Paper Group ANR 782
Recovery of Missing Samples Using Sparse Approximation via a Convex Similarity Measure. Negentropic Planar Symmetry Detector. Process-oriented Iterative Multiple Alignment for Medical Process Mining. Share your Model instead of your Data: Privacy Preserving Mimic Learning for Ranking. One button machine for automating feature engineering in relatio …
Recovery of Missing Samples Using Sparse Approximation via a Convex Similarity Measure
| Title | Recovery of Missing Samples Using Sparse Approximation via a Convex Similarity Measure |
| Authors | Amirhossein Javaheri, Hadi Zayyani, Farokh Marvasti |
| Abstract | In this paper, we study the missing sample recovery problem using methods based on sparse approximation. In this regard, we investigate the algorithms used for solving the inverse problem associated with the restoration of missed samples of image signal. This problem is also known as inpainting in the context of image processing and for this purpose, we suggest an iterative sparse recovery algorithm based on constrained $l_1$-norm minimization with a new fidelity metric. The proposed metric called Convex SIMilarity (CSIM) index, is a simplified version of the Structural SIMilarity (SSIM) index, which is convex and error-sensitive. The optimization problem incorporating this criterion, is then solved via Alternating Direction Method of Multipliers (ADMM). Simulation results show the efficiency of the proposed method for missing sample recovery of 1D patch vectors and inpainting of 2D image signals. |
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| Published | 2017-06-28 |
| URL | http://arxiv.org/abs/1706.09395v1 |
| http://arxiv.org/pdf/1706.09395v1.pdf | |
| PWC | https://paperswithcode.com/paper/recovery-of-missing-samples-using-sparse |
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Negentropic Planar Symmetry Detector
| Title | Negentropic Planar Symmetry Detector |
| Authors | Agata Migalska, JP Lewis |
| Abstract | In this paper we observe that information theoretical concepts are valuable tools for extracting information from images and, in particular, information on image symmetries. It is shown that the problem of detecting reflectional and rotational symmetries in a two-dimensional image can be reduced to the problem of detecting point-symmetry and periodicity in one-dimensional negentropy functions. Based on these findings a detector of reflectional and rotational global symmetries in greyscale images is constructed. We discuss the importance of high precision in symmetry detection in applications arising from quality control and illustrate how the proposed method satisfies this requirement. Finally, a superior performance of our method to other existing methods, demonstrated by the results of a rigorous experimental verification, is an indication that our approach rooted in information theory is a promising direction in a development of a robust and widely applicable symmetry detector. |
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| Published | 2017-03-11 |
| URL | http://arxiv.org/abs/1703.04019v1 |
| http://arxiv.org/pdf/1703.04019v1.pdf | |
| PWC | https://paperswithcode.com/paper/negentropic-planar-symmetry-detector |
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Process-oriented Iterative Multiple Alignment for Medical Process Mining
| Title | Process-oriented Iterative Multiple Alignment for Medical Process Mining |
| Authors | Shuhong Chen, Sen Yang, Moliang Zhou, Randall S. Burd, Ivan Marsic |
| Abstract | Adapted from biological sequence alignment, trace alignment is a process mining technique used to visualize and analyze workflow data. Any analysis done with this method, however, is affected by the alignment quality. The best existing trace alignment techniques use progressive guide-trees to heuristically approximate the optimal alignment in O(N2L2) time. These algorithms are heavily dependent on the selected guide-tree metric, often return sum-of-pairs-score-reducing errors that interfere with interpretation, and are computationally intensive for large datasets. To alleviate these issues, we propose process-oriented iterative multiple alignment (PIMA), which contains specialized optimizations to better handle workflow data. We demonstrate that PIMA is a flexible framework capable of achieving better sum-of-pairs score than existing trace alignment algorithms in only O(NL2) time. We applied PIMA to analyzing medical workflow data, showing how iterative alignment can better represent the data and facilitate the extraction of insights from data visualization. |
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| Published | 2017-09-16 |
| URL | http://arxiv.org/abs/1709.05440v1 |
| http://arxiv.org/pdf/1709.05440v1.pdf | |
| PWC | https://paperswithcode.com/paper/process-oriented-iterative-multiple-alignment |
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Share your Model instead of your Data: Privacy Preserving Mimic Learning for Ranking
