Conditional random fields ( CRFs ) for sequence labeling offer advantages over both generative models like Hidden Markov model ( HMM ) and classifiers applied at each sequence position . First , the CRFs do n’t force to adhere to the independence assumption and thus can depend on arbitrary , non-independent features , without accounting for the distribution of those dependencies . However , there has not been sufficient theoretical analysis of the accuracy of the estimation of latent variables . In a previous study , a distribution-based error function was formulated , and its asymptotic form was calculated for unsupervised learning with generative models . It has been shown that , for the estimation of latent variables , the Bayes method is more accurate than the maximum-likelihood method . We propose a generative model based on latent Dirichlet allocation for mining distinct topics in document collections by integrating the temporal ordering of documents into the generative process . The document collection is divided into time segments where the discovered topics in each segment is propagated to influence the topic discovery in the subsequent time segments . We extend two frequently used special cases of the GLM — a linear and a quadratic model — by assuming that the spike-triggered and non-spike-triggered distributions can be adequately represented using Gaussian mixtures . Because we derive the model from a generative perspective , its components are easy to interpret as they correspond to , for example , the spike-triggered distribution and the interspike interval distribution . The model is able to capture complex dependencies on high-dimensional stimuli with far fewer parameters than other approaches such as histogram-based methods . Noun Phrase in the Generative Perspective ( Studies in Generative Grammar 71 ) . Berlin : Mouton de Gruyter . Experiments on 2 , 430 , 707 English sentences and 33 , 743 Freebase entities show the effectiveness of the two features , where 90 % accuracy can be reached without any labeled data . We also show that discriminative models with proposed split training strategy is robust against over fitting problem , and constantly outperforms the generative ones . In this paper , we present the first discriminative training algorithms for SPNs , combining the high accuracy of the former with the representational power and tractability of the latter . We show that the class of tractable discriminative SPNs is broader than the class of tractable generative ones , and propose an efficient backpropagation-style algorithm for computing the gradient of the conditional log likelihood . Standard gradient descent suffers from the diffusion problem , but networks with many layers can be learned reliably using " hard " gradient descent , where marginal inference is replaced by MPE inference ( i.e. , inferring the most probable state of the non-evidence variables ) . GMMN ) gmmn use a moment matching cost function to learn the data distribution . In this paper , we propose a general approach , called an adversarial autoencoder ( AAE ) that can turn an autoencoder into a generative model . In our model , an autoencoder is trained with dual objectives – a traditional reconstruction error criterion , and an adversarial training criterion gan that matches the aggregated posterior distribution of the latent representation of the autoencoder to an arbitrary prior distribution . IAN is built upon the state-of-the-art object detection framework , i.e. , faster R-CNN , so that high-quality region proposals for pedestrians can be produced in an online manner . In addition , to relieve the negative effect caused by varying visual appearances of the same individual , IAN introduces a novel center loss that can increase the intra-class compactness of feature representations . The engaged center loss encourages persons with the same identity to have similar feature characteristics . We conducted extensive experiments on various sources of text-based information , including social media , electronic health records , and abstracts of scientific articles from PubMed . The results show that Interactive Attention Neural Network ( IAN ) outperformed other models on four corpora in terms of macro F-measure . This work is an abridged version of our recent paper accepted to Programming and Computer Software journal in 2019 . The Inexact Adaptive Newton method ( IAN ) is a modification of the Adaptive Implicit Method/sup 1/ ( AIM ) with improved Newton convergence . Both methods simplify the Jacobian at each time step by zeroing coefficients in regions where saturations are changing slowly . In the last stage , we propose an alternate minimization algorithm to obtain the hybrid precoding vectors . The simulation results show that our proposed suboptimal solutions can achieve a better performance in terms of power consumption and outage probability compared with those for treating interference as noise ( IAN ) systems and the digital precoding schemes . Both AID strategy and SIAS-based hybrid precoding are beneficial to the FD SWIPT system . We describe the protocols along with their signaling mechanism to implement them in a distributed fashion . We then perform Monte Carlo simulations to evaluate the performance and show significant gains over the IAN based CSMA/CA protocol in large random networks . Situations exist where the processors may be in close proximity to each other but still have the requirement for high transmission speeds and the flexibility and modularity of a loosely coupled network . The proposed architecture for an Immediate Area Network ( IAN ) , which falls into this category , is discussed in this paper . In an application of this nature , the importance or priority of the message may be a significant factor . This diversity offers differing support options for each platform , so attention must be paid to HOW decisions to insource or outsource are made . Examples of source-o fisupport deciswns ( including microcomputer repair , LAN design , and IAN training ) are examined . Then the relative strengths of various support source options are reviewed . In a high rise or multi floor building , staircases are commonly used by public safety personnel to move about different floors . As such , a good understanding of wireless signal wave attenuation along the staircase is crucial in order to support the planning of a reliable wireless IAN . This paper presents comparison of different established empirical indoor propagation models when applied to the staircase environment . Perhaps the administration of cortisone would have reduced it.