July 28, 2019

2796 words 14 mins read

Paper Group ANR 203

Nonparametric Independence Screening via Favored Smoothing Bandwidth. Deep Neural Network Based Precursor microRNA Prediction on Eleven Species. Bilingual Document Alignment with Latent Semantic Indexing. Google’s Cloud Vision API Is Not Robust To Noise. Labeled Memory Networks for Online Model Adaptation. Porcellio scaber algorithm (PSA) for solvi …

Nonparametric Independence Screening via Favored Smoothing Bandwidth


Title	Nonparametric Independence Screening via Favored Smoothing Bandwidth
Authors	Yang Feng, Yichao Wu, Leonard Stefanski
Abstract	We propose a flexible nonparametric regression method for ultrahigh-dimensional data. As a first step, we propose a fast screening method based on the favored smoothing bandwidth of the marginal local constant regression. Then, an iterative procedure is developed to recover both the important covariates and the regression function. Theoretically, we prove that the favored smoothing bandwidth based screening possesses the model selection consistency property. Simulation studies as well as real data analysis show the competitive performance of the new procedure.
Tasks	Model Selection
Published	2017-11-28
URL	http://arxiv.org/abs/1711.10411v1
PDF	http://arxiv.org/pdf/1711.10411v1.pdf
PWC	https://paperswithcode.com/paper/nonparametric-independence-screening-via
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Deep Neural Network Based Precursor microRNA Prediction on Eleven Species


Title	Deep Neural Network Based Precursor microRNA Prediction on Eleven Species
Authors	Jaya Thomas, Lee Sael
Abstract	MicroRNA (miRNA) are small non-coding RNAs that regulates the gene expression at the post-transcriptional level. Determining whether a sequence segment is miRNA is experimentally challenging. Also, experimental results are sensitive to the experimental environment. These limitations inspire the development of computational methods for predicting the miRNAs. We propose a deep learning based classification model, called DP-miRNA, for predicting precursor miRNA sequence that contains the miRNA sequence. The feature set based Restricted Boltzmann Machine method, which we call DP-miRNA, uses 58 features that are categorized into four groups: sequence features, folding measures, stem-loop features and statistical feature. We evaluate the performance of the DP-miRNA on eleven twelve data sets of varying species, including the human. The deep neural network based classification outperformed support vector machine, neural network, naive Baye’s classifiers, k-nearest neighbors, random forests, and a hybrid system combining support vector machine and genetic algorithm.
Tasks
Published	2017-04-10
URL	http://arxiv.org/abs/1704.03834v1
PDF	http://arxiv.org/pdf/1704.03834v1.pdf
PWC	https://paperswithcode.com/paper/deep-neural-network-based-precursor-microrna
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Bilingual Document Alignment with Latent Semantic Indexing


Title	Bilingual Document Alignment with Latent Semantic Indexing
Authors	Ulrich Germann
Abstract	We apply cross-lingual Latent Semantic Indexing to the Bilingual Document Alignment Task at WMT16. Reduced-rank singular value decomposition of a bilingual term-document matrix derived from known English/French page pairs in the training data allows us to map monolingual documents into a joint semantic space. Two variants of cosine similarity between the vectors that place each document into the joint semantic space are combined with a measure of string similarity between corresponding URLs to produce 1:1 alignments of English/French web pages in a variety of domains. The system achieves a recall of ca. 88% if no in-domain data is used for building the latent semantic model, and 93% if such data is included. Analysing the system’s errors on the training data, we argue that evaluating aligner performance based on exact URL matches under-estimates their true performance and propose an alternative that is able to account for duplicates and near-duplicates in the underlying data.
Tasks
Published	2017-07-29
URL	http://arxiv.org/abs/1707.09443v1
PDF	http://arxiv.org/pdf/1707.09443v1.pdf
PWC	https://paperswithcode.com/paper/bilingual-document-alignment-with-latent
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Google’s Cloud Vision API Is Not Robust To Noise