| Title | Share your Model instead of your Data: Privacy Preserving Mimic Learning for Ranking |
| Authors | Mostafa Dehghani, Hosein Azarbonyad, Jaap Kamps, Maarten de Rijke |
| Abstract | Deep neural networks have become a primary tool for solving problems in many fields. They are also used for addressing information retrieval problems and show strong performance in several tasks. Training these models requires large, representative datasets and for most IR tasks, such data contains sensitive information from users. Privacy and confidentiality concerns prevent many data owners from sharing the data, thus today the research community can only benefit from research on large-scale datasets in a limited manner. In this paper, we discuss privacy preserving mimic learning, i.e., using predictions from a privacy preserving trained model instead of labels from the original sensitive training data as a supervision signal. We present the results of preliminary experiments in which we apply the idea of mimic learning and privacy preserving mimic learning for the task of document re-ranking as one of the core IR tasks. This research is a step toward laying the ground for enabling researchers from data-rich environments to share knowledge learned from actual users’ data, which should facilitate research collaborations. |
| Tasks | Information Retrieval |
| Published | 2017-07-24 |
| URL | http://arxiv.org/abs/1707.07605v1 |
| http://arxiv.org/pdf/1707.07605v1.pdf | |
| PWC | https://paperswithcode.com/paper/share-your-model-instead-of-your-data-privacy |
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One button machine for automating feature engineering in relational databases
| Title | One button machine for automating feature engineering in relational databases |
| Authors | Hoang Thanh Lam, Johann-Michael Thiebaut, Mathieu Sinn, Bei Chen, Tiep Mai, Oznur Alkan |
| Abstract | Feature engineering is one of the most important and time consuming tasks in predictive analytics projects. It involves understanding domain knowledge and data exploration to discover relevant hand-crafted features from raw data. In this paper, we introduce a system called One Button Machine, or OneBM for short, which automates feature discovery in relational databases. OneBM automatically performs a key activity of data scientists, namely, joining of database tables and applying advanced data transformations to extract useful features from data. We validated OneBM in Kaggle competitions in which OneBM achieved performance as good as top 16% to 24% data scientists in three Kaggle competitions. More importantly, OneBM outperformed the state-of-the-art system in a Kaggle competition in terms of prediction accuracy and ranking on Kaggle leaderboard. The results show that OneBM can be useful for both data scientists and non-experts. It helps data scientists reduce data exploration time allowing them to try and error many ideas in short time. On the other hand, it enables non-experts, who are not familiar with data science, to quickly extract value from their data with a little effort, time and cost. |
| Tasks | Automated Feature Engineering, Feature Engineering |
| Published | 2017-06-01 |
| URL | http://arxiv.org/abs/1706.00327v1 |
| http://arxiv.org/pdf/1706.00327v1.pdf | |
| PWC | https://paperswithcode.com/paper/one-button-machine-for-automating-feature |
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Generating Thematic Chinese Poetry using Conditional Variational Autoencoders with Hybrid Decoders
| Title | Generating Thematic Chinese Poetry using Conditional Variational Autoencoders with Hybrid Decoders |
| Authors | Xiaopeng Yang, Xiaowen Lin, Shunda Suo, Ming Li |
| Abstract | Computer poetry generation is our first step towards computer writing. Writing must have a theme. The current approaches of using sequence-to-sequence models with attention often produce non-thematic poems. We present a novel conditional variational autoencoder with a hybrid decoder adding the deconvolutional neural networks to the general recurrent neural networks to fully learn topic information via latent variables. This approach significantly improves the relevance of the generated poems by representing each line of the poem not only in a context-sensitive manner but also in a holistic way that is highly related to the given keyword and the learned topic. A proposed augmented word2vec model further improves the rhythm and symmetry. Tests show that the generated poems by our approach are mostly satisfying with regulated rules and consistent themes, and 73.42% of them receive an Overall score no less than 3 (the highest score is 5). |
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| Published | 2017-11-21 |
| URL | https://arxiv.org/abs/1711.07632v4 |
| https://arxiv.org/pdf/1711.07632v4.pdf | |
| PWC | https://paperswithcode.com/paper/generating-thematic-chinese-poetry-using |
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Nonconvex Low-Rank Matrix Recovery with Arbitrary Outliers via Median-Truncated Gradient Descent