-I am , etc . , Stock , Essex . IAN G. WICKEs . REFERENCES Henoch , E. , Lectures on Children 's Diseases , translated by John Thomson . Ed . by IAN PHILLIPS AND MAX SUSSMAN . Pp . Abstract : The goal of this work is to characterize the errors committed by an Image Aided Navigation ( IAN ) algorithm that has been developed for use as a navigation tool in GPS denied areas . The filter under study was developed by the Air Force Institute of Technology 's Advanced Navigation Technology center , and has been the focus of numerous research efforts . Text summarization is a helpful process that can be used to alleviate this problem . With the aim of seeking a novel method to enhance the performance of multi-document summarization , this study proposes a novel approach to analyze the problem of multi-document summarization based on a mixture model , consisting of a contextual topic model from a Bayesian hierarchical topic modeling family for selecting candidate summary sentences , and a regression model in machine learning for generating the summary . By investigating hierarchical topics and their correlations with respect to the lexical co-occurrences of words , the proposed contextual topic model can determine the relevance of sentences more effectively , recognize latent topics , and arrange them hierarchically . The quantitative evaluation results from a practical application demonstrates that a system implementing this model can significantly improve the performance of summarization and make it comparable to state-of-the-art summarization systems . In contrast , most previous methods for scene recognition either only modeled one of these two visual contexts , or just totally ignored both of them . We cast these two coupled visual contexts in a discriminative Latent Dirichlet Allocation framework , namely context aware topic model . Then scene recognition is achieved by Bayesian inference given a target image . A variety of heuristic adjustments to these models have been proposed , many of which are based on the use of context information to improve the performance of topic modeling . Existing contextualized topic models rely on arbitrary manipulation of the model structure , by incorporating various context variables into the generative process of classical topic models in an ad hoc manner . Such manipulations usually result in much more complicated model structures , sophisticated inference procedures , and low generalizability to accommodate arbitrary types or combinations of contexts . This thesis focuses on addressing the above challenges within neural topic modeling framework . We propose : ( 1 ) Contextualized topic model that combines a topic and a language model and introduces linguistic structures ( such as word ordering , syntactic and semantic features , etc . ) in topic modeling , ( 2 ) A novel lifelong learning mechanism into neural topic modeling framework to demonstrate continuous learning in sequential document collections and minimizing catastrophic forgetting . Additionally , we perform a selective data augmentation to alleviate the need for complete historical corpora during data hallucination or replay . Accurate prediction of conversation topics can be a valuable signal for creating coherent and engaging dialog systems . In this work , we focus on context-aware topic classification methods for identifying topics in free-form human-chatbot dialogs . We extend previous work on neural topic classification and unsupervised topic keyword detection by incorporating conversational context and dialog act features . We integrate two novel layers into the Recurrent Chinese Restaurant Process ( RCRP ) , respectively one context-aware topic layer and one Long Short Term Memory ( LSTM ) based sequential layer . The context-aware topic layer can help capture the global context-aware semantic coherences and the sequential layer is exploited to learn the local dynamics and semantic dependencies during the dynamic evolutionary process . Experimental results on real datasets show that our method significantly outperforms the state-of-the-art approaches . However , the neglect of context leads traditional approaches to the failure of achieving this goal . In order to overcome this shortcoming , a new context-sensitive topic description method based on ODP(Open Directory Project ) is proposed in this paper . First of all , the best topic feature subset is determined by a new feature selection algorithm . Our system uses the mined context to produce recommendations . We propose a methodology to measure the quality of context topic models along with a novel way to represent context that allows it to be used as side-information in a recommendation engine . Results show that Rich-Context makes more accurate predictions than five well-established recommendation algorithms . This shortcoming of LDA leads to its failing to learn contextual information in sentences and paragraphs . We present a contextual coherent topic model for text learning namely Contextual Latent Dirichlet