Paper Group ANR 343
Model-based free-breathing cardiac MRI reconstruction using deep learned & STORM priors: MoDL-STORM. The steerable graph Laplacian and its application to filtering image data-sets. HyperML: A Boosting Metric Learning Approach in Hyperbolic Space for Recommender Systems. Brain EEG Time Series Selection: A Novel Graph-Based Approach for Classificati …
Model-based free-breathing cardiac MRI reconstruction using deep learned & STORM priors: MoDL-STORM
| Title | Model-based free-breathing cardiac MRI reconstruction using deep learned & STORM priors: MoDL-STORM |
| Authors | Sampurna Biswas, Hemant K. Aggarwal, Sunrita Poddar, Mathews Jacob |
| Abstract | We introduce a model-based reconstruction framework with deep learned (DL) and smoothness regularization on manifolds (STORM) priors to recover free breathing and ungated (FBU) cardiac MRI from highly undersampled measurements. The DL priors enable us to exploit the local correlations, while the STORM prior enables us to make use of the extensive non-local similarities that are subject dependent. We introduce a novel model-based formulation that allows the seamless integration of deep learning methods with available prior information, which current deep learning algorithms are not capable of. The experimental results demonstrate the preliminary potential of this work in accelerating FBU cardiac MRI. |
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| Published | 2018-07-10 |
| URL | http://arxiv.org/abs/1807.03845v1 |
| http://arxiv.org/pdf/1807.03845v1.pdf | |
| PWC | https://paperswithcode.com/paper/model-based-free-breathing-cardiac-mri |
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The steerable graph Laplacian and its application to filtering image data-sets
| Title | The steerable graph Laplacian and its application to filtering image data-sets |
| Authors | Boris Landa, Yoel Shkolnisky |
| Abstract | In recent years, improvements in various image acquisition techniques gave rise to the need for adaptive processing methods, aimed particularly for large datasets corrupted by noise and deformations. In this work, we consider datasets of images sampled from a low-dimensional manifold (i.e. an image-valued manifold), where the images can assume arbitrary planar rotations. To derive an adaptive and rotation-invariant framework for processing such datasets, we introduce a graph Laplacian (GL)-like operator over the dataset, termed ${\textit{steerable graph Laplacian}}$. Essentially, the steerable GL extends the standard GL by accounting for all (infinitely-many) planar rotations of all images. As it turns out, similarly to the standard GL, a properly normalized steerable GL converges to the Laplace-Beltrami operator on the low-dimensional manifold. However, the steerable GL admits an improved convergence rate compared to the GL, where the improved convergence behaves as if the intrinsic dimension of the underlying manifold is lower by one. Moreover, it is shown that the steerable GL admits eigenfunctions of the form of Fourier modes (along the orbits of the images’ rotations) multiplied by eigenvectors of certain matrices, which can be computed efficiently by the FFT. For image datasets corrupted by noise, we employ a subset of these eigenfunctions to “filter” the dataset via a Fourier-like filtering scheme, essentially using all images and their rotations simultaneously. We demonstrate our filtering framework by de-noising simulated single-particle cryo-EM image datasets. |
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| Published | 2018-02-06 |
| URL | http://arxiv.org/abs/1802.01894v2 |
| http://arxiv.org/pdf/1802.01894v2.pdf | |
| PWC | https://paperswithcode.com/paper/the-steerable-graph-laplacian-and-its |
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HyperML: A Boosting Metric Learning Approach in Hyperbolic Space for Recommender Systems
| Title | HyperML: A Boosting Metric Learning Approach in Hyperbolic Space for Recommender Systems |
| Authors | Lucas Vinh Tran, Yi Tay, Shuai Zhang, Gao Cong, Xiaoli Li |
| Abstract | This paper investigates the notion of learning user and item representations in non-Euclidean space. Specifically, we study the connection between metric learning in hyperbolic space and collaborative filtering by exploring Mobius gyrovector spaces where the formalism of the spaces could be utilized to generalize the most common Euclidean vector operations. Overall, this work aims to bridge the gap between Euclidean and hyperbolic geometry in recommender systems through metric learning approach. We propose HyperML (Hyperbolic Metric Learning), a conceptually simple but highly effective model for boosting the performance. Via a series of extensive experiments, we show that our proposed HyperML not only outperforms their Euclidean counterparts, but also achieves state-of-the-art performance on multiple benchmark datasets, demonstrating the effectiveness of personalized recommendation in hyperbolic geometry. |
| Tasks | Metric Learning, Recommendation Systems, Representation Learning |
| Published | 2018-09-05 |
