categories
string
doi
string
id
string
year
float64
venue
string
link
string
updated
string
published
string
title
string
abstract
string
authors
list
cs.CR cs.LG
10.1109/AINA.2013.88
1608.00848
null
null
http://arxiv.org/abs/1608.00848v1
2016-08-02T14:48:49Z
2016-08-02T14:48:49Z
A New Android Malware Detection Approach Using Bayesian Classification
Mobile malware has been growing in scale and complexity as smartphone usage continues to rise. Android has surpassed other mobile platforms as the most popular whilst also witnessing a dramatic increase in malware targeting the platform. A worrying trend that is emerging is the increasing sophistication of Android malware to evade detection by traditional signature-based scanners. As such, Android app marketplaces remain at risk of hosting malicious apps that could evade detection before being downloaded by unsuspecting users. Hence, in this paper we present an effective approach to alleviate this problem based on Bayesian classification models obtained from static code analysis. The models are built from a collection of code and app characteristics that provide indicators of potential malicious activities. The models are evaluated with real malware samples in the wild and results of experiments are presented to demonstrate the effectiveness of the proposed approach.
[ "Suleiman Y. Yerima, Sakir Sezer, Gavin McWilliams, Igor Muttik", "['Suleiman Y. Yerima' 'Sakir Sezer' 'Gavin McWilliams' 'Igor Muttik']" ]
cs.CV cs.LG
null
1608.00853
null
null
http://arxiv.org/pdf/1608.00853v1
2016-08-02T14:57:18Z
2016-08-02T14:57:18Z
A study of the effect of JPG compression on adversarial images
Neural network image classifiers are known to be vulnerable to adversarial images, i.e., natural images which have been modified by an adversarial perturbation specifically designed to be imperceptible to humans yet fool the classifier. Not only can adversarial images be generated easily, but these images will often be adversarial for networks trained on disjoint subsets of data or with different architectures. Adversarial images represent a potential security risk as well as a serious machine learning challenge---it is clear that vulnerable neural networks perceive images very differently from humans. Noting that virtually every image classification data set is composed of JPG images, we evaluate the effect of JPG compression on the classification of adversarial images. For Fast-Gradient-Sign perturbations of small magnitude, we found that JPG compression often reverses the drop in classification accuracy to a large extent, but not always. As the magnitude of the perturbations increases, JPG recompression alone is insufficient to reverse the effect.
[ "['Gintare Karolina Dziugaite' 'Zoubin Ghahramani' 'Daniel M. Roy']", "Gintare Karolina Dziugaite, Zoubin Ghahramani, Daniel M. Roy" ]
cs.LG stat.ML
null
1608.0086
null
null
null
null
null
Hierarchically Compositional Kernels for Scalable Nonparametric Learning
We propose a novel class of kernels to alleviate the high computational cost of large-scale nonparametric learning with kernel methods. The proposed kernel is defined based on a hierarchical partitioning of the underlying data domain, where the Nystr\"om method (a globally low-rank approximation) is married with a locally lossless approximation in a hierarchical fashion. The kernel maintains (strict) positive-definiteness. The corresponding kernel matrix admits a recursively off-diagonal low-rank structure, which allows for fast linear algebra computations. Suppressing the factor of data dimension, the memory and arithmetic complexities for training a regression or a classifier are reduced from $O(n^2)$ and $O(n^3)$ to $O(nr)$ and $O(nr^2)$, respectively, where $n$ is the number of training examples and $r$ is the rank on each level of the hierarchy. Although other randomized approximate kernels entail a similar complexity, empirical results show that the proposed kernel achieves a matching performance with a smaller $r$. We demonstrate comprehensive experiments to show the effective use of the proposed kernel on data sizes up to the order of millions.
[ "Jie Chen, Haim Avron, Vikas Sindhwani" ]
null
null
1608.00860
null
null
http://arxiv.org/pdf/1608.00860v2
2017-08-14T15:11:25Z
2016-08-02T15:07:25Z
Hierarchically Compositional Kernels for Scalable Nonparametric Learning
We propose a novel class of kernels to alleviate the high computational cost of large-scale nonparametric learning with kernel methods. The proposed kernel is defined based on a hierarchical partitioning of the underlying data domain, where the Nystr"om method (a globally low-rank approximation) is married with a locally lossless approximation in a hierarchical fashion. The kernel maintains (strict) positive-definiteness. The corresponding kernel matrix admits a recursively off-diagonal low-rank structure, which allows for fast linear algebra computations. Suppressing the factor of data dimension, the memory and arithmetic complexities for training a regression or a classifier are reduced from $O(n^2)$ and $O(n^3)$ to $O(nr)$ and $O(nr^2)$, respectively, where $n$ is the number of training examples and $r$ is the rank on each level of the hierarchy. Although other randomized approximate kernels entail a similar complexity, empirical results show that the proposed kernel achieves a matching performance with a smaller $r$. We demonstrate comprehensive experiments to show the effective use of the proposed kernel on data sizes up to the order of millions.
[ "['Jie Chen' 'Haim Avron' 'Vikas Sindhwani']" ]
cs.CR cs.LG
10.1145/2811411.2811514
1608.00866
null
null
http://arxiv.org/abs/1608.00866v1
2016-08-02T15:26:41Z
2016-08-02T15:26:41Z
PageRank in Malware Categorization
In this paper, we propose a malware categorization method that models malware behavior in terms of instructions using PageRank. PageRank computes ranks of web pages based on structural information and can also compute ranks of instructions that represent the structural information of the instructions in malware analysis methods. Our malware categorization method uses the computed ranks as features in machine learning algorithms. In the evaluation, we compare the effectiveness of different PageRank algorithms and also investigate bagging and boosting algorithms to improve the categorization accuracy.
[ "['BooJoong Kang' 'Suleiman Y. Yerima' 'Kieran McLaughlin' 'Sakir Sezer']", "BooJoong Kang, Suleiman Y. Yerima, Kieran McLaughlin, Sakir Sezer" ]
stat.ML cs.AI cs.LG
null
1608.00876
null
null
http://arxiv.org/pdf/1608.00876v1
2016-08-02T15:48:58Z
2016-08-02T15:48:58Z
Relational Similarity Machines
This paper proposes Relational Similarity Machines (RSM): a fast, accurate, and flexible relational learning framework for supervised and semi-supervised learning tasks. Despite the importance of relational learning, most existing methods are hard to adapt to different settings, due to issues with efficiency, scalability, accuracy, and flexibility for handling a wide variety of classification problems, data, constraints, and tasks. For instance, many existing methods perform poorly for multi-class classification problems, graphs that are sparsely labeled or network data with low relational autocorrelation. In contrast, the proposed relational learning framework is designed to be (i) fast for learning and inference at real-time interactive rates, and (ii) flexible for a variety of learning settings (multi-class problems), constraints (few labeled instances), and application domains. The experiments demonstrate the effectiveness of RSM for a variety of tasks and data.
[ "Ryan A. Rossi, Rong Zhou, Nesreen K. Ahmed", "['Ryan A. Rossi' 'Rong Zhou' 'Nesreen K. Ahmed']" ]
cs.LG cs.CL cs.NE
null
1608.00895
null
null
http://arxiv.org/pdf/1608.00895v2
2017-01-10T14:25:28Z
2016-08-02T16:43:27Z
RETURNN: The RWTH Extensible Training framework for Universal Recurrent Neural Networks
In this work we release our extensible and easily configurable neural network training software. It provides a rich set of functional layers with a particular focus on efficient training of recurrent neural network topologies on multiple GPUs. The source of the software package is public and freely available for academic research purposes and can be used as a framework or as a standalone tool which supports a flexible configuration. The software allows to train state-of-the-art deep bidirectional long short-term memory (LSTM) models on both one dimensional data like speech or two dimensional data like handwritten text and was used to develop successful submission systems in several evaluation campaigns.
[ "['Patrick Doetsch' 'Albert Zeyer' 'Paul Voigtlaender' 'Ilya Kulikov'\n 'Ralf Schlüter' 'Hermann Ney']", "Patrick Doetsch, Albert Zeyer, Paul Voigtlaender, Ilya Kulikov, Ralf\n Schl\\\"uter, Hermann Ney" ]
cs.SI cs.LG physics.soc-ph
10.7566/JPSJ.85.114802
1608.0092
null
null
null
null
null
Community Detection Algorithm Combining Stochastic Block Model and Attribute Data Clustering
We propose a new algorithm to detect the community structure in a network that utilizes both the network structure and vertex attribute data. Suppose we have the network structure together with the vertex attribute data, that is, the information assigned to each vertex associated with the community to which it belongs. The problem addressed this paper is the detection of the community structure from the information of both the network structure and the vertex attribute data. Our approach is based on the Bayesian approach that models the posterior probability distribution of the community labels. The detection of the community structure in our method is achieved by using belief propagation and an EM algorithm. We numerically verified the performance of our method using computer-generated networks and real-world networks.
[ "Shun Kataoka, Takuto Kobayashi, Muneki Yasuda, and Kazuyuki Tanaka" ]
null
null
1608.00920
null
null
http://arxiv.org/abs/1608.00920v1
2016-07-21T10:21:08Z
2016-07-21T10:21:08Z
Community Detection Algorithm Combining Stochastic Block Model and Attribute Data Clustering
We propose a new algorithm to detect the community structure in a network that utilizes both the network structure and vertex attribute data. Suppose we have the network structure together with the vertex attribute data, that is, the information assigned to each vertex associated with the community to which it belongs. The problem addressed this paper is the detection of the community structure from the information of both the network structure and the vertex attribute data. Our approach is based on the Bayesian approach that models the posterior probability distribution of the community labels. The detection of the community structure in our method is achieved by using belief propagation and an EM algorithm. We numerically verified the performance of our method using computer-generated networks and real-world networks.
[ "['Shun Kataoka' 'Takuto Kobayashi' 'Muneki Yasuda' 'Kazuyuki Tanaka']" ]
cs.LG
null
1608.01072
null
null
http://arxiv.org/pdf/1608.01072v1
2016-08-03T04:42:02Z
2016-08-03T04:42:02Z
Fuzzy c-Shape: A new algorithm for clustering finite time series waveforms
The existence of large volumes of time series data in many applications has motivated data miners to investigate specialized methods for mining time series data. Clustering is a popular data mining method due to its powerful exploratory nature and its usefulness as a preprocessing step for other data mining techniques. This article develops two novel clustering algorithms for time series data that are extensions of a crisp c-shapes algorithm. The two new algorithms are heuristic derivatives of fuzzy c-means (FCM). Fuzzy c-Shapes plus (FCS+) replaces the inner product norm in the FCM model with a shape-based distance function. Fuzzy c-Shapes double plus (FCS++) uses the shape-based distance, and also replaces the FCM cluster centers with shape-extracted prototypes. Numerical experiments on 48 real time series data sets show that the two new algorithms outperform state-of-the-art shape-based clustering algorithms in terms of accuracy and efficiency. Four external cluster validity indices (the Rand index, Adjusted Rand Index, Variation of Information, and Normalized Mutual Information) are used to match candidate partitions generated by each of the studied algorithms. All four indices agree that for these finite waveform data sets, FCS++ gives a small improvement over FCS+, and in turn, FCS+ is better than the original crisp c-shapes method. Finally, we apply two tests of statistical significance to the three algorithms. The Wilcoxon and Friedman statistics both rank the three algorithms in exactly the same way as the four cluster validity indices.
[ "Fateme Fahiman, Jame C.Bezdek, Sarah M.Erfani, Christopher Leckie,\n Marimuthu Palaniswami", "['Fateme Fahiman' 'Jame C. Bezdek' 'Sarah M. Erfani' 'Christopher Leckie'\n 'Marimuthu Palaniswami']" ]
cs.RO cs.AI cs.CV cs.LG
null
1608.01127
null
null
http://arxiv.org/pdf/1608.01127v1
2016-08-03T09:25:35Z
2016-08-03T09:25:35Z
Autonomous Grounding of Visual Field Experience through Sensorimotor Prediction
In a developmental framework, autonomous robots need to explore the world and learn how to interact with it. Without an a priori model of the system, this opens the challenging problem of having robots master their interface with the world: how to perceive their environment using their sensors, and how to act in it using their motors. The sensorimotor approach of perception claims that a naive agent can learn to master this interface by capturing regularities in the way its actions transform its sensory inputs. In this paper, we apply such an approach to the discovery and mastery of the visual field associated with a visual sensor. A computational model is formalized and applied to a simulated system to illustrate the approach.
[ "Alban Laflaqui\\`ere", "['Alban Laflaquière']" ]
cs.LG
null
1608.01198
null
null
http://arxiv.org/pdf/1608.01198v2
2016-08-09T15:28:15Z
2016-08-03T14:19:00Z
Ensemble-driven support vector clustering: From ensemble learning to automatic parameter estimation
Support vector clustering (SVC) is a versatile clustering technique that is able to identify clusters of arbitrary shapes by exploiting the kernel trick. However, one hurdle that restricts the application of SVC lies in its sensitivity to the kernel parameter and the trade-off parameter. Although many extensions of SVC have been developed, to the best of our knowledge, there is still no algorithm that is able to effectively estimate the two crucial parameters in SVC without supervision. In this paper, we propose a novel support vector clustering approach termed ensemble-driven support vector clustering (EDSVC), which for the first time tackles the automatic parameter estimation problem for SVC based on ensemble learning, and is capable of producing robust clustering results in a purely unsupervised manner. Experimental results on multiple real-world datasets demonstrate the effectiveness of our approach.
[ "Dong Huang, Chang-Dong Wang, Jian-Huang Lai, Yun Liang, Shan Bian, Yu\n Chen", "['Dong Huang' 'Chang-Dong Wang' 'Jian-Huang Lai' 'Yun Liang' 'Shan Bian'\n 'Yu Chen']" ]
cs.LG stat.ML
null
1608.0123
null
null
null
null
null
Learning a Driving Simulator
Comma.ai's approach to Artificial Intelligence for self-driving cars is based on an agent that learns to clone driver behaviors and plans maneuvers by simulating future events in the road. This paper illustrates one of our research approaches for driving simulation. One where we learn to simulate. Here we investigate variational autoencoders with classical and learned cost functions using generative adversarial networks for embedding road frames. Afterwards, we learn a transition model in the embedded space using action conditioned Recurrent Neural Networks. We show that our approach can keep predicting realistic looking video for several frames despite the transition model being optimized without a cost function in the pixel space.
