Paper Group ANR 586
Multi-Planar Deep Segmentation Networks for Cardiac Substructures from MRI and CT. Reinforcement learning techniques for Outer Loop Link Adaptation in 4G/5G systems. SARAH: A Novel Method for Machine Learning Problems Using Stochastic Recursive Gradient. On the Fine-Grained Complexity of Empirical Risk Minimization: Kernel Methods and Neural Networ …
Multi-Planar Deep Segmentation Networks for Cardiac Substructures from MRI and CT
| Title | Multi-Planar Deep Segmentation Networks for Cardiac Substructures from MRI and CT |
| Authors | Aliasghar Mortazi, Jeremy Burt, Ulas Bagci |
| Abstract | Non-invasive detection of cardiovascular disorders from radiology scans requires quantitative image analysis of the heart and its substructures. There are well-established measurements that radiologists use for diseases assessment such as ejection fraction, volume of four chambers, and myocardium mass. These measurements are derived as outcomes of precise segmentation of the heart and its substructures. The aim of this paper is to provide such measurements through an accurate image segmentation algorithm that automatically delineates seven substructures of the heart from MRI and/or CT scans. Our proposed method is based on multi-planar deep convolutional neural networks (CNN) with an adaptive fusion strategy where we automatically utilize complementary information from different planes of the 3D scans for improved delineations. For CT and MRI, we have separately designed three CNNs (the same architectural configuration) for three planes, and have trained the networks from scratch for voxel-wise labeling for the following cardiac structures: myocardium of left ventricle (Myo), left atrium (LA), left ventricle (LV), right atrium (RA), right ventricle (RV), ascending aorta (Ao), and main pulmonary artery (PA). We have evaluated the proposed method with 4-fold-cross validation on the multi-modality whole heart segmentation challenge (MM-WHS 2017) dataset. The precision and dice index of 0.93 and 0.90, and 0.87 and 0.85 were achieved for CT and MR images, respectively. While a CT volume was segmented about 50 seconds, an MRI scan was segmented around 17 seconds with the GPUs/CUDA implementation. |
| Tasks | Semantic Segmentation |
| Published | 2017-08-03 |
| URL | http://arxiv.org/abs/1708.00983v1 |
| http://arxiv.org/pdf/1708.00983v1.pdf | |
| PWC | https://paperswithcode.com/paper/multi-planar-deep-segmentation-networks-for |
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Reinforcement learning techniques for Outer Loop Link Adaptation in 4G/5G systems
| Title | Reinforcement learning techniques for Outer Loop Link Adaptation in 4G/5G systems |
| Authors | Saishankar Katri Pulliyakode, Sheetal Kalyani |
| Abstract | Wireless systems perform rate adaptation to transmit at highest possible instantaneous rates. Rate adaptation has been increasingly granular over generations of wireless systems. The base-station uses SINR and packet decode feedback called acknowledgement/no acknowledgement (ACK/NACK) to perform rate adaptation. SINR is used for rate anchoring called inner look adaptation and ACK/NACK is used for fine offset adjustments called Outer Loop Link Adaptation (OLLA). We cast the OLLA as a reinforcement learning problem of the class of Multi-Armed Bandits (MAB) where the different offset values are the arms of the bandit. In OLLA, as the offset values increase, the probability of packet error also increase, and every user equipment (UE) has a desired Block Error Rate (BLER) to meet certain Quality of Service (QoS) requirements. For this MAB we propose a binary search based algorithm which achieves a Probably Approximately Correct (PAC) solution making use of bounds from large deviation theory and confidence bounds. In addition to this we also discuss how a Thompson sampling or UCB based method will not help us meet the target objectives. Finally, simulation results are provided on an LTE system simulator and thereby prove the efficacy of our proposed algorithm. |
| Tasks | Multi-Armed Bandits |
| Published | 2017-08-03 |
| URL | http://arxiv.org/abs/1708.00994v1 |
| http://arxiv.org/pdf/1708.00994v1.pdf | |
| PWC | https://paperswithcode.com/paper/reinforcement-learning-techniques-for-outer |
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SARAH: A Novel Method for Machine Learning Problems Using Stochastic Recursive Gradient
| Title | SARAH: A Novel Method for Machine Learning Problems Using Stochastic Recursive Gradient |
| Authors | Lam M. Nguyen, Jie Liu, Katya Scheinberg, Martin Takáč |