| Title | Nonconvex Low-Rank Matrix Recovery with Arbitrary Outliers via Median-Truncated Gradient Descent |
| Authors | Yuanxin Li, Yuejie Chi, Huishuai Zhang, Yingbin Liang |
| Abstract | Recent work has demonstrated the effectiveness of gradient descent for directly recovering the factors of low-rank matrices from random linear measurements in a globally convergent manner when initialized properly. However, the performance of existing algorithms is highly sensitive in the presence of outliers that may take arbitrary values. In this paper, we propose a truncated gradient descent algorithm to improve the robustness against outliers, where the truncation is performed to rule out the contributions of samples that deviate significantly from the {\em sample median} of measurement residuals adaptively in each iteration. We demonstrate that, when initialized in a basin of attraction close to the ground truth, the proposed algorithm converges to the ground truth at a linear rate for the Gaussian measurement model with a near-optimal number of measurements, even when a constant fraction of the measurements are arbitrarily corrupted. In addition, we propose a new truncated spectral method that ensures an initialization in the basin of attraction at slightly higher requirements. We finally provide numerical experiments to validate the superior performance of the proposed approach. |
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| Published | 2017-09-23 |
| URL | http://arxiv.org/abs/1709.08114v1 |
| http://arxiv.org/pdf/1709.08114v1.pdf | |
| PWC | https://paperswithcode.com/paper/nonconvex-low-rank-matrix-recovery-with |
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Kernel Robust Bias-Aware Prediction under Covariate Shift
| Title | Kernel Robust Bias-Aware Prediction under Covariate Shift |
| Authors | Anqi Liu, Rizal Fathony, Brian D. Ziebart |
| Abstract | Under covariate shift, training (source) data and testing (target) data differ in input space distribution, but share the same conditional label distribution. This poses a challenging machine learning task. Robust Bias-Aware (RBA) prediction provides the conditional label distribution that is robust to the worstcase logarithmic loss for the target distribution while matching feature expectation constraints from the source distribution. However, employing RBA with insufficient feature constraints may result in high certainty predictions for much of the source data, while leaving too much uncertainty for target data predictions. To overcome this issue, we extend the representer theorem to the RBA setting, enabling minimization of regularized expected target risk by a reweighted kernel expectation under the source distribution. By applying kernel methods, we establish consistency guarantees and demonstrate better performance of the RBA classifier than competing methods on synthetically biased UCI datasets as well as datasets that have natural covariate shift. |
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| Published | 2017-12-28 |
| URL | http://arxiv.org/abs/1712.10050v1 |
| http://arxiv.org/pdf/1712.10050v1.pdf | |
| PWC | https://paperswithcode.com/paper/kernel-robust-bias-aware-prediction-under |
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Sensor Transformation Attention Networks
| Title | Sensor Transformation Attention Networks |
| Authors | Stefan Braun, Daniel Neil, Enea Ceolini, Jithendar Anumula, Shih-Chii Liu |
| Abstract | Recent work on encoder-decoder models for sequence-to-sequence mapping has shown that integrating both temporal and spatial attention mechanisms into neural networks increases the performance of the system substantially. In this work, we report on the application of an attentional signal not on temporal and spatial regions of the input, but instead as a method of switching among inputs themselves. We evaluate the particular role of attentional switching in the presence of dynamic noise in the sensors, and demonstrate how the attentional signal responds dynamically to changing noise levels in the environment to achieve increased performance on both audio and visual tasks in three commonly-used datasets: TIDIGITS, Wall Street Journal, and GRID. Moreover, the proposed sensor transformation network architecture naturally introduces a number of advantages that merit exploration, including ease of adding new sensors to existing architectures, attentional interpretability, and increased robustness in a variety of noisy environments not seen during training. Finally, we demonstrate that the sensor selection attention mechanism of a model trained only on the small TIDIGITS dataset can be transferred directly to a pre-existing larger network trained on the Wall Street Journal dataset, maintaining functionality of switching between sensors to yield a dramatic reduction of error in the presence of noise. |
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| Published | 2017-08-03 |
| URL | http://arxiv.org/abs/1708.01015v1 |
| http://arxiv.org/pdf/1708.01015v1.pdf | |