Title	Google’s Cloud Vision API Is Not Robust To Noise
Authors	Hossein Hosseini, Baicen Xiao, Radha Poovendran
Abstract	Google has recently introduced the Cloud Vision API for image analysis. According to the demonstration website, the API “quickly classifies images into thousands of categories, detects individual objects and faces within images, and finds and reads printed words contained within images.” It can be also used to “detect different types of inappropriate content from adult to violent content.” In this paper, we evaluate the robustness of Google Cloud Vision API to input perturbation. In particular, we show that by adding sufficient noise to the image, the API generates completely different outputs for the noisy image, while a human observer would perceive its original content. We show that the attack is consistently successful, by performing extensive experiments on different image types, including natural images, images containing faces and images with texts. For instance, using images from ImageNet dataset, we found that adding an average of 14.25% impulse noise is enough to deceive the API. Our findings indicate the vulnerability of the API in adversarial environments. For example, an adversary can bypass an image filtering system by adding noise to inappropriate images. We then show that when a noise filter is applied on input images, the API generates mostly the same outputs for restored images as for original images. This observation suggests that cloud vision API can readily benefit from noise filtering, without the need for updating image analysis algorithms.
Tasks
Published	2017-04-16
URL	http://arxiv.org/abs/1704.05051v2
PDF	http://arxiv.org/pdf/1704.05051v2.pdf
PWC	https://paperswithcode.com/paper/googles-cloud-vision-api-is-not-robust-to
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Labeled Memory Networks for Online Model Adaptation


Title	Labeled Memory Networks for Online Model Adaptation
Authors	Shiv Shankar, Sunita Sarawagi
Abstract	Augmenting a neural network with memory that can grow without growing the number of trained parameters is a recent powerful concept with many exciting applications. We propose a design of memory augmented neural networks (MANNs) called Labeled Memory Networks (LMNs) suited for tasks requiring online adaptation in classification models. LMNs organize the memory with classes as the primary key.The memory acts as a second boosted stage following a regular neural network thereby allowing the memory and the primary network to play complementary roles. Unlike existing MANNs that write to memory for every instance and use LRU based memory replacement, LMNs write only for instances with non-zero loss and use label-based memory replacement. We demonstrate significant accuracy gains on various tasks including word-modelling and few-shot learning. In this paper, we establish their potential in online adapting a batch trained neural network to domain-relevant labeled data at deployment time. We show that LMNs are better than other MANNs designed for meta-learning. We also found them to be more accurate and faster than state-of-the-art methods of retuning model parameters for adapting to domain-specific labeled data.
Tasks	Few-Shot Learning, Meta-Learning
Published	2017-07-05
URL	http://arxiv.org/abs/1707.01461v3
PDF	http://arxiv.org/pdf/1707.01461v3.pdf
PWC	https://paperswithcode.com/paper/labeled-memory-networks-for-online-model
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Porcellio scaber algorithm (PSA) for solving constrained optimization problems


Title	Porcellio scaber algorithm (PSA) for solving constrained optimization problems
Authors	Yinyan Zhang, Shuai Li, Hongliang Guo
Abstract	In this paper, we extend a bio-inspired algorithm called the porcellio scaber algorithm (PSA) to solve constrained optimization problems, including a constrained mixed discrete-continuous nonlinear optimization problem. Our extensive experiment results based on benchmark optimization problems show that the PSA has a better performance than many existing methods or algorithms. The results indicate that the PSA is a promising algorithm for constrained optimization.
Tasks
Published	2017-10-11
URL	http://arxiv.org/abs/1710.04036v1
PDF	http://arxiv.org/pdf/1710.04036v1.pdf
PWC	https://paperswithcode.com/paper/porcellio-scaber-algorithm-psa-for-solving
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The Stochastic complexity of spin models: Are pairwise models really simple?


Title	The Stochastic complexity of spin models: Are pairwise models really simple?
Authors	Alberto Beretta, Claudia Battistin, Clélia de Mulatier, Iacopo Mastromatteo, Matteo Marsili
Abstract	Models can be simple for different reasons: because they yield a simple and computationally efficient interpretation of a generic dataset (e.g. in terms of pairwise dependences) - as in statistical learning - or because they capture the essential ingredients of a specific phenomenon - as e.g. in physics - leading to non-trivial falsifiable predictions. In information theory and Bayesian inference, the simplicity of a model is precisely quantified in the stochastic complexity, which measures the number of bits needed to encode its parameters. In order to understand how simple models look like, we study the stochastic complexity of spin models with interactions of arbitrary order. We highlight the existence of invariances with respect to bijections within the space of operators, which allow us to partition the space of all models into equivalence classes, in which models share the same complexity. We thus found that the complexity (or simplicity) of a model is not determined by the order of the interactions, but rather by their mutual arrangements. Models where statistical dependencies are localized on non-overlapping groups of few variables (and that afford predictions on independencies that are easy to falsify) are simple. On the contrary, fully connected pairwise models, which are often used in statistical learning, appear to be highly complex, because of their extended set of interactions.
Tasks	Bayesian Inference
Published	2017-02-24
URL	http://arxiv.org/abs/1702.07549v3
PDF	http://arxiv.org/pdf/1702.07549v3.pdf
PWC	https://paperswithcode.com/paper/the-stochastic-complexity-of-spin-models-are
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Dynamic High Resolution Deformable Articulated Tracking