| Abstract | In this paper, we propose a StochAstic Recursive grAdient algoritHm (SARAH), as well as its practical variant SARAH+, as a novel approach to the finite-sum minimization problems. Different from the vanilla SGD and other modern stochastic methods such as SVRG, S2GD, SAG and SAGA, SARAH admits a simple recursive framework for updating stochastic gradient estimates; when comparing to SAG/SAGA, SARAH does not require a storage of past gradients. The linear convergence rate of SARAH is proven under strong convexity assumption. We also prove a linear convergence rate (in the strongly convex case) for an inner loop of SARAH, the property that SVRG does not possess. Numerical experiments demonstrate the efficiency of our algorithm. |
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| Published | 2017-03-01 |
| URL | http://arxiv.org/abs/1703.00102v2 |
| http://arxiv.org/pdf/1703.00102v2.pdf | |
| PWC | https://paperswithcode.com/paper/sarah-a-novel-method-for-machine-learning |
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On the Fine-Grained Complexity of Empirical Risk Minimization: Kernel Methods and Neural Networks
| Title | On the Fine-Grained Complexity of Empirical Risk Minimization: Kernel Methods and Neural Networks |
| Authors | Arturs Backurs, Piotr Indyk, Ludwig Schmidt |
| Abstract | Empirical risk minimization (ERM) is ubiquitous in machine learning and underlies most supervised learning methods. While there has been a large body of work on algorithms for various ERM problems, the exact computational complexity of ERM is still not understood. We address this issue for multiple popular ERM problems including kernel SVMs, kernel ridge regression, and training the final layer of a neural network. In particular, we give conditional hardness results for these problems based on complexity-theoretic assumptions such as the Strong Exponential Time Hypothesis. Under these assumptions, we show that there are no algorithms that solve the aforementioned ERM problems to high accuracy in sub-quadratic time. We also give similar hardness results for computing the gradient of the empirical loss, which is the main computational burden in many non-convex learning tasks. |
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| Published | 2017-04-10 |
| URL | http://arxiv.org/abs/1704.02958v1 |
| http://arxiv.org/pdf/1704.02958v1.pdf | |
| PWC | https://paperswithcode.com/paper/on-the-fine-grained-complexity-of-empirical |
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Redefinition of the concept of fuzzy set based on vague partition from the perspective of axiomatization
| Title | Redefinition of the concept of fuzzy set based on vague partition from the perspective of axiomatization |
| Authors | Xiaodong Pan, Yang Xu |
| Abstract | Based on the in-depth analysis of the essence and features of vague phenomena, this paper focuses on establishing the axiomatical foundation of membership degree theory for vague phenomena, presents an axiomatic system to govern membership degrees and their interconnections. On this basis, the concept of vague partition is introduced, further, the concept of fuzzy set introduced by Zadeh in 1965 is redefined based on vague partition from the perspective of axiomatization. The thesis defended in this paper is that the relationship among vague attribute values should be the starting point to recognize and model vague phenomena from a quantitative view. |
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| Published | 2017-01-27 |
| URL | http://arxiv.org/abs/1701.08665v1 |
| http://arxiv.org/pdf/1701.08665v1.pdf | |
| PWC | https://paperswithcode.com/paper/redefinition-of-the-concept-of-fuzzy-set |
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MS and PAN image fusion by combining Brovey and wavelet methods
| Title | MS and PAN image fusion by combining Brovey and wavelet methods |
| Authors | Hamid Reza Shahdoosti |
| Abstract | Among the existing fusion algorithms, the wavelet fusion method is the most frequently discussed one in recent publications because the wavelet approach preserves the spectral characteristics of the multispectral image better than other methods. The Brovey is also a popular fusion method used for its ability in preserving the spatial information of the PAN image. This study presents a new fusion approach that integrates the advantages of both the Brovey (which preserves a high degree of spatial information) and the wavelet (which preserves a high degree of spectral information) techniques to reduce the colour distortion of fusion results. Visual and statistical analyzes show that the proposed algorithm clearly improves the merging quality in terms of: correlation coefficient and UIQI; compared to fusion methods including, IHS, Brovey, PCA , HPF, discrete wavelet transform (DWT), and a-trous wavelet. |
