May 6, 2019

3022 words 15 mins read

Paper Group ANR 384

Why comparing survival curves between two prognostic subgroups may be misleading. Deep Learning Algorithms for Signal Recognition in Long Perimeter Monitoring Distributed Fiber Optic Sensors. Inexact Proximal Gradient Methods for Non-convex and Non-smooth Optimization. A Probabilistic Generative Grammar for Semantic Parsing. Effective sparse repres …

Why comparing survival curves between two prognostic subgroups may be misleading


Title	Why comparing survival curves between two prognostic subgroups may be misleading
Authors	Damjan Krstajic
Abstract	We consider the validation of prognostic diagnostic tests that predict two prognostic subgroups (high-risk vs low-risk) for a given disease or treatment. When comparing survival curves between two prognostic subgroups the possibility of misclassification arises, i.e. a patient predicted as high-risk might be de facto low-risk and vice versa. This is a fundamental difference from comparing survival curves between two populations (e.g. control vs treatment in RCT), where there is not an option of misclassification between members of populations. We show that there is a relationship between prognostic subgroups’ survival estimates at a time point and positive and negative predictive values in the classification settings. Consequently, the prevalence needs to be taken into account when validating the survival of prognostic subgroups at a time point. Our findings question current methods of comparing survival curves between prognostic subgroups in the validation set because they do not take into account the survival rates of the population.
Tasks
Published	2016-11-04
URL	http://arxiv.org/abs/1611.01480v1
PDF	http://arxiv.org/pdf/1611.01480v1.pdf
PWC	https://paperswithcode.com/paper/why-comparing-survival-curves-between-two
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Deep Learning Algorithms for Signal Recognition in Long Perimeter Monitoring Distributed Fiber Optic Sensors


Title	Deep Learning Algorithms for Signal Recognition in Long Perimeter Monitoring Distributed Fiber Optic Sensors
Authors	A. V. Makarenko
Abstract	In this paper, we show an approach to build deep learning algorithms for recognizing signals in distributed fiber optic monitoring and security systems for long perimeters. Synthesizing such detection algorithms poses a non-trivial research and development challenge, because these systems face stringent error (type I and II) requirements and operate in difficult signal-jamming environments, with intensive signal-like jamming and a variety of changing possible signal portraits of possible recognized events. To address these issues, we have developed a twolevel event detection architecture, where the primary classifier is based on an ensemble of deep convolutional networks, can recognize 7 classes of signals and receives time-space data frames as input. Using real-life data, we have shown that the applied methods result in efficient and robust multiclass detection algorithms that have a high degree of adaptability.
Tasks
Published	2016-10-02
URL	http://arxiv.org/abs/1610.00279v1
PDF	http://arxiv.org/pdf/1610.00279v1.pdf
PWC	https://paperswithcode.com/paper/deep-learning-algorithms-for-signal
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Inexact Proximal Gradient Methods for Non-convex and Non-smooth Optimization


Title	Inexact Proximal Gradient Methods for Non-convex and Non-smooth Optimization
Authors	Bin Gu, De Wang, Zhouyuan Huo, Heng Huang
Abstract	In machine learning research, the proximal gradient methods are popular for solving various optimization problems with non-smooth regularization. Inexact proximal gradient methods are extremely important when exactly solving the proximal operator is time-consuming, or the proximal operator does not have an analytic solution. However, existing inexact proximal gradient methods only consider convex problems. The knowledge of inexact proximal gradient methods in the non-convex setting is very limited. % Moreover, for some machine learning models, there is still no proposed solver for exactly solving the proximal operator. To address this challenge, in this paper, we first propose three inexact proximal gradient algorithms, including the basic version and Nesterov’s accelerated version. After that, we provide the theoretical analysis to the basic and Nesterov’s accelerated versions. The theoretical results show that our inexact proximal gradient algorithms can have the same convergence rates as the ones of exact proximal gradient algorithms in the non-convex setting. Finally, we show the applications of our inexact proximal gradient algorithms on three representative non-convex learning problems. All experimental results confirm the superiority of our new inexact proximal gradient algorithms.
Tasks
Published	2016-12-18
URL	http://arxiv.org/abs/1612.06003v2
PDF	http://arxiv.org/pdf/1612.06003v2.pdf
PWC	https://paperswithcode.com/paper/inexact-proximal-gradient-methods-for-non
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A Probabilistic Generative Grammar for Semantic Parsing


