Paper Group ANR 1141
Linear Time Visualization and Search in Big Data using Pixellated Factor Space Mapping. CRAFT: A multifunction online platform for speech prosody visualisation. On Validating, Repairing and Refining Heuristic ML Explanations. A Multimodal Alerting System for Online Class Quality Assurance. A Temporal Attentive Approach for Video-Based Pedestrian At …
Linear Time Visualization and Search in Big Data using Pixellated Factor Space Mapping
| Title | Linear Time Visualization and Search in Big Data using Pixellated Factor Space Mapping |
| Authors | Fionn Murtagh |
| Abstract | It is demonstrated how linear computational time and storage efficient approaches can be adopted when analyzing very large data sets. More importantly, interpretation is aided and furthermore, basic processing is easily supported. Such basic processing can be the use of supplementary, i.e. contextual, elements, or particular associations. Furthermore pixellated grid cell contents can be utilized as a basic form of imposed clustering. For a given resolution level, here related to an associated m-adic ($m$ here is a non-prime integer) or p-adic ($p$ is prime) number system encoding, such pixellated mapping results in partitioning. The association of a range of m-adic and p-adic representations leads naturally to an imposed hierarchical clustering, with partition levels corresponding to the m-adic-based and p-adic-based representations and displays. In these clustering embedding and imposed cluster structures, some analytical visualization and search applications are described |
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| Published | 2019-02-27 |
| URL | http://arxiv.org/abs/1902.10655v1 |
| http://arxiv.org/pdf/1902.10655v1.pdf | |
| PWC | https://paperswithcode.com/paper/linear-time-visualization-and-search-in-big |
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CRAFT: A multifunction online platform for speech prosody visualisation
| Title | CRAFT: A multifunction online platform for speech prosody visualisation |
| Authors | Dafydd Gibbon |
| Abstract | There are many research tools which are also used for teaching the acoustic phonetics of speech rhythm and speech melody. But they were not purpose-designed for teaching-learning situations, and some have a steep learning curve. CRAFT (Creation and Recovery of Amplitude and Frequency Tracks) is custom-designed as a novel flexible online tool for visualisation and critical comparison of functions and transforms, with implementations of the Reaper, RAPT, PyRapt, YAAPT, YIN and PySWIPE F0 estimators, three Praat configurations, and two purpose-built estimators, PyAMDF, S0FT. Visualisations of amplitude and frequency envelope spectra, spectral edge detection of rhythm zones, and a parametrised spectrogram are included. A selection of audio clips from tone and intonation languages is provided for demonstration purposes. The main advantages of online tools are consistency (users have the same version and the same data selection), interoperability over different platforms, and ease of maintenance. The code is available on GitHub. |
| Tasks | Edge Detection |
| Published | 2019-03-18 |
| URL | http://arxiv.org/abs/1903.08718v1 |
| http://arxiv.org/pdf/1903.08718v1.pdf | |
| PWC | https://paperswithcode.com/paper/craft-a-multifunction-online-platform-for |
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On Validating, Repairing and Refining Heuristic ML Explanations
| Title | On Validating, Repairing and Refining Heuristic ML Explanations |
| Authors | Alexey Ignatiev, Nina Narodytska, Joao Marques-Silva |
| Abstract | Recent years have witnessed a fast-growing interest in computing explanations for Machine Learning (ML) models predictions. For non-interpretable ML models, the most commonly used approaches for computing explanations are heuristic in nature. In contrast, recent work proposed rigorous approaches for computing explanations, which hold for a given ML model and prediction over the entire instance space. This paper extends earlier work to the case of boosted trees and assesses the quality of explanations obtained with state-of-the-art heuristic approaches. On most of the datasets considered, and for the vast majority of instances, the explanations obtained with heuristic approaches are shown to be inadequate when the entire instance space is (implicitly) considered. |
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| Published | 2019-07-04 |
| URL | https://arxiv.org/abs/1907.02509v1 |
| https://arxiv.org/pdf/1907.02509v1.pdf | |
| PWC | https://paperswithcode.com/paper/on-validating-repairing-and-refining |