| URL | https://arxiv.org/abs/1809.01703v3 |
| https://arxiv.org/pdf/1809.01703v3.pdf | |
| PWC | https://paperswithcode.com/paper/hyperbolic-recommender-systems |
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Brain EEG Time Series Selection: A Novel Graph-Based Approach for Classification
| Title | Brain EEG Time Series Selection: A Novel Graph-Based Approach for Classification |
| Authors | Chenglong Dai, Jia Wu, Dechang Pi, Lin Cui |
| Abstract | Brain Electroencephalography (EEG) classification is widely applied to analyze cerebral diseases in recent years. Unfortunately, invalid/noisy EEGs degrade the diagnosis performance and most previously developed methods ignore the necessity of EEG selection for classification. To this end, this paper proposes a novel maximum weight clique-based EEG selection approach, named mwcEEGs, to map EEG selection to searching maximum similarity-weighted cliques from an improved Fr'{e}chet distance-weighted undirected EEG graph simultaneously considering edge weights and vertex weights. Our mwcEEGs improves the classification performance by selecting intra-clique pairwise similar and inter-clique discriminative EEGs with similarity threshold $\delta$. Experimental results demonstrate the algorithm effectiveness compared with the state-of-the-art time series selection algorithms on real-world EEG datasets. |
| Tasks | EEG, Time Series |
| Published | 2018-01-14 |
| URL | http://arxiv.org/abs/1801.04510v2 |
| http://arxiv.org/pdf/1801.04510v2.pdf | |
| PWC | https://paperswithcode.com/paper/brain-eeg-time-series-selection-a-novel-graph |
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Supervised Mixed Norm Autoencoder for Kinship Verification in Unconstrained Videos
| Title | Supervised Mixed Norm Autoencoder for Kinship Verification in Unconstrained Videos |
| Authors | Naman Kohli, Daksha Yadav, Mayank Vatsa, Richa Singh, Afzel Noore |
| Abstract | Identifying kinship relations has garnered interest due to several applications such as organizing and tagging the enormous amount of videos being uploaded on the Internet. Existing research in kinship verification primarily focuses on kinship prediction with image pairs. In this research, we propose a new deep learning framework for kinship verification in unconstrained videos using a novel Supervised Mixed Norm regularization Autoencoder (SMNAE). This new autoencoder formulation introduces class-specific sparsity in the weight matrix. The proposed three-stage SMNAE based kinship verification framework utilizes the learned spatio-temporal representation in the video frames for verifying kinship in a pair of videos. A new kinship video (KIVI) database of more than 500 individuals with variations due to illumination, pose, occlusion, ethnicity, and expression is collected for this research. It comprises a total of 355 true kin video pairs with over 250,000 still frames. The effectiveness of the proposed framework is demonstrated on the KIVI database and six existing kinship databases. On the KIVI database, SMNAE yields video-based kinship verification accuracy of 83.18% which is at least 3.2% better than existing algorithms. The algorithm is also evaluated on six publicly available kinship databases and compared with best-reported results. It is observed that the proposed SMNAE consistently yields best results on all the databases |
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| Published | 2018-05-30 |
| URL | http://arxiv.org/abs/1805.12167v1 |
| http://arxiv.org/pdf/1805.12167v1.pdf | |
| PWC | https://paperswithcode.com/paper/supervised-mixed-norm-autoencoder-for-kinship |
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Adversarial Online Learning with noise
| Title | Adversarial Online Learning with noise |
| Authors | Alon Resler, Yishay Mansour |
| Abstract | We present and study models of adversarial online learning where the feedback observed by the learner is noisy, and the feedback is either full information feedback or bandit feedback. Specifically, we consider binary losses xored with the noise, which is a Bernoulli random variable. We consider both a constant noise rate and a variable noise rate. Our main results are tight regret bounds for learning with noise in the adversarial online learning model. |
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| Published | 2018-10-22 |
| URL | http://arxiv.org/abs/1810.09346v3 |
| http://arxiv.org/pdf/1810.09346v3.pdf | |
| PWC | https://paperswithcode.com/paper/adversarial-online-learning-with-noise |
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Control with Distributed Deep Reinforcement Learning: Learn a Better Policy
| Title | Control with Distributed Deep Reinforcement Learning: Learn a Better Policy |
| Authors | Qihao Liu, Xiaofeng Liu, Guoping Cai |