Title	A Probabilistic Generative Grammar for Semantic Parsing
Authors	Abulhair Saparov, Tom M. Mitchell
Abstract	We present a framework that couples the syntax and semantics of natural language sentences in a generative model, in order to develop a semantic parser that jointly infers the syntactic, morphological, and semantic representations of a given sentence under the guidance of background knowledge. To generate a sentence in our framework, a semantic statement is first sampled from a prior, such as from a set of beliefs in a knowledge base. Given this semantic statement, a grammar probabilistically generates the output sentence. A joint semantic-syntactic parser is derived that returns the $k$-best semantic and syntactic parses for a given sentence. The semantic prior is flexible, and can be used to incorporate background knowledge during parsing, in ways unlike previous semantic parsing approaches. For example, semantic statements corresponding to beliefs in a knowledge base can be given higher prior probability, type-correct statements can be given somewhat lower probability, and beliefs outside the knowledge base can be given lower probability. The construction of our grammar invokes a novel application of hierarchical Dirichlet processes (HDPs), which in turn, requires a novel and efficient inference approach. We present experimental results showing, for a simple grammar, that our parser outperforms a state-of-the-art CCG semantic parser and scales to knowledge bases with millions of beliefs.
Tasks	Semantic Parsing
Published	2016-06-20
URL	http://arxiv.org/abs/1606.06361v1
PDF	http://arxiv.org/pdf/1606.06361v1.pdf
PWC	https://paperswithcode.com/paper/a-probabilistic-generative-grammar-for
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Effective sparse representation of X-Ray medical images


Title	Effective sparse representation of X-Ray medical images
Authors	Laura Rebollo-Neira
Abstract	Effective sparse representation of X-Ray medical images within the context of data reduction is considered. The proposed framework is shown to render an enormous reduction in the cardinality of the data set required to represent this class of images at very good quality. The particularity of the approach is that it can be implemented at very competitive processing time and low memory requirements
Tasks
Published	2016-11-11
URL	http://arxiv.org/abs/1611.03873v1
PDF	http://arxiv.org/pdf/1611.03873v1.pdf
PWC	https://paperswithcode.com/paper/effective-sparse-representation-of-x-ray
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A Bayesian Approach to Policy Recognition and State Representation Learning


Title	A Bayesian Approach to Policy Recognition and State Representation Learning
Authors	Adrian Šošić, Abdelhak M. Zoubir, Heinz Koeppl
Abstract	Learning from demonstration (LfD) is the process of building behavioral models of a task from demonstrations provided by an expert. These models can be used e.g. for system control by generalizing the expert demonstrations to previously unencountered situations. Most LfD methods, however, make strong assumptions about the expert behavior, e.g. they assume the existence of a deterministic optimal ground truth policy or require direct monitoring of the expert’s controls, which limits their practical use as part of a general system identification framework. In this work, we consider the LfD problem in a more general setting where we allow for arbitrary stochastic expert policies, without reasoning about the optimality of the demonstrations. Following a Bayesian methodology, we model the full posterior distribution of possible expert controllers that explain the provided demonstration data. Moreover, we show that our methodology can be applied in a nonparametric context to infer the complexity of the state representation used by the expert, and to learn task-appropriate partitionings of the system state space.
Tasks	Representation Learning
Published	2016-05-04
URL	http://arxiv.org/abs/1605.01278v4
PDF	http://arxiv.org/pdf/1605.01278v4.pdf
PWC	https://paperswithcode.com/paper/a-bayesian-approach-to-policy-recognition-and
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Waterdrop Stereo