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A Multimodal Alerting System for Online Class Quality Assurance
| Title | A Multimodal Alerting System for Online Class Quality Assurance |
| Authors | Jiahao Chen, Hang Li, Wenxin Wang, Wenbiao Ding, Gale Yan Huang, Zitao Liu |
| Abstract | Online 1 on 1 class is created for more personalized learning experience. It demands a large number of teaching resources, which are scarce in China. To alleviate this problem, we build a platform (marketplace), i.e., \emph{Dahai} to allow college students from top Chinese universities to register as part-time instructors for the online 1 on 1 classes. To warn the unqualified instructors and ensure the overall education quality, we build a monitoring and alerting system by utilizing multimodal information from the online environment. Our system mainly consists of two key components: banned word detector and class quality predictor. The system performance is demonstrated both offline and online. By conducting experimental evaluation of real-world online courses, we are able to achieve 74.3% alerting accuracy in our production environment. |
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| Published | 2019-09-01 |
| URL | https://arxiv.org/abs/1909.11765v1 |
| https://arxiv.org/pdf/1909.11765v1.pdf | |
| PWC | https://paperswithcode.com/paper/a-multimodal-alerting-system-for-online-class |
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A Temporal Attentive Approach for Video-Based Pedestrian Attribute Recognition
| Title | A Temporal Attentive Approach for Video-Based Pedestrian Attribute Recognition |
| Authors | Zhiyuan Chen, Annan Li, Yunhong Wang |
| Abstract | In this paper, we first tackle the problem of pedestrian attribute recognition by video-based approach. The challenge mainly lies in spatial and temporal modeling and how to integrating them for effective and dynamic pedestrian representation. To solve this problem, a novel multi-task model based on the conventional neural network and temporal attention strategy is proposed. Since publicly available dataset is rare, two new large-scale video datasets with expanded attribute definition are presented, on which the effectiveness of both video-based pedestrian attribute recognition methods and the proposed new network architecture is well demonstrated. The two datasets are published on http://irip.buaa.edu.cn/mars_duke_attributes/index.html. |
| Tasks | Pedestrian Attribute Recognition |
| Published | 2019-01-17 |
| URL | https://arxiv.org/abs/1901.05742v2 |
| https://arxiv.org/pdf/1901.05742v2.pdf | |
| PWC | https://paperswithcode.com/paper/video-based-pedestrian-attribute-recognition |
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SCATGAN for Reconstruction of Ultrasound Scatterers Using Generative Adversarial Networks
| Title | SCATGAN for Reconstruction of Ultrasound Scatterers Using Generative Adversarial Networks |
| Authors | Andrawes Al Bahou, Christine Tanner, Orcun Goksel |
| Abstract | Computational simulation of ultrasound (US) echography is essential for training sonographers. Realistic simulation of US interaction with microscopic tissue structures is often modeled by a tissue representation in the form of point scatterers, convolved with a spatially varying point spread function. This yields a realistic US B-mode speckle texture, given that a scatterer representation for a particular tissue type is readily available. This is often not the case and scatterers are nontrivial to determine. In this work we propose to estimate scatterer maps from sample US B-mode images of a tissue, by formulating this inverse mapping problem as image translation, where we learn the mapping with Generative Adversarial Networks, using a US simulation software for training. We demonstrate robust reconstruction results, invariant to US viewing and imaging settings such as imaging direction and center frequency. Our method is shown to generalize beyond the trained imaging settings, demonstrated on in-vivo US data. Our inference runs orders of magnitude faster than optimization-based techniques, enabling future extensions for reconstructing 3D B-mode volumes with only linear computational complexity. |
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| Published | 2019-02-01 |
| URL | http://arxiv.org/abs/1902.00469v1 |
| http://arxiv.org/pdf/1902.00469v1.pdf | |
| PWC | https://paperswithcode.com/paper/scatgan-for-reconstruction-of-ultrasound |
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Large-Scale Spectrum Occupancy Learning via Tensor Decomposition and LSTM Networks
| Title | Large-Scale Spectrum Occupancy Learning via Tensor Decomposition and LSTM Networks |
| Authors | Mohsen Joneidi, Ismail Alkhouri, Nazanin Rahnavard |
