May 6, 2019

3359 words 16 mins read

Paper Group ANR 347

Plücker Correction Problem: Analysis and Improvements in Efficiency. Stochastic Alternating Direction Method of Multipliers with Variance Reduction for Nonconvex Optimization. Identification of release sources in advection-diffusion system by machine learning combined with Green function inverse method. Convolutional Gated Recurrent Networks for V …

Plücker Correction Problem: Analysis and Improvements in Efficiency


Title	Plücker Correction Problem: Analysis and Improvements in Efficiency
Authors	João R. Cardoso, Pedro Miraldo, Helder Araujo
Abstract	A given six dimensional vector represents a 3D straight line in Plucker coordinates if its coordinates satisfy the Klein quadric constraint. In many problems aiming to find the Plucker coordinates of lines, noise in the data and other type of errors contribute for obtaining 6D vectors that do not correspond to lines, because of that constraint. A common procedure to overcome this drawback is to find the Plucker coordinates of the lines that are closest to those vectors. This is known as the Plucker correction problem. In this article we propose a simple, closed-form, and global solution for this problem. When compared with the state-of-the-art method, one can conclude that our algorithm is easier and requires much less operations than previous techniques (it does not require Singular Value Decomposition techniques).
Tasks
Published	2016-02-18
URL	http://arxiv.org/abs/1602.05990v1
PDF	http://arxiv.org/pdf/1602.05990v1.pdf
PWC	https://paperswithcode.com/paper/plucker-correction-problem-analysis-and
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Framework

Stochastic Alternating Direction Method of Multipliers with Variance Reduction for Nonconvex Optimization


Title	Stochastic Alternating Direction Method of Multipliers with Variance Reduction for Nonconvex Optimization
Authors	Feihu Huang, Songcan Chen, Zhaosong Lu
Abstract	In the paper, we study the stochastic alternating direction method of multipliers (ADMM) for the nonconvex optimizations, and propose three classes of the nonconvex stochastic ADMM with variance reduction, based on different reduced variance stochastic gradients. Specifically, the first class called the nonconvex stochastic variance reduced gradient ADMM (SVRG-ADMM), uses a multi-stage scheme to progressively reduce the variance of stochastic gradients. The second is the nonconvex stochastic average gradient ADMM (SAG-ADMM), which additionally uses the old gradients estimated in the previous iteration. The third called SAGA-ADMM is an extension of the SAG-ADMM method. Moreover, under some mild conditions, we establish the iteration complexity bound of $O(1/\epsilon)$ of the proposed methods to obtain an $\epsilon$-stationary solution of the nonconvex optimizations. In particular, we provide a general framework to analyze the iteration complexity of these nonconvex stochastic ADMM methods with variance reduction. Finally, some numerical experiments demonstrate the effectiveness of our methods.
Tasks
Published	2016-10-10
URL	http://arxiv.org/abs/1610.02758v5
PDF	http://arxiv.org/pdf/1610.02758v5.pdf
PWC	https://paperswithcode.com/paper/stochastic-alternating-direction-method-of
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Identification of release sources in advection-diffusion system by machine learning combined with Green function inverse method


Title	Identification of release sources in advection-diffusion system by machine learning combined with Green function inverse method
Authors	Valentin G. Stanev, Filip L. Iliev, Scott Hansen, Velimir V. Vesselinov, Boian S. Alexandrov
Abstract	The identification of sources of advection-diffusion transport is based usually on solving complex ill-posed inverse models against the available state- variable data records. However, if there are several sources with different locations and strengths, the data records represent mixtures rather than the separate influences of the original sources. Importantly, the number of these original release sources is typically unknown, which hinders reliability of the classical inverse-model analyses. To address this challenge, we present here a novel hybrid method for identification of the unknown number of release sources. Our hybrid method, called HNMF, couples unsupervised learning based on Nonnegative Matrix Factorization (NMF) and inverse-analysis Green functions method. HNMF synergistically performs decomposition of the recorded mixtures, finds the number of the unknown sources and uses the Green function of advection-diffusion equation to identify their characteristics. In the paper, we introduce the method and demonstrate that it is capable of identifying the advection velocity and dispersivity of the medium as well as the unknown number, locations, and properties of various sets of synthetic release sources with different space and time dependencies, based only on the recorded data. HNMF can be applied directly to any problem controlled by a partial-differential parabolic equation where mixtures of an unknown number of sources are measured at multiple locations.
Tasks
Published	2016-12-12
URL	http://arxiv.org/abs/1612.03948v3
PDF	http://arxiv.org/pdf/1612.03948v3.pdf
PWC	https://paperswithcode.com/paper/identification-of-release-sources-in
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Convolutional Gated Recurrent Networks for Video Segmentation


