May 6, 2019

2746 words 13 mins read

Paper Group ANR 231

Towards Practical Bayesian Parameter and State Estimation. Sequence Segmentation Using Joint RNN and Structured Prediction Models. On the Existence of Synchrostates in Multichannel EEG Signals during Face-perception Tasks. Kullback-Leibler Penalized Sparse Discriminant Analysis for Event-Related Potential Classification. A Representation Theory Per …

Towards Practical Bayesian Parameter and State Estimation


Title	Towards Practical Bayesian Parameter and State Estimation
Authors	Yusuf Bugra Erol, Yi Wu, Lei Li, Stuart Russell
Abstract	Joint state and parameter estimation is a core problem for dynamic Bayesian networks. Although modern probabilistic inference toolkits make it relatively easy to specify large and practically relevant probabilistic models, the silver bullet—an efficient and general online inference algorithm for such problems—remains elusive, forcing users to write special-purpose code for each application. We propose a novel blackbox algorithm – a hybrid of particle filtering for state variables and assumed density filtering for parameter variables. It has following advantages: (a) it is efficient due to its online nature, and (b) it is applicable to both discrete and continuous parameter spaces . On a variety of toy and real models, our system is able to generate more accurate results within a fixed computation budget. This preliminary evidence indicates that the proposed approach is likely to be of practical use.
Tasks
Published	2016-03-29
URL	http://arxiv.org/abs/1603.08988v1
PDF	http://arxiv.org/pdf/1603.08988v1.pdf
PWC	https://paperswithcode.com/paper/towards-practical-bayesian-parameter-and
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Framework

Sequence Segmentation Using Joint RNN and Structured Prediction Models


Title	Sequence Segmentation Using Joint RNN and Structured Prediction Models
Authors	Yossi Adi, Joseph Keshet, Emily Cibelli, Matthew Goldrick
Abstract	We describe and analyze a simple and effective algorithm for sequence segmentation applied to speech processing tasks. We propose a neural architecture that is composed of two modules trained jointly: a recurrent neural network (RNN) module and a structured prediction model. The RNN outputs are considered as feature functions to the structured model. The overall model is trained with a structured loss function which can be designed to the given segmentation task. We demonstrate the effectiveness of our method by applying it to two simple tasks commonly used in phonetic studies: word segmentation and voice onset time segmentation. Results sug- gest the proposed model is superior to previous methods, ob- taining state-of-the-art results on the tested datasets.
Tasks	Structured Prediction
Published	2016-10-25
URL	http://arxiv.org/abs/1610.07918v1
PDF	http://arxiv.org/pdf/1610.07918v1.pdf
PWC	https://paperswithcode.com/paper/sequence-segmentation-using-joint-rnn-and
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On the Existence of Synchrostates in Multichannel EEG Signals during Face-perception Tasks


Title	On the Existence of Synchrostates in Multichannel EEG Signals during Face-perception Tasks
Authors	Wasifa Jamal, Saptarshi Das, Koushik Maharatna, Fabio Apicella, Georgia Chronaki, Federico Sicca, David Cohen, Filippo Muratori
Abstract	Phase synchronisation in multichannel EEG is known as the manifestation of functional brain connectivity. Traditional phase synchronisation studies are mostly based on time average synchrony measures hence do not preserve the temporal evolution of the phase difference. Here we propose a new method to show the existence of a small set of unique phase synchronised patterns or “states” in multi-channel EEG recordings, each “state” being stable of the order of ms, from typical and pathological subjects during face perception tasks. The proposed methodology bridges the concepts of EEG microstates and phase synchronisation in time and frequency domain respectively. The analysis is reported for four groups of children including typical, Autism Spectrum Disorder (ASD), low and high anxiety subjects - a total of 44 subjects. In all cases, we observe consistent existence of these states - termed as synchrostates - within specific cognition related frequency bands (beta and gamma bands), though the topographies of these synchrostates differ for different subject groups with different pathological conditions. The inter-synchrostate switching follows a well-defined sequence capturing the underlying inter-electrode phase relation dynamics in stimulus- and person-centric manner. Our study is motivated from the well-known EEG microstate exhibiting stable potential maps over the scalp. However, here we report a similar observation of quasi-stable phase synchronised states in multichannel EEG. The existence of the synchrostates coupled with their unique switching sequence characteristics could be considered as a potentially new field over contemporary EEG phase synchronisation studies.
Tasks	EEG
Published	2016-11-29
URL	http://arxiv.org/abs/1611.09791v1
PDF	http://arxiv.org/pdf/1611.09791v1.pdf
PWC	https://paperswithcode.com/paper/on-the-existence-of-synchrostates-in
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Framework