Allocation ( Contextual-LDA ) to include the contextual knowledge without increasing the perplexity of the algorithm very much . In our model , a document is segmented into finelydivided word sequences , each corresponded with one distinct latent topic to capture local context , while the global context is obtained by the location a segment appears in the document . Next , context models are learned for each of the concepts in the semantic space , using mixtures of Dirichlet distributions . Finally , an image is represented as a vector of posterior concept probabilities under these contextual concept models . It is shown that these posterior probabilities are remarkably noise-free , and an effective model of the contextual relationships between semantic concepts in natural images . We further introduce general components of contextual topic analysis , by adding priors to contextual topic models to incorporate prior knowledge , regularizing contextual topic models with dependency structure of context , and postprocessing contextual patterns to extract refined patterns . The refinements on the general contextual topic model naturally lead to a variety of probabilistic models which incorporate different types of context and various assumptions and constraints . These special versions of the contextual topic model are proved effective in a variety of real applications involving topics and explicit contexts , implicit contexts , and complex contexts . is also used in the competing method FCN . Our contextual model with CNN pairwise potentials achieves the best performance , which sets a new state - of - the - art result on the NYUDv2 dataset . Note that we do not use any depth information in our model . This work approaches graph generation ( decoding ) as the inverse of graph compression ( encoding ) . We show that in a disentanglement-focused deep autoencoding framework , specifically Beta-Variational Autoencoders ( Beta-VAE ) , choices of generative procedures and their parameters arise naturally in the latent space . Our model is capable of learning disentangled , interpretable latent variables that represent the generative parameters of procedurally generated random graphs and real-world graphs . The degree of disentanglement is quantitatively measured using the Mutual Information Gap ( MIG ) . When training our Beta-VAE model on ER random graphs , its latent variables have a near one-to-one mapping to the ER random graph parameters n and p. We deploy the model to analyse the correlation between graph topology and node attributes measuring their mutual dependence without handpicking topological properties . However , we present evidence that for large databases with multiple possible solutions ( which we argue occur frequently in practice ) , such multiple-context TMSs can be very inefficient . We present a class of problems for which using a multiple-context TMS is both intrinsically interesting and ideal , but which will be computationally infeasible because of the exponential size of the database which the TMS must explore . To circumvent such infeasiblity , appropriate control must be exerted by the problem solver . Data is analyzed using three steps which are data reduction , data display , and summary . The research result showed : Context : TTM program implemented appropriate with the rules and law applicable national education system , suppoted by strategic social environment , as well as being an important requirment for students . Input , material is appropriate with the curriculum of university for teacher or prospective Indonesia laguage teacher of elementary school . Based on the observation of our students learning elec­ tronics in the course of their independent research experi­ ences , the introductory electronics program at Creighton Uni­ versity has been revised to provide a similar contextual set­ ting . From the beginning of the course , the students are involved with the type of instrumentation they will encounter in upper-division laboratories , in research laboratories , and in industrial settings . It uses some of its input and output units for observing its own errors and for explicitly analyzing and modifying its own weight matrix , including those parts of the weight matrix responsible for analyzing and modifying the weight matrix . The result is the first ‘ introspective ’ neural net with explicit potential control over all of its own adaptive parameters . A disadvantage of the algorithm is its high computational complexity per time step which is independent of the sequence length and equals O(nconnlognconn ) , where riconn is the number of connections . We present the Neural Photo Editor , an interface that leverages the power of generative neural networks to make large , semantically coherent changes to existing images . To tackle the challenge of achieving accurate reconstructions without loss of feature quality , we introduce the Introspective Adversarial Network , a novel hybridization of the VAE and GAN . Our model efficiently captures long-range dependencies through use of a computational block based on weight-shared dilated convolutions , and improves generalization performance with Orthogonal Regularization , a novel weight regularization method . The author shows that it is in principle possible to let the network run and improve its own weight change algorithm ( without significant theoretical limits ) . The author derives an initial gradient-based supervised sequence learning algorithm for an ' introspective ' recurrent network that can ' speak ' about its own weight matrix in terms of activations . It uses special subsets of its input and output units for observing its own errors and for explicitly analyzing and manipulating all of its own weights , including those weights responsible for analyzing and manipulating weights .