| Abstract | Distributed approach is a very effective method to improve training efficiency of reinforcement learning. In this paper, we propose a new heuristic distributed architecture for deep reinforcement learning (DRL) algorithm, in which a PSO based network update mechanism is adopted to speed up learning an optimal policy besides using multiple agents for parallel training. In this mechanism, the update of neural network of each agent is not only according to the training result of itself, but also affected by the optimal neural network of all agents. In order to verify the effectiveness of the proposed method, the proposed architecture is implemented on the Deep Q-Network algorithm (DQN) and the Deep Deterministic Policy Gradient algorithm (DDPG) to train several typical control problems. The training results show that the proposed method is effective. |
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| Published | 2018-11-26 |
| URL | http://arxiv.org/abs/1811.10264v2 |
| http://arxiv.org/pdf/1811.10264v2.pdf | |
| PWC | https://paperswithcode.com/paper/control-with-distributed-deep-reinforcement |
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Low-shot Learning via Covariance-Preserving Adversarial Augmentation Networks
| Title | Low-shot Learning via Covariance-Preserving Adversarial Augmentation Networks |
| Authors | Hang Gao, Zheng Shou, Alireza Zareian, Hanwang Zhang, Shih-Fu Chang |
| Abstract | Deep neural networks suffer from over-fitting and catastrophic forgetting when trained with small data. One natural remedy for this problem is data augmentation, which has been recently shown to be effective. However, previous works either assume that intra-class variances can always be generalized to new classes, or employ naive generation methods to hallucinate finite examples without modeling their latent distributions. In this work, we propose Covariance-Preserving Adversarial Augmentation Networks to overcome existing limits of low-shot learning. Specifically, a novel Generative Adversarial Network is designed to model the latent distribution of each novel class given its related base counterparts. Since direct estimation of novel classes can be inductively biased, we explicitly preserve covariance information as the `variability’ of base examples during the generation process. Empirical results show that our model can generate realistic yet diverse examples, leading to substantial improvements on the ImageNet benchmark over the state of the art. | |
| Tasks | Data Augmentation |
| Published | 2018-10-27 |
| URL | http://arxiv.org/abs/1810.11730v3 |
| http://arxiv.org/pdf/1810.11730v3.pdf | |
| PWC | https://paperswithcode.com/paper/low-shot-learning-via-covariance-preserving |
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An Unsupervised Learning Classifier with Competitive Error Performance
| Title | An Unsupervised Learning Classifier with Competitive Error Performance |
| Authors | Daniel N. Nissani |
| Abstract | An unsupervised learning classification model is described. It achieves classification error probability competitive with that of popular supervised learning classifiers such as SVM or kNN. The model is based on the incremental execution of small step shift and rotation operations upon selected discriminative hyperplanes at the arrival of input samples. When applied, in conjunction with a selected feature extractor, to a subset of the ImageNet dataset benchmark, it yields 6.2 % Top 3 probability of error; this exceeds by merely about 2 % the result achieved by (supervised) k-Nearest Neighbor, both using same feature extractor. This result may also be contrasted with popular unsupervised learning schemes such as k-Means which is shown to be practically useless on same dataset. |
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| Published | 2018-06-25 |
| URL | http://arxiv.org/abs/1806.09385v2 |
| http://arxiv.org/pdf/1806.09385v2.pdf | |
| PWC | https://paperswithcode.com/paper/an-unsupervised-learning-classifier-with |
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The Impact of Quantity of Training Data on Recognition of Eating Gestures
| Title | The Impact of Quantity of Training Data on Recognition of Eating Gestures |
| Authors | Yiru Shen, Eric Muth, Adam Hoover |
| Abstract | This paper considers the problem of recognizing eating gestures by tracking wrist motion. Eating gestures can have large variability in motion depending on the subject, utensil, and type of food or beverage being consumed. Previous works have shown viable proofs-of-concept of recognizing eating gestures in laboratory settings with small numbers of subjects and food types, but it is unclear how well these methods would work if tested on a larger population in natural settings. As more subjects, locations and foods are tested, a larger amount of motion variability could cause a decrease in recognition accuracy. To explore this issue, this paper describes the collection and annotation of 51,614 eating gestures taken by 269 subjects eating a meal in a cafeteria. Experiments are described that explore the complexity of hidden Markov models (HMMs) and the amount of training data needed to adequately capture the motion variability across this large data set. Results found that HMMs needed a complexity of 13 states and 5 Gaussians to reach a plateau in accuracy, signifying that a minimum of 65 samples per gesture type are needed. Results also found that 500 training samples per gesture type were needed to identify the point of diminishing returns in recognition accuracy. Overall, the findings provide evidence that the size a data set typically used to demonstrate a laboratory proofs-of-concept may not be sufficiently large enough to capture all the motion variability that could be expected in transitioning to deployment with a larger population. Our data set, which is 1-2 orders of magnitude larger than all data sets tested in previous works, is being made publicly available. |
