We review existing methods and present new techniques to address this problem . We address a common problem with the well-known heatmap visualization , since the often arbitrary ordering of rows and columns renders the heatmap unclear , by using spectral seriation to rearrange the solutions and objectives and thus enhance the clarity of the heatmap . A multiobjective evolutionary optimizer is used to further enhance the simultaneous visualization of solutions in objective and parameter space . With the development of wearable device and augmented reality ( AR ) , the human device interaction in egocentric vision , especially the hand gesture based interaction , has attracted lots of attention among computer vision researchers . In this paper , we build a new dataset named EgoGesture and propose a heatmap-based solution for fingertip detection . Firstly , we discuss the dataset collection detail and as well the comprehensive analysis of this dataset , which shows that the dataset covers substantial data samples in various environments and dynamic hand shapes . Preliminary results indicate that study participants take approximately 15 % longer to learn the heatmap-based frequency analysis technique compared to traditional frequency analysis but demonstrate a 50 % increase in efficacy when tasked with solving simple substitution ciphers . Further , a heatmap-based solution contributes positively to the field insofar as educators have an additional tool to use in the classroom . As well , the heatmap visualization tool may allow researchers to comparatively examine efficacy of visualization tools in the cryptanalysis of mono-alphabetic substitution ciphers . The application is aimed to convert a distributed data or data with multiple variables , in XML format , into heatmap graphics on a computer display unit . The heatmap graphic lets the audience to better understand the content of a distributed data , with ease and appealing interface . Created using Adobe Flash CS4 and ActionScript 3.0 , the application uses XML as the main data format for input and output . The CoM is heavily influenced by outliers , which can lead to imprecise predictions . To fix this problem , we propose a new differentiable layer that computes the spatial median point of the latent heatmap , which is more robust to outliers . We demonstrate that our approach outperforms both strong baselines and a state-of-the-art regression function in synthetic experiments and on 5 real world keypoint regression datasets including faces , human pose , fashion , and document images . So , iRoad is able to scale up to handle real road networks with millions of nodes , and it can process heavy workloads on large numbers of moving objects . During the demo , audience will be able to interact with iRoad through a well designed Graphical User Interface to issue different types of predictive queries on a real road network , to obtain the predictive heatmap of the area of interest , to follow the creation and the dynamic update of the reachability tree around a specific moving object , and finally to examine the system efficiency and scalability . To derive possible display placements , it calculates volumetric heatmaps describing geometric persistence and planarity of structures inside the space . It evaluates visibility of display poses by calculating a volumetric heatmap describing occlusions , position within users ' field of view , and viewing angle . Optimal display size is calculated through a heatmap of average viewing distance . We show several case studies of the proposed analyst 's workflow as applied to the US Health Indicators Warehouse ( HIW ) , which is a medium scale , public dataset regarding community health information as collected by the US federal government . In our case studies , we demonstrate a series of visual analytics efforts targeted at the HIW , including visual analysis according to correlation matrices , multivariate outlier analysis , multiple linear regression of Medicare costs , confirmatory factor analysis , and hybrid scatterplot and heatmap visualization for distributions of a group of health indicators . We conclude by sketching a preliminary framework for examining causal dependence hypotheses for future data science research in public health . To overcome this problem , we first introduce a random survival forest ( RSF ) method to impute the incomplete data effectively . Then , we propose a spatial-appearance heatmap to describe foreground objects ( e.g. players and balls ) in an image . To evaluate the performance of our system , we collect the largest-ever dataset for soccer broadcasting camera selection . The first step regresses bone rotations in order to obtain an initial solution by considering skeleton structure . The second step performs refinement based on heatmap regressor using a 3D pose representation called cross heatmap which stacks heatmaps of xy and zy coordinates . By training the network using the proposed 3D human pose dataset that is comprised of images annotated with 3D skeletal angular poses , we showed that SkeletonNet can predict a full 3D human pose ( joint positions and bone rotations ) from a single image in-the-wild . In particular , a