Conditional random fields ( CRFs ) for sequence labeling offer advantages over both generative models like Hidden Markov model ( HMM ) and classifiers applied at each sequence position . First , the CRFs do n’t force to adhere to the independence assumption and thus can depend on arbitrary , non-independent features , without accounting for the distribution of those dependencies .
However , there has not been sufficient theoretical analysis of the accuracy of the estimation of latent variables . In a previous study , a distribution-based error function was formulated , and its asymptotic form was calculated for unsupervised learning with generative models . It has been shown that , for the estimation of latent variables , the Bayes method is more accurate than the maximum-likelihood method .
We propose a generative model based on latent Dirichlet allocation for mining distinct topics in document collections by integrating the temporal ordering of documents into the generative process . The document collection is divided into time segments where the discovered topics in each segment is propagated to influence the topic discovery in the subsequent time segments .
We extend two frequently used special cases of the GLM — a linear and a quadratic model — by assuming that the spike-triggered and non-spike-triggered distributions can be adequately represented using Gaussian mixtures . Because we derive the model from a generative perspective , its components are easy to interpret as they correspond to , for example , the spike-triggered distribution and the interspike interval distribution . The model is able to capture complex dependencies on high-dimensional stimuli with far fewer parameters than other approaches such as histogram-based methods .
Noun Phrase in the Generative Perspective ( Studies in Generative Grammar 71 ) . Berlin : Mouton de Gruyter .
Experiments on 2 , 430 , 707 English sentences and 33 , 743 Freebase entities show the effectiveness of the two features , where 90 % accuracy can be reached without any labeled data . We also show that discriminative models with proposed split training strategy is robust against over fitting problem , and constantly outperforms the generative ones .
In this paper , we present the first discriminative training algorithms for SPNs , combining the high accuracy of the former with the representational power and tractability of the latter . We show that the class of tractable discriminative SPNs is broader than the class of tractable generative ones , and propose an efficient backpropagation-style algorithm for computing the gradient of the conditional log likelihood . Standard gradient descent suffers from the diffusion problem , but networks with many layers can be learned reliably using " hard " gradient descent , where marginal inference is replaced by MPE inference ( i.e. , inferring the most probable state of the non-evidence variables ) .
GMMN ) gmmn use a moment matching cost function to learn the data distribution . In this paper , we propose a general approach , called an adversarial autoencoder ( AAE ) that can turn an autoencoder into a generative model . In our model , an autoencoder is trained with dual objectives – a traditional reconstruction error criterion , and an adversarial training criterion gan that matches the aggregated posterior distribution of the latent representation of the autoencoder to an arbitrary prior distribution .
IAN is built upon the state-of-the-art object detection framework , i.e. , faster R-CNN , so that high-quality region proposals for pedestrians can be produced in an online manner . In addition , to relieve the negative effect caused by varying visual appearances of the same individual , IAN introduces a novel center loss that can increase the intra-class compactness of feature representations . The engaged center loss encourages persons with the same identity to have similar feature characteristics .
We conducted extensive experiments on various sources of text-based information , including social media , electronic health records , and abstracts of scientific articles from PubMed . The results show that Interactive Attention Neural Network ( IAN ) outperformed other models on four corpora in terms of macro F-measure . This work is an abridged version of our recent paper accepted to Programming and Computer Software journal in 2019 .
The Inexact Adaptive Newton method ( IAN ) is a modification of the Adaptive Implicit Method/sup 1/ ( AIM ) with improved Newton convergence . Both methods simplify the Jacobian at each time step by zeroing coefficients in regions where saturations are changing slowly .