Title	Convolutional Gated Recurrent Networks for Video Segmentation
Authors	Mennatullah Siam, Sepehr Valipour, Martin Jagersand, Nilanjan Ray
Abstract	Semantic segmentation has recently witnessed major progress, where fully convolutional neural networks have shown to perform well. However, most of the previous work focused on improving single image segmentation. To our knowledge, no prior work has made use of temporal video information in a recurrent network. In this paper, we introduce a novel approach to implicitly utilize temporal data in videos for online semantic segmentation. The method relies on a fully convolutional network that is embedded into a gated recurrent architecture. This design receives a sequence of consecutive video frames and outputs the segmentation of the last frame. Convolutional gated recurrent networks are used for the recurrent part to preserve spatial connectivities in the image. Our proposed method can be applied in both online and batch segmentation. This architecture is tested for both binary and semantic video segmentation tasks. Experiments are conducted on the recent benchmarks in SegTrack V2, Davis, CityScapes, and Synthia. Using recurrent fully convolutional networks improved the baseline network performance in all of our experiments. Namely, 5% and 3% improvement of F-measure in SegTrack2 and Davis respectively, 5.7% improvement in mean IoU in Synthia and 3.5% improvement in categorical mean IoU in CityScapes. The performance of the RFCN network depends on its baseline fully convolutional network. Thus RFCN architecture can be seen as a method to improve its baseline segmentation network by exploiting spatiotemporal information in videos.
Tasks	Semantic Segmentation, Video Semantic Segmentation
Published	2016-11-16
URL	http://arxiv.org/abs/1611.05435v2
PDF	http://arxiv.org/pdf/1611.05435v2.pdf
PWC	https://paperswithcode.com/paper/convolutional-gated-recurrent-networks-for
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Framework

Tunable Sensitivity to Large Errors in Neural Network Training


Title	Tunable Sensitivity to Large Errors in Neural Network Training
Authors	Gil Keren, Sivan Sabato, Björn Schuller
Abstract	When humans learn a new concept, they might ignore examples that they cannot make sense of at first, and only later focus on such examples, when they are more useful for learning. We propose incorporating this idea of tunable sensitivity for hard examples in neural network learning, using a new generalization of the cross-entropy gradient step, which can be used in place of the gradient in any gradient-based training method. The generalized gradient is parameterized by a value that controls the sensitivity of the training process to harder training examples. We tested our method on several benchmark datasets. We propose, and corroborate in our experiments, that the optimal level of sensitivity to hard example is positively correlated with the depth of the network. Moreover, the test prediction error obtained by our method is generally lower than that of the vanilla cross-entropy gradient learner. We therefore conclude that tunable sensitivity can be helpful for neural network learning.
Tasks
Published	2016-11-23
URL	http://arxiv.org/abs/1611.07743v1
PDF	http://arxiv.org/pdf/1611.07743v1.pdf
PWC	https://paperswithcode.com/paper/tunable-sensitivity-to-large-errors-in-neural
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Framework

Can Ground Truth Label Propagation from Video help Semantic Segmentation?


Title	Can Ground Truth Label Propagation from Video help Semantic Segmentation?
Authors	Siva Karthik Mustikovela, Michael Ying Yang, Carsten Rother
Abstract	For state-of-the-art semantic segmentation task, training convolutional neural networks (CNNs) requires dense pixelwise ground truth (GT) labeling, which is expensive and involves extensive human effort. In this work, we study the possibility of using auxiliary ground truth, so-called \textit{pseudo ground truth} (PGT) to improve the performance. The PGT is obtained by propagating the labels of a GT frame to its subsequent frames in the video using a simple CRF-based, cue integration framework. Our main contribution is to demonstrate the use of noisy PGT along with GT to improve the performance of a CNN. We perform a systematic analysis to find the right kind of PGT that needs to be added along with the GT for training a CNN. In this regard, we explore three aspects of PGT which influence the learning of a CNN: i) the PGT labeling has to be of good quality; ii) the PGT images have to be different compared to the GT images; iii) the PGT has to be trusted differently than GT. We conclude that PGT which is diverse from GT images and has good quality of labeling can indeed help improve the performance of a CNN. Also, when PGT is multiple folds larger than GT, weighing down the trust on PGT helps in improving the accuracy. Finally, We show that using PGT along with GT, the performance of Fully Convolutional Network (FCN) on Camvid data is increased by $2.7%$ on IoU accuracy. We believe such an approach can be used to train CNNs for semantic video segmentation where sequentially labeled image frames are needed. To this end, we provide recommendations for using PGT strategically for semantic segmentation and hence bypass the need for extensive human efforts in labeling.
Tasks	Semantic Segmentation, Video Semantic Segmentation
Published	2016-10-03
URL	http://arxiv.org/abs/1610.00731v1
PDF	http://arxiv.org/pdf/1610.00731v1.pdf
PWC	https://paperswithcode.com/paper/can-ground-truth-label-propagation-from-video
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Framework

