Paper Group ANR 144
Robustness of Bayesian Pool-based Active Learning Against Prior Misspecification. Learning Shapes by Convex Composition. An Overview on Data Representation Learning: From Traditional Feature Learning to Recent Deep Learning. On-line Bayesian System Identification. A metric for sets of trajectories that is practical and mathematically consistent. No …
Robustness of Bayesian Pool-based Active Learning Against Prior Misspecification
| Title | Robustness of Bayesian Pool-based Active Learning Against Prior Misspecification |
| Authors | Nguyen Viet Cuong, Nan Ye, Wee Sun Lee |
| Abstract | We study the robustness of active learning (AL) algorithms against prior misspecification: whether an algorithm achieves similar performance using a perturbed prior as compared to using the true prior. In both the average and worst cases of the maximum coverage setting, we prove that all $\alpha$-approximate algorithms are robust (i.e., near $\alpha$-approximate) if the utility is Lipschitz continuous in the prior. We further show that robustness may not be achieved if the utility is non-Lipschitz. This suggests we should use a Lipschitz utility for AL if robustness is required. For the minimum cost setting, we can also obtain a robustness result for approximate AL algorithms. Our results imply that many commonly used AL algorithms are robust against perturbed priors. We then propose the use of a mixture prior to alleviate the problem of prior misspecification. We analyze the robustness of the uniform mixture prior and show experimentally that it performs reasonably well in practice. |
| Tasks | Active Learning |
| Published | 2016-03-30 |
| URL | http://arxiv.org/abs/1603.09050v1 |
| http://arxiv.org/pdf/1603.09050v1.pdf | |
| PWC | https://paperswithcode.com/paper/robustness-of-bayesian-pool-based-active |
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Learning Shapes by Convex Composition
| Title | Learning Shapes by Convex Composition |
| Authors | Alireza Aghasi, Justin Romberg |
| Abstract | We present a mathematical and algorithmic scheme for learning the principal geometric elements in an image or 3D object. We build on recent work that convexifies the basic problem of finding a combination of a small number shapes that overlap and occlude one another in such a way that they “match” a given scene as closely as possible. This paper derives general sufficient conditions under which this convex shape composition identifies a target composition. From a computational standpoint, we present two different methods for solving the associated optimization programs. The first method simply recasts the problem as a linear program, while the second uses the alternating direction method of multipliers with a series of easily computed proximal operators. |
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| Published | 2016-02-23 |
| URL | http://arxiv.org/abs/1602.07613v2 |
| http://arxiv.org/pdf/1602.07613v2.pdf | |
| PWC | https://paperswithcode.com/paper/learning-shapes-by-convex-composition |
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An Overview on Data Representation Learning: From Traditional Feature Learning to Recent Deep Learning
| Title | An Overview on Data Representation Learning: From Traditional Feature Learning to Recent Deep Learning |
| Authors | Guoqiang Zhong, Li-Na Wang, Junyu Dong |
| Abstract | Since about 100 years ago, to learn the intrinsic structure of data, many representation learning approaches have been proposed, including both linear ones and nonlinear ones, supervised ones and unsupervised ones. Particularly, deep architectures are widely applied for representation learning in recent years, and have delivered top results in many tasks, such as image classification, object detection and speech recognition. In this paper, we review the development of data representation learning methods. Specifically, we investigate both traditional feature learning algorithms and state-of-the-art deep learning models. The history of data representation learning is introduced, while available resources (e.g. online course, tutorial and book information) and toolboxes are provided. Finally, we conclude this paper with remarks and some interesting research directions on data representation learning. |
| Tasks | Image Classification, Object Detection, Representation Learning, Speech Recognition |
| Published | 2016-11-25 |
| URL | http://arxiv.org/abs/1611.08331v1 |
| http://arxiv.org/pdf/1611.08331v1.pdf | |