| Abstract | A new paradigm for large-scale spectrum occupancy learning based on long short-term memory (LSTM) recurrent neural networks is proposed. Studies have shown that spectrum usage is a highly correlated time series. Moreover, there is a correlation for occupancy of spectrum between different frequency channels. Therefore, revealing all these correlations using learning and prediction of one-dimensional time series is not a trivial task. In this paper, we introduce a new framework for representing the spectrum measurements in a tensor format. Next, a time-series prediction method based on CANDECOMP/PARFAC (CP) tensor decomposition and LSTM recurrent neural networks is proposed. The proposed method is computationally efficient and is able to capture different types of correlation within the measured spectrum. Moreover, it is robust against noise and missing entries of sensed spectrum. The superiority of the proposed method is evaluated over a large-scale synthetic dataset in terms of prediction accuracy and computational efficiency. |
| Tasks | Time Series, Time Series Prediction |
| Published | 2019-05-10 |
| URL | https://arxiv.org/abs/1905.04392v1 |
| https://arxiv.org/pdf/1905.04392v1.pdf | |
| PWC | https://paperswithcode.com/paper/large-scale-spectrum-occupancy-learning-via |
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Optimizing Convolutional Neural Networks for Embedded Systems by Means of Neuroevolution
| Title | Optimizing Convolutional Neural Networks for Embedded Systems by Means of Neuroevolution |
| Authors | Filip Badan, Lukas Sekanina |
| Abstract | Automated design methods for convolutional neural networks (CNNs) have recently been developed in order to increase the design productivity. We propose a neuroevolution method capable of evolving and optimizing CNNs with respect to the classification error and CNN complexity (expressed as the number of tunable CNN parameters), in which the inference phase can partly be executed using fixed point operations to further reduce power consumption. Experimental results are obtained with TinyDNN framework and presented using two common image classification benchmark problems – MNIST and CIFAR-10. |
| Tasks | Image Classification |
| Published | 2019-10-15 |
| URL | https://arxiv.org/abs/1910.06854v1 |
| https://arxiv.org/pdf/1910.06854v1.pdf | |
| PWC | https://paperswithcode.com/paper/optimizing-convolutional-neural-networks-for |
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Meta Decision Trees for Explainable Recommendation Systems
| Title | Meta Decision Trees for Explainable Recommendation Systems |
| Authors | Eyal Shulman, Lior Wolf |
| Abstract | We tackle the problem of building explainable recommendation systems that are based on a per-user decision tree, with decision rules that are based on single attribute values. We build the trees by applying learned regression functions to obtain the decision rules as well as the values at the leaf nodes. The regression functions receive as input the embedding of the user’s training set, as well as the embedding of the samples that arrive at the current node. The embedding and the regressors are learned end-to-end with a loss that encourages the decision rules to be sparse. By applying our method, we obtain a collaborative filtering solution that provides a direct explanation to every rating it provides. With regards to accuracy, it is competitive with other algorithms. However, as expected, explainability comes at a cost and the accuracy is typically slightly lower than the state of the art result reported in the literature. |
| Tasks | Recommendation Systems |
| Published | 2019-12-19 |
| URL | https://arxiv.org/abs/1912.09140v1 |
| https://arxiv.org/pdf/1912.09140v1.pdf | |
| PWC | https://paperswithcode.com/paper/meta-decision-trees-for-explainable-1 |
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Applying ANN, ANFIS, and LSSVM Models for Estimation of Acid Solvent Solubility in Supercritical CO$_2$
| Title | Applying ANN, ANFIS, and LSSVM Models for Estimation of Acid Solvent Solubility in Supercritical CO$_2$ |
| Authors | Amin Bemani, Alireza Baghban, Shahaboddin Shamshirband, Amir Mosavi, Peter Csiba, Annamaria R. Varkonyi-Koczy |