[ "Eder Santana, George Hotz" ]
null
null
1608.01230
null
null
http://arxiv.org/pdf/1608.01230v1
2016-08-03T15:49:12Z
2016-08-03T15:49:12Z
Learning a Driving Simulator
Comma.ai's approach to Artificial Intelligence for self-driving cars is based on an agent that learns to clone driver behaviors and plans maneuvers by simulating future events in the road. This paper illustrates one of our research approaches for driving simulation. One where we learn to simulate. Here we investigate variational autoencoders with classical and learned cost functions using generative adversarial networks for embedding road frames. Afterwards, we learn a transition model in the embedded space using action conditioned Recurrent Neural Networks. We show that our approach can keep predicting realistic looking video for several frames despite the transition model being optimized without a cost function in the pixel space.
[ "['Eder Santana' 'George Hotz']" ]
cs.CL cs.LG
null
1608.01238
null
null
http://arxiv.org/pdf/1608.01238v1
2016-08-03T16:12:23Z
2016-08-03T16:12:23Z
Improving Quality of Hierarchical Clustering for Large Data Series
Brown clustering is a hard, hierarchical, bottom-up clustering of words in a vocabulary. Words are assigned to clusters based on their usage pattern in a given corpus. The resulting clusters and hierarchical structure can be used in constructing class-based language models and for generating features to be used in NLP tasks. Because of its high computational cost, the most-used version of Brown clustering is a greedy algorithm that uses a window to restrict its search space. Like other clustering algorithms, Brown clustering finds a sub-optimal, but nonetheless effective, mapping of words to clusters. Because of its ability to produce high-quality, human-understandable cluster, Brown clustering has seen high uptake the NLP research community where it is used in the preprocessing and feature generation steps. Little research has been done towards improving the quality of Brown clusters, despite the greedy and heuristic nature of the algorithm. The approaches tried so far have focused on: studying the effect of the initialisation in a similar algorithm; tuning the parameters used to define the desired number of clusters and the behaviour of the algorithm; and including a separate parameter to differentiate the window from the desired number of clusters. However, some of these approaches have not yielded significant improvements in cluster quality. In this thesis, a close analysis of the Brown algorithm is provided, revealing important under-specifications and weaknesses in the original algorithm. These have serious effects on cluster quality and reproducibility of research using Brown clustering. In the second part of the thesis, two modifications are proposed. Finally, a thorough evaluation is performed, considering both the optimization criterion of Brown clustering and the performance of the resulting class-based language models.
[ "['Manuel R. Ciosici']", "Manuel R. Ciosici" ]
cs.LG math.OC stat.ML
null
1608.01264
null
null
http://arxiv.org/pdf/1608.01264v1
2016-08-03T17:33:16Z
2016-08-03T17:33:16Z
Fast and Simple Optimization for Poisson Likelihood Models
Poisson likelihood models have been prevalently used in imaging, social networks, and time series analysis. We propose fast, simple, theoretically-grounded, and versatile, optimization algorithms for Poisson likelihood modeling. The Poisson log-likelihood is concave but not Lipschitz-continuous. Since almost all gradient-based optimization algorithms rely on Lipschitz-continuity, optimizing Poisson likelihood models with a guarantee of convergence can be challenging, especially for large-scale problems. We present a new perspective allowing to efficiently optimize a wide range of penalized Poisson likelihood objectives. We show that an appropriate saddle point reformulation enjoys a favorable geometry and a smooth structure. Therefore, we can design a new gradient-based optimization algorithm with $O(1/t)$ convergence rate, in contrast to the usual $O(1/\sqrt{t})$ rate of non-smooth minimization alternatives. Furthermore, in order to tackle problems with large samples, we also develop a randomized block-decomposition variant that enjoys the same convergence rate yet more efficient iteration cost. Experimental results on several point process applications including social network estimation and temporal recommendation show that the proposed algorithm and its randomized block variant outperform existing methods both on synthetic and real-world datasets.
[ "Niao He, Zaid Harchaoui, Yichen Wang, Le Song", "['Niao He' 'Zaid Harchaoui' 'Yichen Wang' 'Le Song']" ]
cs.LG cs.CL
null
1608.01281
null
null
http://arxiv.org/pdf/1608.01281v1
2016-08-03T18:35:12Z
2016-08-03T18:35:12Z
Learning Online Alignments with Continuous Rewards Policy Gradient
Sequence-to-sequence models with soft attention had significant success in machine translation, speech recognition, and question answering. Though capable and easy to use, they require that the entirety of the input sequence is available at the beginning of inference, an assumption that is not valid for instantaneous translation and speech recognition. To address this problem, we present a new method for solving sequence-to-sequence problems using hard online alignments instead of soft offline alignments. The online alignments model is able to start producing outputs without the need to first process the entire input sequence. A highly accurate online sequence-to-sequence model is useful because it can be used to build an accurate voice-based instantaneous translator. Our model uses hard binary stochastic decisions to select the timesteps at which outputs will be produced. The model is trained to produce these stochastic decisions using a standard policy gradient method. In our experiments, we show that this model achieves encouraging performance on TIMIT and Wall Street Journal (WSJ) speech recognition datasets.
[ "['Yuping Luo' 'Chung-Cheng Chiu' 'Navdeep Jaitly' 'Ilya Sutskever']", "Yuping Luo, Chung-Cheng Chiu, Navdeep Jaitly, Ilya Sutskever" ]
cs.LG stat.ML
null
1608.0141
null
null
null
null
null
Bayesian Kernel and Mutual $k$-Nearest Neighbor Regression
We propose Bayesian extensions of two nonparametric regression methods which are kernel and mutual $k$-nearest neighbor regression methods. Derived based on Gaussian process models for regression, the extensions provide distributions for target value estimates and the framework to select the hyperparameters. It is shown that both the proposed methods asymptotically converge to kernel and mutual $k$-nearest neighbor regression methods, respectively. The simulation results show that the proposed methods can select proper hyperparameters and are better than or comparable to the former methods for an artificial data set and a real world data set.
[ "Hyun-Chul Kim" ]
null
null
1608.01410
null
null
http://arxiv.org/pdf/1608.01410v1
2016-08-04T01:33:34Z
2016-08-04T01:33:34Z
Bayesian Kernel and Mutual $k$-Nearest Neighbor Regression
We propose Bayesian extensions of two nonparametric regression methods which are kernel and mutual $k$-nearest neighbor regression methods. Derived based on Gaussian process models for regression, the extensions provide distributions for target value estimates and the framework to select the hyperparameters. It is shown that both the proposed methods asymptotically converge to kernel and mutual $k$-nearest neighbor regression methods, respectively. The simulation results show that the proposed methods can select proper hyperparameters and are better than or comparable to the former methods for an artificial data set and a real world data set.
[ "['Hyun-Chul Kim']" ]
cs.LG stat.ML
null
1608.01747
null
null
http://arxiv.org/pdf/1608.01747v1
2016-08-05T03:37:46Z
2016-08-05T03:37:46Z
A Distance for HMMs based on Aggregated Wasserstein Metric and State Registration
We propose a framework, named Aggregated Wasserstein, for computing a dissimilarity measure or distance between two Hidden Markov Models with state conditional distributions being Gaussian. For such HMMs, the marginal distribution at any time spot follows a Gaussian mixture distribution, a fact exploited to softly match, aka register, the states in two HMMs. We refer to such HMMs as Gaussian mixture model-HMM (GMM-HMM). The registration of states is inspired by the intrinsic relationship of optimal transport and the Wasserstein metric between distributions. Specifically, the components of the marginal GMMs are matched by solving an optimal transport problem where the cost between components is the Wasserstein metric for Gaussian distributions. The solution of the optimization problem is a fast approximation to the Wasserstein metric between two GMMs. The new Aggregated Wasserstein distance is a semi-metric and can be computed without generating Monte Carlo samples. It is invariant to relabeling or permutation of the states. This distance quantifies the dissimilarity of GMM-HMMs by measuring both the difference between the two marginal GMMs and the difference between the two transition matrices. Our new distance is tested on the tasks of retrieval and classification of time series. Experiments on both synthetic data and real data have demonstrated its advantages in terms of accuracy as well as efficiency in comparison with existing distances based on the Kullback-Leibler divergence.
[ "['Yukun Chen' 'Jianbo Ye' 'Jia Li']", "Yukun Chen, Jianbo Ye, and Jia Li" ]
cs.LG
null
1608.01874
null
null
http://arxiv.org/pdf/1608.01874v1
2016-08-05T13:19:47Z
2016-08-05T13:19:47Z
Forward Stagewise Additive Model for Collaborative Multiview Boosting
Multiview assisted learning has gained significant attention in recent years in supervised learning genre. Availability of high performance computing devices enables learning algorithms to search simultaneously over multiple views or feature spaces to obtain an optimum classification performance. The paper is a pioneering attempt of formulating a mathematical foundation for realizing a multiview aided collaborative boosting architecture for multiclass classification. Most of the present algorithms apply multiview learning heuristically without exploring the fundamental mathematical changes imposed on traditional boosting. Also, most of the algorithms are restricted to two class or view setting. Our proposed mathematical framework enables collaborative boosting across any finite dimensional view spaces for multiclass learning. The boosting framework is based on forward stagewise additive model which minimizes a novel exponential loss function. We show that the exponential loss function essentially captures difficulty of a training sample space instead of the traditional `1/0' loss. The new algorithm restricts a weak view from over learning and thereby preventing overfitting. The model is inspired by our earlier attempt on collaborative boosting which was devoid of mathematical justification. The proposed algorithm is shown to converge much nearer to global minimum in the exponential loss space and thus supersedes our previous algorithm. The paper also presents analytical and numerical analysis of convergence and margin bounds for multiview boosting algorithms and we show that our proposed ensemble learning manifests lower error bound and higher margin compared to our previous model. Also, the proposed model is compared with traditional boosting and recent multiview boosting algorithms.
[ "Avisek Lahiri, Biswajit Paria, Prabir Kumar Biswas", "['Avisek Lahiri' 'Biswajit Paria' 'Prabir Kumar Biswas']" ]
stat.ML cs.LG
null
1608.01976
null
null
http://arxiv.org/pdf/1608.01976v1
2016-08-05T19:02:19Z
2016-08-05T19:02:19Z
Kernel Ridge Regression via Partitioning
In this paper, we investigate a divide and conquer approach to Kernel Ridge Regression (KRR). Given n samples, the division step involves separating the points based on some underlying disjoint partition of the input space (possibly via clustering), and then computing a KRR estimate for each partition. The conquering step is simple: for each partition, we only consider its own local estimate for prediction. We establish conditions under which we can give generalization bounds for this estimator, as well as achieve optimal minimax rates. We also show that the approximation error component of the generalization error is lesser than when a single KRR estimate is fit on the data: thus providing both statistical and computational advantages over a single KRR estimate over the entire data (or an averaging over random partitions as in other recent work, [30]). Lastly, we provide experimental validation for our proposed estimator and our assumptions.
[ "['Rashish Tandon' 'Si Si' 'Pradeep Ravikumar' 'Inderjit Dhillon']", "Rashish Tandon, Si Si, Pradeep Ravikumar, Inderjit Dhillon" ]
cs.LG
null
1608.0201
null
null
null
null
null
Communication-Efficient Parallel Block Minimization for Kernel Machines
Kernel machines often yield superior predictive performance on various tasks; however, they suffer from severe computational challenges. In this paper, we show how to overcome the important challenge of speeding up kernel machines. In particular, we develop a parallel block minimization framework for solving kernel machines, including kernel SVM and kernel logistic regression. Our framework proceeds by dividing the problem into smaller subproblems by forming a block-diagonal approximation of the Hessian matrix. The subproblems are then solved approximately in parallel. After that, a communication efficient line search procedure is developed to ensure sufficient reduction of the objective function value at each iteration. We prove global linear convergence rate of the proposed method with a wide class of subproblem solvers, and our analysis covers strongly convex and some non-strongly convex functions. We apply our algorithm to solve large-scale kernel SVM problems on distributed systems, and show a significant improvement over existing parallel solvers. As an example, on the covtype dataset with half-a-million samples, our algorithm can obtain an approximate solution with 96% accuracy in 20 seconds using 32 machines, while all the other parallel kernel SVM solvers require more than 2000 seconds to achieve a solution with 95% accuracy. Moreover, our algorithm can scale to very large data sets, such as the kdd algebra dataset with 8 million samples and 20 million features.
[ "Cho-Jui Hsieh and Si Si and Inderjit S. Dhillon" ]
null
null
1608.02010
null
null
http://arxiv.org/pdf/1608.02010v1
2016-08-05T20:15:51Z
2016-08-05T20:15:51Z
Communication-Efficient Parallel Block Minimization for Kernel Machines
Kernel machines often yield superior predictive performance on various tasks; however, they suffer from severe computational challenges. In this paper, we show how to overcome the important challenge of speeding up kernel machines. In particular, we develop a parallel block minimization framework for solving kernel machines, including kernel SVM and kernel logistic regression. Our framework proceeds by dividing the problem into smaller subproblems by forming a block-diagonal approximation of the Hessian matrix. The subproblems are then solved approximately in parallel. After that, a communication efficient line search procedure is developed to ensure sufficient reduction of the objective function value at each iteration. We prove global linear convergence rate of the proposed method with a wide class of subproblem solvers, and our analysis covers strongly convex and some non-strongly convex functions. We apply our algorithm to solve large-scale kernel SVM problems on distributed systems, and show a significant improvement over existing parallel solvers. As an example, on the covtype dataset with half-a-million samples, our algorithm can obtain an approximate solution with 96% accuracy in 20 seconds using 32 machines, while all the other parallel kernel SVM solvers require more than 2000 seconds to achieve a solution with 95% accuracy. Moreover, our algorithm can scale to very large data sets, such as the kdd algebra dataset with 8 million samples and 20 million features.
[ "['Cho-Jui Hsieh' 'Si Si' 'Inderjit S. Dhillon']" ]
cs.LG cs.CL
null
1608.02071
null
null
http://arxiv.org/pdf/1608.02071v1
2016-08-06T06:24:59Z
2016-08-06T06:24:59Z
Transferring Knowledge from Text to Predict Disease Onset
In many domains such as medicine, training data is in short supply. In such cases, external knowledge is often helpful in building predictive models. We propose a novel method to incorporate publicly available domain expertise to build accurate models. Specifically, we use word2vec models trained on a domain-specific corpus to estimate the relevance of each feature's text description to the prediction problem. We use these relevance estimates to rescale the features, causing more important features to experience weaker regularization. We apply our method to predict the onset of five chronic diseases in the next five years in two genders and two age groups. Our rescaling approach improves the accuracy of the model, particularly when there are few positive examples. Furthermore, our method selects 60% fewer features, easing interpretation by physicians. Our method is applicable to other domains where feature and outcome descriptions are available.