| PWC | https://paperswithcode.com/paper/sensor-transformation-attention-networks |
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Chat Detection in an Intelligent Assistant: Combining Task-oriented and Non-task-oriented Spoken Dialogue Systems
| Title | Chat Detection in an Intelligent Assistant: Combining Task-oriented and Non-task-oriented Spoken Dialogue Systems |
| Authors | Satoshi Akasaki, Nobuhiro Kaji |
| Abstract | Recently emerged intelligent assistants on smartphones and home electronics (e.g., Siri and Alexa) can be seen as novel hybrids of domain-specific task-oriented spoken dialogue systems and open-domain non-task-oriented ones. To realize such hybrid dialogue systems, this paper investigates determining whether or not a user is going to have a chat with the system. To address the lack of benchmark datasets for this task, we construct a new dataset consisting of 15; 160 utterances collected from the real log data of a commercial intelligent assistant (and will release the dataset to facilitate future research activity). In addition, we investigate using tweets and Web search queries for handling open-domain user utterances, which characterize the task of chat detection. Experiments demonstrated that, while simple supervised methods are effective, the use of the tweets and search queries further improves the F1-score from 86.21 to 87.53. |
| Tasks | Spoken Dialogue Systems |
| Published | 2017-05-02 |
| URL | http://arxiv.org/abs/1705.00746v2 |
| http://arxiv.org/pdf/1705.00746v2.pdf | |
| PWC | https://paperswithcode.com/paper/chat-detection-in-an-intelligent-assistant |
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Monocular LSD-SLAM Integration within AR System
| Title | Monocular LSD-SLAM Integration within AR System |
| Authors | Markus Höll, Vincent Lepetit |
| Abstract | In this paper, we cover the process of integrating Large-Scale Direct Simultaneous Localization and Mapping (LSD-SLAM) algorithm into our existing AR stereo engine, developed for our modified “Augmented Reality Oculus Rift”. With that, we are able to track one of our realworld cameras which are mounted on the rift, within a complete unknown environment. This makes it possible to achieve a constant and full augmentation, synchronizing our 3D movement (x, y, z) in both worlds, the real world and the virtual world. The development for the basic AR setup using the Oculus Rift DK1 and two fisheye cameras is fully documented in our previous paper. After an introduction to image-based registration, we detail the LSD-SLAM algorithm and document our code implementing our integration. The AR stereo engine with Oculus Rift support can be accessed via the GIT repository https://github.com/MaXvanHeLL/ARift.git and the modified LSD-SLAM project used for the integration is available here https://github.com/MaXvanHeLL/LSD-SLAM.git. |
| Tasks | Simultaneous Localization and Mapping |
| Published | 2017-02-08 |
| URL | http://arxiv.org/abs/1702.02514v1 |
| http://arxiv.org/pdf/1702.02514v1.pdf | |
| PWC | https://paperswithcode.com/paper/monocular-lsd-slam-integration-within-ar |
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Large-scale Isolated Gesture Recognition Using Convolutional Neural Networks
| Title | Large-scale Isolated Gesture Recognition Using Convolutional Neural Networks |
| Authors | Pichao Wang, Wanqing Li, Song Liu, Zhimin Gao, Chang Tang, Philip Ogunbona |
| Abstract | This paper proposes three simple, compact yet effective representations of depth sequences, referred to respectively as Dynamic Depth Images (DDI), Dynamic Depth Normal Images (DDNI) and Dynamic Depth Motion Normal Images (DDMNI). These dynamic images are constructed from a sequence of depth maps using bidirectional rank pooling to effectively capture the spatial-temporal information. Such image-based representations enable us to fine-tune the existing ConvNets models trained on image data for classification of depth sequences, without introducing large parameters to learn. Upon the proposed representations, a convolutional Neural networks (ConvNets) based method is developed for gesture recognition and evaluated on the Large-scale Isolated Gesture Recognition at the ChaLearn Looking at People (LAP) challenge 2016. The method achieved 55.57% classification accuracy and ranked $2^{nd}$ place in this challenge but was very close to the best performance even though we only used depth data. |
| Tasks | Gesture Recognition |
| Published | 2017-01-07 |
| URL | http://arxiv.org/abs/1701.01814v1 |
| http://arxiv.org/pdf/1701.01814v1.pdf | |
| PWC | https://paperswithcode.com/paper/large-scale-isolated-gesture-recognition |
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Cultural Diffusion and Trends in Facebook Photographs
| Title | Cultural Diffusion and Trends in Facebook Photographs |
| Authors | Quanzeng You, Darío García-García, Mahohar Paluri, Jiebo Luo, Jungseock Joo |