| PWC | https://paperswithcode.com/paper/an-overview-on-data-representation-learning |
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On-line Bayesian System Identification
| Title | On-line Bayesian System Identification |
| Authors | Diego Romeres, Giulia Prando, Gianluigi Pillonetto, Alessandro Chiuso |
| Abstract | We consider an on-line system identification setting, in which new data become available at given time steps. In order to meet real-time estimation requirements, we propose a tailored Bayesian system identification procedure, in which the hyper-parameters are still updated through Marginal Likelihood maximization, but after only one iteration of a suitable iterative optimization algorithm. Both gradient methods and the EM algorithm are considered for the Marginal Likelihood optimization. We compare this “1-step” procedure with the standard one, in which the optimization method is run until convergence to a local minimum. The experiments we perform confirm the effectiveness of the approach we propose. |
| Tasks | |
| Published | 2016-01-17 |
| URL | http://arxiv.org/abs/1601.04251v1 |
| http://arxiv.org/pdf/1601.04251v1.pdf | |
| PWC | https://paperswithcode.com/paper/on-line-bayesian-system-identification |
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A metric for sets of trajectories that is practical and mathematically consistent
| Title | A metric for sets of trajectories that is practical and mathematically consistent |
| Authors | José Bento, Jia Jie Zhu |
| Abstract | Metrics on the space of sets of trajectories are important for scientists in the field of computer vision, machine learning, robotics, and general artificial intelligence. However, existing notions of closeness between sets of trajectories are either mathematically inconsistent or of limited practical use. In this paper, we outline the limitations in the current mathematically-consistent metrics, which are based on OSPA (Schuhmacher et al. 2008); and the inconsistencies in the heuristic notions of closeness used in practice, whose main ideas are common to the CLEAR MOT measures (Keni and Rainer 2008) widely used in computer vision. In two steps, we then propose a new intuitive metric between sets of trajectories and address these limitations. First, we explain a solution that leads to a metric that is hard to compute. Then we modify this formulation to obtain a metric that is easy to compute while keeping the useful properties of the previous metric. Our notion of closeness is the first demonstrating the following three features: the metric 1) can be quickly computed, 2) incorporates confusion of trajectories’ identity in an optimal way, and 3) is a metric in the mathematical sense. |
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| Published | 2016-01-12 |
| URL | http://arxiv.org/abs/1601.03094v2 |
| http://arxiv.org/pdf/1601.03094v2.pdf | |
| PWC | https://paperswithcode.com/paper/a-metric-for-sets-of-trajectories-that-is |
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Non-Gaussian Component Analysis with Log-Density Gradient Estimation
| Title | Non-Gaussian Component Analysis with Log-Density Gradient Estimation |
| Authors | Hiroaki Sasaki, Gang Niu, Masashi Sugiyama |
| Abstract | Non-Gaussian component analysis (NGCA) is aimed at identifying a linear subspace such that the projected data follows a non-Gaussian distribution. In this paper, we propose a novel NGCA algorithm based on log-density gradient estimation. Unlike existing methods, the proposed NGCA algorithm identifies the linear subspace by using the eigenvalue decomposition without any iterative procedures, and thus is computationally reasonable. Furthermore, through theoretical analysis, we prove that the identified subspace converges to the true subspace at the optimal parametric rate. Finally, the practical performance of the proposed algorithm is demonstrated on both artificial and benchmark datasets. |
| Tasks | |
| Published | 2016-01-28 |
| URL | http://arxiv.org/abs/1601.07665v1 |
| http://arxiv.org/pdf/1601.07665v1.pdf | |
| PWC | https://paperswithcode.com/paper/non-gaussian-component-analysis-with-log |
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Low-Rank Matrix Recovery using Gabidulin Codes in Characteristic Zero
| Title | Low-Rank Matrix Recovery using Gabidulin Codes in Characteristic Zero |
| Authors | Sven Müelich, Sven Puchinger, Martin Bossert |