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| Published | 2018-12-11 |
| URL | http://arxiv.org/abs/1812.04513v1 |
| http://arxiv.org/pdf/1812.04513v1.pdf | |
| PWC | https://paperswithcode.com/paper/the-impact-of-quantity-of-training-data-on |
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Estimate the Warfarin Dose by Ensemble of Machine Learning Algorithms
| Title | Estimate the Warfarin Dose by Ensemble of Machine Learning Algorithms |
| Authors | Zhiyuan Ma, Ping Wang, Zehui Gao, Ruobing Wang, Koroush Khalighi |
| Abstract | Warfarin dosing remains challenging due to narrow therapeutic index and highly individual variability. Incorrect warfarin dosing is associated with devastating adverse events. Remarkable efforts have been made to develop the machine learning based warfarin dosing algorithms incorporating clinical factors and genetic variants such as polymorphisms in CYP2C9 and VKORC1. The most widely validated pharmacogenetic algorithm is the IWPC algorithm based on multivariate linear regression (MLR). However, with only a single algorithm, the prediction performance may reach an upper limit even with optimal parameters. Here, we present novel algorithms using stacked generalization frameworks to estimate the warfarin dose, within which different types of machine learning algorithms function together through a meta-machine learning model to maximize the prediction accuracy. Compared to the IWPC-derived MLR algorithm, Stack 1 and 2 based on stacked generalization frameworks performed significantly better overall. Subgroup analysis revealed that the mean of the percentage of patients whose predicted dose of warfarin within 20% of the actual stable therapeutic dose (mean percentage within 20%) for Stack 1 was improved by 12.7% (from 42.47% to 47.86%) in Asians and by 13.5% (from 22.08% to 25.05%) in the low-dose group compared to that for MLR, respectively. These data suggest that our algorithms would especially benefit patients required low warfarin maintenance dose, as subtle changes in warfarin dose could lead to adverse clinical events (thrombosis or bleeding) in patients with low dose. Our study offers novel pharmacogenetic algorithms for clinical trials and practice. |
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| Published | 2018-09-10 |
| URL | http://arxiv.org/abs/1809.04069v2 |
| http://arxiv.org/pdf/1809.04069v2.pdf | |
| PWC | https://paperswithcode.com/paper/estimate-the-warfarin-dose-by-ensemble-of |
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Detecting Syntactic Features of Translated Chinese
| Title | Detecting Syntactic Features of Translated Chinese |
| Authors | Hai Hu, Wen Li, Sandra Kübler |
| Abstract | We present a machine learning approach to distinguish texts translated to Chinese (by humans) from texts originally written in Chinese, with a focus on a wide range of syntactic features. Using Support Vector Machines (SVMs) as classifier on a genre-balanced corpus in translation studies of Chinese, we find that constituent parse trees and dependency triples as features without lexical information perform very well on the task, with an F-measure above 90%, close to the results of lexical n-gram features, without the risk of learning topic information rather than translation features. Thus, we claim syntactic features alone can accurately distinguish translated from original Chinese. Translated Chinese exhibits an increased use of determiners, subject position pronouns, NP + ‘de’ as NP modifiers, multiple NPs or VPs conjoined by a Chinese specific punctuation, among other structures. We also interpret the syntactic features with reference to previous translation studies in Chinese, particularly the usage of pronouns. |
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| Published | 2018-04-23 |
| URL | http://arxiv.org/abs/1804.08756v1 |
| http://arxiv.org/pdf/1804.08756v1.pdf | |
| PWC | https://paperswithcode.com/paper/detecting-syntactic-features-of-translated |
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Estimating Failure in Brittle Materials using Graph Theory
| Title | Estimating Failure in Brittle Materials using Graph Theory |
| Authors | M. K. Mudunuru, N. Panda, S. Karra, G. Srinivasan, V. T. Chau, E. Rougier, A. Hunter, H. S. Viswanathan |