| Abstract | Online social media is a social vehicle in which people share various moments of their lives with their friends, such as playing sports, cooking dinner or just taking a selfie for fun, via visual means, that is, photographs. Our study takes a closer look at the popular visual concepts illustrating various cultural lifestyles from aggregated, de-identified photographs. We perform analysis both at macroscopic and microscopic levels, to gain novel insights about global and local visual trends as well as the dynamics of interpersonal cultural exchange and diffusion among Facebook friends. We processed images by automatically classifying the visual content by a convolutional neural network (CNN). Through various statistical tests, we find that socially tied individuals more likely post images showing similar cultural lifestyles. To further identify the main cause of the observed social correlation, we use the Shuffle test and the Preference-based Matched Estimation (PME) test to distinguish the effects of influence and homophily. The results indicate that the visual content of each user’s photographs are temporally, although not necessarily causally, correlated with the photographs of their friends, which may suggest the effect of influence. Our paper demonstrates that Facebook photographs exhibit diverse cultural lifestyles and preferences and that the social interaction mediated through the visual channel in social media can be an effective mechanism for cultural diffusion. |
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| Published | 2017-05-24 |
| URL | http://arxiv.org/abs/1705.08974v1 |
| http://arxiv.org/pdf/1705.08974v1.pdf | |
| PWC | https://paperswithcode.com/paper/cultural-diffusion-and-trends-in-facebook |
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Multi-Pose Face Recognition Using Hybrid Face Features Descriptor
| Title | Multi-Pose Face Recognition Using Hybrid Face Features Descriptor |
| Authors | I Gede Pasek Suta Wijaya, Keiichi Uchimura, Gou Koutaki |
| Abstract | This paper presents a multi-pose face recognition approach using hybrid face features descriptors (HFFD). The HFFD is a face descriptor containing of rich discriminant information that is created by fusing some frequency-based features extracted using both wavelet and DCT analysis of several different poses of 2D face images. The main aim of this method is to represent the multi-pose face images using a dominant frequency component with still having reasonable achievement compared to the recent multi-pose face recognition methods. The HFFD based face recognition tends to achieve better performance than that of the recent 2D-based face recognition method. In addition, the HFFD-based face recognition also is sufficiently to handle large face variability due to face pose variations . |
| Tasks | Face Recognition |
| Published | 2017-03-12 |
| URL | http://arxiv.org/abs/1703.04062v1 |
| http://arxiv.org/pdf/1703.04062v1.pdf | |
| PWC | https://paperswithcode.com/paper/multi-pose-face-recognition-using-hybrid-face |
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SCAN: Learning Hierarchical Compositional Visual Concepts
| Title | SCAN: Learning Hierarchical Compositional Visual Concepts |
| Authors | Irina Higgins, Nicolas Sonnerat, Loic Matthey, Arka Pal, Christopher P Burgess, Matko Bosnjak, Murray Shanahan, Matthew Botvinick, Demis Hassabis, Alexander Lerchner |
| Abstract | The seemingly infinite diversity of the natural world arises from a relatively small set of coherent rules, such as the laws of physics or chemistry. We conjecture that these rules give rise to regularities that can be discovered through primarily unsupervised experiences and represented as abstract concepts. If such representations are compositional and hierarchical, they can be recombined into an exponentially large set of new concepts. This paper describes SCAN (Symbol-Concept Association Network), a new framework for learning such abstractions in the visual domain. SCAN learns concepts through fast symbol association, grounding them in disentangled visual primitives that are discovered in an unsupervised manner. Unlike state of the art multimodal generative model baselines, our approach requires very few pairings between symbols and images and makes no assumptions about the form of symbol representations. Once trained, SCAN is capable of multimodal bi-directional inference, generating a diverse set of image samples from symbolic descriptions and vice versa. It also allows for traversal and manipulation of the implicit hierarchy of visual concepts through symbolic instructions and learnt logical recombination operations. Such manipulations enable SCAN to break away from its training data distribution and imagine novel visual concepts through symbolically instructed recombination of previously learnt concepts. |
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| Published | 2017-07-11 |
| URL | http://arxiv.org/abs/1707.03389v3 |
| http://arxiv.org/pdf/1707.03389v3.pdf | |
| PWC | https://paperswithcode.com/paper/scan-learning-hierarchical-compositional |
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