Paper Group ANR 1438
Rebalancing Learning on Evolving Data Streams. Offline Recommender Learning Meets Unsupervised Domain Adaptation. Agglomerative Fast Super-Paramagnetic Clustering. Information Theoretic Feature Transformation Learning for Brain Interfaces. Semi-Supervised Ordinal Regression Based on Empirical Risk Minimization. Spectra2pix: Generating Nanostructure …
Rebalancing Learning on Evolving Data Streams
| Title | Rebalancing Learning on Evolving Data Streams |
| Authors | Alessio Bernardo, Emanuele Della Valle, Albert Bifet |
| Abstract | Nowadays, every device connected to the Internet generates an ever-growing stream of data (formally, unbounded). Machine Learning on unbounded data streams is a grand challenge due to its resource constraints. In fact, standard machine learning techniques are not able to deal with data whose statistics is subject to gradual or sudden changes without any warning. Massive Online Analysis (MOA) is the collective name, as well as a software library, for new learners that are able to manage data streams. In this paper, we present a research study on streaming rebalancing. Indeed, data streams can be imbalanced as static data, but there is not a method to rebalance them incrementally, one element at a time. For this reason we propose a new streaming approach able to rebalance data streams online. Our new methodology is evaluated against some synthetically generated datasets using prequential evaluation in order to demonstrate that it outperforms the existing approaches. |
| Tasks | |
| Published | 2019-11-17 |
| URL | https://arxiv.org/abs/1911.07361v1 |
| https://arxiv.org/pdf/1911.07361v1.pdf | |
| PWC | https://paperswithcode.com/paper/rebalancing-learning-on-evolving-data-streams |
| Repo | |
| Framework | |
Offline Recommender Learning Meets Unsupervised Domain Adaptation
| Title | Offline Recommender Learning Meets Unsupervised Domain Adaptation |
| Authors | Yuta Saito |
| Abstract | It is critical to eliminate selection bias of the rating feedback to construct a well-performing recommender offline. Currently, a promising solution to the challenge is the propensity weighting approach that models the missing mechanism of rating feedback. However, the performance of existing propensity-based algorithms can be significantly affected by the propensity estimation bias. To alleviate the problem, we formulate the missing-not-at-random recommendation as the unsupervised domain adaptation problem and drive the propensity-independent generalization error bound. We further propose a corresponding algorithm that minimizes the bound via adversarial learning. Our proposed theoretical framework and algorithm do not depend on the propensity score and can obtain a well-performing rating predictor without the true propensity information. Empirical evaluation using benchmark real-world datasets demonstrates the effectiveness and real-world applicability of the proposed approach. |
| Tasks | Domain Adaptation, Unsupervised Domain Adaptation |
| Published | 2019-10-16 |
| URL | https://arxiv.org/abs/1910.07295v4 |
| https://arxiv.org/pdf/1910.07295v4.pdf | |
| PWC | https://paperswithcode.com/paper/unsupervised-domain-adaptation-meets-offline |
| Repo | |
| Framework | |
Agglomerative Fast Super-Paramagnetic Clustering
| Title | Agglomerative Fast Super-Paramagnetic Clustering |
| Authors | Lionel Yelibi, Tim Gebbie |
| Abstract | We consider the problem of fast time-series data clustering. Building on previous work modeling the correlation-based Hamiltonian of spin variables we present a fast non-expensive agglomerative algorithm. The method is tested on synthetic correlated time-series and noisy synthetic data-sets with built-in cluster structure to demonstrate that the algorithm produces meaningful non-trivial results. We argue that ASPC can reduce compute time costs and resource usage cost for large scale clustering while being serialized and hence has no obvious parallelization requirement. The algorithm can be an effective choice for state-detection for online learning in a fast non-linear data environment because the algorithm requires no prior information about the number of clusters. |
| Tasks | Time Series |
| Published | 2019-08-02 |
| URL | https://arxiv.org/abs/1908.00951v2 |
| https://arxiv.org/pdf/1908.00951v2.pdf | |
| PWC | https://paperswithcode.com/paper/agglomerative-fast-super-paramagnetic |
| Repo | |
| Framework | |
Information Theoretic Feature Transformation Learning for Brain Interfaces
| Title | Information Theoretic Feature Transformation Learning for Brain Interfaces |
| Authors | Ozan Ozdenizci, Deniz Erdogmus |