| Abstract | We present a new approach on low-rank matrix recovery (LRMR) based on Gabidulin Codes. Since most applications of LRMR deal with matrices over infinite fields, we use the recently introduced generalization of Gabidulin codes to fields of characterstic zero. We show that LRMR can be reduced to decoding of Gabidulin codes and discuss which field extensions can be used in the code construction. |
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| Published | 2016-04-15 |
| URL | http://arxiv.org/abs/1604.04397v2 |
| http://arxiv.org/pdf/1604.04397v2.pdf | |
| PWC | https://paperswithcode.com/paper/low-rank-matrix-recovery-using-gabidulin |
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A coarse-to-fine algorithm for registration in 3D street-view cross-source point clouds
| Title | A coarse-to-fine algorithm for registration in 3D street-view cross-source point clouds |
| Authors | Xiaoshui Huang, Jian Zhang, Qiang Wu, Lixin Fan, Chun Yuan |
| Abstract | With the development of numerous 3D sensing technologies, object registration on cross-source point cloud has aroused researchers’ interests. When the point clouds are captured from different kinds of sensors, there are large and different kinds of variations. In this study, we address an even more challenging case in which the differently-source point clouds are acquired from a real street view. One is produced directly by the LiDAR system and the other is generated by using VSFM software on image sequence captured from RGB cameras. When it confronts to large scale point clouds, previous methods mostly focus on point-to-point level registration, and the methods have many limitations.The reason is that the least mean error strategy shows poor ability in registering large variable cross-source point clouds. In this paper, different from previous ICP-based methods, and from a statistic view, we propose a effective coarse-to-fine algorithm to detect and register a small scale SFM point cloud in a large scale Lidar point cloud. Seen from the experimental results, the model can successfully run on LiDAR and SFM point clouds, hence it can make a contribution to many applications, such as robotics and smart city development. |
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| Published | 2016-10-24 |
| URL | http://arxiv.org/abs/1610.07324v1 |
| http://arxiv.org/pdf/1610.07324v1.pdf | |
| PWC | https://paperswithcode.com/paper/a-coarse-to-fine-algorithm-for-registration |
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Interactive Bayesian Hierarchical Clustering
| Title | Interactive Bayesian Hierarchical Clustering |
| Authors | Sharad Vikram, Sanjoy Dasgupta |
| Abstract | Clustering is a powerful tool in data analysis, but it is often difficult to find a grouping that aligns with a user’s needs. To address this, several methods incorporate constraints obtained from users into clustering algorithms, but unfortunately do not apply to hierarchical clustering. We design an interactive Bayesian algorithm that incorporates user interaction into hierarchical clustering while still utilizing the geometry of the data by sampling a constrained posterior distribution over hierarchies. We also suggest several ways to intelligently query a user. The algorithm, along with the querying schemes, shows promising results on real data. |
| Tasks | |
| Published | 2016-02-10 |
| URL | http://arxiv.org/abs/1602.03258v3 |
| http://arxiv.org/pdf/1602.03258v3.pdf | |
| PWC | https://paperswithcode.com/paper/interactive-bayesian-hierarchical-clustering |
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On numerical approximation schemes for expectation propagation
| Title | On numerical approximation schemes for expectation propagation |
| Authors | Alexis Roche |
| Abstract | Several numerical approximation strategies for the expectation-propagation algorithm are studied in the context of large-scale learning: the Laplace method, a faster variant of it, Gaussian quadrature, and a deterministic version of variational sampling (i.e., combining quadrature with variational approximation). Experiments in training linear binary classifiers show that the expectation-propagation algorithm converges best using variational sampling, while it also converges well using Laplace-style methods with smooth factors but tends to be unstable with non-differentiable ones. Gaussian quadrature yields unstable behavior or convergence to a sub-optimal solution in most experiments. |
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| Published | 2016-11-14 |
