This paper proposes a question answering method based on Bi-LSTM and self-attention mechanism model . This method uses Bi-LSTM to encode and align the question and answer respectively , then uses self-attention to obtain the relationship between keywords , and finally performs softmax through the fully connected layer to obtain the similarity between the question and answer . Finally , in the experiment , compared with the traditional attention model , the accuracy rate of this model was increased by 1.6 % , and the recall rate was increased by 1.5 % . As data flows through the network , each node locally learns the best policies to the used under different data flows , different constraints and local network topology . Two learning strategies ( e -greedy and SoftMax ) are adapted to the problem domain and used for tests and comparisons . A proof-of-concept implementation of the proposed resource allocation algorithm is introduced , discussed and tested in simulated networks . All methods are evaluated with respect to novelty detection . The study shows that using pre-softmax activations of the Fully Connected layer provides a better overall confidence estimate than just using the softmax activations . For the MNIST dataset , softmax measures outperform pre-softmax based novelty detection measures , while in the Zurich dataset , pre-softmax based methods not only show better performance in detecting the left-out class , but they also manage to identify particular objects for which no class exists in the ground truth . In this paper , we introduce a new approach based on deep Convolutional Neural Network ( CNN ) to localize the LV in cardiac MRI in short axis views . A six-layer CNN with different kernel sizes was employed for feature extraction , followed by Softmax fully connected layer for classification . The pyramids of scales analysis was introduced in order to take account of the different sizes of the heart . Images were first applied to the CNN . The accuracy of Softmax Fully Connected layer used to classify images obtained 98.67 % . Also , the accuracy of the CNN is obtained with the Radial Basis Function ( RBF ) classifier 97.34 % and the Decision Tree ( DT ) classifier , is 94.24 % . The parameter spaces of these strategies are explored to find the best performing strategies . Results indicate that the softmax strategy is the most successful at maximising trader profit and global allocative efficiency in both adaptive and non-adaptive markets . The @e-decreasing strategy performs well in adaptive markets , while also showing greater stability in its parameter space than softmax . The Softmax loss function is a widely used function in deep learning methods , but it is not the best choice for all kind of problems . For distance-based problems , one new Softmax based loss function called Additive Margin Softmax ( AM-Softmax ) is proving to be a better choice than the traditional Softmax . The AM-Softmax introduces a margin of separation between the classes that forces the samples from the same class to be closer to each other and also maximizes the distance between classes . In this paper , we propose a new approach for speaker recognition systems called AM-SincNet , which is based on the SincNet but uses an improved AM-Softmax layer . In this paper , we propose an additive parameter depending on multiplicative angular margin to improve the discriminative power of feature embedding that can be easily implemented . In additive parameter approach , an automatic adjustment of the seedling element as the result of angular marginal seed is offered in a particular way for the angular softmax to learn angularly discriminative features . We train the model on publically available dataset CASIA-WebFace , and our experiments on famous benchmarks YouTube Faces ( YTF ) and labeled face in the wild ( LFW ) achieve better performance than the various state-of-the-art approaches . LSHTC datasets have large number of categories . In this paper we evaluate and report the performance of normal Softmax Vs Hierarchical Softmax on LSHTC datasets . This evaluation used macro f1 score as a performance measure . The proposed W-Softmax loss not only theoretically formulates the intraclass compactness and inter-class separability , but also can avoid overfitting by enlarging decision margins . Moreover , the size of decision margins can be flexibly controlled by adjusting a hyperparameter $ \alpha$. Extensive experimental results on several benchmark datasets show the superiority of W-Softmax in image classification tasks . This method focuses on different parts of a question , and accordingly retrieves information from the histories of the candidate expert . Since only a small percentage of historical records are relevant to any requester 's question , we introduce a Gumbel-Softmax-based mechanism to select relevant historical records from candidate experts ' answering histories . To evaluate the proposed method , we constructed two large-scale datasets drawn from Stack Overflow and Yahoo ! To solve this problem , we propose a deep-based dynamic interest perception network ( DIPN ) that can trace both positive interest and negative interest . The proposed DIPN model introduces three new parts to the interest-based deep model : a gated recurrent unit ( GRU ) learns the implied interest vector from historical click sequences ; an attention mechanism improves the expressiveness of interest vector with the weight vector ; an interest degree feature scales the weight vector by the d-Softmax function to improve the expressiveness of interest vector . We evaluate the effectiveness of the DIPN model by conducting extensive comparative experiments using real datasets . [ reference ] ) then over the question elements ( Eq . [ reference ] ) , interleaving the sums with affine projections and non - linearities to obtain a final prediction : with , , , learned weights and biases , a ReLU , and a softmax or a logistic function ( see experiments , Section [ reference ] ) . The summations over the scene elements and question elements is a form of pooling that brings the variable number of features ( due to the variable number of words and objects in the input ) to a fixed - size output . A content addressable memory , proposed in , is an external memory with an addressing scheme which selects based upon the similarity of memory words to a given query . Specifically , for the th read weight we define , where is a similarity measure , typically Euclidean distance or cosine similarity , and is a differentiable monotonic transformation , typically a softmax . We can think of this as an instance of kernel smoothing where the network learns to query relevant points . Take the SST - 2 task as an example . The probability that is labeled as class ( i.e. , the sentiment ) is predicted by a logistic regression with softmax : where is the task - specific parameter matrix . paragraph : Text Similarity Output : summarizes several recent papers reporting results on the VQA dataset on arxiv.org and gives a simple but strong baseline model ( iBOWIMG ) on this dataset . This simple baseline concatenates the image features with the bag of word embedding question representation and feeds them into a softmax classifier to predict the answer . The iBOWIMG model beats most VQA models considered in the paper . Our framework ( DSN ) , with the choice of using SVM , softmax or other classifiers , emphasizes the direct supervision of each intermediate layer . In the experiments , we show consistent improvement of DSN - SVM and DSN - Softmax over CNN - SVM and CNN - Softmax respectively . We observe all state - of - the - art results on MNIST , CIFAR - 10 , CIFAR - 100 , and SVHN . Indeed , the updates to the logits with IS are tied whereas in NCE they are independent . subsection : CNN Softmax The character - level features allow for a smoother and compact parametrization of the word embeddings . The first layer consists of K = 8 attention heads computing F = 8 features each ( for a total of 64 features ) , followed by an exponential linear unit ( ELU ) [ reference ] nonlinearity . The second layer is used for classification : a single attention head that computes C features ( where C is the number of classes ) , followed by a softmax activation . For coping with the small training set sizes , regularization is liberally applied within the model . Therefore , we use the following attention mechanism after we get and . Suppose , we then have where is obtained after one - layer MLP for the input , is the importance of word , is calculated by the SoftMax unit on the context vector of the sentence which is randomly initialized and modified during the training , is the attention enhanced vector through multiplying the weight and original vector , where correspond to the forward vector and the backward vector , respectively . Adaptive word direction layer In traditional Bi - LSTM model , the forward and the backward vectors of a word are considered to have equal importance on the word representation . So this convolutional layer has kernels of size connected to the output of CaffeNet and then we add a softmax unit to normalize the output . The size of the result tensor is . We also use the usual learned linear transformation and softmax function to convert the decoder output to predicted next - token probabilities . In our model , we share the same weight matrix between the two embedding layers and the pre - softmax linear transformation , similar to press2016using . In the embedding layers , we multiply those weights by . These pooling layers can also be considered as “ convolutional ” , in the sense that they are using sliding windows . The last two layers are fully connected , with an N - way softmax as the output , where N is the number of categories . The deep network described above needs a fixed image size . To further understand the answers predicted by the model given the visual feature and question sentence , we first decompose the word contribution of the answer into single words of the question sentence , then we visualize the informative image regions relevant to the answer through the technique proposed in . Since there are just two linear transformations ( one is word embedding and the other is softmax matrix multiplication ) from the one hot vector to the answer response , we could easily know the importance of each single word in the question to the predicted answer . In Figure [ reference ] , we plot the ranked word importance for each word in the question sentence . To further reduce the computational complexity of modeling the generation of a word , we use a class - factored softmax . By using classes for a vocabulary of size , this prediction step runs in time rather than the of the full - vocabulary softmax . To obtain clusters , we use the greedy agglomerative clustering algorithm of . This can be easily seen by defining , which turns the MoC parameterization ( [ reference ] ) into . Note that this is equivalent to the Softmax parameterization ( [ reference ] ) . Thus , performing mixture in the feature space can only make the function family more expressive , but does not change the fact that the rank of is upper bounded by the embedding dimension . In this article we analyze probabilistic argumentation frameworks ( PAFs ) , defined as an extension of Dung abstract argumentation frameworks in which each argument n is asserted with a probability pn . The debate around PAFs has so far centered on their theoretical definition and basic properties . Human behavior can be recognized by the motion of keypoints of human bodies . In this paper , we propose a method , which is based on a PAFs approach , for human keypoints detection . The proposed method makes improvements in two aspects : ( 1 ) It perfects the joint points matching algorithm by re-matching . ( 2 ) It uses multi-branch PAFs to correct the keypoint connections , thus improving the wrong connection problem of upper and lower limbs for multi-person keypoint detection . The improved PAFs method , whose mAP reaches 53.6 % on HKD dataset , improved the score of the original method by 2 % . The proposed structure along with its design approach is analyzed . The design procedure to design a second , third , and fourth-order PAFs is presented . It is shown that by increasing the order of the PAF structure , one can either improve the phase accuracy of the structure over the same bandwidth of interest or achieve the same phase accuracy over a wider bandwidth . This paper studies