focus is laid on the explicability of the identified dependencies due to the acceptance of data-driven decisions by workers as well as to initiate tailored improvement measures . For this purpose , in this paper two visual analytics approaches are discussed for uncovering quality-affecting parameters with the claim for a high degree of explicability – on the one hand a method-feature-heatmap that visualizes a multiple backward feature elimination and on the other hand a graph-based visualization of class association rules . An automotive industry use case demonstrates the approaches in the context of gravity die casting of aluminium knuckles . One case worth noting is the middle example where the network initially activates on the visible wrists in the image . Upon further processing the heatmap does not activate at all on the original locations , instead choosing a reasonable position for the occluded wrist . section : Further Analysis Huang first found candidates using MSER and then employed a deep convolutional network as a strong classifier to prune false positives . The method of Jaderberg scanned the image in a sliding - window fashion and produced a dense heatmap for each scale with a convolutional neural network model . Later , Jaderberg employed both a CNN and an ACF to hunt word candidates and further refined them using regression . The precision@recall statistics in Figure [ reference ] again highlight the strength of the attentive approach . We can visualise the attention mechanism as a heatmap over a context document to gain further insight into the models ’ performance . The highlighted words show which tokens in the document were attended to by the model . The points mark the decile in document lengths across the corpus . appendix : Additional Heatmap Analysis We expand on the analysis of the attention mechanism presented in the paper by including visualisations for additional queries from the CNN validation dataset below . Heatmaps represent keypoint locations as Gaussian peaks . Each heatmap layer belongs to a specific keypoint class ( nose , wrists , ankles etc . ) and contains arbitrary number of peaks that pertain to person instances . Person segmentation mask at the last layer of heatmaps encodes the pixelwise spatial layout of people in the image . Given the input shape , the goal of is to regress the shape primitive parameters which is achieved via a CNN . The method of extends classical work on heatmap regression by proposing a 4D representation for regressing the location of sparse 3D landmarks for human pose estimation . Different from , we demonstrate that a 3D volumetric representation is particular effective for learning dense 3D facial geometry . However , we also find that ablating and pruning these heads does not lead to a significant drop in performance , indicating redundancy . By replacing the softmax activation functions with sparsemax activation functions , we find that attention heads behave seemingly more transparent : we can ablate fewer heads and heads score higher on our interpretability metrics . However , if we apply pruning to the sparsemax model we find that we can prune even more heads , raising the question whether enforced sparsity actually improves transparency . Finally , we find that relative positions heads seem integral to summarization performance and persistently remain after pruning . We propose sparsemax , a new activation function similar to the traditional softmax , but able to output sparse probabilities . After deriving its properties , we show how its Jacobian can be efficiently computed , enabling its use in a network trained with backpropagation . We provide the full mathematical analysis of the proposed framework . First , the optimal solution of an MCTE problem is shown to be a sparsemax distribution , whose supporting set can be adjusted . The proposed method has advantages over a softmax distribution in that it can exclude unnecessary actions by assigning zero probability . This paper proposes a novel deep learning architecture for semantic segmentation . The proposed Global and Selective Attention Network ( GSANet ) features Atrous Spatial Pyramid Pooling ( ASPP ) with a novel sparsemax global attention and a novel selective attention that deploys a condensation and diffusion mechanism to aggregate the multi-scale contextual information from the extracted deep features . A selective attention decoder is also proposed to process the GSA-ASPP outputs for optimizing the softmax volume . In our model , an attention memory neural network ( AMNN ) has a variable sized encoding memory and supports semantic compositionality . AMNN captures sentence level semantics and reasons relation between the sentence pairs ; then we use a Sparsemax layer over the output of the generated matching vectors ( sentences ) for classification . Our experiments on the Stanford Natural Language Inference ( SNLI ) Corpus show that our model outperforms the state of the art , achieving an accuracy of 87.4 % on the test data . As topic sparsity of individual documents in online social media increases , so does the difficulty of analyzing the online text sources using traditional methods . In this paper , we propose two