| Abstract | Objective: A variety of pattern analysis techniques for model training in brain interfaces exploit neural feature dimensionality reduction based on feature ranking and selection heuristics. In the light of broad evidence demonstrating the potential sub-optimality of ranking based feature selection by any criterion, we propose to extend this focus with an information theoretic learning driven feature transformation concept. Methods: We present a maximum mutual information linear transformation (MMI-LinT), and a nonlinear transformation (MMI-NonLinT) framework derived by a general definition of the feature transformation learning problem. Empirical assessments are performed based on electroencephalographic (EEG) data recorded during a four class motor imagery brain-computer interface (BCI) task. Exploiting state-of-the-art methods for initial feature vector construction, we compare the proposed approaches with conventional feature selection based dimensionality reduction techniques which are widely used in brain interfaces. Furthermore, for the multi-class problem, we present and exploit a hierarchical graphical model based BCI decoding system. Results: Both binary and multi-class decoding analyses demonstrate significantly better performances with the proposed methods. Conclusion: Information theoretic feature transformations are capable of tackling potential confounders of conventional approaches in various settings. Significance: We argue that this concept provides significant insights to extend the focus on feature selection heuristics to a broader definition of feature transformation learning in brain interfaces. |
| Tasks | Dimensionality Reduction, EEG, Feature Selection |
| Published | 2019-03-28 |
| URL | http://arxiv.org/abs/1903.12235v2 |
| http://arxiv.org/pdf/1903.12235v2.pdf | |
| PWC | https://paperswithcode.com/paper/information-theoretic-feature-transformation |
| Repo | |
| Framework | |
Semi-Supervised Ordinal Regression Based on Empirical Risk Minimization
| Title | Semi-Supervised Ordinal Regression Based on Empirical Risk Minimization |
| Authors | Taira Tsuchiya, Nontawat Charoenphakdee, Issei Sato, Masashi Sugiyama |
| Abstract | Ordinal regression is aimed at predicting an ordinal class label. In this paper, we consider its semi-supervised formulation, in which we have unlabeled data along with ordinal-labeled data to train an ordinal regressor. There are several metrics to evaluate the performance of ordinal regression, such as the mean absolute error, mean zero-one error, and mean squared error. However, the existing studies do not take the evaluation metric into account, has a restriction on the model choice, and has no theoretical guarantee. To overcome these problems, we propose a novel generic framework for semi-supervised ordinal regression based on the empirical risk minimization (ERM) principle that is applicable to optimizing all of the metrics mentioned above. Also, our framework has flexible choices of models, surrogate losses, and optimization algorithms. Moreover, our framework does not require a geometric assumption on unlabeled data such as the cluster assumption or manifold assumption. We provide an estimation error bound to show that our risk estimator is consistent. Finally, we conduct experiments to show the usefulness of our framework. |
| Tasks | |
| Published | 2019-01-31 |
| URL | https://arxiv.org/abs/1901.11351v2 |
| https://arxiv.org/pdf/1901.11351v2.pdf | |
| PWC | https://paperswithcode.com/paper/semi-supervised-ordinal-regression-based-on |
| Repo | |
| Framework | |
Spectra2pix: Generating Nanostructure Images from Spectra
| Title | Spectra2pix: Generating Nanostructure Images from Spectra |
| Authors | Itzik Malkiel, Michael Mrejen, Lior Wolf, Haim Suchowski |
| Abstract | The design of the nanostructures that are used in the field of nano-photonics has remained complex, very often relying on the intuition and expertise of the designer, ultimately limiting the reach and penetration of this groundbreaking approach. Recently, there has been an increasing number of studies suggesting to apply Machine Learning techniques for the design of nanostructures. Most of these studies engage Deep Learning techniques, which entails training a Deep Neural Network (DNN) to approximate the highly non-linear function of the underlying physical process between spectra and nanostructures. At the end of the training, the DNN allows an on-demand design of nanostructures, i.e. the model can infer nanostructure geometries for desired spectra. In this work, we introduce spectra2pix, which is a model DNN trained to generate 2D images of the designed nanostructures. Our model architecture is not limited to a closed set of nanostructure shapes, and can be trained for the design of any geometry. We show, for the first time, a successful generalization ability by designing a completely unseen sub-family of geometries. This generalization capability highlights the importance of our model architecture, and allows higher applicability for real-world design problems. |
| Tasks | |
| Published | 2019-11-26 |
| URL | https://arxiv.org/abs/1911.11525v1 |
