . Additionally , it leads to efficient activation normalization using only arithmetic shifts . We show that the combination of our quantization method and activation normalization maximizes SIMD throughput resulting in 2x to 6x gain in execution time compared to floating point inference . Experimental results demonstrate that our method generalizes to different networks giving same or better accuracy compared to floating point for classification , regression and recurrent networks . Instead of designing them separately , we unify them into a single computation graph , and evolve its structure starting from low-level primitives . Our layer search algorithm leads to the discovery of EvoNorms , a set of new normalization-activation layers that go beyond existing design patterns . Several of these layers enjoy the property of being independent from the batch statistics . To mitigate issues regarding the severe class imbalance and the complex geometry of the atria , the first CNN accurately detects the region of interest ( ROI ) containing the atria and the second CNN performs targeted regional segmentation of the ROI . The CNN comprises of a U-Net backbone enhanced with residual blocks , pre-activation normalization , and a Dice loss to improve accuracy and convergence . The receptive field of the CNN was increased by using 5 × 5 kernels to capture large variations in the atrial geometry . subsection : Gradients To check how the activation variance normalization influences the variance of the gradient , we measure the average variance of the gradient at all layers after 10 mini - batches . The variance is close to for all convolutional layers .
To model the responses of neurons in the early visual system , at least three basic components are required : a receptive field , a normalization term , and a specification of encoding noise . Here , we examine how the receptive field , the normalization factor , and the encoding noise affect the drive to model-neuron responses when stimulated with natural images .
To model the responses of neurons in the early visual system , at least three basic components are required : a receptive field , a normalization term , and a specification of encoding noise . Here , we examine how the receptive field , the normalization factor , and the encoding noise impact the model neuron responses to natural images and the signal-to-noise ratio for natural image discrimination .
The discussion applies to spotted as well as in-situ synthesized microarray data . Existing normalization methods such as curve-fit ( " lowess " ) normalization , parallel and perpendicular translation normalization , and quantile normalization , but also dye-swap normalization are revisited in the light of the affine model and their strengths and weaknesses are investigated in this context . As a direct result from this study , we propose a robust non-parametric multi-dimensional affine normalization method , which can be applied to any number of microarrays with any number of channels either individually or all at once .
It describes a protocol to interconnect workflow management systems based on a simple , reliable and standard messaging protocol . This protocol integrates some WfMC normalization work , especially e-mail Internet MIME Binding and Wf-XML . An experiment of this protocol to interconnect two commercial workflow products is then described and discussed .
Complicated illumination variation is still one key problem in face recognition technique . In view of illumination variation in face recognition , a model-based Local Normalization technique is adopted for the faces illumination treatment , and extracting illumination invariant feature . The contrastive experiments are done with different methods .
The calibration of raw intensity data is therefore a fundamental prerequisite for the analysis of oligonucleotide microarray data . The field of array normalization tools can be separated into tools that use only the perfect match probes ( PMs ) of one chip , tools that use PMs and mismatch probes ( MMs ) of one chip and tools that rely on multichip normalization techniques and reference arrays . The best results are yielded by multichip techniques like RMA or GCRMA .
RESULTS This paper describes a model-based simulation paradigm that compares the classification accuracy provided by these methods over a variety of noise types and presents the results of a study modeled on noise typical of cDNA microarray data . The model consists of four parts : ( 1 ) the measurement equation for genes in the reference state ; ( 2 ) the measurement equation for genes in the test state ; ( 3 ) the ratio and normalization procedure for a dual-channel system ; and ( 4 ) the intensity and normalization procedure for a single-channel system . In the reference state , the mean intensities are modeled as a shifted exponential distribution , and the intensity for a particular gene is modeled via a normal distribution , Normal(I , alphaI ) , about its mean intensity I , with alpha being the coefficient of variation of the cell system .
The test results presented are false match rates and false non-match rates over a range of decision thresholds on a per-test-subject basis . The results reveal considerable differences in the comparison-score frequency distribution among test subjects , which necessitates the setting of user-dependent decision thresholds or comparison-score normalization .
We also propose three new normalization techniques , @D- , @DT- and TC-norm , which also use SMCs to estimate the normalization parameters . Our results show that we can lower the equal error rate and minimum decision cost function with fewer cohort models using SMC-based score normalization approaches .
A new unification method for some equational theories is given . It is a generalization of the Martelli and Montanari ’s algorithm and is based on a decomposition-merging-normalization process . We prove that for the class of decomposable theories this method gives a complete set of unifiers for any equation .