novel neural models by providing sparse posterior distributions over topics based on the Gaussian sparsemax construction , enabling efficient training by stochastic backpropagation . We construct an inference network conditioned on the input data and infer the variational distribution with the relaxed Wasserstein ( RW ) divergence . Therefore , action-related features within the human skeleton sequences can be better represented and analyzed . To further optimize the network , we present a reparameterization that involves batch normalization and sparsemax classifier for the proposed LSTM network . We evaluate our approach on NTU RGB+D benchmark dataset . Our approach allocates fertilities to source words , used to bound the attention each word can receive . We experiment with various sparse and constrained attention transformations and propose a new one , constrained sparsemax , shown to be differentiable and sparse . Empirical evaluation is provided in three languages pairs . This paper introduces and evaluates two novel Hierarchical Attention Network models [ Yang et al. , 2016 ] - i ) Hierarchical Pruned Attention Networks , which remove the irrelevant words and sentences from the classification process in order to reduce potential noise in the document classification accuracy and ii ) Hierarchical Sparsemax Attention Networks , which replace the Softmax function used in the attention mechanism with the Sparsemax [ Martins and Astudillo , 2016 ] , capable of better handling importance distributions where a lot of words or sentences have very low probabilities . Our empirical evaluation on the IMDB Review for sentiment analysis datasets shows both approaches to be able to match the results obtained by the current state-of-the-art ( without , however , any significant benefits ) . We experimented with three types of attention mechanism , data augmentation based on a tempo and pitch perturbations , and a beam search pruning method . Moreover we propose a using of sparsemax function for our task as a probability distribution generator for an attention mechanism . We experimented with a joint CTC-Attention encoder-decoders using deep convolutional networks to compress input features or waveform spectrograms . The bilayer actuator features multiresponsiveness , low-power actuation , fast actuation speed , large-shape deformation , programmable adaptability , robust stability , and low-cost facile fabrication , which are highly desirable for modern soft actuator systems . We believe that these adaptive soft systems are attractive in a wide range of revolutionary technologies such as soft robots , smart switch , information encryption , infrared dynamic display , camouflage , and temperature regulation , as well as human-machine interface such as haptics . In this paper , we aim to conduct empirical study on training Transformers with small parameter sizes in the context of ASR rescoring . By combining techniques including subword units , adaptive softmax , large-scale model pre-training , and knowledge distillation , we show that we are able to successfully train small Transformer LMs with significant relative word error rate reductions ( WERR ) through n-best rescoring . In particular , our experiments on a video speech recognition dataset show that we are able to achieve WERRs ranging from 6.46 % to 7.17 % while only with 5.5 % to 11.9 % parameter sizes of the well-known large GPT model [ 1 ] , whose WERR with rescoring on the same dataset is 7.58 % . In this paper , we propose a novel Domain-aware Visual Bias Eliminating ( DVBE ) network that constructs two complementary visual representations , i.e. , semantic-free and semantic-aligned , to treat seen and unseen domains separately . Specifically , we explore cross-attentive second-order visual statistics to compact the semantic-free representation , and design an adaptive margin Softmax to maximize inter-class divergences . Thus , the semantic-free representation becomes discriminative enough to not only predict seen class accurately but also filter out unseen images , i.e. , domain detection , based on the predicted class entropy . Particularly , an oscillatory convergence behavior due to cycle slipping is explained by means of density evolution . Moreover , based on per-survivor processing ( PSP ) and single-estimator processing ( SEP ) techniques , a family of adaptive soft-input soft-output algorithms are employed to implement joint phase tracking and demodulation in the context of maximum a posteriori symbol detection . Both analytical and simulation results show that for RI-SCS the convergence performance of the SEP method is more robust than that of the PSP method when carrier phase has high and random dynamics . This letter presents an adaptative soft-based method for channel estimation in turbo receivers . The proposed approach is based on the particular algebraic structure of multipath Rayleigh-fading channels , and it is suited for mobile systems where the multipath pattern ( namely , the times of delay ) changes slowly over the time . However , these methods would suffer from sensitivity of hyper-parameters setting . In this paper , we propose an Adaptive Parameters Softmax Loss with different