| https://arxiv.org/pdf/1911.11525v1.pdf | |
| PWC | https://paperswithcode.com/paper/spectra2pix-generating-nanostructure-images |
| Repo | |
| Framework | |
CD2 : Combined Distances of Contrast Distributions for the Assessment of Perceptual Quality of Image Processing
| Title | CD2 : Combined Distances of Contrast Distributions for the Assessment of Perceptual Quality of Image Processing |
| Authors | Sascha Xu, Jan Bauer, Benjamin Axmann |
| Abstract | The quality of visual input is very important for both human and machine perception. Consequently many processing techniques exist that deal with different distortions. Usually image processing is applied freely and lacks redundancy regarding safety. We propose a novel image comparison method called the Combined Distances of Contrast Distributions (CD2) to protect against errors that arise during processing. Based on the distribution of image contrasts a new reduced-reference image quality assessment (IQA) method is introduced. By combining various distance functions excellent performance on IQA benchmarks is achieved with only a small data and computation overhead. |
| Tasks | Image Quality Assessment |
| Published | 2019-11-18 |
| URL | https://arxiv.org/abs/1911.07995v1 |
| https://arxiv.org/pdf/1911.07995v1.pdf | |
| PWC | https://paperswithcode.com/paper/cd2-combined-distances-of-contrast |
| Repo | |
| Framework | |
Entropic Dynamic Time Warping Kernels for Co-evolving Financial Time Series Analysis
| Title | Entropic Dynamic Time Warping Kernels for Co-evolving Financial Time Series Analysis |
| Authors | Lu Bai, Lixin Cui, Lixiang Xu, Yue Wang, Zhihong Zhang, Edwin R. Hancock |
| Abstract | In this work, we develop a novel framework to measure the similarity between dynamic financial networks, i.e., time-varying financial networks. Particularly, we explore whether the proposed similarity measure can be employed to understand the structural evolution of the financial networks with time. For a set of time-varying financial networks with each vertex representing the individual time series of a different stock and each edge between a pair of time series representing the absolute value of their Pearson correlation, our start point is to compute the commute time matrix associated with the weighted adjacency matrix of the network structures, where each element of the matrix can be seen as the enhanced correlation value between pairwise stocks. For each network, we show how the commute time matrix allows us to identify a reliable set of dominant correlated time series as well as an associated dominant probability distribution of the stock belonging to this set. Furthermore, we represent each original network as a discrete dominant Shannon entropy time series computed from the dominant probability distribution. With the dominant entropy time series for each pair of financial networks to hand, we develop a similarity measure based on the classical dynamic time warping framework, for analyzing the financial time-varying networks. We show that the proposed similarity measure is positive definite and thus corresponds to a kernel measure on graphs. The proposed kernel bridges the gap between graph kernels and the classical dynamic time warping framework for multiple financial time series analysis. Experiments on time-varying networks extracted through New York Stock Exchange (NYSE) database demonstrate the effectiveness of the proposed approach. |
| Tasks | Time Series, Time Series Analysis |
| Published | 2019-10-21 |
| URL | https://arxiv.org/abs/1910.09153v1 |
| https://arxiv.org/pdf/1910.09153v1.pdf | |
| PWC | https://paperswithcode.com/paper/entropic-dynamic-time-warping-kernels-for-co |
| Repo | |
| Framework | |
Personalized, Health-Aware Recipe Recommendation: An Ensemble Topic Modeling Based Approach
| Title | Personalized, Health-Aware Recipe Recommendation: An Ensemble Topic Modeling Based Approach |
| Authors | Mansura A. Khan, Ellen Rushe, Barry Smyth, David Coyle |
| Abstract | Food choices are personal and complex and have a significant impact on our long-term health and quality of life. By helping users to make informed and satisfying decisions, Recommender Systems (RS) have the potential to support users in making healthier food choices. Intelligent users-modeling is a key challenge in achieving this potential. This paper investigates Ensemble Topic Modelling (EnsTM) based Feature Identification techniques for efficient user-modeling and recipe recommendation. It builds on findings in EnsTM to propose a reduced data representation format and a smart user-modeling strategy that makes capturing user-preference fast, efficient and interactive. This approach enables personalization, even in a cold-start scenario. This paper proposes two different EnsTM based and one Hybrid EnsTM based recommenders. We compared all three EnsTM based variations through a user study with 48 participants, using a large-scale,real-world corpus of 230,876 recipes, and compare against a conventional Content Based (CB) approach. EnsTM based recommenders performed significantly better than the CB approach. Besides acknowledging multi-domain contents such as taste, demographics and cost, our proposed approach also considers user’s nutritional preference and assists them finding recipes under diverse nutritional categories. Furthermore, it provides excellent coverage and enables implicit understanding of user’s food practices. Subsequent analysis also exposed correlation between certain features and a healthier lifestyle. |