penalty-based functions defined by PAFs . We analyse some properties ( e.g. : idempotency , averaging behavior and shift-invariance ) , providing a characterization of idempotent penalty-based PAFs and a weak characterization of averaging penalty-based PAFs . The use of penalty-based PAFs in spatial/tonal filters is outlined . The MRO also hosts the SKA low frequency pathfinder instrument , the Murchison Widefield Array and will host the initial low frequency instrument of the SKA , SKA1-Low . Commissioning of ASKAP using six antennas equipped with first-generation PAFs is now complete and installation of second-generation PAFs and digital systems is underway . In this paper we review technical progress and commissioning to date , and refer the reader to relevant technical and scientific publications . ConclusionThe RASPRO-PAF system and the associated techniques are found to be useful in many healthcare applications , especially in remote patient monitoring . The personalization , precision , and prevention PAFs presented in the paper successfully shows remarkable performance in satisfying the goals of 3Ps , thereby providing the advantages of three A ’s : availability , affordability , and accessibility in the global health scenario . This paper explores adaptive array cancellation algorithm effectiveness on ASKAP for realistic bandwidths and integration times . With ASKAP 's beamforming PAFs on each dish , followed by a central correlation processor across beamformed signals from all dishes , one may consider algorithms that span multiple levels in the hierarchical signal processing chain . We compare performance for several subspace-projection-based algorithms applied to different tiers of this extended architecture . For underactuated bipeds , control design based on input-output linearization can result in internal dynamics whose stability and convergence rate to the desired gait affect the closed-loop stability and the overall convergence rate . 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 . The invention relates to a direct-current filter circuit for realizing an ASIC ( Application Specific Integrated Circuit ) audio processing function . The direct-current filter circuit comprises an input function processing module and an output function feedback module , wherein the input function processing module is used for performing delay phase reversal on an input signal of the direct-current filter circuit and outputting the input signal which is subjected to delay phase reversal together with an input signal of the direct-current filter circuit ; and the output function feedback module is used for intercepting a high-order field from an output delay signal of the direct-current filter circuit , performing phase reversal on the output delay signal , and adding the output delay signal which is subjected to phase reversal with an output signal of the input function processing module and the output delay signal of the direct-current filter circuit . Through adoption of the direct-current filter circuit for realizing the ASIC audio processing function disclosed by the invention , a direct-current component in an ASIC audio signal can be removed ; moreover , a multiplier is optimized , so that the hardware resource consumption during processing is lowered greatly , and resources accounting for 70 percent of total resources are reduced below 30 percent ; and the circuit is simple and practical in structure , has more stable working performance , and is wider in application range . The strategy is applied in the identification of the key drivers of uncertainty for the complex computer code developed at the National Aeronautics and Space Administration ( NASA ) assessing the risk of lunar space missions . We also introduce a symmetry result that enables the estimation of global sensitivity measures to datasets produced outside a conventional input – output functional framework . A comparison between the cost of these two algorithms and the performance improvement obtained from using these algorithms are discussed . We also propose different output selection functions to be used in switching elements for implementing the adaptive routing . We evaluate and compare the performance of these selection functions and discover that the best selection functions for BMINs are not dependent on the traffic pattern , message size , or system size . We also study the effect of output selection , functions on the network performance . We present six different output selection functions and study their performance for different system parameters and communication patterns through extensive simulation . The results show that three of these selection functions perform similar to each other and outperform the other selection functions consistently . Abstract PTCRIS ( Portuguese Current Research Information System - https://ptcris.pt/ ) is a program aiming at the creation and sustained development of a national integrated information ecosystem to support research management , according to the best international standards and practices . This ecosystem includes outcomes and outputs modules , in particular institutional repositories managed by the nationwide service RCAAP ( Scientific Open Access Repository of Portugal – http://www.rcaap.pt/ ) . In order to achieve such vision , PTCRIS has two main goals . The function blocks currently defined address process automation applications . They include simple input and output function blocks and several control function blocks . The function blocks currently defined are analog input , analog output , discrete input , discrete output , PID , bias & gain , signal select , manual loader , and ratio . The use of these ideas in decomposing a function either for representation as a set of OBDDs or for implementation in a cascode technology is outlined . A characterization is given of a class of multioutput functions which includes addition whose members have particularly small OBDDs.