July 28, 2019

3016 words 15 mins read

Paper Group ANR 312

Robustness of Maximum Correntropy Estimation Against Large Outliers. On the enumeration of sentences by compactness. Meteorology-Aware Multi-Goal Path Planning for Large-Scale Inspection Missions with Long-Endurance Solar-Powered Aircraft. Estimating activity cycles with probabilistic methods II. The Mount Wilson Ca H&K data. Catalyst design using …

Robustness of Maximum Correntropy Estimation Against Large Outliers


Title	Robustness of Maximum Correntropy Estimation Against Large Outliers
Authors	Badong Chen, Lei Xing, Haiquan Zhao, Bin Xu, Jose C. Principe
Abstract	The maximum correntropy criterion (MCC) has recently been successfully applied in robust regression, classification and adaptive filtering, where the correntropy is maximized instead of minimizing the well-known mean square error (MSE) to improve the robustness with respect to outliers (or impulsive noises). Considerable efforts have been devoted to develop various robust adaptive algorithms under MCC, but so far little insight has been gained as to how the optimal solution will be affected by outliers. In this work, we study this problem in the context of parameter estimation for a simple linear errors-in-variables (EIV) model where all variables are scalar. Under certain conditions, we derive an upper bound on the absolute value of the estimation error and show that the optimal solution under MCC can be very close to the true value of the unknown parameter even with outliers (whose values can be arbitrarily large) in both input and output variables. Illustrative examples are presented to verify and clarify the theory.
Tasks
Published	2017-03-23
URL	http://arxiv.org/abs/1703.08065v2
PDF	http://arxiv.org/pdf/1703.08065v2.pdf
PWC	https://paperswithcode.com/paper/robustness-of-maximum-correntropy-estimation
Repo
Framework

On the enumeration of sentences by compactness


Title	On the enumeration of sentences by compactness
Authors	Mark A. Stalzer
Abstract	Presented is a Julia meta-program that discovers compact theories from data if they exist. It writes candidate theories in Julia and then validates: tossing the bad theories and keeping the good theories. Compactness is measured by a metric: such as the number of space-time derivatives. The underlying algorithm is applicable to a wide variety of combinatorics problems and compactness serves to cut down the search space.
Tasks
Published	2017-06-15
URL	http://arxiv.org/abs/1706.06975v1
PDF	http://arxiv.org/pdf/1706.06975v1.pdf
PWC	https://paperswithcode.com/paper/on-the-enumeration-of-sentences-by
Repo
Framework

Meteorology-Aware Multi-Goal Path Planning for Large-Scale Inspection Missions with Long-Endurance Solar-Powered Aircraft


Title	Meteorology-Aware Multi-Goal Path Planning for Large-Scale Inspection Missions with Long-Endurance Solar-Powered Aircraft
Authors	Philipp Oettershagen, Julian Förster, Lukas Wirth, Jacques Ambühl, Roland Siegwart
Abstract	Solar-powered aircraft promise significantly increased flight endurance over conventional aircraft. While this makes them promising candidates for large-scale aerial inspection missions, their structural fragility necessitates that adverse weather is avoided using appropriate path planning methods. This paper therefore presents MetPASS, the Meteorology-aware Path Planning and Analysis Software for Solar-powered UAVs. MetPASS is the first path planning framework in the literature that considers all aspects that influence the safety or performance of solar-powered flight: It avoids environmental risks (thunderstorms, rain, wind, wind gusts and humidity) and exploits advantageous regions (high sun radiation or tailwind). It also avoids system risks such as low battery state of charge and returns safe paths through cluttered terrain. MetPASS imports weather data from global meteorological models, propagates the aircraft state through an energetic system model, and then combines both into a cost function. A combination of dynamic programming techniques and an A*-search-algorithm with a custom heuristic is leveraged to plan globally optimal paths in station-keeping, point-to-point or multi-goal aerial inspection missions with coverage guarantees. A full software implementation including a GUI is provided. The planning methods are verified using three missions of ETH Zurich’s AtlantikSolar UAV: An 81-hour continuous solar-powered station-keeping flight, a 4000km Atlantic crossing from Newfoundland to Portugal, and two multi-glacier aerial inspection missions above the Arctic Ocean performed near Greenland in summer 2017.
Tasks
Published	2017-11-28
URL	http://arxiv.org/abs/1711.10328v1
PDF	http://arxiv.org/pdf/1711.10328v1.pdf
PWC	https://paperswithcode.com/paper/meteorology-aware-multi-goal-path-planning
Repo
Framework