In the last stage , we propose an alternate minimization algorithm to obtain the hybrid precoding vectors . The simulation results show that our proposed suboptimal solutions can achieve a better performance in terms of power consumption and outage probability compared with those for treating interference as noise ( IAN ) systems and the digital precoding schemes . Both AID strategy and SIAS-based hybrid precoding are beneficial to the FD SWIPT system .
We describe the protocols along with their signaling mechanism to implement them in a distributed fashion . We then perform Monte Carlo simulations to evaluate the performance and show significant gains over the IAN based CSMA/CA protocol in large random networks .
Situations exist where the processors may be in close proximity to each other but still have the requirement for high transmission speeds and the flexibility and modularity of a loosely coupled network . The proposed architecture for an Immediate Area Network ( IAN ) , which falls into this category , is discussed in this paper . In an application of this nature , the importance or priority of the message may be a significant factor .
This diversity offers differing support options for each platform , so attention must be paid to HOW decisions to insource or outsource are made . Examples of source-o fisupport deciswns ( including microcomputer repair , LAN design , and IAN training ) are examined . Then the relative strengths of various support source options are reviewed .
In a high rise or multi floor building , staircases are commonly used by public safety personnel to move about different floors . As such , a good understanding of wireless signal wave attenuation along the staircase is crucial in order to support the planning of a reliable wireless IAN . This paper presents comparison of different established empirical indoor propagation models when applied to the staircase environment .
Perhaps the administration of cortisone would have reduced it.-I am , etc . , Stock , Essex . IAN G. WICKEs . REFERENCES Henoch , E. , Lectures on Children 's Diseases , translated by John Thomson .
Ed . by IAN PHILLIPS AND MAX SUSSMAN . Pp .
Abstract : The goal of this work is to characterize the errors committed by an Image Aided Navigation ( IAN ) algorithm that has been developed for use as a navigation tool in GPS denied areas . The filter under study was developed by the Air Force Institute of Technology 's Advanced Navigation Technology center , and has been the focus of numerous research efforts .
Text summarization is a helpful process that can be used to alleviate this problem . With the aim of seeking a novel method to enhance the performance of multi-document summarization , this study proposes a novel approach to analyze the problem of multi-document summarization based on a mixture model , consisting of a contextual topic model from a Bayesian hierarchical topic modeling family for selecting candidate summary sentences , and a regression model in machine learning for generating the summary . By investigating hierarchical topics and their correlations with respect to the lexical co-occurrences of words , the proposed contextual topic model can determine the relevance of sentences more effectively , recognize latent topics , and arrange them hierarchically . The quantitative evaluation results from a practical application demonstrates that a system implementing this model can significantly improve the performance of summarization and make it comparable to state-of-the-art summarization systems .
In contrast , most previous methods for scene recognition either only modeled one of these two visual contexts , or just totally ignored both of them . We cast these two coupled visual contexts in a discriminative Latent Dirichlet Allocation framework , namely context aware topic model . Then scene recognition is achieved by Bayesian inference given a target image .
A variety of heuristic adjustments to these models have been proposed , many of which are based on the use of context information to improve the performance of topic modeling . Existing contextualized topic models rely on arbitrary manipulation of the model structure , by incorporating various context variables into the generative process of classical topic models in an ad hoc manner . Such manipulations usually result in much more complicated model structures , sophisticated inference procedures , and low generalizability to accommodate arbitrary types or combinations of contexts .
This thesis focuses on addressing the above challenges within neural topic modeling framework . We propose : ( 1 ) Contextualized topic model that combines a topic and a language model and introduces linguistic structures ( such as word ordering , syntactic and semantic features , etc . ) in topic modeling , ( 2 ) A novel lifelong learning mechanism into neural topic modeling framework to demonstrate continuous learning in sequential document collections and minimizing catastrophic forgetting . Additionally , we perform a selective data augmentation to alleviate the need for complete historical corpora during data hallucination or replay .
Accurate prediction of conversation topics can be a valuable signal for creating coherent and engaging dialog systems . In this work , we focus on context-aware topic classification methods for identifying topics in free-form human-chatbot dialogs . We extend previous work on neural topic classification and unsupervised topic keyword detection by incorporating conversational context and dialog act features .