<<ETX > > These models are used for space – time monitoring of beach morphodynamics . A series of input – output analyses may reveal certain salient aspects of the spatial – temporal dynamics of a system , which may not be obtainable where reliable data on key system control variables are unavailable . Beaches are open , dynamic , and process-response systems . A unified framework is proposed to study the asymptotic behavior of general systems . From the view-point of signal , Lyapunov-like conditions and output functions based methods are used to help the check of stability . Furthermore , the concept of pseudo distance function is introduced to describe the desired convergence behavior of systems . In this investigation , some of input parameters ( nearly 10 ) were used . For selecting the best model for this study , outputs of models were compared . A three-layer ANN was found to be optimum with architecture of 12 and 5 neurons in the first and second hidden layer , respectively , and 1 neuron in output layer . Our message function is a MLP . Our node function is , Our output function is a MLP with a dropout fraction of 0.5 in the penultimate layer . The last layer has 16 hidden linear units . However , the conventional approach of specifying deterministic ' hard ' subclasses via unsupervised clustering can lead to inaccurate models . The multimodal softmax ( MMS ) model is presented as a new probabilistic discriminative subclass model that overcomes this unreliability . By invoking fully probabilistic latent ' soft ' subclasses , MMS permits learning via standard statistical methods without requiring explicit clustering/relabeling of data . Specifically , the thesis consists of two parts — better architectures to improve generative modeling , and applications of generative modeling in semi-supervised learning . In the first part , we identify an expressiveness bottleneck of prior neural language models , and propose a high-rank language model called the Mixture of Softmaxes ( MoS ) to break through such bottleneck . We later propose a faster highrank language model that trains faster than MoS while maintaining the capacity to break the bottleneck . We propose a simple and effective method to address the Softmax bottleneck . Specifically , we introduce discrete latent variables into a recurrent language model , and formulate the next - token probability distribution as a Mixture of Softmaxes ( MoS ) . Mixture of Softmaxes is more expressive than Softmax and other surrogates considered in prior work . To solve the Softmax bottleneck , DBLP : journals / corr / abs - 1711 - 03953 proposed Mixture of Softmaxes ( MoS ) , which increases the rank of the matrix by combining multiple probability distributions computed from the encoded fixed - length vector . In this study , we propose Direct Output Connection ( DOC ) as a generalization of MoS. For stacked RNNs , DOC computes the probability distributions from the middle layers including input embeddings . With negligible overhead in the number of parameters and training time , our Past Decode Regularization ( PDR ) method achieves a word level perplexity of 55.6 on the Penn Treebank and 63.5 on the WikiText - 2 datasets using a single softmax . We also show gains by using PDR in combination with a mixture - of - softmaxes , achieving a word level perplexity of 53.8 and 60.5 on these datasets . In addition , our method achieves 1.169 bits - per - character on the Penn Treebank Character dataset for character level language modeling . Consequently , the margin between two arbitrary classes can be increased . Compared with the original A - Softmax , Equation [ reference ] replaces norm of with an adjustable scalar factor . In our model , according to Equation [ reference ] , is always equal to 1 . In this paper , a novel two-stream architecture has been designed to improve action recognition accuracy for hockey using three main components . First , pose is estimated via the Part Affinity Fields model to extract meaningful cues from the player . Second , optical flow ( using LiteFlownet ) is used to extract temporal features . explored geometric and appearance constraints among joint candidates . Cao et al . proposed part affinity fields to encode location and orientation of limbs . Newell and Deng presented the associative embedding for grouping joint candidates .
This paper proposes a question answering method based on Bi-LSTM and self-attention mechanism model . This method uses Bi-LSTM to encode and align the question and answer respectively , then uses self-attention to obtain the relationship between keywords , and finally performs softmax through the fully connected layer to obtain the similarity between the question and answer . Finally , in the experiment , compared with the traditional attention model , the accuracy rate of this model was increased by 1.6 % , and the recall rate was increased by 1.5 % .
As data flows through the network , each node locally learns the best policies to the used under different data flows , different constraints and local network topology . Two learning strategies ( e -greedy and SoftMax ) are adapted to the problem domain and used for tests and comparisons . A proof-of-concept implementation of the proposed resource allocation algorithm is introduced , discussed and tested in simulated networks .
All methods are evaluated with respect to novelty detection . The study shows that using pre-softmax activations of the Fully Connected layer provides a better overall confidence estimate than just using the softmax activations . For the MNIST dataset , softmax measures outperform pre-softmax based novelty detection measures , while in the Zurich dataset , pre-softmax based methods not only show better performance in detecting the left-out class , but they also manage to identify particular objects for which no class exists in the ground truth .
In this paper , we introduce a new approach based on deep Convolutional Neural Network ( CNN ) to localize the LV in cardiac MRI in short axis views . A six-layer CNN with different kernel sizes was employed for feature extraction , followed by Softmax fully connected layer for classification . The pyramids of scales analysis was introduced in order to take account of the different sizes of the heart .
Images were first applied to the CNN . The accuracy of Softmax Fully Connected layer used to classify images obtained 98.67 % . Also , the accuracy of the CNN is obtained with the Radial Basis Function ( RBF ) classifier 97.34 % and the Decision Tree ( DT ) classifier , is 94.24 % .