scale parameters for target logits and non-target logits and dynamically adaptive margin parameter . Extensive experiments on Mega-Face and IJB-C demonstrate the effectiveness of our method . “ Yoda , An Adaptive Soft Classification Model : Content-based Similarity Queries and Beyond ” , by Chen and Shahabi , describes the Yoda model , which conceptualizes content-based querying as the task of classifying images into classes . A fuzzy-logic based aggregation function is used to rank images , and a relevance feedback approach is incorporated into the model to improve querying performance . This phenomenon is known as concept drift , and is considered as one of the main challenges for moder learning systems . In this paper , we propose to investigate different methods for handling concept drift with adaptive soft one-class classifiers . One-class classification is a promising direction in data stream analytics , as it allows for a novelty detection , data description and learning with limited access to class labels . Analytical modeling of the computation and memory demands of the GCN shows that this behavior will persist even if the hidden state is increased - which could be needed to improve accuracy or to support a wider vocabulary . We present variations of the adaptive softmax layer that reduce execution time for the layer by 40 % and that scale better with the hidden state . On a large vocabulary speech recognition task , a stochastic gradient ascent implementation of the fDLR and the top hidden layer adaptation is shown to reduce word error rates ( WERs ) by 17 % and 14 % , respectively , compared to the baseline DNN performances . With a batch update implementation , the softmax layer adaptation technique reduces WERs by 10 % . We observe that using bias shift performs as well as doing scaling plus bias shift . Because the stability and the convergence rate of the internal dynamics can only be affected by the output function design , this paper proposes a general output function design to allow for greater freedom in output function optimization and better control performance as compared with previous studies . The proposed output function design and optimization , as well as the input-output linearizing controller , are simulated on a planar bipedal robot to validate the fast convergence rate to desired gait and the high robustness to external disturbances of the proposed method . To test this approach , two N-methylcarbamate pesticides , carbofuran and propoxur , were quantified using a classic peroxyoxalate chemiluminescence reaction as a detection system for chromatographic analysis . Straightforward network topologies ( one and two outputs models ) allow the analytes to be quantified in concentration ratios ranging from 1:7 to 5:1 with an average standard error of prediction for the generalization test of 2.7 and 2.3 % for carbofuran and propoxur , respectively . The reduced dimensions of the selected ANN architectures , especially those obtained after using heuristic rules , allowed simple quantification equations to be developed that transform the input variables into output variables .
We review existing methods and present new techniques to address this problem . We address a common problem with the well-known heatmap visualization , since the often arbitrary ordering of rows and columns renders the heatmap unclear , by using spectral seriation to rearrange the solutions and objectives and thus enhance the clarity of the heatmap . A multiobjective evolutionary optimizer is used to further enhance the simultaneous visualization of solutions in objective and parameter space .
With the development of wearable device and augmented reality ( AR ) , the human device interaction in egocentric vision , especially the hand gesture based interaction , has attracted lots of attention among computer vision researchers . In this paper , we build a new dataset named EgoGesture and propose a heatmap-based solution for fingertip detection . Firstly , we discuss the dataset collection detail and as well the comprehensive analysis of this dataset , which shows that the dataset covers substantial data samples in various environments and dynamic hand shapes .
Preliminary results indicate that study participants take approximately 15 % longer to learn the heatmap-based frequency analysis technique compared to traditional frequency analysis but demonstrate a 50 % increase in efficacy when tasked with solving simple substitution ciphers . Further , a heatmap-based solution contributes positively to the field insofar as educators have an additional tool to use in the classroom . As well , the heatmap visualization tool may allow researchers to comparatively examine efficacy of visualization tools in the cryptanalysis of mono-alphabetic substitution ciphers .
The application is aimed to convert a distributed data or data with multiple variables , in XML format , into heatmap graphics on a computer display unit . The heatmap graphic lets the audience to better understand the content of a distributed data , with ease and appealing interface . Created using Adobe Flash CS4 and ActionScript 3.0 , the application uses XML as the main data format for input and output .