We integrate two novel layers into the Recurrent Chinese Restaurant Process ( RCRP ) , respectively one context-aware topic layer and one Long Short Term Memory ( LSTM ) based sequential layer . The context-aware topic layer can help capture the global context-aware semantic coherences and the sequential layer is exploited to learn the local dynamics and semantic dependencies during the dynamic evolutionary process . Experimental results on real datasets show that our method significantly outperforms the state-of-the-art approaches .
However , the neglect of context leads traditional approaches to the failure of achieving this goal . In order to overcome this shortcoming , a new context-sensitive topic description method based on ODP(Open Directory Project ) is proposed in this paper . First of all , the best topic feature subset is determined by a new feature selection algorithm .
Our system uses the mined context to produce recommendations . We propose a methodology to measure the quality of context topic models along with a novel way to represent context that allows it to be used as side-information in a recommendation engine . Results show that Rich-Context makes more accurate predictions than five well-established recommendation algorithms .
This shortcoming of LDA leads to its failing to learn contextual information in sentences and paragraphs . We present a contextual coherent topic model for text learning namely Contextual Latent Dirichlet Allocation ( Contextual-LDA ) to include the contextual knowledge without increasing the perplexity of the algorithm very much . In our model , a document is segmented into finelydivided word sequences , each corresponded with one distinct latent topic to capture local context , while the global context is obtained by the location a segment appears in the document .
Next , context models are learned for each of the concepts in the semantic space , using mixtures of Dirichlet distributions . Finally , an image is represented as a vector of posterior concept probabilities under these contextual concept models . It is shown that these posterior probabilities are remarkably noise-free , and an effective model of the contextual relationships between semantic concepts in natural images .
We further introduce general components of contextual topic analysis , by adding priors to contextual topic models to incorporate prior knowledge , regularizing contextual topic models with dependency structure of context , and postprocessing contextual patterns to extract refined patterns . The refinements on the general contextual topic model naturally lead to a variety of probabilistic models which incorporate different types of context and various assumptions and constraints . These special versions of the contextual topic model are proved effective in a variety of real applications involving topics and explicit contexts , implicit contexts , and complex contexts .
is also used in the competing method FCN . Our contextual model with CNN pairwise potentials achieves the best performance , which sets a new state - of - the - art result on the NYUDv2 dataset . Note that we do not use any depth information in our model .
This work approaches graph generation ( decoding ) as the inverse of graph compression ( encoding ) . We show that in a disentanglement-focused deep autoencoding framework , specifically Beta-Variational Autoencoders ( Beta-VAE ) , choices of generative procedures and their parameters arise naturally in the latent space . Our model is capable of learning disentangled , interpretable latent variables that represent the generative parameters of procedurally generated random graphs and real-world graphs . The degree of disentanglement is quantitatively measured using the Mutual Information Gap ( MIG ) . When training our Beta-VAE model on ER random graphs , its latent variables have a near one-to-one mapping to the ER random graph parameters n and p. We deploy the model to analyse the correlation between graph topology and node attributes measuring their mutual dependence without handpicking topological properties .
However , we present evidence that for large databases with multiple possible solutions ( which we argue occur frequently in practice ) , such multiple-context TMSs can be very inefficient . We present a class of problems for which using a multiple-context TMS is both intrinsically interesting and ideal , but which will be computationally infeasible because of the exponential size of the database which the TMS must explore . To circumvent such infeasiblity , appropriate control must be exerted by the problem solver .
Data is analyzed using three steps which are data reduction , data display , and summary . The research result showed : Context : TTM program implemented appropriate with the rules and law applicable national education system , suppoted by strategic social environment , as well as being an important requirment for students . Input , material is appropriate with the curriculum of university for teacher or prospective Indonesia laguage teacher of elementary school .
Based on the observation of our students learning elec­ tronics in the course of their independent research experi­ ences , the introductory electronics program at Creighton Uni­ versity has been revised to provide a similar contextual set­ ting . From the beginning of the course , the students are involved with the type of instrumentation they will encounter in upper-division laboratories , in research laboratories , and in industrial settings .