[ "Yun Liu, Kun-Ta Chuang, Fu-Wen Liang, Huey-Jen Su, Collin M. Stultz,\n John V. Guttag", "['Yun Liu' 'Kun-Ta Chuang' 'Fu-Wen Liang' 'Huey-Jen Su' 'Collin M. Stultz'\n 'John V. Guttag']" ]
cs.CL cs.AI cs.LG
null
1608.02076
null
null
http://arxiv.org/pdf/1608.02076v2
2016-09-22T08:52:31Z
2016-08-06T07:16:31Z
Bi-directional Attention with Agreement for Dependency Parsing
We develop a novel bi-directional attention model for dependency parsing, which learns to agree on headword predictions from the forward and backward parsing directions. The parsing procedure for each direction is formulated as sequentially querying the memory component that stores continuous headword embeddings. The proposed parser makes use of {\it soft} headword embeddings, allowing the model to implicitly capture high-order parsing history without dramatically increasing the computational complexity. We conduct experiments on English, Chinese, and 12 other languages from the CoNLL 2006 shared task, showing that the proposed model achieves state-of-the-art unlabeled attachment scores on 6 languages.
[ "['Hao Cheng' 'Hao Fang' 'Xiaodong He' 'Jianfeng Gao' 'Li Deng']", "Hao Cheng and Hao Fang and Xiaodong He and Jianfeng Gao and Li Deng" ]
cs.LG
null
1608.02126
null
null
http://arxiv.org/pdf/1608.02126v1
2016-08-06T16:30:47Z
2016-08-06T16:30:47Z
How Much Did it Rain? Predicting Real Rainfall Totals Based on Radar Data
We applied a variety of parametric and non-parametric machine learning models to predict the probability distribution of rainfall based on 1M training examples over a single year across several U.S. states. Our top performing model based on a squared loss objective was a cross-validated parametric k-nearest-neighbor predictor that took about six days to compute, and was competitive in a world-wide competition.
[ "Adam Lesnikowski", "['Adam Lesnikowski']" ]
cs.CR cs.CV cs.LG
null
1608.02128
null
null
http://arxiv.org/pdf/1608.02128v1
2016-08-06T16:50:26Z
2016-08-06T16:50:26Z
Spoofing 2D Face Detection: Machines See People Who Aren't There
Machine learning is increasingly used to make sense of the physical world yet may suffer from adversarial manipulation. We examine the Viola-Jones 2D face detection algorithm to study whether images can be created that humans do not notice as faces yet the algorithm detects as faces. We show that it is possible to construct images that Viola-Jones recognizes as containing faces yet no human would consider a face. Moreover, we show that it is possible to construct images that fool facial detection even when they are printed and then photographed.
[ "Michael McCoyd and David Wagner", "['Michael McCoyd' 'David Wagner']" ]
cs.LG cs.CV
null
1608.02146
null
null
http://arxiv.org/pdf/1608.02146v2
2017-09-13T21:17:33Z
2016-08-06T19:29:58Z
Leveraging Union of Subspace Structure to Improve Constrained Clustering
Many clustering problems in computer vision and other contexts are also classification problems, where each cluster shares a meaningful label. Subspace clustering algorithms in particular are often applied to problems that fit this description, for example with face images or handwritten digits. While it is straightforward to request human input on these datasets, our goal is to reduce this input as much as possible. We present a pairwise-constrained clustering algorithm that actively selects queries based on the union-of-subspaces model. The central step of the algorithm is in querying points of minimum margin between estimated subspaces; analogous to classifier margin, these lie near the decision boundary. We prove that points lying near the intersection of subspaces are points with low margin. Our procedure can be used after any subspace clustering algorithm that outputs an affinity matrix. We demonstrate on several datasets that our algorithm drives the clustering error down considerably faster than the state-of-the-art active query algorithms on datasets with subspace structure and is competitive on other datasets.
[ "John Lipor and Laura Balzano", "['John Lipor' 'Laura Balzano']" ]
cs.LG cs.CC stat.ML
null
1608.02198
null
null
http://arxiv.org/pdf/1608.02198v3
2017-04-17T06:12:23Z
2016-08-07T09:35:44Z
A General Characterization of the Statistical Query Complexity
Statistical query (SQ) algorithms are algorithms that have access to an {\em SQ oracle} for the input distribution $D$ instead of i.i.d.~ samples from $D$. Given a query function $\phi:X \rightarrow [-1,1]$, the oracle returns an estimate of ${\bf E}_{ x\sim D}[\phi(x)]$ within some tolerance $\tau_\phi$ that roughly corresponds to the number of samples. In this work we demonstrate that the complexity of solving general problems over distributions using SQ algorithms can be captured by a relatively simple notion of statistical dimension that we introduce. SQ algorithms capture a broad spectrum of algorithmic approaches used in theory and practice, most notably, convex optimization techniques. Hence our statistical dimension allows to investigate the power of a variety of algorithmic approaches by analyzing a single linear-algebraic parameter. Such characterizations were investigated over the past 20 years in learning theory but prior characterizations are restricted to the much simpler setting of classification problems relative to a fixed distribution on the domain (Blum et al., 1994; Bshouty and Feldman, 2002; Yang, 2001; Simon, 2007; Feldman, 2012; Szorenyi, 2009). Our characterization is also the first to precisely characterize the necessary tolerance of queries. We give applications of our techniques to two open problems in learning theory and to algorithms that are subject to memory and communication constraints.
[ "['Vitaly Feldman']", "Vitaly Feldman" ]
cs.RO cs.CV cs.LG
null
1608.02239
null
null
http://arxiv.org/pdf/1608.02239v1
2016-08-07T16:30:42Z
2016-08-07T16:30:42Z
Deep Learning a Grasp Function for Grasping under Gripper Pose Uncertainty
This paper presents a new method for parallel-jaw grasping of isolated objects from depth images, under large gripper pose uncertainty. Whilst most approaches aim to predict the single best grasp pose from an image, our method first predicts a score for every possible grasp pose, which we denote the grasp function. With this, it is possible to achieve grasping robust to the gripper's pose uncertainty, by smoothing the grasp function with the pose uncertainty function. Therefore, if the single best pose is adjacent to a region of poor grasp quality, that pose will no longer be chosen, and instead a pose will be chosen which is surrounded by a region of high grasp quality. To learn this function, we train a Convolutional Neural Network which takes as input a single depth image of an object, and outputs a score for each grasp pose across the image. Training data for this is generated by use of physics simulation and depth image simulation with 3D object meshes, to enable acquisition of sufficient data without requiring exhaustive real-world experiments. We evaluate with both synthetic and real experiments, and show that the learned grasp score is more robust to gripper pose uncertainty than when this uncertainty is not accounted for.
[ "Edward Johns, Stefan Leutenegger and Andrew J. Davison", "['Edward Johns' 'Stefan Leutenegger' 'Andrew J. Davison']" ]
cs.LG cs.CR stat.ML
null
1608.02257
null
null
http://arxiv.org/pdf/1608.02257v2
2016-08-09T20:20:17Z
2016-08-07T19:03:52Z
Robust High-Dimensional Linear Regression
The effectiveness of supervised learning techniques has made them ubiquitous in research and practice. In high-dimensional settings, supervised learning commonly relies on dimensionality reduction to improve performance and identify the most important factors in predicting outcomes. However, the economic importance of learning has made it a natural target for adversarial manipulation of training data, which we term poisoning attacks. Prior approaches to dealing with robust supervised learning rely on strong assumptions about the nature of the feature matrix, such as feature independence and sub-Gaussian noise with low variance. We propose an integrated method for robust regression that relaxes these assumptions, assuming only that the feature matrix can be well approximated by a low-rank matrix. Our techniques integrate improved robust low-rank matrix approximation and robust principle component regression, and yield strong performance guarantees. Moreover, we experimentally show that our methods significantly outperform state of the art both in running time and prediction error.
[ "['Chang Liu' 'Bo Li' 'Yevgeniy Vorobeychik' 'Alina Oprea']", "Chang Liu, Bo Li, Yevgeniy Vorobeychik, Alina Oprea" ]
cs.LG cs.NE
null
1608.02292
null
null
http://arxiv.org/pdf/1608.02292v1
2016-08-08T01:10:51Z
2016-08-08T01:10:51Z
Online Adaptation of Deep Architectures with Reinforcement Learning
Online learning has become crucial to many problems in machine learning. As more data is collected sequentially, quickly adapting to changes in the data distribution can offer several competitive advantages such as avoiding loss of prior knowledge and more efficient learning. However, adaptation to changes in the data distribution (also known as covariate shift) needs to be performed without compromising past knowledge already built in into the model to cope with voluminous and dynamic data. In this paper, we propose an online stacked Denoising Autoencoder whose structure is adapted through reinforcement learning. Our algorithm forces the network to exploit and explore favourable architectures employing an estimated utility function that maximises the accuracy of an unseen validation sequence. Different actions, such as Pool, Increment and Merge are available to modify the structure of the network. As we observe through a series of experiments, our approach is more responsive, robust, and principled than its counterparts for non-stationary as well as stationary data distributions. Experimental results indicate that our algorithm performs better at preserving gained prior knowledge and responding to changes in the data distribution.
[ "['Thushan Ganegedara' 'Lionel Ott' 'Fabio Ramos']", "Thushan Ganegedara, Lionel Ott and Fabio Ramos" ]
cs.LG
null
1608.02301
null
null
http://arxiv.org/pdf/1608.02301v1
2016-08-08T02:39:42Z
2016-08-08T02:39:42Z
Uncovering Voice Misuse Using Symbolic Mismatch
Voice disorders affect an estimated 14 million working-aged Americans, and many more worldwide. We present the first large scale study of vocal misuse based on long-term ambulatory data collected by an accelerometer placed on the neck. We investigate an unsupervised data mining approach to uncovering latent information about voice misuse. We segment signals from over 253 days of data from 22 subjects into over a hundred million single glottal pulses (closures of the vocal folds), cluster segments into symbols, and use symbolic mismatch to uncover differences between patients and matched controls, and between patients pre- and post-treatment. Our results show significant behavioral differences between patients and controls, as well as between some pre- and post-treatment patients. Our proposed approach provides an objective basis for helping diagnose behavioral voice disorders, and is a first step towards a more data-driven understanding of the impact of voice therapy.
[ "Marzyeh Ghassemi, Zeeshan Syed, Daryush D. Mehta, Jarrad H. Van Stan,\n Robert E. Hillman, and John V. Guttag", "['Marzyeh Ghassemi' 'Zeeshan Syed' 'Daryush D. Mehta' 'Jarrad H. Van Stan'\n 'Robert E. Hillman' 'John V. Guttag']" ]
cs.LG cs.AI stat.ML
null
1608.02341
null
null
http://arxiv.org/pdf/1608.02341v1
2016-08-08T07:44:24Z
2016-08-08T07:44:24Z
Towards Representation Learning with Tractable Probabilistic Models
Probabilistic models learned as density estimators can be exploited in representation learning beside being toolboxes used to answer inference queries only. However, how to extract useful representations highly depends on the particular model involved. We argue that tractable inference, i.e. inference that can be computed in polynomial time, can enable general schemes to extract features from black box models. We plan to investigate how Tractable Probabilistic Models (TPMs) can be exploited to generate embeddings by random query evaluations. We devise two experimental designs to assess and compare different TPMs as feature extractors in an unsupervised representation learning framework. We show some experimental results on standard image datasets by applying such a method to Sum-Product Networks and Mixture of Trees as tractable models generating embeddings.
[ "Antonio Vergari and Nicola Di Mauro and Floriana Esposito", "['Antonio Vergari' 'Nicola Di Mauro' 'Floriana Esposito']" ]
cs.LG
null
1608.02484
null
null
http://arxiv.org/pdf/1608.02484v1
2016-08-08T15:23:26Z
2016-08-08T15:23:26Z
Interpolated Discretized Embedding of Single Vectors and Vector Pairs for Classification, Metric Learning and Distance Approximation
We propose a new embedding method for a single vector and for a pair of vectors. This embedding method enables: a) efficient classification and regression of functions of single vectors; b) efficient approximation of distance functions; and c) non-Euclidean, semimetric learning. To the best of our knowledge, this is the first work that enables learning any general, non-Euclidean, semimetrics. That is, our method is a universal semimetric learning and approximation method that can approximate any distance function with as high accuracy as needed with or without semimetric constraints. The project homepage including code is at: http://www.ariel.ac.il/sites/ofirpele/ID
[ "Ofir Pele and Yakir Ben-Aliz", "['Ofir Pele' 'Yakir Ben-Aliz']" ]
null
null
1608.02546
null
null
http://arxiv.org/pdf/1608.02546v2
2016-12-08T17:41:49Z
2016-08-08T18:30:22Z
A Stackelberg Game Perspective on the Conflict Between Machine Learning and Data Obfuscation
Data is the new oil; this refrain is repeated extensively in the age of internet tracking, machine learning, and data analytics. As data collection becomes more personal and pervasive, however, public pressure is mounting for privacy protection. In this atmosphere, developers have created applications to add noise to user attributes visible to tracking algorithms. This creates a strategic interaction between trackers and users when incentives to maintain privacy and improve accuracy are misaligned. In this paper, we conceptualize this conflict through an N+1-player, augmented Stackelberg game. First a machine learner declares a privacy protection level, and then users respond by choosing their own perturbation amounts. We use the general frameworks of differential privacy and empirical risk minimization to quantify the utility components due to privacy and accuracy, respectively. In equilibrium, each user perturbs her data independently, which leads to a high net loss in accuracy. To remedy this scenario, we show that the learner improves his utility by proactively perturbing the data himself. While other work in this area has studied privacy markets and mechanism design for truthful reporting of user information, we take a different viewpoint by considering both user and learner perturbation.
[ "['Jeffrey Pawlick' 'Quanyan Zhu']" ]
cs.CL cs.LG
null
1608.02689
null
null
http://arxiv.org/pdf/1608.02689v2
2017-05-30T14:31:13Z
2016-08-09T04:38:38Z
Multi-task Domain Adaptation for Sequence Tagging
Many domain adaptation approaches rely on learning cross domain shared representations to transfer the knowledge learned in one domain to other domains. Traditional domain adaptation only considers adapting for one task. In this paper, we explore multi-task representation learning under the domain adaptation scenario. We propose a neural network framework that supports domain adaptation for multiple tasks simultaneously, and learns shared representations that better generalize for domain adaptation. We apply the proposed framework to domain adaptation for sequence tagging problems considering two tasks: Chinese word segmentation and named entity recognition. Experiments show that multi-task domain adaptation works better than disjoint domain adaptation for each task, and achieves the state-of-the-art results for both tasks in the social media domain.