Title	Dynamic High Resolution Deformable Articulated Tracking
Authors	Aaron Walsman, Weilin Wan, Tanner Schmidt, Dieter Fox
Abstract	The last several years have seen significant progress in using depth cameras for tracking articulated objects such as human bodies, hands, and robotic manipulators. Most approaches focus on tracking skeletal parameters of a fixed shape model, which makes them insufficient for applications that require accurate estimates of deformable object surfaces. To overcome this limitation, we present a 3D model-based tracking system for articulated deformable objects. Our system is able to track human body pose and high resolution surface contours in real time using a commodity depth sensor and GPU hardware. We implement this as a joint optimization over a skeleton to account for changes in pose, and over the vertices of a high resolution mesh to track the subject’s shape. Through experimental results we show that we are able to capture dynamic sub-centimeter surface detail such as folds and wrinkles in clothing. We also show that this shape estimation aids kinematic pose estimation by providing a more accurate target to match against the point cloud. The end result is highly accurate spatiotemporal and semantic information which is well suited for physical human robot interaction as well as virtual and augmented reality systems.
Tasks	Pose Estimation
Published	2017-11-21
URL	http://arxiv.org/abs/1711.07999v1
PDF	http://arxiv.org/pdf/1711.07999v1.pdf
PWC	https://paperswithcode.com/paper/dynamic-high-resolution-deformable
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Variance reduction via empirical variance minimization: convergence and complexity


Title	Variance reduction via empirical variance minimization: convergence and complexity
Authors	D. Belomestny, L. Iosipoi, N. Zhivotovskiy
Abstract	In this paper we propose and study a generic variance reduction approach. The proposed method is based on minimization of the empirical variance over a suitable class of zero mean control functionals. We discuss several possibilities of constructing zero mean control functionals and present non-asymptotic error bounds for the variance reduced Monte Carlo estimates. Finally, a simulation study showing numerical efficiency of the proposed approach is presented.
Tasks
Published	2017-12-13
URL	http://arxiv.org/abs/1712.04667v3
PDF	http://arxiv.org/pdf/1712.04667v3.pdf
PWC	https://paperswithcode.com/paper/variance-reduction-via-empirical-variance
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Deep Architectures for Modulation Recognition


Title	Deep Architectures for Modulation Recognition
Authors	Nathan E West, Timothy J. O’Shea
Abstract	We survey the latest advances in machine learning with deep neural networks by applying them to the task of radio modulation recognition. Results show that radio modulation recognition is not limited by network depth and further work should focus on improving learned synchronization and equalization. Advances in these areas will likely come from novel architectures designed for these tasks or through novel training methods.
Tasks
Published	2017-03-27
URL	http://arxiv.org/abs/1703.09197v1
PDF	http://arxiv.org/pdf/1703.09197v1.pdf
PWC	https://paperswithcode.com/paper/deep-architectures-for-modulation-recognition
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Introduction To The Monogenic Signal


Title	Introduction To The Monogenic Signal
Authors	Christopher P. Bridge
Abstract	The monogenic signal is an image analysis methodology that was introduced by Felsberg and Sommer in 2001 and has been employed for a variety of purposes in image processing and computer vision research. In particular, it has been found to be useful in the analysis of ultrasound imagery in several research scenarios mostly in work done within the BioMedIA lab at Oxford. However, the literature on the monogenic signal can be difficult to penetrate due to the lack of a single resource to explain the various principles from basics. The purpose of this document is therefore to introduce the principles, purpose, applications, and limitations of the methodology. It assumes some background knowledge from the fields of image and signal processing, in particular a good knowledge of Fourier transforms as applied to signals and images. We will not attempt to provide a thorough math- ematical description or derivation of the monogenic signal, but rather focus on developing an intuition for understanding and using the methodology and refer the reader elsewhere for a more mathematical treatment.
Tasks
Published	2017-03-27
URL	http://arxiv.org/abs/1703.09199v1
PDF	http://arxiv.org/pdf/1703.09199v1.pdf
PWC	https://paperswithcode.com/paper/introduction-to-the-monogenic-signal
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An Analysis of the Value of Information when Exploring Stochastic, Discrete Multi-Armed Bandits