It uses some of its input and output units for observing its own errors and for explicitly analyzing and modifying its own weight matrix , including those parts of the weight matrix responsible for analyzing and modifying the weight matrix . The result is the first ‘ introspective ’ neural net with explicit potential control over all of its own adaptive parameters . A disadvantage of the algorithm is its high computational complexity per time step which is independent of the sequence length and equals O(nconnlognconn ) , where riconn is the number of connections .
We present the Neural Photo Editor , an interface that leverages the power of generative neural networks to make large , semantically coherent changes to existing images . To tackle the challenge of achieving accurate reconstructions without loss of feature quality , we introduce the Introspective Adversarial Network , a novel hybridization of the VAE and GAN . Our model efficiently captures long-range dependencies through use of a computational block based on weight-shared dilated convolutions , and improves generalization performance with Orthogonal Regularization , a novel weight regularization method .
The author shows that it is in principle possible to let the network run and improve its own weight change algorithm ( without significant theoretical limits ) . The author derives an initial gradient-based supervised sequence learning algorithm for an ' introspective ' recurrent network that can ' speak ' about its own weight matrix in terms of activations . It uses special subsets of its input and output units for observing its own errors and for explicitly analyzing and manipulating all of its own weights , including those weights responsible for analyzing and manipulating weights .
The Application of CRFs in Part-of-Speech Tagging Accuracy of latent-variable estimation in Bayesian semi-supervised learning Finding topic trends in digital libraries Beyond GLMs: A Generative Mixture Modeling Approach to Neural System Identification Alexiadou, Artemis & Liliane Haegemann & Melita Stavrou. 2006. Noun Phrase in the Generative Perspective (Studies in Generative Grammar 71). Berlin: Mouton de Gruyter Entity Disambiguation with Freebase Discriminative Learning of Sum-Product Networks Adversarial Autoencoders IAN: The Individual Aggregation Network for Person Search Entity-level Classification of Adverse Drug Reactions: a Comparison of Neural Network Models Inexact Adaptive Newton Methods Hybrid Precoding for an Adaptive Interference Decoding SWIPT System With Full-Duplex IoT Devices CSMA k-SIC — A class of distributed MAC protocols and their performance evaluation A fuzzy algorithm for priority estimation in Immediate Area Networks Downsizing and outsourcing opportunities: right-sizing requires right-sourcing Comparison of indoor propagation models for multi floor staircase at 900 MHz and 1800 MHz Cold Injury in the Newborn Occupational Lung Disorders Error Characterization of Vision-Aided Navigation Systems Using Contextual Topic Model for a Query-Focused Multi-Document Summarizer Context aware topic model for scene recognition One theme in all views: modeling consensus topics in multiple contexts Lifelong Neural Topic Learning in Contextualized Autoregressive Topic Models of Language via Informative Transfers Contextual Topic Modeling For Dialog Systems An Evolutionary Context-aware Sequential Model for topic evolution of text stream A Novel Context-Sensitive Topic Description Method Based on ODP Unsupervised Context-Driven Recommendations Based On User Reviews Contextual-LDA: A Context Coherent Latent Topic Model for Mining Large Corpora Holistic context modeling using semantic co-occurrences Contextual text mining Efficient Piecewise Training of Deep Structured Models for Semantic Segmentation Disentangling Interpretable Generative Parameters of Random and Real-World Graphs Efficiency Analysis of Multiple-Context TMSs in Scene Representation Implementasi Program Tutorial Tatap Muka Mata Kuliah Materi Dan Pembelajaran Bahasa Indonesia Sekolah Dasar (Penelitian Evaluasi Program Pada Program Studi S1 PGSD Di Unit Program Belajar Jarak Jauh Universitas Terbuka Kendari Sulawesi Tenggara) A Project-Motivated Approach to an Electronics Curriculum A self-referential weight matrix Neural Photo Editing with Introspective Adversarial Networks An 'introspective' network that can learn to run its own weight change algorithm
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JCDL Web Intelligence NIPS ArXiv INFOCOM SIGUCCS ISTT CVPR KDD SLT RecSys BigMM CVPR 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) AAAI
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