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Syntax-Directed Variational Autoencoder for Structured Data [article]

Hanjun Dai, Yingtao Tian, Bo Dai, Steven Skiena, Le Song
2018 arXiv   pre-print
Inspired by the theory of compiler where the syntax and semantics check is done via syntax-directed translation (SDT), we propose a novel syntax-directed variational autoencoder (SD-VAE) by introducing  ...  The results demonstrate the effectiveness in incorporating syntactic and semantic constraints in discrete generative models, which is significantly better than current state-of-the-art approaches.  ...  DIVERSITY OF GENERATED MOLECULES VISUALIZING THE LATENT SPACE We seek to visualize the latent space as an assessment of how well our generative model is able to produces a coherent and smooth space of  ... 
arXiv:1802.08786v1 fatcat:j7orkkafvvfb5ccuz6wy7ztr6i

Greater lifestyle engagement is associated with better age-adjusted cognitive abilities

G. Sophia Borgeest, Richard N. Henson, Meredith Shafto, David Samu, Rogier A. Kievit, Cam-CAN, Angel Blanch
2020 PLoS ONE  
A joint path model of all lifestyle factors on crystallized and fluid abilities, which allowed a simultaneous assessment of the lifestyle domains, showed that physical health, social and intellectual engagement  ...  Specifically, we assessed the relative associations of the following five lifestyle factors on age-related differences of fluid and crystallized age-adjusted abilities: education/SES, physical health,  ...  Acknowledgments The Cambridge Centre for Ageing and Neuroscience (Cam-CAN) research was supported by the Biotechnology and Biological Sciences Research Council (grant number BB/H008217/1).  ... 
doi:10.1371/journal.pone.0230077 pmid:32437448 fatcat:m5fnqhnfe5bf3mxkouh7hv76di

Quantifying the Importance of Latent Features in Neural Networks

Amany Alshareef, Nicolas Berthier, Sven Schewe, Xiaowei Huang
2022 AAAI Conference on Artificial Intelligence  
To achieve this, we first abstract a given neural network model into a Bayesian Network, where each random variable represents the value of a hidden feature.  ...  We specifically investigate how the distribution of features in their latent space changes in the presence of distortions.  ...  (2021) , which elicited semantic assumptions by advancing an approach that relies on a Bayesian Network (BN) abstraction to examine whether latent features are adequately exercised by a set of inputs  ... 
dblp:conf/aaai/AlshareefBSH22 fatcat:m2blqkue3vccdmyufalgk3xerq

Comparative Analysis of Exemplar-Based Approaches for Students' Learning Style Diagnosis Purposes

Daiva Goštautaitė, Jevgenij Kurilov
2021 Applied Sciences  
A comparative analysis of approaches combining exemplar-based modelling and case-based reasoning leads to the choice of the Bayesian Case model for diagnosing a student's learning style based on the data  ...  A lot of computational models recently are undergoing rapid development. However, there is a conceptual and analytical gap in understanding the driving forces behind them.  ...  An explanation usually relates the feature values of an instance to its model prediction in a humanly understandable way [56] .  ... 
doi:10.3390/app11157083 fatcat:q6kbj6qvvfhh7fmc7c4havjp7y

Topic Modeling: A Comprehensive Review

Pooja Kherwa, Poonam Bansal
2018 EAI Endorsed Transactions on Scalable Information Systems  
Topic modelling is the new revolution in text mining. It is a statistical technique for revealing the underlying semantic structure in large collection of documents.  ...  At the end paper is concluded with detailed discussion on challenges of topic modelling, which will definitely give researchers an insight for good research.  ...  Correlated Topic Model Latent Dirichlet allocation is capable enough for considering an effective tool for the statistics analysis of document collections.  ... 
doi:10.4108/eai.13-7-2018.159623 fatcat:lu6al57vp5aahbytyejhqrlzry

Latent Regression Bayesian Network for Data Representation [article]

Siqi Nie, Qiang Ji
2015 arXiv   pre-print
Qualitative and quantitative evaluations of our model against state of the art deep models on benchmark datasets demonstrate the effectiveness of the proposed algorithm in data representation and reconstruction  ...  Driven by this idea, we propose an inference method based on the conditional pseudo-likelihood that preserves the dependencies among the latent variables.  ...  Latent Regression Bayesian Network We propose a generalized directed graphical model, called latent regression Bayesian network (LRBN), as shown in Fig. 1 (a) .  ... 
arXiv:1506.04720v1 fatcat:cpqwc5hsqfannkzjchse4obxsu

Data Fusion for MaaS: Opportunities and Challenges

Jianqing Wu, Luping Zhou, Chen Cai, Jun Shen, Sim Kim Lau, Jianming Yong
2018 2018 IEEE 22nd International Conference on Computer Supported Cooperative Work in Design ((CSCWD))  
In this paper, we provide an overview of the recent advances in data fusion for Mobility as a Service (MaaS), including the basics of data fusion theory and the related machine learning methods.  ...  We also highlight the opportunities and challenges on MaaS, and discuss potential future directions of research on the integrated mobility modelling.  ...  Moreover, a topic model based on Latent Dirichlet Allocation (LDA) and Dirichlet Multinomial Regression (DMR) can infer the functional regions in a city by utilizing Gibbs sampling [40] .  ... 
doi:10.1109/cscwd.2018.8465224 dblp:conf/cscwd/WuZCSLY18 fatcat:ug4kbo33qbd33gqpnqx3gfpkve