Title	Waterdrop Stereo
Authors	Shaodi You, Robby T. Tan, Rei Kawakami, Yasuhiro Mukaigawa, Katsushi Ikeuchi
Abstract	This paper introduces depth estimation from water drops. The key idea is that a single water drop adhered to window glass is totally transparent and convex, and thus optically acts like a fisheye lens. If we have more than one water drop in a single image, then through each of them we can see the environment with different view points, similar to stereo. To realize this idea, we need to rectify every water drop imagery to make radially distorted planar surfaces look flat. For this rectification, we consider two physical properties of water drops: (1) A static water drop has constant volume, and its geometric convex shape is determined by the balance between the tension force and gravity. This implies that the 3D geometric shape can be obtained by minimizing the overall potential energy, which is the sum of the tension energy and the gravitational potential energy. (2) The imagery inside a water-drop is determined by the water-drop 3D shape and total reflection at the boundary. This total reflection generates a dark band commonly observed in any adherent water drops. Hence, once the 3D shape of water drops are recovered, we can rectify the water drop images through backward raytracing. Subsequently, we can compute depth using stereo. In addition to depth estimation, we can also apply image refocusing. Experiments on real images and a quantitative evaluation show the effectiveness of our proposed method. To our best knowledge, never before have adherent water drops been used to estimate depth.
Tasks	Depth Estimation
Published	2016-04-04
URL	http://arxiv.org/abs/1604.00730v1
PDF	http://arxiv.org/pdf/1604.00730v1.pdf
PWC	https://paperswithcode.com/paper/waterdrop-stereo
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Online Updating of Word Representations for Part-of-Speech Tagging


Title	Online Updating of Word Representations for Part-of-Speech Tagging
Authors	Wenpeng Yin, Tobias Schnabel, Hinrich Schütze
Abstract	We propose online unsupervised domain adaptation (DA), which is performed incrementally as data comes in and is applicable when batch DA is not possible. In a part-of-speech (POS) tagging evaluation, we find that online unsupervised DA performs as well as batch DA.
Tasks	Domain Adaptation, Part-Of-Speech Tagging, Unsupervised Domain Adaptation
Published	2016-04-02
URL	http://arxiv.org/abs/1604.00502v1
PDF	http://arxiv.org/pdf/1604.00502v1.pdf
PWC	https://paperswithcode.com/paper/online-updating-of-word-representations-for
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Title	Mixture model modal clustering
Authors	José E. Chacón
Abstract	The two most extended density-based approaches to clustering are surely mixture model clustering and modal clustering. In the mixture model approach, the density is represented as a mixture and clusters are associated to the different mixture components. In modal clustering, clusters are understood as regions of high density separated from each other by zones of lower density, so that they are closely related to certain regions around the density modes. If the true density is indeed in the assumed class of mixture densities, then mixture model clustering allows to scrutinize more subtle situations than modal clustering. However, when mixture modeling is used in a nonparametric way, taking advantage of the denseness of the sieve of mixture densities to approximate any density, then the correspondence between clusters and mixture components may become questionable. In this paper we introduce two methods to adopt a modal clustering point of view after a mixture model fit. Numerous examples are provided to illustrate that mixture modeling can also be used for clustering in a nonparametric sense, as long as clusters are understood as the domains of attraction of the density modes.
Tasks
Published	2016-09-15
URL	http://arxiv.org/abs/1609.04721v1
PDF	http://arxiv.org/pdf/1609.04721v1.pdf
PWC	https://paperswithcode.com/paper/mixture-model-modal-clustering
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Monotone Retargeting for Unsupervised Rank Aggregation with Object Features