| Abstract | In the present work, a novel and the robust computational investigation is carried out to estimate solubility of different acids in supercritical carbon dioxide. Four different algorithms such as radial basis function artificial neural network, Multi-layer Perceptron (MLP) artificial neural network (ANN), Least squares support vector machine (LSSVM) and adaptive neuro-fuzzy inference system (ANFIS) are developed to predict the solubility of different acids in carbon dioxide based on the temperature, pressure, hydrogen number, carbon number, molecular weight, and acid dissociation constant of acid. In the purpose of best evaluation of proposed models, different graphical and statistical analyses and also a novel sensitivity analysis are carried out. The present study proposed the great manners for best acid solubility estimation in supercritical carbon dioxide, which can be helpful for engineers and chemists to predict operational conditions in industries. |
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| Published | 2019-11-21 |
| URL | https://arxiv.org/abs/1912.05612v1 |
| https://arxiv.org/pdf/1912.05612v1.pdf | |
| PWC | https://paperswithcode.com/paper/applying-ann-anfis-and-lssvm-models-for |
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Hierarchical Expert Networks for Meta-Learning
| Title | Hierarchical Expert Networks for Meta-Learning |
| Authors | Heinke Hihn, Daniel A. Braun |
| Abstract | The goal of meta-learning is to train a model on a variety of learning tasks, such that it can adapt to new problems within only a few iterations. Here we propose a principled information-theoretic model that optimally partitions the underlying problem space such that the resulting partitions are processed by specialized expert decision-makers. To drive this specialization we impose the same kind of information processing constraints both on the partitioning and the expert decision-makers. We argue that this specialization leads to efficient adaptation to new tasks. To demonstrate the generality of our approach we evaluate on three meta-learning domains: image classification, regression, and reinforcement learning. |
| Tasks | Image Classification, Meta-Learning |
| Published | 2019-10-31 |
| URL | https://arxiv.org/abs/1911.00348v6 |
| https://arxiv.org/pdf/1911.00348v6.pdf | |
| PWC | https://paperswithcode.com/paper/hierarchical-expert-networks-for-meta |
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Decoupling Adaptation from Modeling with Meta-Optimizers for Meta Learning
| Title | Decoupling Adaptation from Modeling with Meta-Optimizers for Meta Learning |
| Authors | Sébastien M. R. Arnold, Shariq Iqbal, Fei Sha |
| Abstract | Meta-learning methods, most notably Model-Agnostic Meta-Learning or MAML, have achieved great success in adapting to new tasks quickly, after having been trained on similar tasks. The mechanism behind their success, however, is poorly understood. We begin this work with an experimental analysis of MAML, finding that deep models are crucial for its success, even given sets of simple tasks where a linear model would suffice on any individual task. Furthermore, on image-recognition tasks, we find that the early layers of MAML-trained models learn task-invariant features, while later layers are used for adaptation, providing further evidence that these models require greater capacity than is strictly necessary for their individual tasks. Following our findings, we propose a method which enables better use of model capacity at inference time by separating the adaptation aspect of meta-learning into parameters that are only used for adaptation but are not part of the forward model. We find that our approach enables more effective meta-learning in smaller models, which are suitably sized for the individual tasks. |
| Tasks | Meta-Learning |
| Published | 2019-10-30 |
| URL | https://arxiv.org/abs/1910.13603v1 |
| https://arxiv.org/pdf/1910.13603v1.pdf | |
| PWC | https://paperswithcode.com/paper/decoupling-adaptation-from-modeling-with-meta-1 |
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The Challenge of Predicting Meal-to-meal Blood Glucose Concentrations for Patients with Type I Diabetes
| Title | The Challenge of Predicting Meal-to-meal Blood Glucose Concentrations for Patients with Type I Diabetes |
| Authors | Neil C. Borle, Edmond A. Ryan, Russell Greiner |
| Abstract | Patients with Type I Diabetes (T1D) must take insulin injections to prevent the serious long term effects of hyperglycemia - high blood glucose (BG). Patients must also be careful not to inject too much insulin because this could induce hypoglycemia (low BG), which can potentially be fatal. Patients therefore follow a “regimen” that determines how much insulin to inject at certain times. Current methods for managing this disease require adjusting the patient’s regimen over time based on the disease’s behavior (recorded in the patient’s diabetes diary). If we can accurately predict a patient’s future BG values from his/her current features (e.g., predicting today’s lunch BG value given today’s diabetes diary entry