| URL | http://arxiv.org/abs/1611.04416v1 |
| http://arxiv.org/pdf/1611.04416v1.pdf | |
| PWC | https://paperswithcode.com/paper/on-numerical-approximation-schemes-for |
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Incremental Nonparametric Weighted Feature Extraction for OnlineSubspace Pattern Classification
| Title | Incremental Nonparametric Weighted Feature Extraction for OnlineSubspace Pattern Classification |
| Authors | Hamid Abrishami Moghaddam, Elaheh Raisi |
| Abstract | In this paper, a new online method based on nonparametric weighted feature extraction (NWFE) is proposed. NWFE was introduced to enjoy optimum characteristics of linear discriminant analysis (LDA) and nonparametric discriminant analysis (NDA) while rectifying their drawbacks. It emphasizes the points near decision boundary by putting greater weights on them and deemphasizes other points. Incremental nonparametric weighted feature extraction (INWFE) is the online version of NWFE. INWFE has advantages of NWFE method such as extracting more than L-1 features in contrast to LDA. It is independent of the class distribution and performs well in complex distributed data. The effects of outliers are reduced due to the nature of its nonparametric scatter matrix. Furthermore, it is possible to add new samples asynchronously, i.e. whenever a new sample becomes available at any given time, it can be added to the algorithm. This is useful for many real world applications since all data cannot be available in advance. This method is implemented on Gaussian and non-Gaussian multidimensional data, a number of UCI datasets and Indian Pine dataset. Results are compared with NWFE in terms of classification accuracy and execution time. For nearest neighbour classifier it shows that this technique converges to NWFE at the end of learning process. In addition, the computational complexity is reduced in comparison with NWFE in terms of execution time. |
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| Published | 2016-10-26 |
| URL | http://arxiv.org/abs/1610.08133v1 |
| http://arxiv.org/pdf/1610.08133v1.pdf | |
| PWC | https://paperswithcode.com/paper/incremental-nonparametric-weighted-feature |
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POSEidon: Face-from-Depth for Driver Pose Estimation
| Title | POSEidon: Face-from-Depth for Driver Pose Estimation |
| Authors | Guido Borghi, Marco Venturelli, Roberto Vezzani, Rita Cucchiara |
| Abstract | Fast and accurate upper-body and head pose estimation is a key task for automatic monitoring of driver attention, a challenging context characterized by severe illumination changes, occlusions and extreme poses. In this work, we present a new deep learning framework for head localization and pose estimation on depth images. The core of the proposal is a regression neural network, called POSEidon, which is composed of three independent convolutional nets followed by a fusion layer, specially conceived for understanding the pose by depth. In addition, to recover the intrinsic value of face appearance for understanding head position and orientation, we propose a new Face-from-Depth approach for learning image faces from depth. Results in face reconstruction are qualitatively impressive. We test the proposed framework on two public datasets, namely Biwi Kinect Head Pose and ICT-3DHP, and on Pandora, a new challenging dataset mainly inspired by the automotive setup. Results show that our method overcomes all recent state-of-art works, running in real time at more than 30 frames per second. |
| Tasks | Face Reconstruction, Head Pose Estimation, Pose Estimation |
| Published | 2016-11-30 |
| URL | http://arxiv.org/abs/1611.10195v3 |
| http://arxiv.org/pdf/1611.10195v3.pdf | |
| PWC | https://paperswithcode.com/paper/poseidon-face-from-depth-for-driver-pose |
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The Myopia of Crowds: A Study of Collective Evaluation on Stack Exchange
| Title | The Myopia of Crowds: A Study of Collective Evaluation on Stack Exchange |
| Authors | Keith Burghardt, Emanuel F. Alsina, Michelle Girvan, William Rand, Kristina Lerman |