The parameter spaces of these strategies are explored to find the best performing strategies . Results indicate that the softmax strategy is the most successful at maximising trader profit and global allocative efficiency in both adaptive and non-adaptive markets . The @e-decreasing strategy performs well in adaptive markets , while also showing greater stability in its parameter space than softmax .
The Softmax loss function is a widely used function in deep learning methods , but it is not the best choice for all kind of problems . For distance-based problems , one new Softmax based loss function called Additive Margin Softmax ( AM-Softmax ) is proving to be a better choice than the traditional Softmax . The AM-Softmax introduces a margin of separation between the classes that forces the samples from the same class to be closer to each other and also maximizes the distance between classes . In this paper , we propose a new approach for speaker recognition systems called AM-SincNet , which is based on the SincNet but uses an improved AM-Softmax layer .
In this paper , we propose an additive parameter depending on multiplicative angular margin to improve the discriminative power of feature embedding that can be easily implemented . In additive parameter approach , an automatic adjustment of the seedling element as the result of angular marginal seed is offered in a particular way for the angular softmax to learn angularly discriminative features . We train the model on publically available dataset CASIA-WebFace , and our experiments on famous benchmarks YouTube Faces ( YTF ) and labeled face in the wild ( LFW ) achieve better performance than the various state-of-the-art approaches .
LSHTC datasets have large number of categories . In this paper we evaluate and report the performance of normal Softmax Vs Hierarchical Softmax on LSHTC datasets . This evaluation used macro f1 score as a performance measure .
The proposed W-Softmax loss not only theoretically formulates the intraclass compactness and inter-class separability , but also can avoid overfitting by enlarging decision margins . Moreover , the size of decision margins can be flexibly controlled by adjusting a hyperparameter $ \alpha$. Extensive experimental results on several benchmark datasets show the superiority of W-Softmax in image classification tasks .
This method focuses on different parts of a question , and accordingly retrieves information from the histories of the candidate expert . Since only a small percentage of historical records are relevant to any requester 's question , we introduce a Gumbel-Softmax-based mechanism to select relevant historical records from candidate experts ' answering histories . To evaluate the proposed method , we constructed two large-scale datasets drawn from Stack Overflow and Yahoo !
To solve this problem , we propose a deep-based dynamic interest perception network ( DIPN ) that can trace both positive interest and negative interest . The proposed DIPN model introduces three new parts to the interest-based deep model : a gated recurrent unit ( GRU ) learns the implied interest vector from historical click sequences ; an attention mechanism improves the expressiveness of interest vector with the weight vector ; an interest degree feature scales the weight vector by the d-Softmax function to improve the expressiveness of interest vector . We evaluate the effectiveness of the DIPN model by conducting extensive comparative experiments using real datasets .
[ reference ] ) then over the question elements ( Eq . [ reference ] ) , interleaving the sums with affine projections and non - linearities to obtain a final prediction : with , , , learned weights and biases , a ReLU , and a softmax or a logistic function ( see experiments , Section [ reference ] ) . The summations over the scene elements and question elements is a form of pooling that brings the variable number of features ( due to the variable number of words and objects in the input ) to a fixed - size output .
A content addressable memory , proposed in , is an external memory with an addressing scheme which selects based upon the similarity of memory words to a given query . Specifically , for the th read weight we define , where is a similarity measure , typically Euclidean distance or cosine similarity , and is a differentiable monotonic transformation , typically a softmax . We can think of this as an instance of kernel smoothing where the network learns to query relevant points .
Take the SST - 2 task as an example . The probability that is labeled as class ( i.e. , the sentiment ) is predicted by a logistic regression with softmax : where is the task - specific parameter matrix . paragraph : Text Similarity Output :
summarizes several recent papers reporting results on the VQA dataset on arxiv.org and gives a simple but strong baseline model ( iBOWIMG ) on this dataset . This simple baseline concatenates the image features with the bag of word embedding question representation and feeds them into a softmax classifier to predict the answer . The iBOWIMG model beats most VQA models considered in the paper .
Our framework ( DSN ) , with the choice of using SVM , softmax or other classifiers , emphasizes the direct supervision of each intermediate layer . In the experiments , we show consistent improvement of DSN - SVM and DSN - Softmax over CNN - SVM and CNN - Softmax respectively . We observe all state - of - the - art results on MNIST , CIFAR - 10 , CIFAR - 100 , and SVHN .
Indeed , the updates to the logits with IS are tied whereas in NCE they are independent . subsection : CNN Softmax The character - level features allow for a smoother and compact parametrization of the word embeddings .
The first layer consists of K = 8 attention heads computing F = 8 features each ( for a total of 64 features ) , followed by an exponential linear unit ( ELU ) [ reference ] nonlinearity . The second layer is used for classification : a single attention head that computes C features ( where C is the number of classes ) , followed by a softmax activation . For coping with the small training set sizes , regularization is liberally applied within the model .