[ "['Nanyun Peng' 'Mark Dredze']", "Nanyun Peng and Mark Dredze" ]
cs.AI cs.CV cs.LG cs.LO
null
1608.02693
null
null
http://arxiv.org/pdf/1608.02693v1
2016-08-09T05:48:51Z
2016-08-09T05:48:51Z
Deeply Semantic Inductive Spatio-Temporal Learning
We present an inductive spatio-temporal learning framework rooted in inductive logic programming. With an emphasis on visuo-spatial language, logic, and cognition, the framework supports learning with relational spatio-temporal features identifiable in a range of domains involving the processing and interpretation of dynamic visuo-spatial imagery. We present a prototypical system, and an example application in the domain of computing for visual arts and computational cognitive science.
[ "['Jakob Suchan' 'Mehul Bhatt' 'Carl Schultz']", "Jakob Suchan and Mehul Bhatt and Carl Schultz" ]
cs.CV cs.AI cs.CL cs.LG
null
1608.02717
null
null
http://arxiv.org/pdf/1608.02717v1
2016-08-09T08:24:02Z
2016-08-09T08:24:02Z
Mean Box Pooling: A Rich Image Representation and Output Embedding for the Visual Madlibs Task
We present Mean Box Pooling, a novel visual representation that pools over CNN representations of a large number, highly overlapping object proposals. We show that such representation together with nCCA, a successful multimodal embedding technique, achieves state-of-the-art performance on the Visual Madlibs task. Moreover, inspired by the nCCA's objective function, we extend classical CNN+LSTM approach to train the network by directly maximizing the similarity between the internal representation of the deep learning architecture and candidate answers. Again, such approach achieves a significant improvement over the prior work that also uses CNN+LSTM approach on Visual Madlibs.
[ "Ashkan Mokarian and Mateusz Malinowski and Mario Fritz", "['Ashkan Mokarian' 'Mateusz Malinowski' 'Mario Fritz']" ]
cs.CV cs.LG cs.NE
null
1608.02728
null
null
http://arxiv.org/pdf/1608.02728v1
2016-08-09T08:59:47Z
2016-08-09T08:59:47Z
OnionNet: Sharing Features in Cascaded Deep Classifiers
The focus of our work is speeding up evaluation of deep neural networks in retrieval scenarios, where conventional architectures may spend too much time on negative examples. We propose to replace a monolithic network with our novel cascade of feature-sharing deep classifiers, called OnionNet, where subsequent stages may add both new layers as well as new feature channels to the previous ones. Importantly, intermediate feature maps are shared among classifiers, preventing them from the necessity of being recomputed. To accomplish this, the model is trained end-to-end in a principled way under a joint loss. We validate our approach in theory and on a synthetic benchmark. As a result demonstrated in three applications (patch matching, object detection, and image retrieval), our cascade can operate significantly faster than both monolithic networks and traditional cascades without sharing at the cost of marginal decrease in precision.
[ "Martin Simonovsky and Nikos Komodakis", "['Martin Simonovsky' 'Nikos Komodakis']" ]
stat.ML cs.LG
null
1608.02731
null
null
http://arxiv.org/pdf/1608.02731v1
2016-08-09T09:01:13Z
2016-08-09T09:01:13Z
Posterior Sampling for Reinforcement Learning Without Episodes
This is a brief technical note to clarify some of the issues with applying the application of the algorithm posterior sampling for reinforcement learning (PSRL) in environments without fixed episodes. In particular, this paper aims to: - Review some of results which have been proven for finite horizon MDPs (Osband et al 2013, 2014a, 2014b, 2016) and also for MDPs with finite ergodic structure (Gopalan et al 2014). - Review similar results for optimistic algorithms in infinite horizon problems (Jaksch et al 2010, Bartlett and Tewari 2009, Abbasi-Yadkori and Szepesvari 2011), with particular attention to the dynamic episode growth. - Highlight the delicate technical issue which has led to a fault in the proof of the lazy-PSRL algorithm (Abbasi-Yadkori and Szepesvari 2015). We present an explicit counterexample to this style of argument. Therefore, we suggest that the Theorem 2 in (Abbasi-Yadkori and Szepesvari 2015) be instead considered a conjecture, as it has no rigorous proof. - Present pragmatic approaches to apply PSRL in infinite horizon problems. We conjecture that, under some additional assumptions, it will be possible to obtain bounds $O( \sqrt{T} )$ even without episodic reset. We hope that this note serves to clarify existing results in the field of reinforcement learning and provides interesting motivation for future work.
[ "Ian Osband, Benjamin Van Roy", "['Ian Osband' 'Benjamin Van Roy']" ]
stat.ML cs.LG
null
1608.02732
null
null
http://arxiv.org/pdf/1608.02732v1
2016-08-09T09:02:01Z
2016-08-09T09:02:01Z
On Lower Bounds for Regret in Reinforcement Learning
This is a brief technical note to clarify the state of lower bounds on regret for reinforcement learning. In particular, this paper: - Reproduces a lower bound on regret for reinforcement learning, similar to the result of Theorem 5 in the journal UCRL2 paper (Jaksch et al 2010). - Clarifies that the proposed proof of Theorem 6 in the REGAL paper (Bartlett and Tewari 2009) does not hold using the standard techniques without further work. We suggest that this result should instead be considered a conjecture as it has no rigorous proof. - Suggests that the conjectured lower bound given by (Bartlett and Tewari 2009) is incorrect and, in fact, it is possible to improve the scaling of the upper bound to match the weaker lower bounds presented in this paper. We hope that this note serves to clarify existing results in the field of reinforcement learning and provides interesting motivation for future work.
[ "Ian Osband, Benjamin Van Roy", "['Ian Osband' 'Benjamin Van Roy']" ]
stat.ML cs.LG
null
1608.02861
null
null
http://arxiv.org/pdf/1608.02861v1
2016-08-09T16:46:53Z
2016-08-09T16:46:53Z
Classification with the pot-pot plot
We propose a procedure for supervised classification that is based on potential functions. The potential of a class is defined as a kernel density estimate multiplied by the class's prior probability. The method transforms the data to a potential-potential (pot-pot) plot, where each data point is mapped to a vector of potentials. Separation of the classes, as well as classification of new data points, is performed on this plot. For this, either the $\alpha$-procedure ($\alpha$-P) or $k$-nearest neighbors ($k$-NN) are employed. For data that are generated from continuous distributions, these classifiers prove to be strongly Bayes-consistent. The potentials depend on the kernel and its bandwidth used in the density estimate. We investigate several variants of bandwidth selection, including joint and separate pre-scaling and a bandwidth regression approach. The new method is applied to benchmark data from the literature, including simulated data sets as well as 50 sets of real data. It compares favorably to known classification methods such as LDA, QDA, max kernel density estimates, $k$-NN, and $DD$-plot classification using depth functions.
[ "Oleksii Pokotylo and Karl Mosler", "['Oleksii Pokotylo' 'Karl Mosler']" ]
cs.LG
10.14569/IJACSA.2016.070710
1608.02888
null
null
http://arxiv.org/abs/1608.02888v1
2016-08-06T12:48:40Z
2016-08-06T12:48:40Z
Effective Data Mining Technique for Classification Cancers via Mutations in Gene using Neural Network
The prediction plays the important role in detecting efficient protection and therapy of cancer. The prediction of mutations in gene needs a diagnostic and classification, which is based on the whole database (big dataset), to reach sufficient accuracy results. Since the tumor suppressor P53 is approximately about fifty percentage of all human tumors because mutations that occur in the TP53 gene into the cells. So, this paper is applied on tumor p53, where the problem is there are several primitive databases (excel database) contain datasets of TP53 gene with its tumor protein p53, these databases are rich datasets that cover all mutations and cause diseases (cancers). But these Data Bases cannot reach to predict and diagnosis cancers, i.e. the big datasets have not efficient Data Mining method, which can predict, diagnosis the mutation, and classify the cancer of patient. The goal of this paper to reach a Data Mining technique, that employs neural network, which bases on the big datasets. Also, offers friendly predictions, flexible, and effective classified cancers, in order to overcome the previous techniques drawbacks. This proposed technique is done by using two approaches, first, bioinformatics techniques by using BLAST, CLUSTALW, etc, in order to know if there are malignant mutations or not. The second, data mining by using neural network; it is selected (12) out of (53) TP53 gene database fields. To clarify, one of these 12 fields (gene location field) did not exists in TP53 gene database; therefore, it is added to the database of TP53 gene in training and testing back propagation algorithm, in order to classify specifically the types of cancers. Feed Forward Back Propagation supports this Data Mining method with data training rate (1) and Mean Square Error (MSE) (0.00000000000001). This effective technique allows in a quick, accurate and easy way to classify the type of cancer.
[ "['Ayad Ghany Ismaeel' 'Dina Yousif Mikhail']", "Ayad Ghany Ismaeel, Dina Yousif Mikhail" ]
cs.LG cs.CL cs.IT math.IT
null
1608.02893
null
null
http://arxiv.org/pdf/1608.02893v2
2016-08-26T20:55:41Z
2016-08-08T01:30:45Z
Syntactically Informed Text Compression with Recurrent Neural Networks
We present a self-contained system for constructing natural language models for use in text compression. Our system improves upon previous neural network based models by utilizing recent advances in syntactic parsing -- Google's SyntaxNet -- to augment character-level recurrent neural networks. RNNs have proven exceptional in modeling sequence data such as text, as their architecture allows for modeling of long-term contextual information.
[ "['David Cox']", "David Cox" ]
cs.NE cs.AI cs.LG
null
1608.02971
null
null
http://arxiv.org/pdf/1608.02971v1
2016-08-09T20:04:40Z
2016-08-09T20:04:40Z
Neuroevolution-Based Inverse Reinforcement Learning
The problem of Learning from Demonstration is targeted at learning to perform tasks based on observed examples. One approach to Learning from Demonstration is Inverse Reinforcement Learning, in which actions are observed to infer rewards. This work combines a feature based state evaluation approach to Inverse Reinforcement Learning with neuroevolution, a paradigm for modifying neural networks based on their performance on a given task. Neural networks are used to learn from a demonstrated expert policy and are evolved to generate a policy similar to the demonstration. The algorithm is discussed and evaluated against competitive feature-based Inverse Reinforcement Learning approaches. At the cost of execution time, neural networks allow for non-linear combinations of features in state evaluations. These valuations may correspond to state value or state reward. This results in better correspondence to observed examples as opposed to using linear combinations. This work also extends existing work on Bayesian Non-Parametric Feature Construction for Inverse Reinforcement Learning by using non-linear combinations of intermediate data to improve performance. The algorithm is observed to be specifically suitable for a linearly solvable non-deterministic Markov Decision Processes in which multiple rewards are sparsely scattered in state space. A conclusive performance hierarchy between evaluated algorithms is presented.
[ "['Karan K. Budhraja' 'Tim Oates']", "Karan K. Budhraja and Tim Oates" ]
cs.CL cs.LG cs.NE
null
1608.02996
null
null
http://arxiv.org/pdf/1608.02996v1
2016-08-09T22:24:16Z
2016-08-09T22:24:16Z
Towards cross-lingual distributed representations without parallel text trained with adversarial autoencoders
Current approaches to learning vector representations of text that are compatible between different languages usually require some amount of parallel text, aligned at word, sentence or at least document level. We hypothesize however, that different natural languages share enough semantic structure that it should be possible, in principle, to learn compatible vector representations just by analyzing the monolingual distribution of words. In order to evaluate this hypothesis, we propose a scheme to map word vectors trained on a source language to vectors semantically compatible with word vectors trained on a target language using an adversarial autoencoder. We present preliminary qualitative results and discuss possible future developments of this technique, such as applications to cross-lingual sentence representations.
[ "Antonio Valerio Miceli Barone", "['Antonio Valerio Miceli Barone']" ]
cs.MM cs.LG
10.1109/TMM.2017.2690144
1608.03016
null
null
http://arxiv.org/abs/1608.03016v2
2017-04-15T05:26:23Z
2016-08-10T01:11:32Z
Mining Fashion Outfit Composition Using An End-to-End Deep Learning Approach on Set Data
Composing fashion outfits involves deep understanding of fashion standards while incorporating creativity for choosing multiple fashion items (e.g., Jewelry, Bag, Pants, Dress). In fashion websites, popular or high-quality fashion outfits are usually designed by fashion experts and followed by large audiences. In this paper, we propose a machine learning system to compose fashion outfits automatically. The core of the proposed automatic composition system is to score fashion outfit candidates based on the appearances and meta-data. We propose to leverage outfit popularity on fashion oriented websites to supervise the scoring component. The scoring component is a multi-modal multi-instance deep learning system that evaluates instance aesthetics and set compatibility simultaneously. In order to train and evaluate the proposed composition system, we have collected a large scale fashion outfit dataset with 195K outfits and 368K fashion items from Polyvore. Although the fashion outfit scoring and composition is rather challenging, we have achieved an AUC of 85% for the scoring component, and an accuracy of 77% for a constrained composition task.
[ "Yuncheng Li, LiangLiang Cao, Jiang Zhu, Jiebo Luo", "['Yuncheng Li' 'LiangLiang Cao' 'Jiang Zhu' 'Jiebo Luo']" ]
cs.LG stat.ML
null
1608.03023
null
null
http://arxiv.org/pdf/1608.03023v3
2017-03-08T07:58:32Z
2016-08-10T01:51:36Z
Stochastic Rank-1 Bandits
We propose stochastic rank-$1$ bandits, a class of online learning problems where at each step a learning agent chooses a pair of row and column arms, and receives the product of their values as a reward. The main challenge of the problem is that the individual values of the row and column are unobserved. We assume that these values are stochastic and drawn independently. We propose a computationally-efficient algorithm for solving our problem, which we call Rank1Elim. We derive a $O((K + L) (1 / \Delta) \log n)$ upper bound on its $n$-step regret, where $K$ is the number of rows, $L$ is the number of columns, and $\Delta$ is the minimum of the row and column gaps; under the assumption that the mean row and column rewards are bounded away from zero. To the best of our knowledge, we present the first bandit algorithm that finds the maximum entry of a rank-$1$ matrix whose regret is linear in $K + L$, $1 / \Delta$, and $\log n$. We also derive a nearly matching lower bound. Finally, we evaluate Rank1Elim empirically on multiple problems. We observe that it leverages the structure of our problems and can learn near-optimal solutions even if our modeling assumptions are mildly violated.
[ "['Sumeet Katariya' 'Branislav Kveton' 'Csaba Szepesvari' 'Claire Vernade'\n 'Zheng Wen']", "Sumeet Katariya, Branislav Kveton, Csaba Szepesvari, Claire Vernade,\n and Zheng Wen" ]
stat.ML cs.LG
null
1608.031
null
null
null
null
null
Estimation from Indirect Supervision with Linear Moments
In structured prediction problems where we have indirect supervision of the output, maximum marginal likelihood faces two computational obstacles: non-convexity of the objective and intractability of even a single gradient computation. In this paper, we bypass both obstacles for a class of what we call linear indirectly-supervised problems. Our approach is simple: we solve a linear system to estimate sufficient statistics of the model, which we then use to estimate parameters via convex optimization. We analyze the statistical properties of our approach and show empirically that it is effective in two settings: learning with local privacy constraints and learning from low-cost count-based annotations.