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| Published | 2017-01-08 |
| URL | http://arxiv.org/abs/1701.01996v1 |
| http://arxiv.org/pdf/1701.01996v1.pdf | |
| PWC | https://paperswithcode.com/paper/ms-and-pan-image-fusion-by-combining-brovey |
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Stock-out Prediction in Multi-echelon Networks
| Title | Stock-out Prediction in Multi-echelon Networks |
| Authors | Afshin Oroojlooyjadid, Lawrence Snyder, Martin Takáč |
| Abstract | In multi-echelon inventory systems the performance of a given node is affected by events that occur at many other nodes and in many other time periods. For example, a supply disruption upstream will have an effect on downstream, customer-facing nodes several periods later as the disruption “cascades” through the system. There is very little research on stock-out prediction in single-echelon systems and (to the best of our knowledge) none on multi-echelon systems. However, in real the world, it is clear that there is significant interest in techniques for this sort of stock-out prediction. Therefore, our research aims to fill this gap by using deep neural networks (DNN) to predict stock-outs in multi-echelon supply chains. |
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| Published | 2017-09-20 |
| URL | http://arxiv.org/abs/1709.06922v2 |
| http://arxiv.org/pdf/1709.06922v2.pdf | |
| PWC | https://paperswithcode.com/paper/stock-out-prediction-in-multi-echelon |
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On Finding Maximum Cardinality Subset of Vectors with a Constraint on Normalized Squared Length of Vectors Sum
| Title | On Finding Maximum Cardinality Subset of Vectors with a Constraint on Normalized Squared Length of Vectors Sum |
| Authors | Anton V. Eremeev, Alexander V. Kelmanov, Artem V. Pyatkin, Igor A. Ziegler |
| Abstract | In this paper, we consider the problem of finding a maximum cardinality subset of vectors, given a constraint on the normalized squared length of vectors sum. This problem is closely related to Problem 1 from (Eremeev, Kel’manov, Pyatkin, 2016). The main difference consists in swapping the constraint with the optimization criterion. We prove that the problem is NP-hard even in terms of finding a feasible solution. An exact algorithm for solving this problem is proposed. The algorithm has a pseudo-polynomial time complexity in the special case of the problem, where the dimension of the space is bounded from above by a constant and the input data are integer. A computational experiment is carried out, where the proposed algorithm is compared to COINBONMIN solver, applied to a mixed integer quadratic programming formulation of the problem. The results of the experiment indicate superiority of the proposed algorithm when the dimension of Euclidean space is low, while the COINBONMIN has an advantage for larger dimensions. |
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| Published | 2017-07-19 |
| URL | http://arxiv.org/abs/1707.06068v1 |
| http://arxiv.org/pdf/1707.06068v1.pdf | |
| PWC | https://paperswithcode.com/paper/on-finding-maximum-cardinality-subset-of |
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Guiding human gaze with convolutional neural networks
| Title | Guiding human gaze with convolutional neural networks |
| Authors | Leon A. Gatys, Matthias Kümmerer, Thomas S. A. Wallis, Matthias Bethge |
| Abstract | The eye fixation patterns of human observers are a fundamental indicator of the aspects of an image to which humans attend. Thus, manipulating fixation patterns to guide human attention is an exciting challenge in digital image processing. Here, we present a new model for manipulating images to change the distribution of human fixations in a controlled fashion. We use the state-of-the-art model for fixation prediction to train a convolutional neural network to transform images so that they satisfy a given fixation distribution. For network training, we carefully design a loss function to achieve a perceptual effect while preserving naturalness of the transformed images. Finally, we evaluate the success of our model by measuring human fixations for a set of manipulated images. On our test images we can in-/decrease the probability to fixate on selected objects on average by 43/22% but show that the effectiveness of the model depends on the semantic content of the manipulated images. |
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| Published | 2017-12-18 |