| Abstract | Crowds can often make better decisions than individuals or small groups of experts by leveraging their ability to aggregate diverse information. Question answering sites, such as Stack Exchange, rely on the “wisdom of crowds” effect to identify the best answers to questions asked by users. We analyze data from 250 communities on the Stack Exchange network to pinpoint factors affecting which answers are chosen as the best answers. Our results suggest that, rather than evaluate all available answers to a question, users rely on simple cognitive heuristics to choose an answer to vote for or accept. These cognitive heuristics are linked to an answer’s salience, such as the order in which it is listed and how much screen space it occupies. While askers appear to depend more on heuristics, compared to voting users, when choosing an answer to accept as the most helpful one, voters use acceptance itself as a heuristic: they are more likely to choose the answer after it is accepted than before that very same answer was accepted. These heuristics become more important in explaining and predicting behavior as the number of available answers increases. Our findings suggest that crowd judgments may become less reliable as the number of answers grow. |
| Tasks | Question Answering |
| Published | 2016-02-24 |
| URL | http://arxiv.org/abs/1602.07388v1 |
| http://arxiv.org/pdf/1602.07388v1.pdf | |
| PWC | https://paperswithcode.com/paper/the-myopia-of-crowds-a-study-of-collective |
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Dictionary Learning for Robotic Grasp Recognition and Detection
| Title | Dictionary Learning for Robotic Grasp Recognition and Detection |
| Authors | Ludovic Trottier, Philippe Giguère, Brahim Chaib-draa |
| Abstract | The ability to grasp ordinary and potentially never-seen objects is an important feature in both domestic and industrial robotics. For a system to accomplish this, it must autonomously identify grasping locations by using information from various sensors, such as Microsoft Kinect 3D camera. Despite numerous progress, significant work still remains to be done in this field. To this effect, we propose a dictionary learning and sparse representation (DLSR) framework for representing RGBD images from 3D sensors in the context of determining such good grasping locations. In contrast to previously proposed approaches that relied on sophisticated regularization or very large datasets, the derived perception system has a fast training phase and can work with small datasets. It is also theoretically founded for dealing with masked-out entries, which are common with 3D sensors. We contribute by presenting a comparative study of several DLSR approach combinations for recognizing and detecting grasp candidates on the standard Cornell dataset. Importantly, experimental results show a performance improvement of 1.69% in detection and 3.16% in recognition over current state-of-the-art convolutional neural network (CNN). Even though nowadays most popular vision-based approach is CNN, this suggests that DLSR is also a viable alternative with interesting advantages that CNN has not. |
| Tasks | Dictionary Learning |
| Published | 2016-06-02 |
| URL | http://arxiv.org/abs/1606.00538v1 |
| http://arxiv.org/pdf/1606.00538v1.pdf | |
| PWC | https://paperswithcode.com/paper/dictionary-learning-for-robotic-grasp |
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Modeling Group Dynamics Using Probabilistic Tensor Decompositions
| Title | Modeling Group Dynamics Using Probabilistic Tensor Decompositions |
| Authors | Lin Li, Ananthram Swami, Anna Scaglione |
| Abstract | We propose a probabilistic modeling framework for learning the dynamic patterns in the collective behaviors of social agents and developing profiles for different behavioral groups, using data collected from multiple information sources. The proposed model is based on a hierarchical Bayesian process, in which each observation is a finite mixture of an set of latent groups and the mixture proportions (i.e., group probabilities) are drawn randomly. Each group is associated with some distributions over a finite set of outcomes. Moreover, as time evolves, the structure of these groups also changes; we model the change in the group structure by a hidden Markov model (HMM) with a fixed transition probability. We present an efficient inference method based on tensor decompositions and the expectation-maximization (EM) algorithm for parameter estimation. |
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| Published | 2016-06-24 |
| URL | http://arxiv.org/abs/1606.07840v1 |
| http://arxiv.org/pdf/1606.07840v1.pdf | |
| PWC | https://paperswithcode.com/paper/modeling-group-dynamics-using-probabilistic |
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