[ "Aditi Raghunathan, Roy Frostig, John Duchi, Percy Liang" ]
null
null
1608.03100
null
null
http://arxiv.org/pdf/1608.03100v1
2016-08-10T09:19:07Z
2016-08-10T09:19:07Z
Estimation from Indirect Supervision with Linear Moments
In structured prediction problems where we have indirect supervision of the output, maximum marginal likelihood faces two computational obstacles: non-convexity of the objective and intractability of even a single gradient computation. In this paper, we bypass both obstacles for a class of what we call linear indirectly-supervised problems. Our approach is simple: we solve a linear system to estimate sufficient statistics of the model, which we then use to estimate parameters via convex optimization. We analyze the statistical properties of our approach and show empirically that it is effective in two settings: learning with local privacy constraints and learning from low-cost count-based annotations.
[ "['Aditi Raghunathan' 'Roy Frostig' 'John Duchi' 'Percy Liang']" ]
math.OC cs.IT cs.LG math.IT
null
1608.03248
null
null
http://arxiv.org/pdf/1608.03248v2
2017-11-19T22:48:13Z
2016-08-10T18:15:58Z
Combination of LMS Adaptive Filters with Coefficients Feedback
Parallel combinations of adaptive filters have been effectively used to improve the performance of adaptive algorithms and address well-known trade-offs, such as convergence rate vs. steady-state error. Nevertheless, typical combinations suffer from a convergence stagnation issue due to the fact that the component filters run independently. Solutions to this issue usually involve conditional transfers of coefficients between filters, which although effective, are hard to generalize to combinations with more filters or when there is no clearly faster adaptive filter. In this work, a more natural solution is proposed by cyclically feeding back the combined coefficient vector to all component filters. Besides coping with convergence stagnation, this new topology improves tracking and supervisor stability, and bridges an important conceptual gap between combinations of adaptive filters and variable step size schemes. We analyze the steady-state, tracking, and transient performance of this topology for LMS component filters and supervisors with generic activation functions. Numerical examples are used to illustrate how coefficients feedback can improve the performance of parallel combinations at a small computational overhead.
[ "Luiz F. O. Chamon and Cassio G. Lopes", "['Luiz F. O. Chamon' 'Cassio G. Lopes']" ]
cs.LG math.FA
null
1608.03287
null
null
http://arxiv.org/pdf/1608.03287v1
2016-08-10T20:02:40Z
2016-08-10T20:02:40Z
Deep vs. shallow networks : An approximation theory perspective
The paper briefy reviews several recent results on hierarchical architectures for learning from examples, that may formally explain the conditions under which Deep Convolutional Neural Networks perform much better in function approximation problems than shallow, one-hidden layer architectures. The paper announces new results for a non-smooth activation function - the ReLU function - used in present-day neural networks, as well as for the Gaussian networks. We propose a new definition of relative dimension to encapsulate different notions of sparsity of a function class that can possibly be exploited by deep networks but not by shallow ones to drastically reduce the complexity required for approximation and learning.
[ "['Hrushikesh Mhaskar' 'Tomaso Poggio']", "Hrushikesh Mhaskar and Tomaso Poggio" ]
cs.LG stat.ML
10.1145/2987538.2987540
1608.03333
null
null
http://arxiv.org/abs/1608.03333v1
2016-08-11T00:48:00Z
2016-08-11T00:48:00Z
Temporal Learning and Sequence Modeling for a Job Recommender System
We present our solution to the job recommendation task for RecSys Challenge 2016. The main contribution of our work is to combine temporal learning with sequence modeling to capture complex user-item activity patterns to improve job recommendations. First, we propose a time-based ranking model applied to historical observations and a hybrid matrix factorization over time re-weighted interactions. Second, we exploit sequence properties in user-items activities and develop a RNN-based recommendation model. Our solution achieved 5$^{th}$ place in the challenge among more than 100 participants. Notably, the strong performance of our RNN approach shows a promising new direction in employing sequence modeling for recommendation systems.
[ "Kuan Liu, Xing Shi, Anoop Kumar, Linhong Zhu, Prem Natarajan", "['Kuan Liu' 'Xing Shi' 'Anoop Kumar' 'Linhong Zhu' 'Prem Natarajan']" ]
cs.LG stat.ML
null
1608.03339
null
null
http://arxiv.org/pdf/1608.03339v2
2017-03-11T07:45:26Z
2016-08-11T01:20:23Z
Distributed learning with regularized least squares
We study distributed learning with the least squares regularization scheme in a reproducing kernel Hilbert space (RKHS). By a divide-and-conquer approach, the algorithm partitions a data set into disjoint data subsets, applies the least squares regularization scheme to each data subset to produce an output function, and then takes an average of the individual output functions as a final global estimator or predictor. We show with error bounds in expectation in both the $L^2$-metric and RKHS-metric that the global output function of this distributed learning is a good approximation to the algorithm processing the whole data in one single machine. Our error bounds are sharp and stated in a general setting without any eigenfunction assumption. The analysis is achieved by a novel second order decomposition of operator differences in our integral operator approach. Even for the classical least squares regularization scheme in the RKHS associated with a general kernel, we give the best learning rate in the literature.
[ "['Shao-Bo Lin' 'Xin Guo' 'Ding-Xuan Zhou']", "Shao-Bo Lin, Xin Guo, Ding-Xuan Zhou" ]
cs.DB cs.LG
null
1608.03344
null
null
http://arxiv.org/pdf/1608.03344v1
2016-08-11T01:55:04Z
2016-08-11T01:55:04Z
Multi-source Hierarchical Prediction Consolidation
In big data applications such as healthcare data mining, due to privacy concerns, it is necessary to collect predictions from multiple information sources for the same instance, with raw features being discarded or withheld when aggregating multiple predictions. Besides, crowd-sourced labels need to be aggregated to estimate the ground truth of the data. Because of the imperfect predictive models or human crowdsourcing workers, noisy and conflicting information is ubiquitous and inevitable. Although state-of-the-art aggregation methods have been proposed to handle label spaces with flat structures, as the label space is becoming more and more complicated, aggregation under a label hierarchical structure becomes necessary but has been largely ignored. These label hierarchies can be quite informative as they are usually created by domain experts to make sense of highly complex label correlations for many real-world cases like protein functionality interactions or disease relationships. We propose a novel multi-source hierarchical prediction consolidation method to effectively exploits the complicated hierarchical label structures to resolve the noisy and conflicting information that inherently originates from multiple imperfect sources. We formulate the problem as an optimization problem with a closed-form solution. The proposed method captures the smoothness overall information sources as well as penalizing any consolidation result that violates the constraints derived from the label hierarchy. The hierarchical instance similarity, as well as the consolidation result, are inferred in a totally unsupervised, iterative fashion. Experimental results on both synthetic and real-world datasets show the effectiveness of the proposed method over existing alternatives.
[ "['Chenwei Zhang' 'Sihong Xie' 'Yaliang Li' 'Jing Gao' 'Wei Fan'\n 'Philip S. Yu']", "Chenwei Zhang, Sihong Xie, Yaliang Li, Jing Gao, Wei Fan, Philip S. Yu" ]
cs.LG q-bio.QM stat.ML
null
1608.0353
null
null
null
null
null
Semi-Supervised Prediction of Gene Regulatory Networks Using Machine Learning Algorithms
Use of computational methods to predict gene regulatory networks (GRNs) from gene expression data is a challenging task. Many studies have been conducted using unsupervised methods to fulfill the task; however, such methods usually yield low prediction accuracies due to the lack of training data. In this article, we propose semi-supervised methods for GRN prediction by utilizing two machine learning algorithms, namely support vector machines (SVM) and random forests (RF). The semi-supervised methods make use of unlabeled data for training. We investigate inductive and transductive learning approaches, both of which adopt an iterative procedure to obtain reliable negative training data from the unlabeled data. We then apply our semi-supervised methods to gene expression data of Escherichia coli and Saccharomyces cerevisiae, and evaluate the performance of our methods using the expression data. Our analysis indicated that the transductive learning approach outperformed the inductive learning approach for both organisms. However, there was no conclusive difference identified in the performance of SVM and RF. Experimental results also showed that the proposed semi-supervised methods performed better than existing supervised methods for both organisms.
[ "Nihir Patel and Jason T. L. Wang" ]
null
null
1608.03530
null
null
http://arxiv.org/pdf/1608.03530v1
2016-08-11T16:52:03Z
2016-08-11T16:52:03Z
Semi-Supervised Prediction of Gene Regulatory Networks Using Machine Learning Algorithms
Use of computational methods to predict gene regulatory networks (GRNs) from gene expression data is a challenging task. Many studies have been conducted using unsupervised methods to fulfill the task; however, such methods usually yield low prediction accuracies due to the lack of training data. In this article, we propose semi-supervised methods for GRN prediction by utilizing two machine learning algorithms, namely support vector machines (SVM) and random forests (RF). The semi-supervised methods make use of unlabeled data for training. We investigate inductive and transductive learning approaches, both of which adopt an iterative procedure to obtain reliable negative training data from the unlabeled data. We then apply our semi-supervised methods to gene expression data of Escherichia coli and Saccharomyces cerevisiae, and evaluate the performance of our methods using the expression data. Our analysis indicated that the transductive learning approach outperformed the inductive learning approach for both organisms. However, there was no conclusive difference identified in the performance of SVM and RF. Experimental results also showed that the proposed semi-supervised methods performed better than existing supervised methods for both organisms.
[ "['Nihir Patel' 'Jason T. L. Wang']" ]
stat.ML cs.LG
null
1608.03533
null
null
null
null
null
Sequence Graph Transform (SGT): A Feature Embedding Function for Sequence Data Mining
Sequence feature embedding is a challenging task due to the unstructuredness of sequence, i.e., arbitrary strings of arbitrary length. Existing methods are efficient in extracting short-term dependencies but typically suffer from computation issues for the long-term. Sequence Graph Transform (SGT), a feature embedding function, that can extract a varying amount of short- to long-term dependencies without increasing the computation is proposed. SGT's properties are analytically proved for interpretation under normal and uniform distribution assumptions. SGT features yield significantly superior results in sequence clustering and classification with higher accuracy and lower computation as compared to the existing methods, including the state-of-the-art sequence/string Kernels and LSTM.
[ "Chitta Ranjan, Samaneh Ebrahimi and Kamran Paynabar" ]
null
null
1608.03533v
null
null
http://arxiv.org/pdf/1608.03533v15
2021-10-05T00:32:17Z
2016-08-11T16:59:19Z
Sequence Graph Transform (SGT): A Feature Embedding Function for Sequence Data Mining
Sequence feature embedding is a challenging task due to the unstructuredness of sequence, i.e., arbitrary strings of arbitrary length. Existing methods are efficient in extracting short-term dependencies but typically suffer from computation issues for the long-term. Sequence Graph Transform (SGT), a feature embedding function, that can extract a varying amount of short- to long-term dependencies without increasing the computation is proposed. SGT's properties are analytically proved for interpretation under normal and uniform distribution assumptions. SGT features yield significantly superior results in sequence clustering and classification with higher accuracy and lower computation as compared to the existing methods, including the state-of-the-art sequence/string Kernels and LSTM.
[ "['Chitta Ranjan' 'Samaneh Ebrahimi' 'Kamran Paynabar']" ]
cs.LG cs.AI cs.IR stat.ML
null
1608.03544
null
null
http://arxiv.org/pdf/1608.03544v2
2017-02-27T17:16:22Z
2016-08-06T14:13:28Z
On Context-Dependent Clustering of Bandits
We investigate a novel cluster-of-bandit algorithm CAB for collaborative recommendation tasks that implements the underlying feedback sharing mechanism by estimating the neighborhood of users in a context-dependent manner. CAB makes sharp departures from the state of the art by incorporating collaborative effects into inference as well as learning processes in a manner that seamlessly interleaving explore-exploit tradeoffs and collaborative steps. We prove regret bounds under various assumptions on the data, which exhibit a crisp dependence on the expected number of clusters over the users, a natural measure of the statistical difficulty of the learning task. Experiments on production and real-world datasets show that CAB offers significantly increased prediction performance against a representative pool of state-of-the-art methods.
[ "['Claudio Gentile' 'Shuai Li' 'Purushottam Kar' 'Alexandros Karatzoglou'\n 'Evans Etrue' 'Giovanni Zappella']", "Claudio Gentile, Shuai Li, Purushottam Kar, Alexandros Karatzoglou,\n Evans Etrue, Giovanni Zappella" ]
stat.ML cs.LG stat.AP
null
1608.03585
null
null
http://arxiv.org/pdf/1608.03585v1
2016-08-11T19:56:27Z
2016-08-11T19:56:27Z
Warm Starting Bayesian Optimization
We develop a framework for warm-starting Bayesian optimization, that reduces the solution time required to solve an optimization problem that is one in a sequence of related problems. This is useful when optimizing the output of a stochastic simulator that fails to provide derivative information, for which Bayesian optimization methods are well-suited. Solving sequences of related optimization problems arises when making several business decisions using one optimization model and input data collected over different time periods or markets. While many gradient-based methods can be warm started by initiating optimization at the solution to the previous problem, this warm start approach does not apply to Bayesian optimization methods, which carry a full metamodel of the objective function from iteration to iteration. Our approach builds a joint statistical model of the entire collection of related objective functions, and uses a value of information calculation to recommend points to evaluate.
[ "Matthias Poloczek, Jialei Wang, and Peter I. Frazier", "['Matthias Poloczek' 'Jialei Wang' 'Peter I. Frazier']" ]
stat.ML cs.LG cs.NE
null
1608.03639
null
null
http://arxiv.org/pdf/1608.03639v1
2016-08-11T23:48:44Z
2016-08-11T23:48:44Z
Faster Training of Very Deep Networks Via p-Norm Gates
A major contributing factor to the recent advances in deep neural networks is structural units that let sensory information and gradients to propagate easily. Gating is one such structure that acts as a flow control. Gates are employed in many recent state-of-the-art recurrent models such as LSTM and GRU, and feedforward models such as Residual Nets and Highway Networks. This enables learning in very deep networks with hundred layers and helps achieve record-breaking results in vision (e.g., ImageNet with Residual Nets) and NLP (e.g., machine translation with GRU). However, there is limited work in analysing the role of gating in the learning process. In this paper, we propose a flexible $p$-norm gating scheme, which allows user-controllable flow and as a consequence, improve the learning speed. This scheme subsumes other existing gating schemes, including those in GRU, Highway Networks and Residual Nets as special cases. Experiments on large sequence and vector datasets demonstrate that the proposed gating scheme helps improve the learning speed significantly without extra overhead.