The CoM is heavily influenced by outliers , which can lead to imprecise predictions . To fix this problem , we propose a new differentiable layer that computes the spatial median point of the latent heatmap , which is more robust to outliers . We demonstrate that our approach outperforms both strong baselines and a state-of-the-art regression function in synthetic experiments and on 5 real world keypoint regression datasets including faces , human pose , fashion , and document images .
So , iRoad is able to scale up to handle real road networks with millions of nodes , and it can process heavy workloads on large numbers of moving objects . During the demo , audience will be able to interact with iRoad through a well designed Graphical User Interface to issue different types of predictive queries on a real road network , to obtain the predictive heatmap of the area of interest , to follow the creation and the dynamic update of the reachability tree around a specific moving object , and finally to examine the system efficiency and scalability .
To derive possible display placements , it calculates volumetric heatmaps describing geometric persistence and planarity of structures inside the space . It evaluates visibility of display poses by calculating a volumetric heatmap describing occlusions , position within users ' field of view , and viewing angle . Optimal display size is calculated through a heatmap of average viewing distance .
We show several case studies of the proposed analyst 's workflow as applied to the US Health Indicators Warehouse ( HIW ) , which is a medium scale , public dataset regarding community health information as collected by the US federal government . In our case studies , we demonstrate a series of visual analytics efforts targeted at the HIW , including visual analysis according to correlation matrices , multivariate outlier analysis , multiple linear regression of Medicare costs , confirmatory factor analysis , and hybrid scatterplot and heatmap visualization for distributions of a group of health indicators . We conclude by sketching a preliminary framework for examining causal dependence hypotheses for future data science research in public health .
To overcome this problem , we first introduce a random survival forest ( RSF ) method to impute the incomplete data effectively . Then , we propose a spatial-appearance heatmap to describe foreground objects ( e.g. players and balls ) in an image . To evaluate the performance of our system , we collect the largest-ever dataset for soccer broadcasting camera selection .
The first step regresses bone rotations in order to obtain an initial solution by considering skeleton structure . The second step performs refinement based on heatmap regressor using a 3D pose representation called cross heatmap which stacks heatmaps of xy and zy coordinates . By training the network using the proposed 3D human pose dataset that is comprised of images annotated with 3D skeletal angular poses , we showed that SkeletonNet can predict a full 3D human pose ( joint positions and bone rotations ) from a single image in-the-wild .
In particular , a focus is laid on the explicability of the identified dependencies due to the acceptance of data-driven decisions by workers as well as to initiate tailored improvement measures . For this purpose , in this paper two visual analytics approaches are discussed for uncovering quality-affecting parameters with the claim for a high degree of explicability – on the one hand a method-feature-heatmap that visualizes a multiple backward feature elimination and on the other hand a graph-based visualization of class association rules . An automotive industry use case demonstrates the approaches in the context of gravity die casting of aluminium knuckles .
One case worth noting is the middle example where the network initially activates on the visible wrists in the image . Upon further processing the heatmap does not activate at all on the original locations , instead choosing a reasonable position for the occluded wrist . section : Further Analysis
Huang first found candidates using MSER and then employed a deep convolutional network as a strong classifier to prune false positives . The method of Jaderberg scanned the image in a sliding - window fashion and produced a dense heatmap for each scale with a convolutional neural network model . Later , Jaderberg employed both a CNN and an ACF to hunt word candidates and further refined them using regression .
The precision@recall statistics in Figure [ reference ] again highlight the strength of the attentive approach . We can visualise the attention mechanism as a heatmap over a context document to gain further insight into the models ’ performance . The highlighted words show which tokens in the document were attended to by the model .
The points mark the decile in document lengths across the corpus . appendix : Additional Heatmap Analysis We expand on the analysis of the attention mechanism presented in the paper by including visualisations for additional queries from the CNN validation dataset below .
Heatmaps represent keypoint locations as Gaussian peaks . Each heatmap layer belongs to a specific keypoint class ( nose , wrists , ankles etc . ) and contains arbitrary number of peaks that pertain to person instances . Person segmentation mask at the last layer of heatmaps encodes the pixelwise spatial layout of people in the image .