Estimating activity cycles with probabilistic methods II. The Mount Wilson Ca H&K data


Title	Estimating activity cycles with probabilistic methods II. The Mount Wilson Ca H&K data
Authors	N. Olspert, J. Lehtinen, M. J. Käpylä, J. Pelt, A. Grigorievskiy
Abstract	Debate over the existence of branches in the stellar activity-rotation diagrams continues. Application of modern time series analysis tools to study the mean cycle periods in chromospheric activity index is lacking. We develop such models, based on Gaussian processes, for one-dimensional time series and apply it to the extended Mount Wilson Ca H&K sample. Our main aim is to study how the previously commonly used assumption of strict harmonicity of the stellar cycles as well as handling of the linear trends affects the results. We introduce three methods of different complexity, starting with the simple Bayesian harmonic model and followed by Gaussian Process models with periodic and quasi-periodic covariance functions. We confirm the existence of two populations in the activity-period diagram. We find only one significant trend in the inactive population, namely that the cycle periods get shorter with increasing rotation. This is in contrast with earlier studies, that postulate the existence of trends in both of the populations. In terms of rotation to cycle period ratio, our data is consistent with only two activity branches such that the active branch merges together with the transitional one. The retrieved stellar cycles are uniformly distributed over the R’HK activity index, indicating that the operation of stellar large-scale dynamos carries smoothly over the Vaughan-Preston gap. At around the solar activity index, however, indications of a disruption in the cyclic dynamo action are seen. Our study shows that stellar cycle estimates depend significantly on the model applied. Such model-dependent aspects include the improper treatment of linear trends, while the assumption of strict harmonicity can result in the appearance of double cyclicities that seem more likely to be explained by the quasi-periodicity of the cycles.
Tasks	Gaussian Processes, Time Series, Time Series Analysis
Published	2017-12-21
URL	http://arxiv.org/abs/1712.08240v4
PDF	http://arxiv.org/pdf/1712.08240v4.pdf
PWC	https://paperswithcode.com/paper/estimating-activity-cycles-with-probabilistic-1
Repo
Framework

Catalyst design using actively learned machine with non-ab initio input features towards CO2 reduction reactions


Title	Catalyst design using actively learned machine with non-ab initio input features towards CO2 reduction reactions
Authors	Juhwan Noh, Jaehoon Kim, Seoin Back, Yousung Jung
Abstract	In conventional chemisorption model, the d-band center theory (augmented sometimes with the upper edge of d-band for imporved accuarcy) plays a central role in predicting adsorption energies and catalytic activity as a function of d-band center of the solid surfaces, but it requires density functional calculations that can be quite costly for large scale screening purposes of materials. In this work, we propose to use the d-band width of the muffin-tin orbital theory (to account for local coordination environment) plus electronegativity (to account for adsorbate renormalization) as a simple set of alternative descriptors for chemisorption, which do not demand the ab initio calculations. This pair of descriptors are then combined with machine learning methods, namely, artificial neural network (ANN) and kernel ridge regression (KRR), to allow large scale materials screenings. We show, for a toy set of 263 alloy systems, that the CO adsorption energy can be predicted with a remarkably small mean absolute deviation error of 0.05 eV, a significantly improved result as compared to 0.13 eV obtained with descriptors including costly d-band center calculations in literature. We achieved this high accuracy by utilizing an active learning algorithm, without which the accuracy was 0.18 eV otherwise. As a practical application of this machine, we identified Cu3Y@Cu as a highly active and cost-effective electrochemical CO2 reduction catalyst to produce CO with the overpotential 0.37 V lower than Au catalyst.
Tasks	Active Learning
Published	2017-09-14
URL	http://arxiv.org/abs/1709.04576v1
PDF	http://arxiv.org/pdf/1709.04576v1.pdf
PWC	https://paperswithcode.com/paper/catalyst-design-using-actively-learned
Repo
Framework