Title	An Analysis of the Value of Information when Exploring Stochastic, Discrete Multi-Armed Bandits
Authors	Isaac J. Sledge, Jose C. Principe
Abstract	In this paper, we propose an information-theoretic exploration strategy for stochastic, discrete multi-armed bandits that achieves optimal regret. Our strategy is based on the value of information criterion. This criterion measures the trade-off between policy information and obtainable rewards. High amounts of policy information are associated with exploration-dominant searches of the space and yield high rewards. Low amounts of policy information favor the exploitation of existing knowledge. Information, in this criterion, is quantified by a parameter that can be varied during search. We demonstrate that a simulated-annealing-like update of this parameter, with a sufficiently fast cooling schedule, leads to an optimal regret that is logarithmic with respect to the number of episodes.
Tasks	Multi-Armed Bandits
Published	2017-10-08
URL	http://arxiv.org/abs/1710.02869v2
PDF	http://arxiv.org/pdf/1710.02869v2.pdf
PWC	https://paperswithcode.com/paper/an-analysis-of-the-value-of-information-when
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Practical Reasoning with Norms for Autonomous Software Agents (Full Edition)


Title	Practical Reasoning with Norms for Autonomous Software Agents (Full Edition)
Authors	Zohreh Shams, Marina De Vos, Julian Padget, Wamberto W. Vasconcelos
Abstract	Autonomous software agents operating in dynamic environments need to constantly reason about actions in pursuit of their goals, while taking into consideration norms which might be imposed on those actions. Normative practical reasoning supports agents making decisions about what is best for them to (not) do in a given situation. What makes practical reasoning challenging is the interplay between goals that agents are pursuing and the norms that the agents are trying to uphold. We offer a formalisation to allow agents to plan for multiple goals and norms in the presence of durative actions that can be executed concurrently. We compare plans based on decision-theoretic notions (i.e. utility) such that the utility gain of goals and utility loss of norm violations are the basis for this comparison. The set of optimal plans consists of plans that maximise the overall utility, each of which can be chosen by the agent to execute. We provide an implementation of our proposal in Answer Set Programming, thus allowing us to state the original problem in terms of a logic program that can be queried for solutions with specific properties. The implementation is proven to be sound and complete.
Tasks
Published	2017-01-28
URL	http://arxiv.org/abs/1701.08306v1
PDF	http://arxiv.org/pdf/1701.08306v1.pdf
PWC	https://paperswithcode.com/paper/practical-reasoning-with-norms-for-autonomous
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Cascade Attribute Learning Network


Title	Cascade Attribute Learning Network
Authors	Zhuo Xu, Haonan Chang, Masayoshi Tomizuka
Abstract	We propose the cascade attribute learning network (CALNet), which can learn attributes in a control task separately and assemble them together. Our contribution is twofold: first we propose attribute learning in reinforcement learning (RL). Attributes used to be modeled using constraint functions or terms in the objective function, making it hard to transfer. Attribute learning, on the other hand, models these task properties as modules in the policy network. We also propose using novel cascading compensative networks in the CALNet to learn and assemble attributes. Using the CALNet, one can zero shoot an unseen task by separately learning all its attributes, and assembling the attribute modules. We have validated the capacity of our model on a wide variety of control problems with attributes in time, position, velocity and acceleration phases.
Tasks
Published	2017-11-24
URL	http://arxiv.org/abs/1711.09142v1
PDF	http://arxiv.org/pdf/1711.09142v1.pdf
PWC	https://paperswithcode.com/paper/cascade-attribute-learning-network
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A Learning-based Variable Size Part Extraction Architecture for 6D Object Pose Recovery in Depth


Title	A Learning-based Variable Size Part Extraction Architecture for 6D Object Pose Recovery in Depth
Authors	Caner Sahin, Rigas Kouskouridas, Tae-Kyun Kim
Abstract	State-of-the-art techniques for 6D object pose recovery depend on occlusion-free point clouds to accurately register objects in 3D space. To deal with this shortcoming, we introduce a novel architecture called Iterative Hough Forest with Histogram of Control Points that is capable of estimating the 6D pose of occluded and cluttered objects given a candidate 2D bounding box. Our Iterative Hough Forest (IHF) is learnt using parts extracted only from the positive samples. These parts are represented with Histogram of Control Points (HoCP), a “scale-variant” implicit volumetric description, which we derive from recently introduced Implicit B-Splines (IBS). The rich discriminative information provided by the scale-variant HoCP features is leveraged during inference. An automatic variable size part extraction framework iteratively refines the object’s initial pose that is roughly aligned due to the extraction of coarsest parts, the ones occupying the largest area in image pixels. The iterative refinement is accomplished based on finer (smaller) parts that are represented with more discriminative control point descriptors by using our Iterative Hough Forest. Experiments conducted on a publicly available dataset report that our approach show better registration performance than the state-of-the-art methods.
Tasks
Published	2017-01-09
URL	http://arxiv.org/abs/1701.02166v1
PDF	http://arxiv.org/pdf/1701.02166v1.pdf
PWC	https://paperswithcode.com/paper/a-learning-based-variable-size-part
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