Given the input shape , the goal of is to regress the shape primitive parameters which is achieved via a CNN . The method of extends classical work on heatmap regression by proposing a 4D representation for regressing the location of sparse 3D landmarks for human pose estimation . Different from , we demonstrate that a 3D volumetric representation is particular effective for learning dense 3D facial geometry .
However , we also find that ablating and pruning these heads does not lead to a significant drop in performance , indicating redundancy . By replacing the softmax activation functions with sparsemax activation functions , we find that attention heads behave seemingly more transparent : we can ablate fewer heads and heads score higher on our interpretability metrics . However , if we apply pruning to the sparsemax model we find that we can prune even more heads , raising the question whether enforced sparsity actually improves transparency . Finally , we find that relative positions heads seem integral to summarization performance and persistently remain after pruning .
We propose sparsemax , a new activation function similar to the traditional softmax , but able to output sparse probabilities . After deriving its properties , we show how its Jacobian can be efficiently computed , enabling its use in a network trained with backpropagation .
We provide the full mathematical analysis of the proposed framework . First , the optimal solution of an MCTE problem is shown to be a sparsemax distribution , whose supporting set can be adjusted . The proposed method has advantages over a softmax distribution in that it can exclude unnecessary actions by assigning zero probability .
This paper proposes a novel deep learning architecture for semantic segmentation . The proposed Global and Selective Attention Network ( GSANet ) features Atrous Spatial Pyramid Pooling ( ASPP ) with a novel sparsemax global attention and a novel selective attention that deploys a condensation and diffusion mechanism to aggregate the multi-scale contextual information from the extracted deep features . A selective attention decoder is also proposed to process the GSA-ASPP outputs for optimizing the softmax volume .
In our model , an attention memory neural network ( AMNN ) has a variable sized encoding memory and supports semantic compositionality . AMNN captures sentence level semantics and reasons relation between the sentence pairs ; then we use a Sparsemax layer over the output of the generated matching vectors ( sentences ) for classification . Our experiments on the Stanford Natural Language Inference ( SNLI ) Corpus show that our model outperforms the state of the art , achieving an accuracy of 87.4 % on the test data .
As topic sparsity of individual documents in online social media increases , so does the difficulty of analyzing the online text sources using traditional methods . In this paper , we propose two novel neural models by providing sparse posterior distributions over topics based on the Gaussian sparsemax construction , enabling efficient training by stochastic backpropagation . We construct an inference network conditioned on the input data and infer the variational distribution with the relaxed Wasserstein ( RW ) divergence .
Therefore , action-related features within the human skeleton sequences can be better represented and analyzed . To further optimize the network , we present a reparameterization that involves batch normalization and sparsemax classifier for the proposed LSTM network . We evaluate our approach on NTU RGB+D benchmark dataset .
Our approach allocates fertilities to source words , used to bound the attention each word can receive . We experiment with various sparse and constrained attention transformations and propose a new one , constrained sparsemax , shown to be differentiable and sparse . Empirical evaluation is provided in three languages pairs .
This paper introduces and evaluates two novel Hierarchical Attention Network models [ Yang et al. , 2016 ] - i ) Hierarchical Pruned Attention Networks , which remove the irrelevant words and sentences from the classification process in order to reduce potential noise in the document classification accuracy and ii ) Hierarchical Sparsemax Attention Networks , which replace the Softmax function used in the attention mechanism with the Sparsemax [ Martins and Astudillo , 2016 ] , capable of better handling importance distributions where a lot of words or sentences have very low probabilities . Our empirical evaluation on the IMDB Review for sentiment analysis datasets shows both approaches to be able to match the results obtained by the current state-of-the-art ( without , however , any significant benefits ) .
We experimented with three types of attention mechanism , data augmentation based on a tempo and pitch perturbations , and a beam search pruning method . Moreover we propose a using of sparsemax function for our task as a probability distribution generator for an attention mechanism . We experimented with a joint CTC-Attention encoder-decoders using deep convolutional networks to compress input features or waveform spectrograms .