for breakfast, including insulin injections, and perhaps earlier entries), then it is relatively easy to produce an effective regimen. This study explores the challenges of BG modeling by applying several machine learning algorithms and various data preprocessing variations (corresponding to 312 [learner, preprocessed-dataset] combinations), to a new T1D dataset containing 29 601 entries from 47 different patients. Our most accurate predictor is a weighted ensemble of two Gaussian Process Regression models, which achieved an errL1 loss of 2.70 mmol/L (48.65 mg/dl). This was an unexpectedly poor result given that one can obtain an errL1 of 2.91 mmol/L (52.43 mg/dl) using the naive approach of simply predicting the patient’s average BG. For each of data-variant/model combination we report several evaluation metrics, including glucose-specific metrics, and find similarly disappointing results (the best model was only incrementally better than the simplest measure). These results suggest that the diabetes diary data that is typically collected may not be sufficient to produce accurate BG prediction models; additional data may be necessary to build accurate BG prediction models. |
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| Published | 2019-03-29 |
| URL | http://arxiv.org/abs/1903.12347v1 |
| http://arxiv.org/pdf/1903.12347v1.pdf | |
| PWC | https://paperswithcode.com/paper/the-challenge-of-predicting-meal-to-meal |
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Efficient Segmentation: Learning Downsampling Near Semantic Boundaries
| Title | Efficient Segmentation: Learning Downsampling Near Semantic Boundaries |
| Authors | Dmitrii Marin, Zijian He, Peter Vajda, Priyam Chatterjee, Sam Tsai, Fei Yang, Yuri Boykov |
| Abstract | Many automated processes such as auto-piloting rely on a good semantic segmentation as a critical component. To speed up performance, it is common to downsample the input frame. However, this comes at the cost of missed small objects and reduced accuracy at semantic boundaries. To address this problem, we propose a new content-adaptive downsampling technique that learns to favor sampling locations near semantic boundaries of target classes. Cost-performance analysis shows that our method consistently outperforms the uniform sampling improving balance between accuracy and computational efficiency. Our adaptive sampling gives segmentation with better quality of boundaries and more reliable support for smaller-size objects. |
| Tasks | Semantic Segmentation |
| Published | 2019-07-16 |
| URL | https://arxiv.org/abs/1907.07156v1 |
| https://arxiv.org/pdf/1907.07156v1.pdf | |
| PWC | https://paperswithcode.com/paper/efficient-segmentation-learning-downsampling |
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Multi-scale Graph-based Grading for Alzheimer’s Disease Prediction
| Title | Multi-scale Graph-based Grading for Alzheimer’s Disease Prediction |
| Authors | Kilian Hett, Vinh-Thong Ta, José V. Manjón, Pierrick Coupé |
| Abstract | The prediction of subjects with mild cognitive impairment (MCI) who will progress to Alzheimer’s disease (AD) is clinically relevant, and may above all have a significant impact on accelerate the development of new treatments. In this paper, we present a new MRI-based biomarker that enables us to predict conversion of MCI subjects to AD accurately. In order to better capture the AD signature, we introduce two main contributions. First, we present a new graph-based grading framework to combine inter-subject similarity features and intra-subject variability features. This framework involves patch-based grading of anatomical structures and graph-based modeling of structure alteration relationships. Second, we propose an innovative multiscale brain analysis to capture alterations caused by AD at different anatomical levels. Based on a cascade of classifiers, this multiscale approach enables the analysis of alterations of whole brain structures and hippocampus subfields at the same time. During our experiments using the ADNI-1 dataset, the proposed multiscale graph-based grading method obtained an area under the curve (AUC) of 81% to predict conversion of MCI subjects to AD within three years. Moreover, when combined with cognitive scores, the proposed method obtained 85% of AUC. These results are competitive in comparison to state-of-the-art methods evaluated on the same dataset. |
| Tasks | Disease Prediction |
| Published | 2019-07-15 |
| URL | https://arxiv.org/abs/1907.06625v1 |
| https://arxiv.org/pdf/1907.06625v1.pdf | |
| PWC | https://paperswithcode.com/paper/multi-scale-graph-based-grading-for |
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