Sinusoidal wave generating network based on adversarial learning and its application: synthesizing frog sounds for data augmentation

Jan 07, 2019

Sangwook Park, David K. Han, Hanseok Ko

Jan 07, 2019

Sangwook Park, David K. Han, Hanseok Ko

* This paper has been revised from our previous manuscripts as following reviewer's comments in ICML, NIP, and IEEE TSP

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Heterogeneous Network Motifs

Feb 04, 2019

Ryan A. Rossi, Nesreen K. Ahmed, Aldo Carranza, David Arbour, Anup Rao, Sungchul Kim, Eunyee Koh

Feb 04, 2019

Ryan A. Rossi, Nesreen K. Ahmed, Aldo Carranza, David Arbour, Anup Rao, Sungchul Kim, Eunyee Koh

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* Latex source, needs aclap.sty, 8 pages, to appear in ACL-95

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Generating Different Story Tellings from Semantic Representations of Narrative

Aug 29, 2017

Elena Rishes, Stephanie M. Lukin, David K. Elson, Marilyn A. Walker

Aug 29, 2017

Elena Rishes, Stephanie M. Lukin, David K. Elson, Marilyn A. Walker

* International Conference on Interactive Digital Storytelling (ICIDS 2013)

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Overdispersed Black-Box Variational Inference

Mar 03, 2016

Francisco J. R. Ruiz, Michalis K. Titsias, David M. Blei

We introduce overdispersed black-box variational inference, a method to reduce the variance of the Monte Carlo estimator of the gradient in black-box variational inference. Instead of taking samples from the variational distribution, we use importance sampling to take samples from an overdispersed distribution in the same exponential family as the variational approximation. Our approach is general since it can be readily applied to any exponential family distribution, which is the typical choice for the variational approximation. We run experiments on two non-conjugate probabilistic models to show that our method effectively reduces the variance, and the overhead introduced by the computation of the proposal parameters and the importance weights is negligible. We find that our overdispersed importance sampling scheme provides lower variance than black-box variational inference, even when the latter uses twice the number of samples. This results in faster convergence of the black-box inference procedure.
Mar 03, 2016

Francisco J. R. Ruiz, Michalis K. Titsias, David M. Blei

* 10 pages, 6 figures

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Variational Inducing Kernels for Sparse Convolved Multiple Output Gaussian Processes

Dec 16, 2009

Mauricio A. Álvarez, David Luengo, Michalis K. Titsias, Neil D. Lawrence

Interest in multioutput kernel methods is increasing, whether under the guise of multitask learning, multisensor networks or structured output data. From the Gaussian process perspective a multioutput Mercer kernel is a covariance function over correlated output functions. One way of constructing such kernels is based on convolution processes (CP). A key problem for this approach is efficient inference. Alvarez and Lawrence (2009) recently presented a sparse approximation for CPs that enabled efficient inference. In this paper, we extend this work in two directions: we introduce the concept of variational inducing functions to handle potential non-smooth functions involved in the kernel CP construction and we consider an alternative approach to approximate inference based on variational methods, extending the work by Titsias (2009) to the multiple output case. We demonstrate our approaches on prediction of school marks, compiler performance and financial time series.
Dec 16, 2009

Mauricio A. Álvarez, David Luengo, Michalis K. Titsias, Neil D. Lawrence

* Technical report, 22 pages, 8 figures

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Action2Activity: Recognizing Complex Activities from Sensor Data

Nov 07, 2016

Ye Liu, Liqiang Nie, Lei Han, Luming Zhang, David S Rosenblum

Nov 07, 2016

Ye Liu, Liqiang Nie, Lei Han, Luming Zhang, David S Rosenblum

* IJCAI 2015

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Image Set based Collaborative Representation for Face Recognition

Aug 30, 2013

Pengfei Zhu, Wangmeng Zuo, Lei Zhang, Simon C. K. Shiu, David Zhang

Aug 30, 2013

Pengfei Zhu, Wangmeng Zuo, Lei Zhang, Simon C. K. Shiu, David Zhang

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Exponentially Weighted Moving Average Charts for Detecting Concept Drift

Dec 25, 2012

Gordon J. Ross, Niall M. Adams, Dimitris K. Tasoulis, David J. Hand

Dec 25, 2012

Gordon J. Ross, Niall M. Adams, Dimitris K. Tasoulis, David J. Hand

* Pattern Recognition Letters, 33(2) 191-198, 2012

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Interpretable Deep Learning applied to Plant Stress Phenotyping