The bilayer actuator features multiresponsiveness , low-power actuation , fast actuation speed , large-shape deformation , programmable adaptability , robust stability , and low-cost facile fabrication , which are highly desirable for modern soft actuator systems . We believe that these adaptive soft systems are attractive in a wide range of revolutionary technologies such as soft robots , smart switch , information encryption , infrared dynamic display , camouflage , and temperature regulation , as well as human-machine interface such as haptics .
In this paper , we aim to conduct empirical study on training Transformers with small parameter sizes in the context of ASR rescoring . By combining techniques including subword units , adaptive softmax , large-scale model pre-training , and knowledge distillation , we show that we are able to successfully train small Transformer LMs with significant relative word error rate reductions ( WERR ) through n-best rescoring . In particular , our experiments on a video speech recognition dataset show that we are able to achieve WERRs ranging from 6.46 % to 7.17 % while only with 5.5 % to 11.9 % parameter sizes of the well-known large GPT model [ 1 ] , whose WERR with rescoring on the same dataset is 7.58 % .
In this paper , we propose a novel Domain-aware Visual Bias Eliminating ( DVBE ) network that constructs two complementary visual representations , i.e. , semantic-free and semantic-aligned , to treat seen and unseen domains separately . Specifically , we explore cross-attentive second-order visual statistics to compact the semantic-free representation , and design an adaptive margin Softmax to maximize inter-class divergences . Thus , the semantic-free representation becomes discriminative enough to not only predict seen class accurately but also filter out unseen images , i.e. , domain detection , based on the predicted class entropy .
Particularly , an oscillatory convergence behavior due to cycle slipping is explained by means of density evolution . Moreover , based on per-survivor processing ( PSP ) and single-estimator processing ( SEP ) techniques , a family of adaptive soft-input soft-output algorithms are employed to implement joint phase tracking and demodulation in the context of maximum a posteriori symbol detection . Both analytical and simulation results show that for RI-SCS the convergence performance of the SEP method is more robust than that of the PSP method when carrier phase has high and random dynamics .
This letter presents an adaptative soft-based method for channel estimation in turbo receivers . The proposed approach is based on the particular algebraic structure of multipath Rayleigh-fading channels , and it is suited for mobile systems where the multipath pattern ( namely , the times of delay ) changes slowly over the time .
However , these methods would suffer from sensitivity of hyper-parameters setting . In this paper , we propose an Adaptive Parameters Softmax Loss with different scale parameters for target logits and non-target logits and dynamically adaptive margin parameter . Extensive experiments on Mega-Face and IJB-C demonstrate the effectiveness of our method .
“ Yoda , An Adaptive Soft Classification Model : Content-based Similarity Queries and Beyond ” , by Chen and Shahabi , describes the Yoda model , which conceptualizes content-based querying as the task of classifying images into classes . A fuzzy-logic based aggregation function is used to rank images , and a relevance feedback approach is incorporated into the model to improve querying performance .
This phenomenon is known as concept drift , and is considered as one of the main challenges for moder learning systems . In this paper , we propose to investigate different methods for handling concept drift with adaptive soft one-class classifiers . One-class classification is a promising direction in data stream analytics , as it allows for a novelty detection , data description and learning with limited access to class labels .
Analytical modeling of the computation and memory demands of the GCN shows that this behavior will persist even if the hidden state is increased - which could be needed to improve accuracy or to support a wider vocabulary . We present variations of the adaptive softmax layer that reduce execution time for the layer by 40 % and that scale better with the hidden state .
On a large vocabulary speech recognition task , a stochastic gradient ascent implementation of the fDLR and the top hidden layer adaptation is shown to reduce word error rates ( WERs ) by 17 % and 14 % , respectively , compared to the baseline DNN performances . With a batch update implementation , the softmax layer adaptation technique reduces WERs by 10 % . We observe that using bias shift performs as well as doing scaling plus bias shift .