[ "Trang Pham, Truyen Tran, Dinh Phung, Svetha Venkatesh", "['Trang Pham' 'Truyen Tran' 'Dinh Phung' 'Svetha Venkatesh']" ]
cs.LG cs.DS stat.ML
null
1608.03643
null
null
http://arxiv.org/pdf/1608.03643v2
2016-11-16T16:31:27Z
2016-08-12T00:36:42Z
Chi-squared Amplification: Identifying Hidden Hubs
We consider the following general hidden hubs model: an $n \times n$ random matrix $A$ with a subset $S$ of $k$ special rows (hubs): entries in rows outside $S$ are generated from the probability distribution $p_0 \sim N(0,\sigma_0^2)$; for each row in $S$, some $k$ of its entries are generated from $p_1 \sim N(0,\sigma_1^2)$, $\sigma_1>\sigma_0$, and the rest of the entries from $p_0$. The problem is to identify the high-degree hubs efficiently. This model includes and significantly generalizes the planted Gaussian Submatrix Model, where the special entries are all in a $k \times k$ submatrix. There are two well-known barriers: if $k\geq c\sqrt{n\ln n}$, just the row sums are sufficient to find $S$ in the general model. For the submatrix problem, this can be improved by a $\sqrt{\ln n}$ factor to $k \ge c\sqrt{n}$ by spectral methods or combinatorial methods. In the variant with $p_0=\pm 1$ (with probability $1/2$ each) and $p_1\equiv 1$, neither barrier has been broken. We give a polynomial-time algorithm to identify all the hidden hubs with high probability for $k \ge n^{0.5-\delta}$ for some $\delta >0$, when $\sigma_1^2>2\sigma_0^2$. The algorithm extends to the setting where planted entries might have different variances each at least as large as $\sigma_1^2$. We also show a nearly matching lower bound: for $\sigma_1^2 \le 2\sigma_0^2$, there is no polynomial-time Statistical Query algorithm for distinguishing between a matrix whose entries are all from $N(0,\sigma_0^2)$ and a matrix with $k=n^{0.5-\delta}$ hidden hubs for any $\delta >0$. The lower bound as well as the algorithm are related to whether the chi-squared distance of the two distributions diverges. At the critical value $\sigma_1^2=2\sigma_0^2$, we show that the general hidden hubs problem can be solved for $k\geq c\sqrt n(\ln n)^{1/4}$, improving on the naive row sum-based method.
[ "['Ravi Kannan' 'Santosh Vempala']", "Ravi Kannan and Santosh Vempala" ]
cs.LG cs.CV cs.NE
null
1608.03644
null
null
http://arxiv.org/pdf/1608.03644v4
2016-10-18T20:20:22Z
2016-08-12T00:43:59Z
Deep Motif Dashboard: Visualizing and Understanding Genomic Sequences Using Deep Neural Networks
Deep neural network (DNN) models have recently obtained state-of-the-art prediction accuracy for the transcription factor binding (TFBS) site classification task. However, it remains unclear how these approaches identify meaningful DNA sequence signals and give insights as to why TFs bind to certain locations. In this paper, we propose a toolkit called the Deep Motif Dashboard (DeMo Dashboard) which provides a suite of visualization strategies to extract motifs, or sequence patterns from deep neural network models for TFBS classification. We demonstrate how to visualize and understand three important DNN models: convolutional, recurrent, and convolutional-recurrent networks. Our first visualization method is finding a test sequence's saliency map which uses first-order derivatives to describe the importance of each nucleotide in making the final prediction. Second, considering recurrent models make predictions in a temporal manner (from one end of a TFBS sequence to the other), we introduce temporal output scores, indicating the prediction score of a model over time for a sequential input. Lastly, a class-specific visualization strategy finds the optimal input sequence for a given TFBS positive class via stochastic gradient optimization. Our experimental results indicate that a convolutional-recurrent architecture performs the best among the three architectures. The visualization techniques indicate that CNN-RNN makes predictions by modeling both motifs as well as dependencies among them.
[ "Jack Lanchantin, Ritambhara Singh, Beilun Wang, and Yanjun Qi", "['Jack Lanchantin' 'Ritambhara Singh' 'Beilun Wang' 'Yanjun Qi']" ]
cs.LG
null
1608.03647
null
null
http://arxiv.org/pdf/1608.03647v2
2017-04-23T16:46:17Z
2016-08-12T07:24:57Z
Learning with Value-Ramp
We study a learning principle based on the intuition of forming ramps. The agent tries to follow an increasing sequence of values until the agent meets a peak of reward. The resulting Value-Ramp algorithm is natural, easy to configure, and has a robust implementation with natural numbers.
[ "Tom J. Ameloot and Jan Van den Bussche", "['Tom J. Ameloot' 'Jan Van den Bussche']" ]
cs.NE cs.LG stat.ML
null
1608.03665
null
null
http://arxiv.org/pdf/1608.03665v4
2016-10-18T04:03:41Z
2016-08-12T03:20:43Z
Learning Structured Sparsity in Deep Neural Networks
High demand for computation resources severely hinders deployment of large-scale Deep Neural Networks (DNN) in resource constrained devices. In this work, we propose a Structured Sparsity Learning (SSL) method to regularize the structures (i.e., filters, channels, filter shapes, and layer depth) of DNNs. SSL can: (1) learn a compact structure from a bigger DNN to reduce computation cost; (2) obtain a hardware-friendly structured sparsity of DNN to efficiently accelerate the DNNs evaluation. Experimental results show that SSL achieves on average 5.1x and 3.1x speedups of convolutional layer computation of AlexNet against CPU and GPU, respectively, with off-the-shelf libraries. These speedups are about twice speedups of non-structured sparsity; (3) regularize the DNN structure to improve classification accuracy. The results show that for CIFAR-10, regularization on layer depth can reduce 20 layers of a Deep Residual Network (ResNet) to 18 layers while improve the accuracy from 91.25% to 92.60%, which is still slightly higher than that of original ResNet with 32 layers. For AlexNet, structure regularization by SSL also reduces the error by around ~1%. Open source code is in https://github.com/wenwei202/caffe/tree/scnn
[ "Wei Wen, Chunpeng Wu, Yandan Wang, Yiran Chen, Hai Li", "['Wei Wen' 'Chunpeng Wu' 'Yandan Wang' 'Yiran Chen' 'Hai Li']" ]
cs.RO cs.LG
null
1608.03694
null
null
http://arxiv.org/pdf/1608.03694v1
2016-08-12T07:26:59Z
2016-08-12T07:26:59Z
Density Matching Reward Learning
In this paper, we focus on the problem of inferring the underlying reward function of an expert given demonstrations, which is often referred to as inverse reinforcement learning (IRL). In particular, we propose a model-free density-based IRL algorithm, named density matching reward learning (DMRL), which does not require model dynamics. The performance of DMRL is analyzed theoretically and the sample complexity is derived. Furthermore, the proposed DMRL is extended to handle nonlinear IRL problems by assuming that the reward function is in the reproducing kernel Hilbert space (RKHS) and kernel DMRL (KDMRL) is proposed. The parameters for KDMRL can be computed analytically, which greatly reduces the computation time. The performance of KDMRL is extensively evaluated in two sets of experiments: grid world and track driving experiments. In grid world experiments, the proposed KDMRL method is compared with both model-based and model-free IRL methods and shows superior performance on a nonlinear reward setting and competitive performance on a linear reward setting in terms of expected value differences. Then we move on to more realistic experiments of learning different driving styles for autonomous navigation in complex and dynamic tracks using KDMRL and receding horizon control.
[ "['Sungjoon Choi' 'Kyungjae Lee' 'Andy Park' 'Songhwai Oh']", "Sungjoon Choi, Kyungjae Lee, Andy Park, Songhwai Oh" ]
cond-mat.dis-nn cond-mat.stat-mech cs.LG q-bio.NC
10.1103/PhysRevE.94.062310
1608.03714
null
null
http://arxiv.org/abs/1608.03714v2
2016-11-11T01:49:13Z
2016-08-12T08:35:22Z
Unsupervised feature learning from finite data by message passing: discontinuous versus continuous phase transition
Unsupervised neural network learning extracts hidden features from unlabeled training data. This is used as a pretraining step for further supervised learning in deep networks. Hence, understanding unsupervised learning is of fundamental importance. Here, we study the unsupervised learning from a finite number of data, based on the restricted Boltzmann machine learning. Our study inspires an efficient message passing algorithm to infer the hidden feature, and estimate the entropy of candidate features consistent with the data. Our analysis reveals that the learning requires only a few data if the feature is salient and extensively many if the feature is weak. Moreover, the entropy of candidate features monotonically decreases with data size and becomes negative (i.e., entropy crisis) before the message passing becomes unstable, suggesting a discontinuous phase transition. In terms of convergence time of the message passing algorithm, the unsupervised learning exhibits an easy-hard-easy phenomenon as the training data size increases. All these properties are reproduced in an approximate Hopfield model, with an exception that the entropy crisis is absent, and only continuous phase transition is observed. This key difference is also confirmed in a handwritten digits dataset. This study deepens our understanding of unsupervised learning from a finite number of data, and may provide insights into its role in training deep networks.
[ "['Haiping Huang' 'Taro Toyoizumi']", "Haiping Huang and Taro Toyoizumi" ]
cs.NE cs.CV cs.LG
null
1608.03793
null
null
http://arxiv.org/pdf/1608.03793v2
2016-08-16T18:36:44Z
2016-08-12T13:50:24Z
Applying Deep Learning to Basketball Trajectories
One of the emerging trends for sports analytics is the growing use of player and ball tracking data. A parallel development is deep learning predictive approaches that use vast quantities of data with less reliance on feature engineering. This paper applies recurrent neural networks in the form of sequence modeling to predict whether a three-point shot is successful. The models are capable of learning the trajectory of a basketball without any knowledge of physics. For comparison, a baseline static machine learning model with a full set of features, such as angle and velocity, in addition to the positional data is also tested. Using a dataset of over 20,000 three pointers from NBA SportVu data, the models based simply on sequential positional data outperform a static feature rich machine learning model in predicting whether a three-point shot is successful. This suggests deep learning models may offer an improvement to traditional feature based machine learning methods for tracking data.
[ "['Rajiv Shah' 'Rob Romijnders']", "Rajiv Shah and Rob Romijnders" ]
stat.ML cs.IR cs.LG
null
1608.03811
null
null
http://arxiv.org/pdf/1608.03811v1
2016-08-12T14:40:46Z
2016-08-12T14:40:46Z
Content-based image retrieval tutorial
This paper functions as a tutorial for individuals interested to enter the field of information retrieval but wouldn't know where to begin from. It describes two fundamental yet efficient image retrieval techniques, the first being k - nearest neighbors (knn) and the second support vector machines(svm). The goal is to provide the reader with both the theoretical and practical aspects in order to acquire a better understanding. Along with this tutorial we have also developed the equivalent software1 using the MATLAB environment in order to illustrate the techniques, so that the reader can have a hands-on experience.
[ "['Joani Mitro']", "Joani Mitro" ]
cs.DC cs.LG math.OC
null
1608.03866
null
null
http://arxiv.org/pdf/1608.03866v2
2016-12-19T15:19:25Z
2016-08-12T18:34:06Z
Distributed Optimization for Client-Server Architecture with Negative Gradient Weights
Availability of both massive datasets and computing resources have made machine learning and predictive analytics extremely pervasive. In this work we present a synchronous algorithm and architecture for distributed optimization motivated by privacy requirements posed by applications in machine learning. We present an algorithm for the recently proposed multi-parameter-server architecture. We consider a group of parameter servers that learn a model based on randomized gradients received from clients. Clients are computational entities with private datasets (inducing a private objective function), that evaluate and upload randomized gradients to the parameter servers. The parameter servers perform model updates based on received gradients and share the model parameters with other servers. We prove that the proposed algorithm can optimize the overall objective function for a very general architecture involving $C$ clients connected to $S$ parameter servers in an arbitrary time varying topology and the parameter servers forming a connected network.
[ "['Shripad Gade' 'Nitin H. Vaidya']", "Shripad Gade and Nitin H. Vaidya" ]
cs.CL cs.LG cs.SI
null
1608.03902
null
null
http://arxiv.org/pdf/1608.03902v1
2016-08-12T20:19:16Z
2016-08-12T20:19:16Z
Rapid Classification of Crisis-Related Data on Social Networks using Convolutional Neural Networks
The role of social media, in particular microblogging platforms such as Twitter, as a conduit for actionable and tactical information during disasters is increasingly acknowledged. However, time-critical analysis of big crisis data on social media streams brings challenges to machine learning techniques, especially the ones that use supervised learning. The Scarcity of labeled data, particularly in the early hours of a crisis, delays the machine learning process. The current state-of-the-art classification methods require a significant amount of labeled data specific to a particular event for training plus a lot of feature engineering to achieve best results. In this work, we introduce neural network based classification methods for binary and multi-class tweet classification task. We show that neural network based models do not require any feature engineering and perform better than state-of-the-art methods. In the early hours of a disaster when no labeled data is available, our proposed method makes the best use of the out-of-event data and achieves good results.
[ "Dat Tien Nguyen, Kamela Ali Al Mannai, Shafiq Joty, Hassan Sajjad,\n Muhammad Imran, Prasenjit Mitra", "['Dat Tien Nguyen' 'Kamela Ali Al Mannai' 'Shafiq Joty' 'Hassan Sajjad'\n 'Muhammad Imran' 'Prasenjit Mitra']" ]
cs.LG
null
1608.03933
null
null
http://arxiv.org/pdf/1608.03933v3
2017-11-02T07:15:50Z
2016-08-13T03:24:11Z
Improved Dynamic Regret for Non-degenerate Functions
Recently, there has been a growing research interest in the analysis of dynamic regret, which measures the performance of an online learner against a sequence of local minimizers. By exploiting the strong convexity, previous studies have shown that the dynamic regret can be upper bounded by the path-length of the comparator sequence. In this paper, we illustrate that the dynamic regret can be further improved by allowing the learner to query the gradient of the function multiple times, and meanwhile the strong convexity can be weakened to other non-degenerate conditions. Specifically, we introduce the squared path-length, which could be much smaller than the path-length, as a new regularity of the comparator sequence. When multiple gradients are accessible to the learner, we first demonstrate that the dynamic regret of strongly convex functions can be upper bounded by the minimum of the path-length and the squared path-length. We then extend our theoretical guarantee to functions that are semi-strongly convex or self-concordant. To the best of our knowledge, this is the first time that semi-strong convexity and self-concordance are utilized to tighten the dynamic regret.