Hybridization of Expectation-Maximization and K-Means Algorithms for Better Clustering Performance


Title	Hybridization of Expectation-Maximization and K-Means Algorithms for Better Clustering Performance
Authors	D. Raja Kishor, N. B. Venkateswarlu
Abstract	The present work proposes hybridization of Expectation-Maximization (EM) and K-Means techniques as an attempt to speed-up the clustering process. Though both K-Means and EM techniques look into different areas, K-means can be viewed as an approximate way to obtain maximum likelihood estimates for the means. Along with the proposed algorithm for hybridization, the present work also experiments with the Standard EM algorithm. Six different datasets are used for the experiments of which three are synthetic datasets. Clustering fitness and Sum of Squared Errors (SSE) are computed for measuring the clustering performance. In all the experiments it is observed that the proposed algorithm for hybridization of EM and K-Means techniques is consistently taking less execution time with acceptable Clustering Fitness value and less SSE than the standard EM algorithm. It is also observed that the proposed algorithm is producing better clustering results than the Cluster package of Purdue University.
Tasks
Published	2016-03-25
URL	http://arxiv.org/abs/1603.07879v1
PDF	http://arxiv.org/pdf/1603.07879v1.pdf
PWC	https://paperswithcode.com/paper/hybridization-of-expectation-maximization-and
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Framework

Approximate Policy Iteration for Budgeted Semantic Video Segmentation


Title	Approximate Policy Iteration for Budgeted Semantic Video Segmentation
Authors	Behrooz Mahasseni, Sinisa Todorovic, Alan Fern
Abstract	This paper formulates and presents a solution to the new problem of budgeted semantic video segmentation. Given a video, the goal is to accurately assign a semantic class label to every pixel in the video within a specified time budget. Typical approaches to such labeling problems, such as Conditional Random Fields (CRFs), focus on maximizing accuracy but do not provide a principled method for satisfying a time budget. For video data, the time required by CRF and related methods is often dominated by the time to compute low-level descriptors of supervoxels across the video. Our key contribution is the new budgeted inference framework for CRF models that intelligently selects the most useful subsets of descriptors to run on subsets of supervoxels within the time budget. The objective is to maintain an accuracy as close as possible to the CRF model with no time bound, while remaining within the time budget. Our second contribution is the algorithm for learning a policy for the sparse selection of supervoxels and their descriptors for budgeted CRF inference. This learning algorithm is derived by casting our problem in the framework of Markov Decision Processes, and then instantiating a state-of-the-art policy learning algorithm known as Classification-Based Approximate Policy Iteration. Our experiments on multiple video datasets show that our learning approach and framework is able to significantly reduce computation time, and maintain competitive accuracy under varying budgets.
Tasks	Video Semantic Segmentation
Published	2016-07-26
URL	http://arxiv.org/abs/1607.07770v1
PDF	http://arxiv.org/pdf/1607.07770v1.pdf
PWC	https://paperswithcode.com/paper/approximate-policy-iteration-for-budgeted
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Framework

Encoding the Local Connectivity Patterns of fMRI for Cognitive State Classification