Title	Kullback-Leibler Penalized Sparse Discriminant Analysis for Event-Related Potential Classification
Authors	Victoria Peterson, Hugo Leonardo Rufiner, Ruben Daniel Spies
Abstract	A brain computer interface (BCI) is a system which provides direct communication between the mind of a person and the outside world by using only brain activity (EEG). The event-related potential (ERP)-based BCI problem consists of a binary pattern recognition. Linear discriminant analysis (LDA) is widely used to solve this type of classification problems, but it fails when the number of features is large relative to the number of observations. In this work we propose a penalized version of the sparse discriminant analysis (SDA), called Kullback-Leibler penalized sparse discriminant analysis (KLSDA). This method inherits both the discriminative feature selection and classification properties of SDA and it also improves SDA performance through the addition of Kullback-Leibler class discrepancy information. The KLSDA method is design to automatically select the optimal regularization parameters. Numerical experiments with two real ERP-EEG datasets show that this new method outperforms standard SDA.
Tasks	EEG, Feature Selection
Published	2016-08-24
URL	http://arxiv.org/abs/1608.06863v1
PDF	http://arxiv.org/pdf/1608.06863v1.pdf
PWC	https://paperswithcode.com/paper/kullback-leibler-penalized-sparse
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Framework

A Representation Theory Perspective on Simultaneous Alignment and Classification


Title	A Representation Theory Perspective on Simultaneous Alignment and Classification
Authors	Roy R. Lederman, Amit Singer
Abstract	One of the difficulties in 3D reconstruction of molecules from images in single particle Cryo-Electron Microscopy (Cryo-EM), in addition to high levels of noise and unknown image orientations, is heterogeneity in samples: in many cases, the samples contain a mixture of molecules, or multiple conformations of one molecule. Many algorithms for the reconstruction of molecules from images in heterogeneous Cryo-EM experiments are based on iterative approximations of the molecules in a non-convex optimization that is prone to reaching suboptimal local minima. Other algorithms require an alignment in order to perform classification, or vice versa. The recently introduced Non-Unique Games framework provides a representation theoretic approach to studying problems of alignment over compact groups, and offers convex relaxations for alignment problems which are formulated as semidefinite programs (SDPs) with certificates of global optimality under certain circumstances. In this manuscript, we propose to extend Non-Unique Games to the problem of simultaneous alignment and classification with the goal of simultaneously classifying Cryo-EM images and aligning them within their respective classes. Our proposed approach can also be extended to the case of continuous heterogeneity.
Tasks	3D Reconstruction
Published	2016-07-12
URL	http://arxiv.org/abs/1607.03464v1
PDF	http://arxiv.org/pdf/1607.03464v1.pdf
PWC	https://paperswithcode.com/paper/a-representation-theory-perspective-on
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Fairness as a Program Property