ISS-MULT: Intelligent Sample Selection for Multi-Task Learning in Question Answering


Title	ISS-MULT: Intelligent Sample Selection for Multi-Task Learning in Question Answering
Authors	Ali Ahmadvand, Jinho D. Choi
Abstract	Transferring knowledge from a source domain to another domain is useful, especially when gathering new data is very expensive and time-consuming. Deep networks have been well-studied for question answering tasks in recent years; however, no prominent research for transfer learning through deep neural networks exists in the question answering field. In this paper, two main methods (INIT and MULT) in this field are examined. Then, a new method named Intelligent sample selection (ISS-MULT) is proposed to improve the MULT method for question answering tasks. Different datasets, specificay SQuAD, SelQA, WikiQA, NewWikiQA and InforBoxQA, are used for evaluation. Moreover, two different tasks of question answering - answer selection and answer triggering - are evaluated to examine the effectiveness of transfer learning. The results show that using transfer learning generally improves the performance if the corpora are related and are based on the same policy. In addition, using ISS-MULT could finely improve the MULT method for question answering tasks, and these improvements prove more significant in the answer triggering task.
Tasks	Answer Selection, Multi-Task Learning, Question Answering, Transfer Learning
Published	2017-08-07
URL	https://arxiv.org/abs/1708.02267v2
PDF	https://arxiv.org/pdf/1708.02267v2.pdf
PWC	https://paperswithcode.com/paper/iss-mult-intelligent-sample-selection-for
Repo
Framework

Counterfactual Learning for Machine Translation: Degeneracies and Solutions


Title	Counterfactual Learning for Machine Translation: Degeneracies and Solutions
Authors	Carolin Lawrence, Pratik Gajane, Stefan Riezler
Abstract	Counterfactual learning is a natural scenario to improve web-based machine translation services by offline learning from feedback logged during user interactions. In order to avoid the risk of showing inferior translations to users, in such scenarios mostly exploration-free deterministic logging policies are in place. We analyze possible degeneracies of inverse and reweighted propensity scoring estimators, in stochastic and deterministic settings, and relate them to recently proposed techniques for counterfactual learning under deterministic logging.
Tasks	Machine Translation
Published	2017-11-23
URL	http://arxiv.org/abs/1711.08621v3
PDF	http://arxiv.org/pdf/1711.08621v3.pdf
PWC	https://paperswithcode.com/paper/counterfactual-learning-for-machine
Repo
Framework

Digital image splicing detection based on Markov features in QDCT and QWT domain


Title	Digital image splicing detection based on Markov features in QDCT and QWT domain
Authors	Ruxin Wang, Wei Lu, Shijun Xiang, Xianfeng Zhao, Jinwei Wang
Abstract	Image splicing detection is of fundamental importance in digital forensics and therefore has attracted increasing attention recently. In this paper, a color image splicing detection approach is proposed based on Markov transition probability of quaternion component separation in quaternion discrete cosine transform (QDCT) domain and quaternion wavelet transform (QWT) domain. Firstly, Markov features of the intra-block and inter-block between block QDCT coefficients are obtained from the real part and three imaginary parts of QDCT coefficients respectively. Then, additional Markov features are extracted from luminance (Y) channel in quaternion wavelet transform domain to characterize the dependency of position among quaternion wavelet subband coefficients. Finally, ensemble classifier (EC) is exploited to classify the spliced and authentic color images. The experiment results demonstrate that the proposed approach can outperforms some state-of-the-art methods.
Tasks
Published	2017-08-28
URL	http://arxiv.org/abs/1708.08245v3
PDF	http://arxiv.org/pdf/1708.08245v3.pdf
PWC	https://paperswithcode.com/paper/digital-image-splicing-detection-based-on
Repo
Framework