Title	Encoding the Local Connectivity Patterns of fMRI for Cognitive State Classification
Authors	Itir Onal Ertugrul, Mete Ozay, Fatos T. Yarman Vural
Abstract	In this work, we propose a novel framework to encode the local connectivity patterns of brain, using Fisher Vectors (FV), Vector of Locally Aggregated Descriptors (VLAD) and Bag-of-Words (BoW) methods. We first obtain local descriptors, called Mesh Arc Descriptors (MADs) from fMRI data, by forming local meshes around anatomical regions, and estimating their relationship within a neighborhood. Then, we extract a dictionary of relationships, called \textit{brain connectivity dictionary} by fitting a generative Gaussian mixture model (GMM) to a set of MADs, and selecting the codewords at the mean of each component of the mixture. Codewords represent the connectivity patterns among anatomical regions. We also encode MADs by VLAD and BoW methods using the k-Means clustering. We classify the cognitive states of Human Connectome Project (HCP) task fMRI dataset, where we train support vector machines (SVM) by the encoded MADs. Results demonstrate that, FV encoding of MADs can be successfully employed for classification of cognitive tasks, and outperform the VLAD and BoW representations. Moreover, we identify the significant Gaussians in mixture models by computing energy of their corresponding FV parts, and analyze their effect on classification accuracy. Finally, we suggest a new method to visualize the codewords of brain connectivity dictionary.
Tasks
Published	2016-10-17
URL	http://arxiv.org/abs/1610.05036v1
PDF	http://arxiv.org/pdf/1610.05036v1.pdf
PWC	https://paperswithcode.com/paper/encoding-the-local-connectivity-patterns-of
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Framework

Semi-supervised evidential label propagation algorithm for graph data


Title	Semi-supervised evidential label propagation algorithm for graph data
Authors	Kuang Zhou, Arnaud Martin, Quan Pan
Abstract	In the task of community detection, there often exists some useful prior information. In this paper, a Semi-supervised clustering approach using a new Evidential Label Propagation strategy (SELP) is proposed to incorporate the domain knowledge into the community detection model. The main advantage of SELP is that it can take limited supervised knowledge to guide the detection process. The prior information of community labels is expressed in the form of mass functions initially. Then a new evidential label propagation rule is adopted to propagate the labels from labeled data to unlabeled ones. The outliers can be identified to be in a special class. The experimental results demonstrate the effectiveness of SELP.
Tasks	Community Detection
Published	2016-07-29
URL	http://arxiv.org/abs/1607.08695v1
PDF	http://arxiv.org/pdf/1607.08695v1.pdf
PWC	https://paperswithcode.com/paper/semi-supervised-evidential-label-propagation
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Framework

Multiscale Inverse Reinforcement Learning using Diffusion Wavelets


Title	Multiscale Inverse Reinforcement Learning using Diffusion Wavelets
Authors	Jung-Su Ha, Han-Lim Choi
Abstract	This work presents a multiscale framework to solve an inverse reinforcement learning (IRL) problem for continuous-time/state stochastic systems. We take advantage of a diffusion wavelet representation of the associated Markov chain to abstract the state space. This not only allows for effectively handling the large (and geometrically complex) decision space but also provides more interpretable representations of the demonstrated state trajectories and also of the resulting policy of IRL. In the proposed framework, the problem is divided into the global and local IRL, where the global approximation of the optimal value functions are obtained using coarse features and the local details are quantified using fine local features. An illustrative numerical example on robot path control in a complex environment is presented to verify the proposed method.
Tasks
Published	2016-11-24
URL	http://arxiv.org/abs/1611.08070v1
PDF	http://arxiv.org/pdf/1611.08070v1.pdf
PWC	https://paperswithcode.com/paper/multiscale-inverse-reinforcement-learning
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Sequential Bayesian optimal experimental design via approximate dynamic programming


Title	Sequential Bayesian optimal experimental design via approximate dynamic programming
Authors	Xun Huan, Youssef M. Marzouk
Abstract	The design of multiple experiments is commonly undertaken via suboptimal strategies, such as batch (open-loop) design that omits feedback or greedy (myopic) design that does not account for future effects. This paper introduces new strategies for the optimal design of sequential experiments. First, we rigorously formulate the general sequential optimal experimental design (sOED) problem as a dynamic program. Batch and greedy designs are shown to result from special cases of this formulation. We then focus on sOED for parameter inference, adopting a Bayesian formulation with an information theoretic design objective. To make the problem tractable, we develop new numerical approaches for nonlinear design with continuous parameter, design, and observation spaces. We approximate the optimal policy by using backward induction with regression to construct and refine value function approximations in the dynamic program. The proposed algorithm iteratively generates trajectories via exploration and exploitation to improve approximation accuracy in frequently visited regions of the state space. Numerical results are verified against analytical solutions in a linear-Gaussian setting. Advantages over batch and greedy design are then demonstrated on a nonlinear source inversion problem where we seek an optimal policy for sequential sensing.
Tasks
Published	2016-04-28
URL	http://arxiv.org/abs/1604.08320v1
PDF	http://arxiv.org/pdf/1604.08320v1.pdf
PWC	https://paperswithcode.com/paper/sequential-bayesian-optimal-experimental
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Framework