[ "Lijun Zhang, Tianbao Yang, Jinfeng Yi, Rong Jin, Zhi-Hua Zhou", "['Lijun Zhang' 'Tianbao Yang' 'Jinfeng Yi' 'Rong Jin' 'Zhi-Hua Zhou']" ]
stat.ML cs.CV cs.LG
null
1608.03974
null
null
http://arxiv.org/pdf/1608.03974v1
2016-08-13T11:19:22Z
2016-08-13T11:19:22Z
Recurrent Fully Convolutional Neural Networks for Multi-slice MRI Cardiac Segmentation
In cardiac magnetic resonance imaging, fully-automatic segmentation of the heart enables precise structural and functional measurements to be taken, e.g. from short-axis MR images of the left-ventricle. In this work we propose a recurrent fully-convolutional network (RFCN) that learns image representations from the full stack of 2D slices and has the ability to leverage inter-slice spatial dependences through internal memory units. RFCN combines anatomical detection and segmentation into a single architecture that is trained end-to-end thus significantly reducing computational time, simplifying the segmentation pipeline, and potentially enabling real-time applications. We report on an investigation of RFCN using two datasets, including the publicly available MICCAI 2009 Challenge dataset. Comparisons have been carried out between fully convolutional networks and deep restricted Boltzmann machines, including a recurrent version that leverages inter-slice spatial correlation. Our studies suggest that RFCN produces state-of-the-art results and can substantially improve the delineation of contours near the apex of the heart.
[ "['Rudra P K Poudel' 'Pablo Lamata' 'Giovanni Montana']", "Rudra P K Poudel and Pablo Lamata and Giovanni Montana" ]
cs.LG cs.NE math.OC
null
1608.03983
null
null
http://arxiv.org/pdf/1608.03983v5
2017-05-03T16:28:09Z
2016-08-13T13:46:05Z
SGDR: Stochastic Gradient Descent with Warm Restarts
Restart techniques are common in gradient-free optimization to deal with multimodal functions. Partial warm restarts are also gaining popularity in gradient-based optimization to improve the rate of convergence in accelerated gradient schemes to deal with ill-conditioned functions. In this paper, we propose a simple warm restart technique for stochastic gradient descent to improve its anytime performance when training deep neural networks. We empirically study its performance on the CIFAR-10 and CIFAR-100 datasets, where we demonstrate new state-of-the-art results at 3.14% and 16.21%, respectively. We also demonstrate its advantages on a dataset of EEG recordings and on a downsampled version of the ImageNet dataset. Our source code is available at https://github.com/loshchil/SGDR
[ "['Ilya Loshchilov' 'Frank Hutter']", "Ilya Loshchilov and Frank Hutter" ]
cs.LG cs.IR stat.ML
null
1608.04037
null
null
http://arxiv.org/pdf/1608.04037v1
2016-08-13T23:45:21Z
2016-08-13T23:45:21Z
An approach to dealing with missing values in heterogeneous data using k-nearest neighbors
Techniques such as clusterization, neural networks and decision making usually rely on algorithms that are not well suited to deal with missing values. However, real world data frequently contains such cases. The simplest solution is to either substitute them by a best guess value or completely disregard the missing values. Unfortunately, both approaches can lead to biased results. In this paper, we propose a technique for dealing with missing values in heterogeneous data using imputation based on the k-nearest neighbors algorithm. It can handle real (which we refer to as crisp henceforward), interval and fuzzy data. The effectiveness of the algorithm is tested on several datasets and the numerical results are promising.
[ "Davi E. N. Frossard, Igor O. Nunes, Renato A. Krohling", "['Davi E. N. Frossard' 'Igor O. Nunes' 'Renato A. Krohling']" ]
cs.LG cs.CV
null
1608.04062
null
null
http://arxiv.org/pdf/1608.04062v2
2016-09-08T17:46:13Z
2016-08-14T05:35:11Z
Stacked Approximated Regression Machine: A Simple Deep Learning Approach
With the agreement of my coauthors, I Zhangyang Wang would like to withdraw the manuscript "Stacked Approximated Regression Machine: A Simple Deep Learning Approach". Some experimental procedures were not included in the manuscript, which makes a part of important claims not meaningful. In the relevant research, I was solely responsible for carrying out the experiments; the other coauthors joined in the discussions leading to the main algorithm. Please see the updated text for more details.
[ "['Zhangyang Wang' 'Shiyu Chang' 'Qing Ling' 'Shuai Huang' 'Xia Hu'\n 'Honghui Shi' 'Thomas S. Huang']", "Zhangyang Wang, Shiyu Chang, Qing Ling, Shuai Huang, Xia Hu, Honghui\n Shi, Thomas S. Huang" ]
cs.LG stat.ML
null
1608.04063
null
null
http://arxiv.org/pdf/1608.04063v1
2016-08-14T06:01:21Z
2016-08-14T06:01:21Z
Bayesian Model Selection Methods for Mutual and Symmetric $k$-Nearest Neighbor Classification
The $k$-nearest neighbor classification method ($k$-NNC) is one of the simplest nonparametric classification methods. The mutual $k$-NN classification method (M$k$NNC) is a variant of $k$-NNC based on mutual neighborship. We propose another variant of $k$-NNC, the symmetric $k$-NN classification method (S$k$NNC) based on both mutual neighborship and one-sided neighborship. The performance of M$k$NNC and S$k$NNC depends on the parameter $k$ as the one of $k$-NNC does. We propose the ways how M$k$NN and S$k$NN classification can be performed based on Bayesian mutual and symmetric $k$-NN regression methods with the selection schemes for the parameter $k$. Bayesian mutual and symmetric $k$-NN regression methods are based on Gaussian process models, and it turns out that they can do M$k$NN and S$k$NN classification with new encodings of target values (class labels). The simulation results show that the proposed methods are better than or comparable to $k$-NNC, M$k$NNC and S$k$NNC with the parameter $k$ selected by the leave-one-out cross validation method not only for an artificial data set but also for real world data sets.
[ "['Hyun-Chul Kim']", "Hyun-Chul Kim" ]
cs.LG
null
1608.04077
null
null
http://arxiv.org/pdf/1608.04077v3
2017-02-28T08:25:33Z
2016-08-14T09:19:26Z
Generative Knowledge Transfer for Neural Language Models
In this paper, we propose a generative knowledge transfer technique that trains an RNN based language model (student network) using text and output probabilities generated from a previously trained RNN (teacher network). The text generation can be conducted by either the teacher or the student network. We can also improve the performance by taking the ensemble of soft labels obtained from multiple teacher networks. This method can be used for privacy conscious language model adaptation because no user data is directly used for training. Especially, when the soft labels of multiple devices are aggregated via a trusted third party, we can expect very strong privacy protection.
[ "['Sungho Shin' 'Kyuyeon Hwang' 'Wonyong Sung']", "Sungho Shin, Kyuyeon Hwang, and Wonyong Sung" ]
cs.CV cs.LG
null
1608.0408
null
null
null
null
null
Dynamic Hand Gesture Recognition for Wearable Devices with Low Complexity Recurrent Neural Networks
Gesture recognition is a very essential technology for many wearable devices. While previous algorithms are mostly based on statistical methods including the hidden Markov model, we develop two dynamic hand gesture recognition techniques using low complexity recurrent neural network (RNN) algorithms. One is based on video signal and employs a combined structure of a convolutional neural network (CNN) and an RNN. The other uses accelerometer data and only requires an RNN. Fixed-point optimization that quantizes most of the weights into two bits is conducted to optimize the amount of memory size for weight storage and reduce the power consumption in hardware and software based implementations.
[ "Sungho Shin and Wonyong Sung" ]
null
null
1608.04080
null
null
http://arxiv.org/pdf/1608.04080v1
2016-08-14T09:32:17Z
2016-08-14T09:32:17Z
Dynamic Hand Gesture Recognition for Wearable Devices with Low Complexity Recurrent Neural Networks
Gesture recognition is a very essential technology for many wearable devices. While previous algorithms are mostly based on statistical methods including the hidden Markov model, we develop two dynamic hand gesture recognition techniques using low complexity recurrent neural network (RNN) algorithms. One is based on video signal and employs a combined structure of a convolutional neural network (CNN) and an RNN. The other uses accelerometer data and only requires an RNN. Fixed-point optimization that quantizes most of the weights into two bits is conducted to optimize the amount of memory size for weight storage and reduce the power consumption in hardware and software based implementations.
[ "['Sungho Shin' 'Wonyong Sung']" ]
cs.NE cs.LG
null
1608.04171
null
null
http://arxiv.org/pdf/1608.04171v4
2017-06-07T04:34:06Z
2016-08-15T02:49:17Z
Power Data Classification: A Hybrid of a Novel Local Time Warping and LSTM
In this paper, for the purpose of data centre energy consumption monitoring and analysis, we propose to detect the running programs in a server by classifying the observed power consumption series. Time series classification problem has been extensively studied with various distance measurements developed; also recently the deep learning based sequence models have been proved to be promising. In this paper, we propose a novel distance measurement and build a time series classification algorithm hybridizing nearest neighbour and long short term memory (LSTM) neural network. More specifically, first we propose a new distance measurement termed as Local Time Warping (LTW), which utilizes a user-specified set for local warping, and is designed to be non-commutative and non-dynamic programming. Second we hybridize the 1NN-LTW and LSTM together. In particular, we combine the prediction probability vector of 1NN-LTW and LSTM to determine the label of the test cases. Finally, using the power consumption data from a real data center, we show that the proposed LTW can improve the classification accuracy of DTW from about 84% to 90%. Our experimental results prove that the proposed LTW is competitive on our data set compared with existed DTW variants and its non-commutative feature is indeed beneficial. We also test a linear version of LTW and it can significantly outperform existed linear runtime lower bound methods like LB_Keogh. Furthermore, with the hybrid algorithm, for the power series classification task we achieve an accuracy up to about 93%. Our research can inspire more studies on time series distance measurement and the hybrid of the deep learning models with other traditional models.
[ "Yuanlong Li, Han Hu, Yonggang Wen, Jun Zhang", "['Yuanlong Li' 'Han Hu' 'Yonggang Wen' 'Jun Zhang']" ]
cs.SC cs.LG
10.1109/SYNASC.2016.020
1608.04219
null
null
http://arxiv.org/abs/1608.04219v1
2016-08-15T09:44:29Z
2016-08-15T09:44:29Z
Using Machine Learning to Decide When to Precondition Cylindrical Algebraic Decomposition With Groebner Bases
Cylindrical Algebraic Decomposition (CAD) is a key tool in computational algebraic geometry, particularly for quantifier elimination over real-closed fields. However, it can be expensive, with worst case complexity doubly exponential in the size of the input. Hence it is important to formulate the problem in the best manner for the CAD algorithm. One possibility is to precondition the input polynomials using Groebner Basis (GB) theory. Previous experiments have shown that while this can often be very beneficial to the CAD algorithm, for some problems it can significantly worsen the CAD performance. In the present paper we investigate whether machine learning, specifically a support vector machine (SVM), may be used to identify those CAD problems which benefit from GB preconditioning. We run experiments with over 1000 problems (many times larger than previous studies) and find that the machine learned choice does better than the human-made heuristic.
[ "['Zongyan Huang' 'Matthew England' 'James H. Davenport'\n 'Lawrence C. Paulson']", "Zongyan Huang, Matthew England, James H. Davenport and Lawrence C.\n Paulson" ]
cs.CV cs.HC cs.LG stat.ML
null
1608.04236
null
null
http://arxiv.org/pdf/1608.04236v2
2016-08-16T08:06:24Z
2016-08-15T11:14:35Z
Generative and Discriminative Voxel Modeling with Convolutional Neural Networks
When working with three-dimensional data, choice of representation is key. We explore voxel-based models, and present evidence for the viability of voxellated representations in applications including shape modeling and object classification. Our key contributions are methods for training voxel-based variational autoencoders, a user interface for exploring the latent space learned by the autoencoder, and a deep convolutional neural network architecture for object classification. We address challenges unique to voxel-based representations, and empirically evaluate our models on the ModelNet benchmark, where we demonstrate a 51.5% relative improvement in the state of the art for object classification.
[ "Andrew Brock, Theodore Lim, J.M. Ritchie, Nick Weston", "['Andrew Brock' 'Theodore Lim' 'J. M. Ritchie' 'Nick Weston']" ]
stat.ML cs.LG
null
1608.04245
null
null
http://arxiv.org/pdf/1608.04245v2
2016-08-16T10:41:32Z
2016-08-15T11:42:51Z
The Bayesian Low-Rank Determinantal Point Process Mixture Model
Determinantal point processes (DPPs) are an elegant model for encoding probabilities over subsets, such as shopping baskets, of a ground set, such as an item catalog. They are useful for a number of machine learning tasks, including product recommendation. DPPs are parametrized by a positive semi-definite kernel matrix. Recent work has shown that using a low-rank factorization of this kernel provides remarkable scalability improvements that open the door to training on large-scale datasets and computing online recommendations, both of which are infeasible with standard DPP models that use a full-rank kernel. In this paper we present a low-rank DPP mixture model that allows us to represent the latent structure present in observed subsets as a mixture of a number of component low-rank DPPs, where each component DPP is responsible for representing a portion of the observed data. The mixture model allows us to effectively address the capacity constraints of the low-rank DPP model. We present an efficient and scalable Markov Chain Monte Carlo (MCMC) learning algorithm for our model that uses Gibbs sampling and stochastic gradient Hamiltonian Monte Carlo (SGHMC). Using an evaluation on several real-world product recommendation datasets, we show that our low-rank DPP mixture model provides substantially better predictive performance than is possible with a single low-rank or full-rank DPP, and significantly better performance than several other competing recommendation methods in many cases.
[ "['Mike Gartrell' 'Ulrich Paquet' 'Noam Koenigstein']", "Mike Gartrell, Ulrich Paquet, Noam Koenigstein" ]
cs.LG cs.IT math.IT
null
1608.0432
null
null
null
null
null
Correlated-PCA: Principal Components' Analysis when Data and Noise are Correlated
Given a matrix of observed data, Principal Components Analysis (PCA) computes a small number of orthogonal directions that contain most of its variability. Provably accurate solutions for PCA have been in use for a long time. However, to the best of our knowledge, all existing theoretical guarantees for it assume that the data and the corrupting noise are mutually independent, or at least uncorrelated. This is valid in practice often, but not always. In this paper, we study the PCA problem in the setting where the data and noise can be correlated. Such noise is often also referred to as "data-dependent noise". We obtain a correctness result for the standard eigenvalue decomposition (EVD) based solution to PCA under simple assumptions on the data-noise correlation. We also develop and analyze a generalization of EVD, cluster-EVD, that improves upon EVD in certain regimes.