Coresets for Kernel Regression


Title	Coresets for Kernel Regression
Authors	Yan Zheng, Jeff M. Phillips
Abstract	Kernel regression is an essential and ubiquitous tool for non-parametric data analysis, particularly popular among time series and spatial data. However, the central operation which is performed many times, evaluating a kernel on the data set, takes linear time. This is impractical for modern large data sets. In this paper we describe coresets for kernel regression: compressed data sets which can be used as proxy for the original data and have provably bounded worst case error. The size of the coresets are independent of the raw number of data points, rather they only depend on the error guarantee, and in some cases the size of domain and amount of smoothing. We evaluate our methods on very large time series and spatial data, and demonstrate that they incur negligible error, can be constructed extremely efficiently, and allow for great computational gains.
Tasks	Time Series
Published	2017-02-13
URL	http://arxiv.org/abs/1702.03644v2
PDF	http://arxiv.org/pdf/1702.03644v2.pdf
PWC	https://paperswithcode.com/paper/coresets-for-kernel-regression
Repo
Framework

STARK: Structured Dictionary Learning Through Rank-one Tensor Recovery


Title	STARK: Structured Dictionary Learning Through Rank-one Tensor Recovery
Authors	Mohsen Ghassemi, Zahra Shakeri, Anand D. Sarwate, Waheed U. Bajwa
Abstract	In recent years, a class of dictionaries have been proposed for multidimensional (tensor) data representation that exploit the structure of tensor data by imposing a Kronecker structure on the dictionary underlying the data. In this work, a novel algorithm called “STARK” is provided to learn Kronecker structured dictionaries that can represent tensors of any order. By establishing that the Kronecker product of any number of matrices can be rearranged to form a rank-1 tensor, we show that Kronecker structure can be enforced on the dictionary by solving a rank-1 tensor recovery problem. Because rank-1 tensor recovery is a challenging nonconvex problem, we resort to solving a convex relaxation of this problem. Empirical experiments on synthetic and real data show promising results for our proposed algorithm.
Tasks	Dictionary Learning
Published	2017-11-13
URL	http://arxiv.org/abs/1711.04887v1
PDF	http://arxiv.org/pdf/1711.04887v1.pdf
PWC	https://paperswithcode.com/paper/stark-structured-dictionary-learning-through
Repo
Framework

Binary Generative Adversarial Networks for Image Retrieval


Title	Binary Generative Adversarial Networks for Image Retrieval
Authors	Jingkuan Song
Abstract	The most striking successes in image retrieval using deep hashing have mostly involved discriminative models, which require labels. In this paper, we use binary generative adversarial networks (BGAN) to embed images to binary codes in an unsupervised way. By restricting the input noise variable of generative adversarial networks (GAN) to be binary and conditioned on the features of each input image, BGAN can simultaneously learn a binary representation per image, and generate an image plausibly similar to the original one. In the proposed framework, we address two main problems: 1) how to directly generate binary codes without relaxation? 2) how to equip the binary representation with the ability of accurate image retrieval? We resolve these problems by proposing new sign-activation strategy and a loss function steering the learning process, which consists of new models for adversarial loss, a content loss, and a neighborhood structure loss. Experimental results on standard datasets (CIFAR-10, NUSWIDE, and Flickr) demonstrate that our BGAN significantly outperforms existing hashing methods by up to 107% in terms of~mAP (See Table tab.res.map.comp) Our anonymous code is available at: https://github.com/htconquer/BGAN.
Tasks	Image Retrieval
Published	2017-08-08
URL	http://arxiv.org/abs/1708.04150v1
PDF	http://arxiv.org/pdf/1708.04150v1.pdf
PWC	https://paperswithcode.com/paper/binary-generative-adversarial-networks-for
Repo
Framework

Unperturbed: spectral analysis beyond Davis-Kahan


Title	Unperturbed: spectral analysis beyond Davis-Kahan
Authors	Justin Eldridge, Mikhail Belkin, Yusu Wang
Abstract	Classical matrix perturbation results, such as Weyl’s theorem for eigenvalues and the Davis-Kahan theorem for eigenvectors, are general purpose. These classical bounds are tight in the worst case, but in many settings sub-optimal in the typical case. In this paper, we present perturbation bounds which consider the nature of the perturbation and its interaction with the unperturbed structure in order to obtain significant improvements over the classical theory in many scenarios, such as when the perturbation is random. We demonstrate the utility of these new results by analyzing perturbations in the stochastic blockmodel where we derive much tighter bounds than provided by the classical theory. We use our new perturbation theory to show that a very simple and natural clustering algorithm – whose analysis was difficult using the classical tools – nevertheless recovers the communities of the blockmodel exactly even in very sparse graphs.
Tasks
Published	2017-06-20
URL	http://arxiv.org/abs/1706.06516v1
PDF	http://arxiv.org/pdf/1706.06516v1.pdf
PWC	https://paperswithcode.com/paper/unperturbed-spectral-analysis-beyond-davis
Repo
Framework