Therefore , we use the following attention mechanism after we get and . Suppose , we then have where is obtained after one - layer MLP for the input , is the importance of word , is calculated by the SoftMax unit on the context vector of the sentence which is randomly initialized and modified during the training , is the attention enhanced vector through multiplying the weight and original vector , where correspond to the forward vector and the backward vector , respectively . Adaptive word direction layer In traditional Bi - LSTM model , the forward and the backward vectors of a word are considered to have equal importance on the word representation .
So this convolutional layer has kernels of size connected to the output of CaffeNet and then we add a softmax unit to normalize the output . The size of the result tensor is .
We also use the usual learned linear transformation and softmax function to convert the decoder output to predicted next - token probabilities . In our model , we share the same weight matrix between the two embedding layers and the pre - softmax linear transformation , similar to press2016using . In the embedding layers , we multiply those weights by .
These pooling layers can also be considered as “ convolutional ” , in the sense that they are using sliding windows . The last two layers are fully connected , with an N - way softmax as the output , where N is the number of categories . The deep network described above needs a fixed image size .
To further understand the answers predicted by the model given the visual feature and question sentence , we first decompose the word contribution of the answer into single words of the question sentence , then we visualize the informative image regions relevant to the answer through the technique proposed in . Since there are just two linear transformations ( one is word embedding and the other is softmax matrix multiplication ) from the one hot vector to the answer response , we could easily know the importance of each single word in the question to the predicted answer . In Figure [ reference ] , we plot the ranked word importance for each word in the question sentence .
To further reduce the computational complexity of modeling the generation of a word , we use a class - factored softmax . By using classes for a vocabulary of size , this prediction step runs in time rather than the of the full - vocabulary softmax . To obtain clusters , we use the greedy agglomerative clustering algorithm of .
This can be easily seen by defining , which turns the MoC parameterization ( [ reference ] ) into . Note that this is equivalent to the Softmax parameterization ( [ reference ] ) . Thus , performing mixture in the feature space can only make the function family more expressive , but does not change the fact that the rank of is upper bounded by the embedding dimension .
In this article we analyze probabilistic argumentation frameworks ( PAFs ) , defined as an extension of Dung abstract argumentation frameworks in which each argument n is asserted with a probability pn . The debate around PAFs has so far centered on their theoretical definition and basic properties .
Human behavior can be recognized by the motion of keypoints of human bodies . In this paper , we propose a method , which is based on a PAFs approach , for human keypoints detection . The proposed method makes improvements in two aspects : ( 1 ) It perfects the joint points matching algorithm by re-matching . ( 2 ) It uses multi-branch PAFs to correct the keypoint connections , thus improving the wrong connection problem of upper and lower limbs for multi-person keypoint detection . The improved PAFs method , whose mAP reaches 53.6 % on HKD dataset , improved the score of the original method by 2 % .
The proposed structure along with its design approach is analyzed . The design procedure to design a second , third , and fourth-order PAFs is presented . It is shown that by increasing the order of the PAF structure , one can either improve the phase accuracy of the structure over the same bandwidth of interest or achieve the same phase accuracy over a wider bandwidth .
This paper studies penalty-based functions defined by PAFs . We analyse some properties ( e.g. : idempotency , averaging behavior and shift-invariance ) , providing a characterization of idempotent penalty-based PAFs and a weak characterization of averaging penalty-based PAFs . The use of penalty-based PAFs in spatial/tonal filters is outlined .
The MRO also hosts the SKA low frequency pathfinder instrument , the Murchison Widefield Array and will host the initial low frequency instrument of the SKA , SKA1-Low . Commissioning of ASKAP using six antennas equipped with first-generation PAFs is now complete and installation of second-generation PAFs and digital systems is underway . In this paper we review technical progress and commissioning to date , and refer the reader to relevant technical and scientific publications .
ConclusionThe RASPRO-PAF system and the associated techniques are found to be useful in many healthcare applications , especially in remote patient monitoring . The personalization , precision , and prevention PAFs presented in the paper successfully shows remarkable performance in satisfying the goals of 3Ps , thereby providing the advantages of three A ’s : availability , affordability , and accessibility in the global health scenario .
This paper explores adaptive array cancellation algorithm effectiveness on ASKAP for realistic bandwidths and integration times . With ASKAP 's beamforming PAFs on each dish , followed by a central correlation processor across beamformed signals from all dishes , one may consider algorithms that span multiple levels in the hierarchical signal processing chain . We compare performance for several subspace-projection-based algorithms applied to different tiers of this extended architecture .
For underactuated bipeds , control design based on input-output linearization can result in internal dynamics whose stability and convergence rate to the desired gait affect the closed-loop stability and the overall convergence rate . 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 .