Title	Monotone Retargeting for Unsupervised Rank Aggregation with Object Features
Authors	Avradeep Bhowmik, Joydeep Ghosh
Abstract	Learning the true ordering between objects by aggregating a set of expert opinion rank order lists is an important and ubiquitous problem in many applications ranging from social choice theory to natural language processing and search aggregation. We study the problem of unsupervised rank aggregation where no ground truth ordering information in available, neither about the true preference ordering between any set of objects nor about the quality of individual rank lists. Aggregating the often inconsistent and poor quality rank lists in such an unsupervised manner is a highly challenging problem, and standard consensus-based methods are often ill-defined, and difficult to solve. In this manuscript we propose a novel framework to bypass these issues by using object attributes to augment the standard rank aggregation framework. We design algorithms that learn joint models on both rank lists and object features to obtain an aggregated rank ordering that is more accurate and robust, and also helps weed out rank lists of dubious validity. We validate our techniques on synthetic datasets where our algorithm is able to estimate the true rank ordering even when the rank lists are corrupted. Experiments on three real datasets, MQ2008, MQ2008 and OHSUMED, show that using object features can result in significant improvement in performance over existing rank aggregation methods that do not use object information. Furthermore, when at least some of the rank lists are of high quality, our methods are able to effectively exploit their high expertise to output an aggregated rank ordering of great accuracy.
Tasks
Published	2016-05-14
URL	http://arxiv.org/abs/1605.04465v1
PDF	http://arxiv.org/pdf/1605.04465v1.pdf
PWC	https://paperswithcode.com/paper/monotone-retargeting-for-unsupervised-rank
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Revisiting Causality Inference in Memory-less Transition Networks


Title	Revisiting Causality Inference in Memory-less Transition Networks
Authors	Abbas Shojaee, Isuru Ranasinghe, Alireza Ani
Abstract	Several methods exist to infer causal networks from massive volumes of observational data. However, almost all existing methods require a considerable length of time series data to capture cause and effect relationships. In contrast, memory-less transition networks or Markov Chain data, which refers to one-step transitions to and from an event, have not been explored for causality inference even though such data is widely available. We find that causal network can be inferred from characteristics of four unique distribution zones around each event. We call this Composition of Transitions and show that cause, effect, and random events exhibit different behavior in their compositions. We applied machine learning models to learn these different behaviors and to infer causality. We name this new method Causality Inference using Composition of Transitions (CICT). To evaluate CICT, we used an administrative inpatient healthcare dataset to set up a network of patients transitions between different diagnoses. We show that CICT is highly accurate in inferring whether the transition between a pair of events is causal or random and performs well in identifying the direction of causality in a bi-directional association.
Tasks	Time Series
Published	2016-08-08
URL	http://arxiv.org/abs/1608.02658v3
PDF	http://arxiv.org/pdf/1608.02658v3.pdf
PWC	https://paperswithcode.com/paper/revisiting-causality-inference-in-memory-less
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Object Manipulation Learning by Imitation


Title	Object Manipulation Learning by Imitation
Authors	Zhen Zeng, Benjamin Kuipers
Abstract	We aim to enable robot to learn object manipulation by imitation. Given external observations of demonstrations on object manipulations, we believe that two underlying problems to address in learning by imitation is 1) segment a given demonstration into skills that can be individually learned and reused, and 2) formulate the correct RL (Reinforcement Learning) problem that only considers the relevant aspects of each skill so that the policy for each skill can be effectively learned. Previous works made certain progress in this direction, but none has taken private information into account. The public information is the information that is available in the external observations of demonstration, and the private information is the information that are only available to the agent that executes the actions, such as tactile sensations. Our contribution is that we provide a method for the robot to automatically segment the demonstration of object manipulations into multiple skills, and formulate the correct RL problem for each skill, and automatically decide whether the private information is an important aspect of each skill based on interaction with the world. Our experiment shows that our robot learns to pick up a block, and stack it onto another block by imitating an observed demonstration. The evaluation is based on 1) whether the demonstration is reasonably segmented, 2) whether the correct RL problems are formulated, 3) and whether a good policy is learned.
Tasks
Published	2016-03-03
URL	http://arxiv.org/abs/1603.00964v3
PDF	http://arxiv.org/pdf/1603.00964v3.pdf
PWC	https://paperswithcode.com/paper/object-manipulation-learning-by-imitation
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Ballpark Learning: Estimating Labels from Rough Group Comparisons