Title	Fairness as a Program Property
Authors	Aws Albarghouthi, Loris D’Antoni, Samuel Drews, Aditya Nori
Abstract	We explore the following question: Is a decision-making program fair, for some useful definition of fairness? First, we describe how several algorithmic fairness questions can be phrased as program verification problems. Second, we discuss an automated verification technique for proving or disproving fairness of decision-making programs with respect to a probabilistic model of the population.
Tasks	Decision Making
Published	2016-10-19
URL	http://arxiv.org/abs/1610.06067v1
PDF	http://arxiv.org/pdf/1610.06067v1.pdf
PWC	https://paperswithcode.com/paper/fairness-as-a-program-property
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Framework

Counting Grid Aggregation for Event Retrieval and Recognition


Title	Counting Grid Aggregation for Event Retrieval and Recognition
Authors	Zhanning Gao, Gang Hua, Dongqing Zhang, Jianru Xue, Nanning Zheng
Abstract	Event retrieval and recognition in a large corpus of videos necessitates a holistic fixed-size visual representation at the video clip level that is comprehensive, compact, and yet discriminative. It shall comprehensively aggregate information across relevant video frames, while suppress redundant information, leading to a compact representation that can effectively differentiate among different visual events. In search for such a representation, we propose to build a spatially consistent counting grid model to aggregate together deep features extracted from different video frames. The spatial consistency of the counting grid model is achieved by introducing a prior model estimated from a large corpus of video data. The counting grid model produces an intermediate tensor representation for each video, which automatically identifies and removes the feature redundancy across the different frames. The tensor representation is subsequently reduced to a fixed-size vector representation by averaging over the counting grid. When compared to existing methods on both event retrieval and event classification benchmarks, we achieve significantly better accuracy with much more compact representation.
Tasks
Published	2016-04-05
URL	http://arxiv.org/abs/1604.01109v3
PDF	http://arxiv.org/pdf/1604.01109v3.pdf
PWC	https://paperswithcode.com/paper/counting-grid-aggregation-for-event-retrieval
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Framework

SimDoc: Topic Sequence Alignment based Document Similarity Framework


Title	SimDoc: Topic Sequence Alignment based Document Similarity Framework
Authors	Gaurav Maheshwari, Priyansh Trivedi, Harshita Sahijwani, Kunal Jha, Sourish Dasgupta, Jens Lehmann
Abstract	Document similarity is the problem of estimating the degree to which a given pair of documents has similar semantic content. An accurate document similarity measure can improve several enterprise relevant tasks such as document clustering, text mining, and question-answering. In this paper, we show that a document’s thematic flow, which is often disregarded by bag-of-word techniques, is pivotal in estimating their similarity. To this end, we propose a novel semantic document similarity framework, called SimDoc. We model documents as topic-sequences, where topics represent latent generative clusters of related words. Then, we use a sequence alignment algorithm to estimate their semantic similarity. We further conceptualize a novel mechanism to compute topic-topic similarity to fine tune our system. In our experiments, we show that SimDoc outperforms many contemporary bag-of-words techniques in accurately computing document similarity, and on practical applications such as document clustering.
Tasks	Question Answering, Semantic Similarity, Semantic Textual Similarity
Published	2016-11-15
URL	http://arxiv.org/abs/1611.04822v2
PDF	http://arxiv.org/pdf/1611.04822v2.pdf
PWC	https://paperswithcode.com/paper/simdoc-topic-sequence-alignment-based
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Framework

Enhancing Transparency and Control when Drawing Data-Driven Inferences about Individuals


Title	Enhancing Transparency and Control when Drawing Data-Driven Inferences about Individuals
Authors	Daizhuo Chen, Samuel P. Fraiberger, Robert Moakler, Foster Provost
Abstract	Recent studies have shown that information disclosed on social network sites (such as Facebook) can be used to predict personal characteristics with surprisingly high accuracy. In this paper we examine a method to give online users transparency into why certain inferences are made about them by statistical models, and control to inhibit those inferences by hiding (“cloaking”) certain personal information from inference. We use this method to examine whether such transparency and control would be a reasonable goal by assessing how difficult it would be for users to actually inhibit inferences. Applying the method to data from a large collection of real users on Facebook, we show that a user must cloak only a small portion of her Facebook Likes in order to inhibit inferences about their personal characteristics. However, we also show that in response a firm could change its modeling of users to make cloaking more difficult.
Tasks
Published	2016-06-26
URL	http://arxiv.org/abs/1606.08063v1
PDF	http://arxiv.org/pdf/1606.08063v1.pdf
PWC	https://paperswithcode.com/paper/enhancing-transparency-and-control-when
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Framework