The invention relates to a direct-current filter circuit for realizing an ASIC ( Application Specific Integrated Circuit ) audio processing function . The direct-current filter circuit comprises an input function processing module and an output function feedback module , wherein the input function processing module is used for performing delay phase reversal on an input signal of the direct-current filter circuit and outputting the input signal which is subjected to delay phase reversal together with an input signal of the direct-current filter circuit ; and the output function feedback module is used for intercepting a high-order field from an output delay signal of the direct-current filter circuit , performing phase reversal on the output delay signal , and adding the output delay signal which is subjected to phase reversal with an output signal of the input function processing module and the output delay signal of the direct-current filter circuit . Through adoption of the direct-current filter circuit for realizing the ASIC audio processing function disclosed by the invention , a direct-current component in an ASIC audio signal can be removed ; moreover , a multiplier is optimized , so that the hardware resource consumption during processing is lowered greatly , and resources accounting for 70 percent of total resources are reduced below 30 percent ; and the circuit is simple and practical in structure , has more stable working performance , and is wider in application range .
The strategy is applied in the identification of the key drivers of uncertainty for the complex computer code developed at the National Aeronautics and Space Administration ( NASA ) assessing the risk of lunar space missions . We also introduce a symmetry result that enables the estimation of global sensitivity measures to datasets produced outside a conventional input – output functional framework .
A comparison between the cost of these two algorithms and the performance improvement obtained from using these algorithms are discussed . We also propose different output selection functions to be used in switching elements for implementing the adaptive routing . We evaluate and compare the performance of these selection functions and discover that the best selection functions for BMINs are not dependent on the traffic pattern , message size , or system size .
We also study the effect of output selection , functions on the network performance . We present six different output selection functions and study their performance for different system parameters and communication patterns through extensive simulation . The results show that three of these selection functions perform similar to each other and outperform the other selection functions consistently .
Abstract PTCRIS ( Portuguese Current Research Information System - https://ptcris.pt/ ) is a program aiming at the creation and sustained development of a national integrated information ecosystem to support research management , according to the best international standards and practices . This ecosystem includes outcomes and outputs modules , in particular institutional repositories managed by the nationwide service RCAAP ( Scientific Open Access Repository of Portugal – http://www.rcaap.pt/ ) . In order to achieve such vision , PTCRIS has two main goals .
The function blocks currently defined address process automation applications . They include simple input and output function blocks and several control function blocks . The function blocks currently defined are analog input , analog output , discrete input , discrete output , PID , bias & gain , signal select , manual loader , and ratio .
The use of these ideas in decomposing a function either for representation as a set of OBDDs or for implementation in a cascode technology is outlined . A characterization is given of a class of multioutput functions which includes addition whose members have particularly small OBDDs.<<ETX > >
These models are used for space – time monitoring of beach morphodynamics . A series of input – output analyses may reveal certain salient aspects of the spatial – temporal dynamics of a system , which may not be obtainable where reliable data on key system control variables are unavailable . Beaches are open , dynamic , and process-response systems .
A unified framework is proposed to study the asymptotic behavior of general systems . From the view-point of signal , Lyapunov-like conditions and output functions based methods are used to help the check of stability . Furthermore , the concept of pseudo distance function is introduced to describe the desired convergence behavior of systems .
In this investigation , some of input parameters ( nearly 10 ) were used . For selecting the best model for this study , outputs of models were compared . A three-layer ANN was found to be optimum with architecture of 12 and 5 neurons in the first and second hidden layer , respectively , and 1 neuron in output layer .
Our message function is a MLP . Our node function is , Our output function is a MLP with a dropout fraction of 0.5 in the penultimate layer . The last layer has 16 hidden linear units .
However , the conventional approach of specifying deterministic ' hard ' subclasses via unsupervised clustering can lead to inaccurate models . The multimodal softmax ( MMS ) model is presented as a new probabilistic discriminative subclass model that overcomes this unreliability . By invoking fully probabilistic latent ' soft ' subclasses , MMS permits learning via standard statistical methods without requiring explicit clustering/relabeling of data .
Specifically , the thesis consists of two parts — better architectures to improve generative modeling , and applications of generative modeling in semi-supervised learning . In the first part , we identify an expressiveness bottleneck of prior neural language models , and propose a high-rank language model called the Mixture of Softmaxes ( MoS ) to break through such bottleneck . We later propose a faster highrank language model that trains faster than MoS while maintaining the capacity to break the bottleneck .
We propose a simple and effective method to address the Softmax bottleneck . Specifically , we introduce discrete latent variables into a recurrent language model , and formulate the next - token probability distribution as a Mixture of Softmaxes ( MoS ) . Mixture of Softmaxes is more expressive than Softmax and other surrogates considered in prior work .
To solve the Softmax bottleneck , DBLP : journals / corr / abs - 1711 - 03953 proposed Mixture of Softmaxes ( MoS ) , which increases the rank of the matrix by combining multiple probability distributions computed from the encoded fixed - length vector . In this study , we propose Direct Output Connection ( DOC ) as a generalization of MoS. For stacked RNNs , DOC computes the probability distributions from the middle layers including input embeddings .