Title	Ballpark Learning: Estimating Labels from Rough Group Comparisons
Authors	Tom Hope, Dafna Shahaf
Abstract	We are interested in estimating individual labels given only coarse, aggregated signal over the data points. In our setting, we receive sets (“bags”) of unlabeled instances with constraints on label proportions. We relax the unrealistic assumption of known label proportions, made in previous work; instead, we assume only to have upper and lower bounds, and constraints on bag differences. We motivate the problem, propose an intuitive formulation and algorithm, and apply our methods to real-world scenarios. Across several domains, we show how using only proportion constraints and no labeled examples, we can achieve surprisingly high accuracy. In particular, we demonstrate how to predict income level using rough stereotypes and how to perform sentiment analysis using very little information. We also apply our method to guide exploratory analysis, recovering geographical differences in twitter dialect.
Tasks	Sentiment Analysis
Published	2016-06-30
URL	http://arxiv.org/abs/1607.00034v1
PDF	http://arxiv.org/pdf/1607.00034v1.pdf
PWC	https://paperswithcode.com/paper/ballpark-learning-estimating-labels-from
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Cell assemblies at multiple time scales with arbitrary lag constellations


Title	Cell assemblies at multiple time scales with arbitrary lag constellations
Authors	Eleonora Russo, Daniel Durstewitz
Abstract	Hebb’s idea of a cell assembly as the fundamental unit of neural information processing has dominated neuroscience like no other theoretical concept within the past 60 years. A range of different physiological phenomena, from precisely synchronized spiking to broadly simultaneous rate increases, has been subsumed under this term. Yet progress in this area is hampered by the lack of statistical tools that would enable to extract assemblies with arbitrary constellations of time lags, and at multiple temporal scales, partly due to the severe computational burden. Here we present such a unifying methodological and conceptual framework which detects assembly structure at many different time scales, levels of precision, and with arbitrary internal organization. Applying this methodology to multiple single unit recordings from various cortical areas, we find that there is no universal cortical coding scheme, but that assembly structure and precision significantly depends on brain area recorded and ongoing task demands.
Tasks
Published	2016-07-04
URL	http://arxiv.org/abs/1607.00969v2
PDF	http://arxiv.org/pdf/1607.00969v2.pdf
PWC	https://paperswithcode.com/paper/cell-assemblies-at-multiple-time-scales-with
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Facial age estimation using BSIF and LBP


Title	Facial age estimation using BSIF and LBP
Authors	Salah Eddine Bekhouche, Abdelkrim Ouafi, Abdelmalik Taleb-Ahmed, Abdenour Hadid, Azeddine Benlamoudi
Abstract	Human face aging is irreversible process causing changes in human face characteristics such us hair whitening, muscles drop and wrinkles. Due to the importance of human face aging in biometrics systems, age estimation became an attractive area for researchers. This paper presents a novel method to estimate the age from face images, using binarized statistical image features (BSIF) and local binary patterns (LBP)histograms as features performed by support vector regression (SVR) and kernel ridge regression (KRR). We applied our method on FG-NET and PAL datasets. Our proposed method has shown superiority to that of the state-of-the-art methods when using the whole PAL database.
Tasks	Age Estimation
Published	2016-01-08
URL	http://arxiv.org/abs/1601.01876v1
PDF	http://arxiv.org/pdf/1601.01876v1.pdf
PWC	https://paperswithcode.com/paper/facial-age-estimation-using-bsif-and-lbp
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