Doubly Accelerated Stochastic Variance Reduced Dual Averaging Method for Regularized Empirical Risk Minimization


Title	Doubly Accelerated Stochastic Variance Reduced Dual Averaging Method for Regularized Empirical Risk Minimization
Authors	Tomoya Murata, Taiji Suzuki
Abstract	In this paper, we develop a new accelerated stochastic gradient method for efficiently solving the convex regularized empirical risk minimization problem in mini-batch settings. The use of mini-batches is becoming a golden standard in the machine learning community, because mini-batch settings stabilize the gradient estimate and can easily make good use of parallel computing. The core of our proposed method is the incorporation of our new “double acceleration” technique and variance reduction technique. We theoretically analyze our proposed method and show that our method much improves the mini-batch efficiencies of previous accelerated stochastic methods, and essentially only needs size $\sqrt{n}$ mini-batches for achieving the optimal iteration complexities for both non-strongly and strongly convex objectives, where $n$ is the training set size. Further, we show that even in non-mini-batch settings, our method achieves the best known convergence rate for both non-strongly and strongly convex objectives.
Tasks
Published	2017-03-01
URL	http://arxiv.org/abs/1703.00439v4
PDF	http://arxiv.org/pdf/1703.00439v4.pdf
PWC	https://paperswithcode.com/paper/doubly-accelerated-stochastic-variance
Repo
Framework

DOC: Deep Open Classification of Text Documents


Title	DOC: Deep Open Classification of Text Documents
Authors	Lei Shu, Hu Xu, Bing Liu
Abstract	Traditional supervised learning makes the closed-world assumption that the classes appeared in the test data must have appeared in training. This also applies to text learning or text classification. As learning is used increasingly in dynamic open environments where some new/test documents may not belong to any of the training classes, identifying these novel documents during classification presents an important problem. This problem is called open-world classification or open classification. This paper proposes a novel deep learning based approach. It outperforms existing state-of-the-art techniques dramatically.
Tasks	Text Classification
Published	2017-09-25
URL	http://arxiv.org/abs/1709.08716v1
PDF	http://arxiv.org/pdf/1709.08716v1.pdf
PWC	https://paperswithcode.com/paper/doc-deep-open-classification-of-text
Repo
Framework

Pixel-variant Local Homography for Fisheye Stereo Rectification Minimizing Resampling Distortion


Title	Pixel-variant Local Homography for Fisheye Stereo Rectification Minimizing Resampling Distortion
Authors	Dingfu Zhou, Yuchao Dai, Hongdong Li
Abstract	Large field-of-view fisheye lens cameras have attracted more and more researchers’ attention in the field of robotics. However, there does not exist a convenient off-the-shelf stereo rectification approach which can be applied directly to fisheye stereo rig. One obvious drawback of existing methods is that the resampling distortion (which is defined as the loss of pixels due to under-sampling and the creation of new pixels due to over-sampling during rectification process) is severe if we want to obtain a rectification with epipolar line (not epipolar circle) constraint. To overcome this weakness, we propose a novel pixel-wise local homography technique for stereo rectification. First, we prove that there indeed exist enough degrees of freedom to apply pixel-wise local homography for stereo rectification. Then we present a method to exploit these freedoms and the solution via an optimization framework. Finally, the robustness and effectiveness of the proposed method have been verified on real fisheye lens images. The rectification results show that the proposed approach can effectively reduce the resampling distortion in comparison with existing methods while satisfying the epipolar line constraint. By employing the proposed method, dense stereo matching and 3D reconstruction for fisheye lens camera become as easy as perspective lens cameras.
Tasks	3D Reconstruction, Stereo Matching, Stereo Matching Hand
Published	2017-07-12
URL	http://arxiv.org/abs/1707.03775v1
PDF	http://arxiv.org/pdf/1707.03775v1.pdf
PWC	https://paperswithcode.com/paper/pixel-variant-local-homography-for-fisheye
Repo
Framework