Determining Lead-Lag Structure between Sentiment Index and Stock Price Returns

Alex Momotov, Xianghua Xie
2019 Proceedings of the 3rd International Conference on Vision, Image and Signal Processing  
Latent Semantic Analysis (LSA) and Latent Semantic Indexing (LSI) were early developments within topic modelling which applied singular value decomposition on sentence-level term matrix and have been extensively  ...  The methodology of document-encoding representations, in turn, has expanded from bag-of-words to TF-IDF matrices, and more recently to thematic modelling techniques of Latent Semantic Indexing (LSI) and  ... 
doi:10.1145/3387168.3389115 dblp:conf/icvisp/MomotovX19 fatcat:vuhuxqds5ng4larojctbetimzy

A Latent Variable Model Approach to PMI-based Word Embeddings

Sanjeev Arora, Yuanzhi Li, Yingyu Liang, Tengyu Ma, Andrej Risteski
2016 Transactions of the Association for Computational Linguistics  
Semantic word embeddings represent the meaning of a word via a vector, and are created by diverse methods.  ...  This paper proposes a new generative model, a dynamic version of the log-linear topic model of Mnih and Hinton (2007) .  ...  Acknowledgements We thank the editors of TACL for granting a special relaxation of the page limit for our paper. We thank Yann LeCun, Christopher D.  ... 
doi:10.1162/tacl_a_00106 fatcat:4c5aoo66hrfafew57khr5bueuu

Semantic Sentiment Analysis Based on Probabilistic Graphical Models and Recurrent Neural Network [article]

Ukachi Osisiogu
2020 arXiv   pre-print
After this empirical study, we conclude that the inclusion of semantics for sentiment analysis tasks can greatly improve the performance of a classifier, as the semantic feature extraction methods reduce  ...  Sentiment Analysis is the task of classifying documents based on the sentiments expressed in textual form, this can be achieved by using lexical and semantic methods.  ...  which does not require a lot of hyper-parameter tuning a lot of tuning has to be made to get the best out of the application of this technique.  ... 
arXiv:2009.00234v1 fatcat:qtkgokmozrfkfor4auw77sokvi

News-based forecasts of macroeconomic indicators: A semantic path model for interpretable predictions [article]

Stefan Feuerriegel, Julius Gordon
2018 arXiv   pre-print
a projection of words onto latent semantic structures as a means of feature engineering. (3) We propose a semantic path model, together with estimation technique based on regularization, in order to yield  ...  In order to reduce forecast errors, this paper presents an innovative methodology that extends lag variables with unstructured data in the form of financial news: (1) we apply a variety of models from  ...  Third, one could weigh corporate disclosures according to their market capitalization, since larger firms presumably have a great impact on the general economy than smaller firms.  ... 
arXiv:1801.07047v2 fatcat:yejshz2sjbfehihhnm7zjdumxu

A Survey of Collaborative Filtering Techniques

Xiaoyuan Su, Taghi M. Khoshgoftaar
2009 Advances in Artificial Intelligence  
As one of the most successful approaches to building recommender systems, collaborative filtering (CF) uses the known preferences of a group of users to make recommendations or predictions of the unknown  ...  From basic techniques to the state-of-the-art, we attempt to present a comprehensive survey for CF techniques, which can be served as a roadmap for research and practice in this area.  ...  Acknowledgment The authors are grateful to Drs. Miroslav Kubat and Moiez A. Tapia for their help during the early stage of this paper and also to Drs.  ... 
doi:10.1155/2009/421425 fatcat:qtbk7gfqtvhzhg6dqqrf2drjzy

The Future of Data Analysis in the Neurosciences [article]

Danilo Bzdok, B. T. Thomas Yeo
2016 arXiv   pre-print
We believe that large-scale data analysis will use more models that are non-parametric, generative, mixing frequentist and Bayesian aspects, and grounded in different statistical inferences.  ...  While growing data availability and information granularity have been amply discussed, we direct attention to a routinely neglected question: How will the unprecedented data richness shape data analysis  ...  As a second, more intuitive explanation, non-parametric models do not assume that the structure of the statistical model is fixed.  ... 
arXiv:1608.03465v1 fatcat:roen4d2axncufftj3ifjjimqpe

The Hidden Uncertainty in a Neural Networks Activations [article]

Janis Postels, Hermann Blum, Yannick Strümpler, Cesar Cadena, Roland Siegwart, Luc Van Gool, Federico Tombari
2021 arXiv   pre-print
We verify our findings on both classification and regression models.  ...  The distribution of a neural network's latent representations has been successfully used to detect out-of-distribution (OOD) data.  ...  By applying perturbations of increasing magnitude to the test data (section 4.1.1), we showed that latent densities denote an effective proxy for epistemic uncertainty and, thus, quantify a model's ability  ... 
arXiv:2012.03082v2 fatcat:2txmq45dhbaytgoc7vn6dyvwuu

Individual Differences in Cortical Processing Speed Predict Cognitive Abilities: a Model-Based Cognitive Neuroscience Account

Anna-Lena Schubert, Michael D. Nunez, Dirk Hagemann, Joachim Vandekerckhove
2018 Computational Brain & Behavior  
We used a hierarchical Bayesian cognitive modeling approach to test the hypothesis that individual differences in the velocity of evidence accumulation mediate the relationship between neural processing  ...  The model demonstrated impressive forecasting abilities by predicting 36% of individual variation in cognitive ability test scores in an entirely new sample solely based on their electrophysiological and  ...  Acknowledgments The authors thank Gidon T. Frischkorn, Ramesh Srinivasan, and members of the Human Neuroscience Laboratory for their constructive criticism on work related to this manuscript.  ... 
doi:10.1007/s42113-018-0021-5 fatcat:xzhmrv2pm5gbhjqgsj3jbh76lm
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