Oct 28, 2017

Sambuddha Ghosal, David Blystone, Asheesh K. Singh, Baskar Ganapathysubramanian, Arti Singh, Soumik Sarkar

Availability of an explainable deep learning model that can be applied to practical real world scenarios and in turn, can consistently, rapidly and accurately identify specific and minute traits in applicable fields of biological sciences, is scarce. Here we consider one such real world example viz., accurate identification, classification and quantification of biotic and abiotic stresses in crop research and production. Up until now, this has been predominantly done manually by visual inspection and require specialized training. However, such techniques are hindered by subjectivity resulting from inter- and intra-rater cognitive variability. Here, we demonstrate the ability of a machine learning framework to identify and classify a diverse set of foliar stresses in the soybean plant with remarkable accuracy. We also present an explanation mechanism using gradient-weighted class activation mapping that isolates the visual symptoms used by the model to make predictions. This unsupervised identification of unique visual symptoms for each stress provides a quantitative measure of stress severity, allowing for identification, classification and quantification in one framework. The learnt model appears to be agnostic to species and make good predictions for other (non-soybean) species, demonstrating an ability of transfer learning.
Oct 28, 2017

Sambuddha Ghosal, David Blystone, Asheesh K. Singh, Baskar Ganapathysubramanian, Arti Singh, Soumik Sarkar

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Augment and Reduce: Stochastic Inference for Large Categorical Distributions

Jun 07, 2018

Francisco J. R. Ruiz, Michalis K. Titsias, Adji B. Dieng, David M. Blei

Jun 07, 2018

Francisco J. R. Ruiz, Michalis K. Titsias, Adji B. Dieng, David M. Blei

* Francisco J. R. Ruiz, Michalis K. Titsias, Adji B. Dieng, and David M. Blei. Augment and Reduce: Stochastic Inference for Large Categorical Distributions. International Conference on Machine Learning. Stockholm (Sweden), July 2018

* 11 pages, 2 figures

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Measuring dependence powerfully and equitably

Jul 06, 2016

Yakir A. Reshef, David N. Reshef, Hilary K. Finucane, Pardis C. Sabeti, Michael M. Mitzenmacher

Jul 06, 2016

Yakir A. Reshef, David N. Reshef, Hilary K. Finucane, Pardis C. Sabeti, Michael M. Mitzenmacher

* J.Mach.Learn.Res. 17 (2016), 1-63

* Yakir A. Reshef and David N. Reshef are co-first authors, Pardis C. Sabeti and Michael M. Mitzenmacher are co-last authors. This paper, together with arXiv:1505.02212, subsumes arXiv:1408.4908. v3 includes new analyses and exposition

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Payment Rules through Discriminant-Based Classifiers

Aug 06, 2012

Paul Duetting, Felix Fischer, Pitchayut Jirapinyo, John K. Lai, Benjamin Lubin, David C. Parkes

In mechanism design it is typical to impose incentive compatibility and then derive an optimal mechanism subject to this constraint. By replacing the incentive compatibility requirement with the goal of minimizing expected ex post regret, we are able to adapt statistical machine learning techniques to the design of payment rules. This computational approach to mechanism design is applicable to domains with multi-dimensional types and situations where computational efficiency is a concern. Specifically, given an outcome rule and access to a type distribution, we train a support vector machine with a special discriminant function structure such that it implicitly establishes a payment rule with desirable incentive properties. We discuss applications to a multi-minded combinatorial auction with a greedy winner-determination algorithm and to an assignment problem with egalitarian outcome rule. Experimental results demonstrate both that the construction produces payment rules with low ex post regret, and that penalizing classification errors is effective in preventing failures of ex post individual rationality.
Aug 06, 2012

Paul Duetting, Felix Fischer, Pitchayut Jirapinyo, John K. Lai, Benjamin Lubin, David C. Parkes

* Proceedings of the 13th ACM Conference on Electronic Commerce (EC '12), pages 477-494, 2012

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Interactive Learning from Policy-Dependent Human Feedback

Jan 21, 2017

James MacGlashan, Mark K Ho, Robert Loftin, Bei Peng, David Roberts, Matthew E. Taylor, Michael L. Littman