A single-phase, proximal path-following framework


Title	A single-phase, proximal path-following framework
Authors	Quoc Tran-Dinh, Anastasios Kyrillidis, Volkan Cevher
Abstract	We propose a new proximal, path-following framework for a class of constrained convex problems. We consider settings where the nonlinear—and possibly non-smooth—objective part is endowed with a proximity operator, and the constraint set is equipped with a self-concordant barrier. Our approach relies on the following two main ideas. First, we re-parameterize the optimality condition as an auxiliary problem, such that a good initial point is available; by doing so, a family of alternative paths towards the optimum is generated. Second, we combine the proximal operator with path-following ideas to design a single-phase, proximal, path-following algorithm. Our method has several advantages. First, it allows handling non-smooth objectives via proximal operators; this avoids lifting the problem dimension in order to accommodate non-smooth components in optimization. Second, it consists of only a \emph{single phase}: While the overall convergence rate of classical path-following schemes for self-concordant objectives does not suffer from the initialization phase, proximal path-following schemes undergo slow convergence, in order to obtain a good starting point \cite{TranDinh2013e}. In this work, we show how to overcome this limitation in the proximal setting and prove that our scheme has the same $\mathcal{O}(\sqrt{\nu}\log(1/\varepsilon))$ worst-case iteration-complexity with standard approaches \cite{Nesterov2004,Nesterov1994} without requiring an initial phase, where $\nu$ is the barrier parameter and $\varepsilon$ is a desired accuracy. Finally, our framework allows errors in the calculation of proximal-Newton directions, without sacrificing the worst-case iteration complexity. We demonstrate the merits of our algorithm via three numerical examples, where proximal operators play a key role.
Tasks
Published	2016-03-05
URL	http://arxiv.org/abs/1603.01681v2
PDF	http://arxiv.org/pdf/1603.01681v2.pdf
PWC	https://paperswithcode.com/paper/a-single-phase-proximal-path-following
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Framework

Chaining Bounds for Empirical Risk Minimization


Title	Chaining Bounds for Empirical Risk Minimization
Authors	Gábor Balázs, András György, Csaba Szepesvári
Abstract	This paper extends the standard chaining technique to prove excess risk upper bounds for empirical risk minimization with random design settings even if the magnitude of the noise and the estimates is unbounded. The bound applies to many loss functions besides the squared loss, and scales only with the sub-Gaussian or subexponential parameters without further statistical assumptions such as the bounded kurtosis condition over the hypothesis class. A detailed analysis is provided for slope constrained and penalized linear least squares regression with a sub-Gaussian setting, which often proves tight sample complexity bounds up to logartihmic factors.
Tasks
Published	2016-09-07
URL	http://arxiv.org/abs/1609.01872v1
PDF	http://arxiv.org/pdf/1609.01872v1.pdf
PWC	https://paperswithcode.com/paper/chaining-bounds-for-empirical-risk
Repo
Framework

Interpreting extracted rules from ensemble of trees: Application to computer-aided diagnosis of breast MRI


Title	Interpreting extracted rules from ensemble of trees: Application to computer-aided diagnosis of breast MRI
Authors	Cristina Gallego-Ortiz, Anne L. Martel
Abstract	High predictive performance and ease of use and interpretability are important requirements for the applicability of a computer-aided diagnosis (CAD) to human reading studies. We propose a CAD system specifically designed to be more comprehensible to the radiologist reviewing screening breast MRI studies. Multiparametric imaging features are combined to produce a CAD system for differentiating cancerous and non-cancerous lesions. The complete system uses a rule-extraction algorithm to present lesion classification results in an easy to understand graph visualization.
Tasks
Published	2016-06-27
URL	http://arxiv.org/abs/1606.08288v1
PDF	http://arxiv.org/pdf/1606.08288v1.pdf
PWC	https://paperswithcode.com/paper/interpreting-extracted-rules-from-ensemble-of
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Framework