| Tasks | Recommendation Systems |
| Published | 2019-07-31 |
| URL | https://arxiv.org/abs/1908.00148v1 |
| https://arxiv.org/pdf/1908.00148v1.pdf | |
| PWC | https://paperswithcode.com/paper/personalized-health-aware-recipe |
| Repo | |
| Framework | |
Domain Fingerprints for No-reference Image Quality Assessment
| Title | Domain Fingerprints for No-reference Image Quality Assessment |
| Authors | Weihao Xia, Yujiu Yang, Jing-Hao Xue, Jing Xiao |
| Abstract | Human fingerprints are detailed and nearly unique markers of human identity. Such a unique and stable fingerprint is also left on each acquired image. It can reveal how an image was degraded during the image acquisition procedure and thus is closely related to the quality of an image. In this work, we propose a new no-reference image quality assessment (NR-IQA) approach called domain-aware IQA (DA-IQA), which for the first time introduces the concept of domain fingerprint to the NR-IQA field. The domain fingerprint of an image is learned from image collections of different degradations and then used as the unique characteristics to identify the degradation sources and assess the quality of the image. To this end, we design a new domain-aware architecture, which enables simultaneous determination of both the distortion sources and the quality of an image. With the distortion in an image better characterized, the image quality can be more accurately assessed, as verified by extensive experiments, which show that the proposed DA-IQA performs better than almost all the compared state-of-the-art NR-IQA methods. |
| Tasks | Image Quality Assessment, No-Reference Image Quality Assessment |
| Published | 2019-11-02 |
| URL | https://arxiv.org/abs/1911.00673v2 |
| https://arxiv.org/pdf/1911.00673v2.pdf | |
| PWC | https://paperswithcode.com/paper/domain-aware-no-reference-image-quality |
| Repo | |
| Framework | |
Emotion Detection using Data Driven Models
| Title | Emotion Detection using Data Driven Models |
| Authors | Naveenkumar K S, Vinayakumar R, Soman KP |
| Abstract | Text is the major method that is used for communication now a days, each and every day lots of text are created. In this paper the text data is used for the classification of the emotions. Emotions are the way of expression of the persons feelings which has an high influence on the decision making tasks. Datasets are collected which are available publically and combined together based on the three emotions that are considered here positive, negative and neutral. In this paper we have proposed the text representation method TFIDF and keras embedding and then given to the classical machine learning algorithms of which Logistics Regression gives the highest accuracy of about 75.6%, after which it is passed to the deep learning algorithm which is the CNN which gives the state of art accuracy of about 45.25%. For the research purpose the datasets that has been collected are released. |
| Tasks | Decision Making |
| Published | 2019-01-10 |
| URL | http://arxiv.org/abs/1901.03141v1 |
| http://arxiv.org/pdf/1901.03141v1.pdf | |
| PWC | https://paperswithcode.com/paper/emotion-detection-using-data-driven-models |
| Repo | |
| Framework | |
Domain adaptation for part-of-speech tagging of noisy user-generated text
| Title | Domain adaptation for part-of-speech tagging of noisy user-generated text |
| Authors | Luisa März, Dietrich Trautmann, Benjamin Roth |
| Abstract | The performance of a Part-of-speech (POS) tagger is highly dependent on the domain ofthe processed text, and for many domains there is no or only very little training data available. This work addresses the problem of POS tagging noisy user-generated text using a neural network. We propose an architecture that trains an out-of-domain model on a large newswire corpus, and transfers those weights by using them as a prior for a model trained on the target domain (a data-set of German Tweets) for which there is very little an-notations available. The neural network has two standard bidirectional LSTMs at its core. However, we find it crucial to also encode a set of task-specific features, and to obtain reliable (source-domain and target-domain) word representations. Experiments with different regularization techniques such as early stopping, dropout and fine-tuning the domain adaptation prior weights are conducted. Our best model uses external weights from the out-of-domain model, as well as feature embeddings, pre-trained word and sub-word embeddings and achieves a tagging accuracy of slightly over 90%, improving on the previous state of the art for this task. |