Jan 21, 2017

James MacGlashan, Mark K Ho, Robert Loftin, Bei Peng, David Roberts, Matthew E. Taylor, Michael L. Littman

* 7 pages, 2 figures

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Task-Driven Convolutional Recurrent Models of the Visual System

Oct 27, 2018

Aran Nayebi, Daniel Bear, Jonas Kubilius, Kohitij Kar, Surya Ganguli, David Sussillo, James J. DiCarlo, Daniel L. K. Yamins

Oct 27, 2018

Aran Nayebi, Daniel Bear, Jonas Kubilius, Kohitij Kar, Surya Ganguli, David Sussillo, James J. DiCarlo, Daniel L. K. Yamins

* NIPS 2018 Camera Ready Version, 16 pages including supplementary information, 6 figures

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A new approach for pedestrian density estimation using moving sensors and computer vision

Nov 12, 2018

Eric K. Tokuda, Yitzchak Lockerman, Gabriel B. A. Ferreira, Ethan Sorrelgreen, David Boyle, Roberto M. Cesar-Jr., Claudio T. Silva

Nov 12, 2018

Eric K. Tokuda, Yitzchak Lockerman, Gabriel B. A. Ferreira, Ethan Sorrelgreen, David Boyle, Roberto M. Cesar-Jr., Claudio T. Silva

* Submitted to ACM-TSAS

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Accuracy to Throughput Trade-offs for Reduced Precision Neural Networks on Reconfigurable Logic

Jul 17, 2018

Jiang Su, Nicholas J. Fraser, Giulio Gambardella, Michaela Blott, Gianluca Durelli, David B. Thomas, Philip Leong, Peter Y. K. Cheung

Jul 17, 2018

Jiang Su, Nicholas J. Fraser, Giulio Gambardella, Michaela Blott, Gianluca Durelli, David B. Thomas, Philip Leong, Peter Y. K. Cheung

* Accepted by ARC 2018

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Efficient Multi-Scale 3D CNN with Fully Connected CRF for Accurate Brain Lesion Segmentation

Jan 08, 2017

Konstantinos Kamnitsas, Christian Ledig, Virginia F. J. Newcombe, Joanna P. Simpson, Andrew D. Kane, David K. Menon, Daniel Rueckert, Ben Glocker

We propose a dual pathway, 11-layers deep, three-dimensional Convolutional Neural Network for the challenging task of brain lesion segmentation. The devised architecture is the result of an in-depth analysis of the limitations of current networks proposed for similar applications. To overcome the computational burden of processing 3D medical scans, we have devised an efficient and effective dense training scheme which joins the processing of adjacent image patches into one pass through the network while automatically adapting to the inherent class imbalance present in the data. Further, we analyze the development of deeper, thus more discriminative 3D CNNs. In order to incorporate both local and larger contextual information, we employ a dual pathway architecture that processes the input images at multiple scales simultaneously. For post-processing of the network's soft segmentation, we use a 3D fully connected Conditional Random Field which effectively removes false positives. Our pipeline is extensively evaluated on three challenging tasks of lesion segmentation in multi-channel MRI patient data with traumatic brain injuries, brain tumors, and ischemic stroke. We improve on the state-of-the-art for all three applications, with top ranking performance on the public benchmarks BRATS 2015 and ISLES 2015. Our method is computationally efficient, which allows its adoption in a variety of research and clinical settings. The source code of our implementation is made publicly available.
Jan 08, 2017

Konstantinos Kamnitsas, Christian Ledig, Virginia F. J. Newcombe, Joanna P. Simpson, Andrew D. Kane, David K. Menon, Daniel Rueckert, Ben Glocker

* This version was accepted in the journal Medical Image Analysis (MedIA)

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Interpretable Neural Networks for Predicting Mortality Risk using Multi-modal Electronic Health Records

Jan 23, 2019

Alvaro E. Ulloa Cerna, Marios Pattichis, David P. vanMaanen, Linyuan Jing, Aalpen A. Patel, Joshua V. Stough, Christopher M. Haggerty, Brandon K. Fornwalt

Jan 23, 2019

Alvaro E. Ulloa Cerna, Marios Pattichis, David P. vanMaanen, Linyuan Jing, Aalpen A. Patel, Joshua V. Stough, Christopher M. Haggerty, Brandon K. Fornwalt

* Submitted to IEEE JBHI

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