Because the stability and the convergence rate of the internal dynamics can only be affected by the output function design , this paper proposes a general output function design to allow for greater freedom in output function optimization and better control performance as compared with previous studies . The proposed output function design and optimization , as well as the input-output linearizing controller , are simulated on a planar bipedal robot to validate the fast convergence rate to desired gait and the high robustness to external disturbances of the proposed method .
To test this approach , two N-methylcarbamate pesticides , carbofuran and propoxur , were quantified using a classic peroxyoxalate chemiluminescence reaction as a detection system for chromatographic analysis . Straightforward network topologies ( one and two outputs models ) allow the analytes to be quantified in concentration ratios ranging from 1:7 to 5:1 with an average standard error of prediction for the generalization test of 2.7 and 2.3 % for carbofuran and propoxur , respectively . The reduced dimensions of the selected ANN architectures , especially those obtained after using heuristic rules , allowed simple quantification equations to be developed that transform the input variables into output variables .
Visualizing Mutually Nondominating Solution Sets in Many-Objective Optimization YOLSE: Egocentric Fingertip Detection from Single RGB Images An Enhanced Visualization Tool for Teaching Monoalphabetic Substitution Cipher Frequency Analysis Implementasi Metode Heatmap 2-D Untuk Visualisasi Data Terdistribusi Robust Keypoint Detection iRoad: A Framework For Scalable Predictive Query Processing On Road Networks HeatSpace: Automatic Placement of Displays by Empirical Analysis of User Behavior Moving from descriptive to causal analytics: case study of discovering knowledge from us health indicators warehouse Learning Sports Camera Selection From Internet Videos Skeleton Transformer Networks: 3D Human Pose and Skinned Mesh from Single RGB Image Feature Selection Based on Visual Analytics for Quality Prediction in Aluminium Die Casting Stacked Hourglass Networks for Human Pose Estimation EAST: An Efficient and Accurate Scene Text Detector Teaching Machines to Read and Comprehend MultiPoseNet: Fast Multi-Person Pose Estimation using Pose Residual Network Large Pose 3D Face Reconstruction from a Single Image via Direct Volumetric CNN Regression Understanding Multi-Head Attention in Abstractive Summarization From Softmax to Sparsemax: A Sparse Model of Attention and Multi-Label Classification Maximum Causal Tsallis Entropy Imitation Learning GSANet: Semantic Segmentation with Global and Selective Attention Research on attention memory networks as a model for learning natural language inference Sparsemax and Relaxed Wasserstein for Topic Sparsity Spatio-Temporal LSTM with Aggregated Feature Learning for Human Action Recognition Sparse and Constrained Attention for Neural Machine Translation Pruning and Sparsemax Methods for Hierarchical Attention Networks Investigating Joint CTC-Attention Models for End-to-End Russian Speech Recognition Leaf-inspired multiresponsive MXene-based actuator for programmable smart devices An Empirical Study of Efficient ASR Rescoring with Transformers Domain-Aware Visual Bias Eliminating for Generalized Zero-Shot Learning Convergence behavior of iterative phase synchronization and detection Soft Iterative Channel Estimation With Subspace and Rank Tracking Adaptive Parameters Softmax Loss for Deep Face Recognition Introduction to the Special Issue on Content-Based Image Retrieval (CBIR) Guest Editors Reacting to different types of concept drift with adaptive and incremental one-class classifiers Deep Learning Language Modeling Workloads: Where Time Goes on Graphics Processors Adaptation of context-dependent deep neural networks for automatic speech recognition Optimization of Output Functions with Nonholonomic Virtual Constraints in Underactuated Bipedal Walking Control Heuristic Extraction of Rules in Pruned Artificial Neural Networks Models Used for Quantifying Highly Overlapping Chromatographic Peaks
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ICCV Workshops CPR WML@ICDAR UIST SHB ACCV (4) INDIN ECCV 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) NIPS ECCV 2017 IEEE International Conference on Computer Vision (ICCV) SPNLP@EMNLP WSDM RCAR SPECOM CYBCONF IISWC SLT ACC
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