| Tasks | Domain Adaptation, Part-Of-Speech Tagging, Word Embeddings |
| Published | 2019-05-21 |
| URL | https://arxiv.org/abs/1905.08920v1 |
| https://arxiv.org/pdf/1905.08920v1.pdf | |
| PWC | https://paperswithcode.com/paper/domain-adaptation-for-part-of-speech-tagging |
| Repo | |
| Framework | |
Automatic Target Recognition Using Discrimination Based on Optimal Transport
| Title | Automatic Target Recognition Using Discrimination Based on Optimal Transport |
| Authors | Ali Sadeghian, Deoksu Lim, Johan Karlsson, Jian Li |
| Abstract | The use of distances based on optimal transportation has recently shown promise for discrimination of power spectra. In particular, spectral estimation methods based on l1 regularization as well as covariance based methods can be shown to be robust with respect to such distances. These transportation distances provide a geometric framework where geodesics corresponds to smooth transition of spectral mass, and have been useful for tracking. In this paper, we investigate the use of these distances for automatic target recognition. We study the use of the Monge-Kantorovich distance compared to the standard l2 distance for classifying civilian vehicles based on SAR images. We use a version of the Monge-Kantorovich distance that applies also for the case where the spectra may have different total mass, and we formulate the optimization problem as a minimum flow problem that can be computed using efficient algorithms. |
| Tasks | |
| Published | 2019-04-06 |
| URL | http://arxiv.org/abs/1904.03534v1 |
| http://arxiv.org/pdf/1904.03534v1.pdf | |
| PWC | https://paperswithcode.com/paper/automatic-target-recognition-using |
| Repo | |
| Framework | |
Measuring Calibration in Deep Learning
| Title | Measuring Calibration in Deep Learning |
| Authors | Jeremy Nixon, Mike Dusenberry, Linchuan Zhang, Ghassen Jerfel, Dustin Tran |
| Abstract | The reliability of a machine learning model’s confidence in its predictions is critical for highrisk applications. Calibration-the idea that a model’s predicted probabilities of outcomes reflect true probabilities of those outcomes-formalizes this notion. While analyzing the calibration of deep neural networks, we’ve identified core problems with the way calibration is currently measured. We design the Thresholded Adaptive Calibration Error (TACE) metric to resolve these pathologies and show that it outperforms other metrics, especially in settings where predictions beyond the maximum prediction that is chosen as the output class matter. There are many cases where what a practitioner cares about is the calibration of a specific prediction, and so we introduce a dynamic programming based Prediction Specific Calibration Error (PSCE) that smoothly considers the calibration of nearby predictions to give an estimate of the calibration error of a specific prediction. |
| Tasks | Calibration |
| Published | 2019-04-02 |
| URL | http://arxiv.org/abs/1904.01685v1 |
| http://arxiv.org/pdf/1904.01685v1.pdf | |
| PWC | https://paperswithcode.com/paper/measuring-calibration-in-deep-learning |
| Repo | |
| Framework | |
Towards Adversarially Robust Object Detection
| Title | Towards Adversarially Robust Object Detection |
| Authors | Haichao Zhang, Jianyu Wang |
| Abstract | Object detection is an important vision task and has emerged as an indispensable component in many vision system, rendering its robustness as an increasingly important performance factor for practical applications. While object detection models have been demonstrated to be vulnerable against adversarial attacks by many recent works, very few efforts have been devoted to improving their robustness. In this work, we take an initial attempt towards this direction. We first revisit and systematically analyze object detectors and many recently developed attacks from the perspective of model robustness. We then present a multi-task learning perspective of object detection and identify an asymmetric role of task losses. We further develop an adversarial training approach which can leverage the multiple sources of attacks for improving the robustness of detection models. Extensive experiments on PASCAL-VOC and MS-COCO verified the effectiveness of the proposed approach. |
| Tasks | Multi-Task Learning, Object Detection, Robust Object Detection |
| Published | 2019-07-24 |
| URL | https://arxiv.org/abs/1907.10310v1 |
| https://arxiv.org/pdf/1907.10310v1.pdf | |
| PWC | https://paperswithcode.com/paper/towards-adversarially-robust-object-detection |
| Repo | |
| Framework | |