To Swap or Not to Swap? Exploiting Dependency Word Pairs for Reordering in Statistical Machine Translation


Title	To Swap or Not to Swap? Exploiting Dependency Word Pairs for Reordering in Statistical Machine Translation
Authors	Christian Hadiwinoto, Yang Liu, Hwee Tou Ng
Abstract	Reordering poses a major challenge in machine translation (MT) between two languages with significant differences in word order. In this paper, we present a novel reordering approach utilizing sparse features based on dependency word pairs. Each instance of these features captures whether two words, which are related by a dependency link in the source sentence dependency parse tree, follow the same order or are swapped in the translation output. Experiments on Chinese-to-English translation show a statistically significant improvement of 1.21 BLEU point using our approach, compared to a state-of-the-art statistical MT system that incorporates prior reordering approaches.
Tasks	Machine Translation
Published	2016-08-03
URL	http://arxiv.org/abs/1608.01084v1
PDF	http://arxiv.org/pdf/1608.01084v1.pdf
PWC	https://paperswithcode.com/paper/to-swap-or-not-to-swap-exploiting-dependency
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Framework

Retinal Vessel Segmentation Using A New Topological Method


Title	Retinal Vessel Segmentation Using A New Topological Method
Authors	Martin Brooks
Abstract	A novel topological segmentation of retinal images represents blood vessels as connected regions in the continuous image plane, having shape-related analytic and geometric properties. This paper presents topological segmentation results from the DRIVE retinal image database.
Tasks	Retinal Vessel Segmentation
Published	2016-08-03
URL	http://arxiv.org/abs/1608.01339v1
PDF	http://arxiv.org/pdf/1608.01339v1.pdf
PWC	https://paperswithcode.com/paper/retinal-vessel-segmentation-using-a-new
Repo
Framework

DASA: Domain Adaptation in Stacked Autoencoders using Systematic Dropout


Title	DASA: Domain Adaptation in Stacked Autoencoders using Systematic Dropout
Authors	Abhijit Guha Roy, Debdoot Sheet
Abstract	Domain adaptation deals with adapting behaviour of machine learning based systems trained using samples in source domain to their deployment in target domain where the statistics of samples in both domains are dissimilar. The task of directly training or adapting a learner in the target domain is challenged by lack of abundant labeled samples. In this paper we propose a technique for domain adaptation in stacked autoencoder (SAE) based deep neural networks (DNN) performed in two stages: (i) unsupervised weight adaptation using systematic dropouts in mini-batch training, (ii) supervised fine-tuning with limited number of labeled samples in target domain. We experimentally evaluate performance in the problem of retinal vessel segmentation where the SAE-DNN is trained using large number of labeled samples in the source domain (DRIVE dataset) and adapted using less number of labeled samples in target domain (STARE dataset). The performance of SAE-DNN measured using $logloss$ in source domain is $0.19$, without and with adaptation are $0.40$ and $0.18$, and $0.39$ when trained exclusively with limited samples in target domain. The area under ROC curve is observed respectively as $0.90$, $0.86$, $0.92$ and $0.87$. The high efficiency of vessel segmentation with DASA strongly substantiates our claim.
Tasks	Domain Adaptation, Retinal Vessel Segmentation
Published	2016-03-19
URL	http://arxiv.org/abs/1603.06060v1
PDF	http://arxiv.org/pdf/1603.06060v1.pdf
PWC	https://paperswithcode.com/paper/dasa-domain-adaptation-in-stacked
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Framework