The technique involves normalizing the nerve-to-electrode transfer functions ( NETFs ) at a plurality of closely spaced recording sites . CVD 's are estimated from short-segment nerve recordings before and after NETF normalization and the results are compared with a long-distance CVD estimated from the same nerve . Normalization is found to dramatically improve short-distance CVD and to substantially duplicate the long-distance estimate .
One is dropout optionally applied to the input to the top layer . The other is response normalization as in , which in our case scales the output of the pooling layer at each location by multiplying . subsection : Baseline methods
The ordering of the kernel maps is of course arbitrary and determined before training begins . This sort of response normalization implements a form of lateral inhibition inspired by the type found in real neurons , creating competition for big activities amongst neuron outputs computed using different kernels . The constants k , n , ↵ , and are hyper - parameters whose values are determined using a validation set ; we used k = 2 , n = 5 , ↵ = 10 4 , and = 0.75 .
We take a first step towards answering this question by studying the most simple architecture we could conceive : a homogeneous network solely consisting of convolutional layers , with occasional dimensionality reduction by using a stride of 2 . Surprisingly , we find that this basic architecture – trained using vanilla stochastic gradient descent with momentum – reaches state of the art performance without the need for complicated activation functions , any response normalization or max - pooling . We empirically study the effect of transitioning from a more standard architecture to our simplified CNN by performing an ablation study on CIFAR - 10 and compare our model to the state of the art on CIFAR - 10 , CIFAR - 100 and the ILSVRC - 2012 ImageNet dataset .
With SNIPER , we show that the image resolution bottleneck can be alleviated for instance level recognition tasks . As long as we can cover negatives and use appropriate scale normalization methods , we can train with a large batch size of resampled low resolution chips , even on challenging datasets like COCO . Our results suggest that context beyond a certain field of view may not be beneficial during training .
These cells are known to exhibit similar phase - invariance properties . subsection : Local response normalization Our networks also include response normalization layers .
AlexNet is the eight - layer that won the ImageNet Large Scale Visual Recognition Challenge ( ILSVRC ) in 2012 by a large margin . It employs five convolutional layers with optional pooling and local response normalization . These are then followed by three fully - connected layers and a softmax classifier ( cf .
Therefore , our PointNet variant has all batch normalization layers removed . paragraph : Input normalization As described in the setup , the corresponding 3D point cloud of an image bounding box is obtained by finding all points in the scene that can be projected onto the box .
paragraph : QUAD We extend the smoothed - L1 loss proposed in by adding an extra normalization term designed for word quadrangles , which is typically longer in one direction . Let all coordinate values of be an ordered set then the loss can be written as where the normalization term is the shorted edge length of the quadrangle , given by and is the set of all equivalent quadrangles of with different vertices ordering .
We do not update the word vectors during training . Performance was highest when adding ELMo without layer normalization to both the input and output of the contextual GRU layer and leaving the ELMo weights unregularized ( ) . Table [ reference ] compares test set results from the SQuAD leaderboard as of November 17 , 2017 when we submitted our system .
It is empirically set to . The offset normalization is necessary to make the offset learning invariant to RoI size . The fc layer is learned by back - propagation , as detailed in appendix [ reference ] .
In this paper , we propose a light-weight integration between , and thus harness the best of , the two schema . We present Attentive Normalization ( AN ) which generalizes the common affine transformation component in the vanilla feature normalization . Instead of learning a single affine transformation , AN learns a mixture of affine transformations and utilizes their weighted-sum as the final affine transformation applied to re-calibrate features in an instance-specific way .
Batch normalization ( BN ) is an effective method to accelerate model training and improve the generalization performance of neural networks . In this paper , we propose an improved batch normalization technique called attentive batch normalization ( ABN ) in Long Short Term Memory ( LSTM ) based acoustic modeling for automatic speech recognition ( ASR ) . In the proposed method , an auxiliary network is used to dynamically generate the scaling and shifting parameters in batch normalization , and attention mechanisms are introduced to improve their regularized performance .
Moreover , the GP-based classifier can not well distinguish less separable activities in the presence of multiple activities . Our work aims to address these issues by improving the GP-based classifier via : ( 1 ) using the area under the receiver operating characteristic curve ( AUC ) as fitness function , ( 2 ) using an online local time series normalization procedure to pre-smooth undesirable features , and ( 3 ) using a binary tree based classification framework to force GP to learn key discriminating features that can better distinguish less separable activities . We test the proposed method on a sensor data set collected from a smartphone , consisting of four common human activities , sitting , standing , walking and running .