Assessing Human Error Against a Benchmark of Perfection


Title	Assessing Human Error Against a Benchmark of Perfection
Authors	Ashton Anderson, Jon Kleinberg, Sendhil Mullainathan
Abstract	An increasing number of domains are providing us with detailed trace data on human decisions in settings where we can evaluate the quality of these decisions via an algorithm. Motivated by this development, an emerging line of work has begun to consider whether we can characterize and predict the kinds of decisions where people are likely to make errors. To investigate what a general framework for human error prediction might look like, we focus on a model system with a rich history in the behavioral sciences: the decisions made by chess players as they select moves in a game. We carry out our analysis at a large scale, employing datasets with several million recorded games, and using chess tablebases to acquire a form of ground truth for a subset of chess positions that have been completely solved by computers but remain challenging even for the best players in the world. We organize our analysis around three categories of features that we argue are present in most settings where the analysis of human error is applicable: the skill of the decision-maker, the time available to make the decision, and the inherent difficulty of the decision. We identify rich structure in all three of these categories of features, and find strong evidence that in our domain, features describing the inherent difficulty of an instance are significantly more powerful than features based on skill or time.
Tasks
Published	2016-06-15
URL	http://arxiv.org/abs/1606.04956v1
PDF	http://arxiv.org/pdf/1606.04956v1.pdf
PWC	https://paperswithcode.com/paper/assessing-human-error-against-a-benchmark-of
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Framework

Compensating for Large In-Plane Rotations in Natural Images


Title	Compensating for Large In-Plane Rotations in Natural Images
Authors	Lokesh Boominathan, Suraj Srinivas, R. Venkatesh Babu
Abstract	Rotation invariance has been studied in the computer vision community primarily in the context of small in-plane rotations. This is usually achieved by building invariant image features. However, the problem of achieving invariance for large rotation angles remains largely unexplored. In this work, we tackle this problem by directly compensating for large rotations, as opposed to building invariant features. This is inspired by the neuro-scientific concept of mental rotation, which humans use to compare pairs of rotated objects. Our contributions here are three-fold. First, we train a Convolutional Neural Network (CNN) to detect image rotations. We find that generic CNN architectures are not suitable for this purpose. To this end, we introduce a convolutional template layer, which learns representations for canonical ‘unrotated’ images. Second, we use Bayesian Optimization to quickly sift through a large number of candidate images to find the canonical ‘unrotated’ image. Third, we use this method to achieve robustness to large angles in an image retrieval scenario. Our method is task-agnostic, and can be used as a pre-processing step in any computer vision system.
Tasks	Image Retrieval
Published	2016-11-17
URL	http://arxiv.org/abs/1611.05744v1
PDF	http://arxiv.org/pdf/1611.05744v1.pdf
PWC	https://paperswithcode.com/paper/compensating-for-large-in-plane-rotations-in
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Evaluation method of word embedding by roots and affixes


Title	Evaluation method of word embedding by roots and affixes
Authors	KeBin Peng
Abstract	Word embedding has been shown to be remarkably effective in a lot of Natural Language Processing tasks. However, existing models still have a couple of limitations in interpreting the dimensions of word vector. In this paper, we provide a new approach—roots and affixes model(RAAM)—to interpret it from the intrinsic structures of natural language. Also it can be used as an evaluation measure of the quality of word embedding. We introduce the information entropy into our model and divide the dimensions into two categories, just like roots and affixes in lexical semantics. Then considering each category as a whole rather than individually. We experimented with English Wikipedia corpus. Our result show that there is a negative linear relation between the two attributes and a high positive correlation between our model and downstream semantic evaluation tasks.
Tasks
Published	2016-06-24
URL	http://arxiv.org/abs/1606.07601v1
PDF	http://arxiv.org/pdf/1606.07601v1.pdf
PWC	https://paperswithcode.com/paper/evaluation-method-of-word-embedding-by-roots
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