| Abstract | In brittle fracture applications, failure paths, regions where the failure occurs and damage statistics, are some of the key quantities of interest (QoI). High-fidelity models for brittle failure that accurately predict these QoI exist but are highly computationally intensive, making them infeasible to incorporate in upscaling and uncertainty quantification frameworks. The goal of this paper is to provide a fast heuristic to reasonably estimate quantities such as failure path and damage in the process of brittle failure. Towards this goal, we first present a method to predict failure paths under tensile loading conditions and low-strain rates. The method uses a $k$-nearest neighbors algorithm built on fracture process zone theory, and identifies the set of all possible pre-existing cracks that are likely to join early to form a large crack. The method then identifies zone of failure and failure paths using weighted graphs algorithms. We compare these failure paths to those computed with a high-fidelity model called the Hybrid Optimization Software Simulation Suite (HOSS). A probabilistic evolution model for average damage in a system is also developed that is trained using 150 HOSS simulations and tested on 40 simulations. A non-parametric approach based on confidence intervals is used to determine the damage evolution over time along the dominant failure path. For upscaling, damage is the key QoI needed as an input by the continuum models. This needs to be informed accurately by the surrogate models for calculating effective modulii at continuum-scale. We show that for the proposed average damage evolution model, the prediction accuracy on the test data is more than 90%. In terms of the computational time, the proposed models are $\approx \mathcal{O}(10^6)$ times faster compared to high-fidelity HOSS. |
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| Published | 2018-07-30 |
| URL | http://arxiv.org/abs/1807.11537v1 |
| http://arxiv.org/pdf/1807.11537v1.pdf | |
| PWC | https://paperswithcode.com/paper/estimating-failure-in-brittle-materials-using |
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Modeling 4D fMRI Data via Spatio-Temporal Convolutional Neural Networks (ST-CNN)
| Title | Modeling 4D fMRI Data via Spatio-Temporal Convolutional Neural Networks (ST-CNN) |
| Authors | Yu Zhao, Xiang Li, Wei Zhang, Shijie Zhao, Milad Makkie, Mo Zhang, Quanzheng Li, Tianming Liu |
| Abstract | Simultaneous modeling of the spatio-temporal variation patterns of brain functional network from 4D fMRI data has been an important yet challenging problem for the field of cognitive neuroscience and medical image analysis. Inspired by the recent success in applying deep learning for functional brain decoding and encoding, in this work we propose a spatio-temporal convolutional neural network (ST-CNN)to jointly learn the spatial and temporal patterns of targeted network from the training data and perform automatic, pin-pointing functional network identification. The proposed ST-CNN is evaluated by the task of identifying the Default Mode Network (DMN) from fMRI data. Results show that while the framework is only trained on one fMRI dataset,it has the sufficient generalizability to identify the DMN from different populations of data as well as different cognitive tasks. Further investigation into the results show that the superior performance of ST-CNN is driven by the jointly-learning scheme, which capture the intrinsic relationship between the spatial and temporal characteristic of DMN and ensures the accurate identification. |
| Tasks | Brain Decoding |
| Published | 2018-05-31 |
| URL | http://arxiv.org/abs/1805.12564v3 |
| http://arxiv.org/pdf/1805.12564v3.pdf | |
| PWC | https://paperswithcode.com/paper/modeling-4d-fmri-data-via-spatio-temporal |
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Maximizing Monotone DR-submodular Continuous Functions by Derivative-free Optimization
| Title | Maximizing Monotone DR-submodular Continuous Functions by Derivative-free Optimization |
| Authors | Yibo Zhang, Chao Qian, Ke Tang |
| Abstract | In this paper, we study the problem of monotone (weakly) DR-submodular continuous maximization. While previous methods require the gradient information of the objective function, we propose a derivative-free algorithm LDGM for the first time. We define $\beta$ and $\alpha$ to characterize how close a function is to continuous DR-submodulr and submodular, respectively. Under a convex polytope constraint, we prove that LDGM can achieve a $(1-e^{-\beta}-\epsilon)$-approximation guarantee after $O(1/\epsilon)$ iterations, which is the same as the best previous gradient-based algorithm. Moreover, in some special cases, a variant of LDGM can achieve a $((\alpha/2)(1-e^{-\alpha})-\epsilon)$-approximation guarantee for (weakly) submodular functions. We also compare LDGM with the gradient-based algorithm Frank-Wolfe under noise, and show that LDGM can be more robust. Empirical results on budget allocation verify the effectiveness of LDGM. |
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| Published | 2018-10-16 |
| URL | http://arxiv.org/abs/1810.06833v2 |
| http://arxiv.org/pdf/1810.06833v2.pdf | |
| PWC | https://paperswithcode.com/paper/maximizing-monotone-dr-submodular-continuous |
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