With negligible overhead in the number of parameters and training time , our Past Decode Regularization ( PDR ) method achieves a word level perplexity of 55.6 on the Penn Treebank and 63.5 on the WikiText - 2 datasets using a single softmax . We also show gains by using PDR in combination with a mixture - of - softmaxes , achieving a word level perplexity of 53.8 and 60.5 on these datasets . In addition , our method achieves 1.169 bits - per - character on the Penn Treebank Character dataset for character level language modeling .
Consequently , the margin between two arbitrary classes can be increased . Compared with the original A - Softmax , Equation [ reference ] replaces norm of with an adjustable scalar factor . In our model , according to Equation [ reference ] , is always equal to 1 .
In this paper , a novel two-stream architecture has been designed to improve action recognition accuracy for hockey using three main components . First , pose is estimated via the Part Affinity Fields model to extract meaningful cues from the player . Second , optical flow ( using LiteFlownet ) is used to extract temporal features .
explored geometric and appearance constraints among joint candidates . Cao et al . proposed part affinity fields to encode location and orientation of limbs . Newell and Deng presented the associative embedding for grouping joint candidates .
Research on Question Answering System Based on Bi-LSTM and Self-attention Mechanism Distributed resource coordination strategies for mobile ad hoc networks Novelty Detection in Convolutional Neural Networks Using Density Forests Automatic localization of the left ventricle in cardiac MRI images using deep learning Brain Tumor Detection Using Deep Neural Network and Machine Learning Algorithm An assessment of strategies for choosing between competitive marketplaces Additive Margin SincNet for Speaker Recognition Additive Parameter for Deep Face Recognition Effectiveness of Hierarchical Softmax in Large Scale Classification Tasks Learning Discriminative Features Via Weights-biased Softmax Loss Recurrent Memory Reasoning Network for Expert Finding in Community Question Answering CTR Prediction Models Considering the Dynamics of User Interest Graph-Structured Representations for Visual Question Answering Scaling Memory-Augmented Neural Networks with Sparse Reads and Writes Multi-Task Deep Neural Networks for Natural Language Understanding Ask, Attend and Answer: Exploring Question-Guided Spatial Attention for Visual Question Answering Deeply-Supervised Nets Exploring the Limits of Language Modeling Graph Attention Networks Attention Boosted Sequential Inference Model A Discriminatively Learned CNN Embedding for Person Reidentification Attention is All you Need Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition Simple Baseline for Visual Question Answering Recurrent Neural Network Grammars Breaking the Softmax Bottleneck: A High-Rank RNN Language Model TOWARD A COMPUTATIONAL ANALYSIS OF PROBABILISTIC ARGUMENTATION FRAMEWORKS Development of Multi-person Pose Estimation Method Based on PAFs On the Design of  $n$ th-Order Polyphase All-Pass Filters Penalty-Based Functions Defined by Pre-aggregation Functions The Australian SKA Pathfinder: project update and initial operations Data to diagnosis in global health: a 3P approach Multi-tier interference-cancelling array processing for the ASKAP radio telescope Optimization of Output Functions with Nonholonomic Virtual Constraints in Underactuated Bipedal Walking Control Direct-current filter circuit for realizing ASIC (Application Specific Integrated Circuit) audio processing function Global sensitivity measures from given data Adaptive Routing on the New Switch Chip for IBM SP Systems Adaptive routing in RS/6000 SP-like bidirectional multistage interconnection networks Integrating a National Network of Institutional Repositories into the National/International Research Management Ecosystem The Interoperable Systems Project (ISP) Ordered binary decision diagrams and circuit structure Chapter 8 Space Time Monitoring of Beach Morphodynamics: A Black Box Approach Invariance Principles on Signal Sets Evaluation of effect of coal chemical properties on coal swelling index using artificial neural networks Recurrent Relational Networks On estimating simple probabilistic discriminative models with subclasses Deep Generative Modeling with Applications in Semi-Supervised Learning Breaking the Softmax Bottleneck: A High-Rank RNN Language Model Direct Output Connection for a High-Rank Language Model Orthogonal Deep Features Decomposition for Age-Invariant Face Recognition Temporal Hockey Action Recognition via Pose and Optical Flows Generative Partition Networks for Multi-Person Pose Estimation
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EMBC IJCNN ICACCI WSDM 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) NIPS ACL ECCV AISTATS ArXiv ICLR ArXiv ACM Trans. Multim. Comput. Commun. Appl. NIPS IEEE Transactions on Pattern Analysis and Machine Intelligence ArXiv HLT-NAACL ICLR ICCSIP (2) IPMU (2) SP/SPE ACC IPDPS CRIS UKSim NeurIPS ICLR EMNLP ECCV ArXiv
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