| URL | http://arxiv.org/abs/1712.06492v1 |
| http://arxiv.org/pdf/1712.06492v1.pdf | |
| PWC | https://paperswithcode.com/paper/guiding-human-gaze-with-convolutional-neural |
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Large-Scale Quadratically Constrained Quadratic Program via Low-Discrepancy Sequences
| Title | Large-Scale Quadratically Constrained Quadratic Program via Low-Discrepancy Sequences |
| Authors | Kinjal Basu, Ankan Saha, Shaunak Chatterjee |
| Abstract | We consider the problem of solving a large-scale Quadratically Constrained Quadratic Program. Such problems occur naturally in many scientific and web applications. Although there are efficient methods which tackle this problem, they are mostly not scalable. In this paper, we develop a method that transforms the quadratic constraint into a linear form by sampling a set of low-discrepancy points. The transformed problem can then be solved by applying any state-of-the-art large-scale quadratic programming solvers. We show the convergence of our approximate solution to the true solution as well as some finite sample error bounds. Experimental results are also shown to prove scalability as well as improved quality of approximation in practice. |
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| Published | 2017-10-02 |
| URL | http://arxiv.org/abs/1710.01163v1 |
| http://arxiv.org/pdf/1710.01163v1.pdf | |
| PWC | https://paperswithcode.com/paper/large-scale-quadratically-constrained |
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Syntactic and Semantic Features For Code-Switching Factored Language Models
| Title | Syntactic and Semantic Features For Code-Switching Factored Language Models |
| Authors | Heike Adel, Ngoc Thang Vu, Katrin Kirchhoff, Dominic Telaar, Tanja Schultz |
| Abstract | This paper presents our latest investigations on different features for factored language models for Code-Switching speech and their effect on automatic speech recognition (ASR) performance. We focus on syntactic and semantic features which can be extracted from Code-Switching text data and integrate them into factored language models. Different possible factors, such as words, part-of-speech tags, Brown word clusters, open class words and clusters of open class word embeddings are explored. The experimental results reveal that Brown word clusters, part-of-speech tags and open-class words are the most effective at reducing the perplexity of factored language models on the Mandarin-English Code-Switching corpus SEAME. In ASR experiments, the model containing Brown word clusters and part-of-speech tags and the model also including clusters of open class word embeddings yield the best mixed error rate results. In summary, the best language model can significantly reduce the perplexity on the SEAME evaluation set by up to 10.8% relative and the mixed error rate by up to 3.4% relative. |
| Tasks | Language Modelling, Speech Recognition, Word Embeddings |
| Published | 2017-10-04 |
| URL | http://arxiv.org/abs/1710.01809v1 |
| http://arxiv.org/pdf/1710.01809v1.pdf | |
| PWC | https://paperswithcode.com/paper/syntactic-and-semantic-features-for-code |
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Optimal DNN Primitive Selection with Partitioned Boolean Quadratic Programming
| Title | Optimal DNN Primitive Selection with Partitioned Boolean Quadratic Programming |
| Authors | Andrew Anderson, David Gregg |
| Abstract | Deep Neural Networks (DNNs) require very large amounts of computation both for training and for inference when deployed in the field. Many different algorithms have been proposed to implement the most computationally expensive layers of DNNs. Further, each of these algorithms has a large number of variants, which offer different trade-offs of parallelism, data locality, memory footprint, and execution time. In addition, specific algorithms operate much more efficiently on specialized data layouts and formats. We state the problem of optimal primitive selection in the presence of data format transformations, and show that it is NP-hard by demonstrating an embedding in the Partitioned Boolean Quadratic Assignment problem (PBQP). We propose an analytic solution via a PBQP solver, and evaluate our approach experimentally by optimizing several popular DNNs using a library of more than 70 DNN primitives, on an embedded platform and a general purpose platform. We show experimentally that significant gains are possible versus the state of the art vendor libraries by using a principled analytic solution to the problem of layout selection in the presence of data format transformations. |