Like Batch Normalization , it normalizes the sample dimension . While Online Normalization does not use batches , it is as accurate as Batch Normalization . We resolve a theoretical limitation of Batch Normalization by introducing an unbiased technique for computing the gradient of normalized activations .
Regardless of the level of the class hierarchy and the number of classes for each level , ARTMAP-HC is able to incrementally learn sequentially added input data belonging to new classes . By using a novel online normalization process , ARTMAP-HC can classify the new data without prior knowledge of the maximum value of the dataset . By adopting the prior labels appending process , the class dependency between class hierarchy levels is reflected in ARTMAP-HC .
Specifically , we first extend the segmentation network , U-Net with a Self-Attention module , named SAU-Net , for cell counting . Second , we design an online version of Batch Normalization to mitigate the generalization gap caused by data augmentation in small datasets . We evaluate the proposed method on four public cell counting benchmarks - synthetic fluorescence microscopy ( VGG ) dataset , Modified Bone Marrow ( MBM ) dataset , human subcutaneous adipose tissue ( ADI ) dataset , and Dublin Cell Counting ( DCC ) dataset .
Additionally , it leads to efficient activation normalization using only arithmetic shifts . We show that the combination of our quantization method and activation normalization maximizes SIMD throughput resulting in 2x to 6x gain in execution time compared to floating point inference . Experimental results demonstrate that our method generalizes to different networks giving same or better accuracy compared to floating point for classification , regression and recurrent networks .
Instead of designing them separately , we unify them into a single computation graph , and evolve its structure starting from low-level primitives . Our layer search algorithm leads to the discovery of EvoNorms , a set of new normalization-activation layers that go beyond existing design patterns . Several of these layers enjoy the property of being independent from the batch statistics .
To mitigate issues regarding the severe class imbalance and the complex geometry of the atria , the first CNN accurately detects the region of interest ( ROI ) containing the atria and the second CNN performs targeted regional segmentation of the ROI . The CNN comprises of a U-Net backbone enhanced with residual blocks , pre-activation normalization , and a Dice loss to improve accuracy and convergence . The receptive field of the CNN was increased by using 5 × 5 kernels to capture large variations in the atrial geometry .
subsection : Gradients To check how the activation variance normalization influences the variance of the gradient , we measure the average variance of the gradient at all layers after 10 mini - batches . The variance is close to for all convolutional layers .
The statistics of how natural images drive the responses of neurons Model neuron response statistics to natural images Methodological study of affine transformations of gene expression data with proposed robust non-parametric multi-dimensional normalization method A Mail and XML based Protocol to Support Workflow Interoperability Illumination Study in Face Recognition Based on Local Normalization Technique A new program package for model-based microarray analysis Which is better for cDNA-microarray-based classification: ratios or direct intensities Handwritten Signature On-Card Matching Performance Testing Speaker verification score normalization using speaker model clusters A New Equational Unification Method: A Generalization of Martelli-Montanari's Algorithm Evaluation of a new method for CVD estimation Effective Use of Word Order for Text Categorization with Convolutional Neural Networks ImageNet classification with deep convolutional neural networks Striving for Simplicity: The All Convolutional Net SNIPER: Efficient Multi-Scale Training Improving neural networks by preventing co-adaptation of feature detectors Cutting the Error by Half: Investigation of Very Deep CNN and Advanced Training Strategies for Document Image Classification PointFusion: Deep Sensor Fusion for 3D Bounding Box Estimation EAST: An Efficient and Accurate Scene Text Detector Deep contextualized word representations Deformable Convolutional Networks Attentive Normalization Attentive batch normalization for lstm-based acoustic modeling of speech recognition Sensor-based activity recognition with improved GP-based classifier Online Normalization for Training Neural Networks Incremental Class Learning for Hierarchical Classification SAU-Net: A Universal Deep Network for Cell Counting Accurate and Efficient Fixed Point Inference for Deep Neural Networks Evolving Normalization-Activation Layers Fully Automatic 3D Bi-Atria Segmentation from Late Gadolinium-Enhanced MRIs Using Double Convolutional Neural Networks All you need is a good init
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COST 2101/2102 Conference CADE HLT-NAACL CACM ICLR NeurIPS ArXiv 2017 14th IAPR International Conference on Document Analysis and Recognition (ICDAR) 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) NAACL-HLT 2017 IEEE International Conference on Computer Vision (ICCV) IEEE Congress on Evolutionary Computation NeurIPS BCB ICIP STACOM@MICCAI ICLR
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