[ "Namrata Vaswani, Han Guo" ]
null
null
1608.04320
null
null
http://arxiv.org/pdf/1608.04320v2
2016-11-02T17:55:02Z
2016-08-15T16:32:57Z
Correlated-PCA: Principal Components' Analysis when Data and Noise are Correlated
Given a matrix of observed data, Principal Components Analysis (PCA) computes a small number of orthogonal directions that contain most of its variability. Provably accurate solutions for PCA have been in use for a long time. However, to the best of our knowledge, all existing theoretical guarantees for it assume that the data and the corrupting noise are mutually independent, or at least uncorrelated. This is valid in practice often, but not always. In this paper, we study the PCA problem in the setting where the data and noise can be correlated. Such noise is often also referred to as "data-dependent noise". We obtain a correctness result for the standard eigenvalue decomposition (EVD) based solution to PCA under simple assumptions on the data-noise correlation. We also develop and analyze a generalization of EVD, cluster-EVD, that improves upon EVD in certain regimes.
[ "['Namrata Vaswani' 'Han Guo']" ]
cs.LG stat.ML
null
1608.04331
null
null
http://arxiv.org/pdf/1608.04331v1
2016-08-15T17:12:09Z
2016-08-15T17:12:09Z
Consistency constraints for overlapping data clustering
We examine overlapping clustering schemes with functorial constraints, in the spirit of Carlsson--Memoli. This avoids issues arising from the chaining required by partition-based methods. Our principal result shows that any clustering functor is naturally constrained to refine single-linkage clusters and be refined by maximal-linkage clusters. We work in the context of metric spaces with non-expansive maps, which is appropriate for modeling data processing which does not increase information content.
[ "['Jared Culbertson' 'Dan P. Guralnik' 'Jakob Hansen' 'Peter F. Stiller']", "Jared Culbertson, Dan P. Guralnik, Jakob Hansen, Peter F. Stiller" ]
cs.LG cs.CV cs.DB
10.1137/17M1121184
1608.04348
null
null
http://arxiv.org/abs/1608.04348v2
2017-03-15T19:50:07Z
2016-08-15T18:03:51Z
Anomaly detection and classification for streaming data using PDEs
Nondominated sorting, also called Pareto Depth Analysis (PDA), is widely used in multi-objective optimization and has recently found important applications in multi-criteria anomaly detection. Recently, a partial differential equation (PDE) continuum limit was discovered for nondominated sorting leading to a very fast approximate sorting algorithm called PDE-based ranking. We propose in this paper a fast real-time streaming version of the PDA algorithm for anomaly detection that exploits the computational advantages of PDE continuum limits. Furthermore, we derive new PDE continuum limits for sorting points within their nondominated layers and show how the new PDEs can be used to classify anomalies based on which criterion was more significantly violated. We also prove statistical convergence rates for PDE-based ranking, and present the results of numerical experiments with both synthetic and real data.
[ "Bilal Abbasi, Jeff Calder, Adam M. Oberman", "['Bilal Abbasi' 'Jeff Calder' 'Adam M. Oberman']" ]
cs.SD cs.CV cs.LG cs.NE
10.1109/LSP.2017.2657381
1608.04363
null
null
http://arxiv.org/abs/1608.04363v2
2016-11-28T17:48:04Z
2016-08-15T18:57:10Z
Deep Convolutional Neural Networks and Data Augmentation for Environmental Sound Classification
The ability of deep convolutional neural networks (CNN) to learn discriminative spectro-temporal patterns makes them well suited to environmental sound classification. However, the relative scarcity of labeled data has impeded the exploitation of this family of high-capacity models. This study has two primary contributions: first, we propose a deep convolutional neural network architecture for environmental sound classification. Second, we propose the use of audio data augmentation for overcoming the problem of data scarcity and explore the influence of different augmentations on the performance of the proposed CNN architecture. Combined with data augmentation, the proposed model produces state-of-the-art results for environmental sound classification. We show that the improved performance stems from the combination of a deep, high-capacity model and an augmented training set: this combination outperforms both the proposed CNN without augmentation and a "shallow" dictionary learning model with augmentation. Finally, we examine the influence of each augmentation on the model's classification accuracy for each class, and observe that the accuracy for each class is influenced differently by each augmentation, suggesting that the performance of the model could be improved further by applying class-conditional data augmentation.
[ "Justin Salamon and Juan Pablo Bello", "['Justin Salamon' 'Juan Pablo Bello']" ]
cs.LG stat.ML
null
1608.04414
null
null
http://arxiv.org/pdf/1608.04414v3
2016-12-26T06:37:48Z
2016-08-15T21:19:51Z
Generalization of ERM in Stochastic Convex Optimization: The Dimension Strikes Back
In stochastic convex optimization the goal is to minimize a convex function $F(x) \doteq {\mathbf E}_{{\mathbf f}\sim D}[{\mathbf f}(x)]$ over a convex set $\cal K \subset {\mathbb R}^d$ where $D$ is some unknown distribution and each $f(\cdot)$ in the support of $D$ is convex over $\cal K$. The optimization is commonly based on i.i.d.~samples $f^1,f^2,\ldots,f^n$ from $D$. A standard approach to such problems is empirical risk minimization (ERM) that optimizes $F_S(x) \doteq \frac{1}{n}\sum_{i\leq n} f^i(x)$. Here we consider the question of how many samples are necessary for ERM to succeed and the closely related question of uniform convergence of $F_S$ to $F$ over $\cal K$. We demonstrate that in the standard $\ell_p/\ell_q$ setting of Lipschitz-bounded functions over a $\cal K$ of bounded radius, ERM requires sample size that scales linearly with the dimension $d$. This nearly matches standard upper bounds and improves on $\Omega(\log d)$ dependence proved for $\ell_2/\ell_2$ setting by Shalev-Shwartz et al. (2009). In stark contrast, these problems can be solved using dimension-independent number of samples for $\ell_2/\ell_2$ setting and $\log d$ dependence for $\ell_1/\ell_\infty$ setting using other approaches. We further show that our lower bound applies even if the functions in the support of $D$ are smooth and efficiently computable and even if an $\ell_1$ regularization term is added. Finally, we demonstrate that for a more general class of bounded-range (but not Lipschitz-bounded) stochastic convex programs an infinite gap appears already in dimension 2.
[ "['Vitaly Feldman']", "Vitaly Feldman" ]
cs.LG cs.NE
null
1608.04426
null
null
http://arxiv.org/pdf/1608.04426v4
2017-02-17T02:49:12Z
2016-08-15T22:28:05Z
Regularization for Unsupervised Deep Neural Nets
Unsupervised neural networks, such as restricted Boltzmann machines (RBMs) and deep belief networks (DBNs), are powerful tools for feature selection and pattern recognition tasks. We demonstrate that overfitting occurs in such models just as in deep feedforward neural networks, and discuss possible regularization methods to reduce overfitting. We also propose a "partial" approach to improve the efficiency of Dropout/DropConnect in this scenario, and discuss the theoretical justification of these methods from model convergence and likelihood bounds. Finally, we compare the performance of these methods based on their likelihood and classification error rates for various pattern recognition data sets.
[ "['Baiyang Wang' 'Diego Klabjan']", "Baiyang Wang, Diego Klabjan" ]
cs.LG cs.AI cs.NE
null
1608.04428
null
null
http://arxiv.org/pdf/1608.04428v1
2016-08-15T22:34:50Z
2016-08-15T22:34:50Z
TerpreT: A Probabilistic Programming Language for Program Induction
We study machine learning formulations of inductive program synthesis; given input-output examples, we try to synthesize source code that maps inputs to corresponding outputs. Our aims are to develop new machine learning approaches based on neural networks and graphical models, and to understand the capabilities of machine learning techniques relative to traditional alternatives, such as those based on constraint solving from the programming languages community. Our key contribution is the proposal of TerpreT, a domain-specific language for expressing program synthesis problems. TerpreT is similar to a probabilistic programming language: a model is composed of a specification of a program representation (declarations of random variables) and an interpreter describing how programs map inputs to outputs (a model connecting unknowns to observations). The inference task is to observe a set of input-output examples and infer the underlying program. TerpreT has two main benefits. First, it enables rapid exploration of a range of domains, program representations, and interpreter models. Second, it separates the model specification from the inference algorithm, allowing like-to-like comparisons between different approaches to inference. From a single TerpreT specification we automatically perform inference using four different back-ends. These are based on gradient descent, linear program (LP) relaxations for graphical models, discrete satisfiability solving, and the Sketch program synthesis system. We illustrate the value of TerpreT by developing several interpreter models and performing an empirical comparison between alternative inference algorithms. Our key empirical finding is that constraint solvers dominate the gradient descent and LP-based formulations. We conclude with suggestions for the machine learning community to make progress on program synthesis.
[ "['Alexander L. Gaunt' 'Marc Brockschmidt' 'Rishabh Singh' 'Nate Kushman'\n 'Pushmeet Kohli' 'Jonathan Taylor' 'Daniel Tarlow']", "Alexander L. Gaunt, Marc Brockschmidt, Rishabh Singh, Nate Kushman,\n Pushmeet Kohli, Jonathan Taylor, Daniel Tarlow" ]
cs.IR cs.LG
null
1608.04468
null
null
http://arxiv.org/pdf/1608.04468v1
2016-08-16T02:56:24Z
2016-08-16T02:56:24Z
Unbiased Learning-to-Rank with Biased Feedback
Implicit feedback (e.g., clicks, dwell times, etc.) is an abundant source of data in human-interactive systems. While implicit feedback has many advantages (e.g., it is inexpensive to collect, user centric, and timely), its inherent biases are a key obstacle to its effective use. For example, position bias in search rankings strongly influences how many clicks a result receives, so that directly using click data as a training signal in Learning-to-Rank (LTR) methods yields sub-optimal results. To overcome this bias problem, we present a counterfactual inference framework that provides the theoretical basis for unbiased LTR via Empirical Risk Minimization despite biased data. Using this framework, we derive a Propensity-Weighted Ranking SVM for discriminative learning from implicit feedback, where click models take the role of the propensity estimator. In contrast to most conventional approaches to de-bias the data using click models, this allows training of ranking functions even in settings where queries do not repeat. Beyond the theoretical support, we show empirically that the proposed learning method is highly effective in dealing with biases, that it is robust to noise and propensity model misspecification, and that it scales efficiently. We also demonstrate the real-world applicability of our approach on an operational search engine, where it substantially improves retrieval performance.
[ "Thorsten Joachims, Adith Swaminathan, Tobias Schnabel", "['Thorsten Joachims' 'Adith Swaminathan' 'Tobias Schnabel']" ]
stat.ML cs.LG
null
1608.04471
null
null
http://arxiv.org/pdf/1608.04471v3
2019-09-09T17:31:39Z
2016-08-16T03:24:20Z
Stein Variational Gradient Descent: A General Purpose Bayesian Inference Algorithm
We propose a general purpose variational inference algorithm that forms a natural counterpart of gradient descent for optimization. Our method iteratively transports a set of particles to match the target distribution, by applying a form of functional gradient descent that minimizes the KL divergence. Empirical studies are performed on various real world models and datasets, on which our method is competitive with existing state-of-the-art methods. The derivation of our method is based on a new theoretical result that connects the derivative of KL divergence under smooth transforms with Stein's identity and a recently proposed kernelized Stein discrepancy, which is of independent interest.
[ "Qiang Liu and Dilin Wang", "['Qiang Liu' 'Dilin Wang']" ]
stat.ME cs.LG stat.ML
null
1608.04478
null
null
http://arxiv.org/pdf/1608.04478v1
2016-08-16T04:31:52Z
2016-08-16T04:31:52Z
A Geometrical Approach to Topic Model Estimation
In the probabilistic topic models, the quantity of interest---a low-rank matrix consisting of topic vectors---is hidden in the text corpus matrix, masked by noise, and the Singular Value Decomposition (SVD) is a potentially useful tool for learning such a low-rank matrix. However, the connection between this low-rank matrix and the singular vectors of the text corpus matrix are usually complicated and hard to spell out, so how to use SVD for learning topic models faces challenges. In this paper, we overcome the challenge by revealing a surprising insight: there is a low-dimensional simplex structure which can be viewed as a bridge between the low-rank matrix of interest and the SVD of the text corpus matrix, and allows us to conveniently reconstruct the former using the latter. Such an insight motivates a new SVD approach to learning topic models, which we analyze with delicate random matrix theory and derive the rate of convergence. We support our methods and theory numerically, using both simulated data and real data.
[ "Zheng Tracy Ke", "['Zheng Tracy Ke']" ]
cs.NE cs.CV cs.LG
null
1608.04493
null
null
http://arxiv.org/pdf/1608.04493v2
2016-11-10T00:17:25Z
2016-08-16T06:23:05Z
Dynamic Network Surgery for Efficient DNNs
Deep learning has become a ubiquitous technology to improve machine intelligence. However, most of the existing deep models are structurally very complex, making them difficult to be deployed on the mobile platforms with limited computational power. In this paper, we propose a novel network compression method called dynamic network surgery, which can remarkably reduce the network complexity by making on-the-fly connection pruning. Unlike the previous methods which accomplish this task in a greedy way, we properly incorporate connection splicing into the whole process to avoid incorrect pruning and make it as a continual network maintenance. The effectiveness of our method is proved with experiments. Without any accuracy loss, our method can efficiently compress the number of parameters in LeNet-5 and AlexNet by a factor of $\bm{108}\times$ and $\bm{17.7}\times$ respectively, proving that it outperforms the recent pruning method by considerable margins. Code and some models are available at https://github.com/yiwenguo/Dynamic-Network-Surgery.
[ "Yiwen Guo, Anbang Yao, Yurong Chen", "['Yiwen Guo' 'Anbang Yao' 'Yurong Chen']" ]
cs.CE cs.LG stat.ML
null
1608.0455
null
null
null
null
null
Fast Calculation of the Knowledge Gradient for Optimization of Deterministic Engineering Simulations
A novel efficient method for computing the Knowledge-Gradient policy for Continuous Parameters (KGCP) for deterministic optimization is derived. The differences with Expected Improvement (EI), a popular choice for Bayesian optimization of deterministic engineering simulations, are explored. Both policies and the Upper Confidence Bound (UCB) policy are compared on a number of benchmark functions including a problem from structural dynamics. It is empirically shown that KGCP has similar performance as the EI policy for many problems, but has better convergence properties for complex (multi-modal) optimization problems as it emphasizes more on exploration when the model is confident about the shape of optimal regions. In addition, the relationship between Maximum Likelihood Estimation (MLE) and slice sampling for estimation of the hyperparameters of the underlying models, and the complexity of the problem at hand, is studied.
[ "Joachim van der Herten and Ivo Couckuyt and Dirk Deschrijver and Tom\n Dhaene" ]