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| Published | 2017-10-03 |
| URL | http://arxiv.org/abs/1710.01079v2 |
| http://arxiv.org/pdf/1710.01079v2.pdf | |
| PWC | https://paperswithcode.com/paper/optimal-dnn-primitive-selection-with |
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Stochastic Backward Euler: An Implicit Gradient Descent Algorithm for $k$-means Clustering
| Title | Stochastic Backward Euler: An Implicit Gradient Descent Algorithm for $k$-means Clustering |
| Authors | Penghang Yin, Minh Pham, Adam Oberman, Stanley Osher |
| Abstract | In this paper, we propose an implicit gradient descent algorithm for the classic $k$-means problem. The implicit gradient step or backward Euler is solved via stochastic fixed-point iteration, in which we randomly sample a mini-batch gradient in every iteration. It is the average of the fixed-point trajectory that is carried over to the next gradient step. We draw connections between the proposed stochastic backward Euler and the recent entropy stochastic gradient descent (Entropy-SGD) for improving the training of deep neural networks. Numerical experiments on various synthetic and real datasets show that the proposed algorithm provides better clustering results compared to $k$-means algorithms in the sense that it decreased the objective function (the cluster) and is much more robust to initialization. |
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| Published | 2017-10-21 |
| URL | http://arxiv.org/abs/1710.07746v3 |
| http://arxiv.org/pdf/1710.07746v3.pdf | |
| PWC | https://paperswithcode.com/paper/stochastic-backward-euler-an-implicit |
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Exploiting Domain Knowledge via Grouped Weight Sharing with Application to Text Categorization
| Title | Exploiting Domain Knowledge via Grouped Weight Sharing with Application to Text Categorization |
| Authors | Ye Zhang, Matthew Lease, Byron C. Wallace |
| Abstract | A fundamental advantage of neural models for NLP is their ability to learn representations from scratch. However, in practice this often means ignoring existing external linguistic resources, e.g., WordNet or domain specific ontologies such as the Unified Medical Language System (UMLS). We propose a general, novel method for exploiting such resources via weight sharing. Prior work on weight sharing in neural networks has considered it largely as a means of model compression. In contrast, we treat weight sharing as a flexible mechanism for incorporating prior knowledge into neural models. We show that this approach consistently yields improved performance on classification tasks compared to baseline strategies that do not exploit weight sharing. |
| Tasks | Model Compression, Text Categorization |
| Published | 2017-02-08 |
| URL | http://arxiv.org/abs/1702.02535v3 |
| http://arxiv.org/pdf/1702.02535v3.pdf | |
| PWC | https://paperswithcode.com/paper/exploiting-domain-knowledge-via-grouped |
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Online Learning for Changing Environments using Coin Betting
| Title | Online Learning for Changing Environments using Coin Betting |
| Authors | Kwang-Sung Jun, Francesco Orabona, Stephen Wright, Rebecca Willett |
| Abstract | A key challenge in online learning is that classical algorithms can be slow to adapt to changing environments. Recent studies have proposed “meta” algorithms that convert any online learning algorithm to one that is adaptive to changing environments, where the adaptivity is analyzed in a quantity called the strongly-adaptive regret. This paper describes a new meta algorithm that has a strongly-adaptive regret bound that is a factor of $\sqrt{\log(T)}$ better than other algorithms with the same time complexity, where $T$ is the time horizon. We also extend our algorithm to achieve a first-order (i.e., dependent on the observed losses) strongly-adaptive regret bound for the first time, to our knowledge. At its heart is a new parameter-free algorithm for the learning with expert advice (LEA) problem in which experts sometimes do not output advice for consecutive time steps (i.e., \emph{sleeping} experts). This algorithm is derived by a reduction from optimal algorithms for the so-called coin betting problem. Empirical results show that our algorithm outperforms state-of-the-art methods in both learning with expert advice and metric learning scenarios. |
| Tasks | Metric Learning |
| Published | 2017-11-06 |
| URL | http://arxiv.org/abs/1711.02545v1 |
| http://arxiv.org/pdf/1711.02545v1.pdf | |
| PWC | https://paperswithcode.com/paper/online-learning-for-changing-environments |
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