October 18, 2019

3009 words 15 mins read

Paper Group ANR 596

Real-valued parametric conditioning of an RNN for interactive sound synthesis. Online Learning Algorithms for Statistical Arbitrage. Generalized Logical Operations among Conditional Events. Waveform to Single Sinusoid Regression to Estimate the F0 Contour from Noisy Speech Using Recurrent Deep Neural Networks. A Review on The Use of Deep Learning i …

Real-valued parametric conditioning of an RNN for interactive sound synthesis


Title	Real-valued parametric conditioning of an RNN for interactive sound synthesis
Authors	Lonce Wyse
Abstract	A Recurrent Neural Network (RNN) for audio synthesis is trained by augmenting the audio input with information about signal characteristics such as pitch, amplitude, and instrument. The result after training is an audio synthesizer that is played like a musical instrument with the desired musical characteristics provided as continuous parametric control. The focus of this paper is on conditioning data-driven synthesis models with real-valued parameters, and in particular, on the ability of the system a) to generalize and b) to be responsive to parameter values and sequences not seen during training.
Tasks
Published	2018-05-28
URL	http://arxiv.org/abs/1805.10808v2
PDF	http://arxiv.org/pdf/1805.10808v2.pdf
PWC	https://paperswithcode.com/paper/real-valued-parametric-conditioning-of-an-rnn
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Online Learning Algorithms for Statistical Arbitrage


Title	Online Learning Algorithms for Statistical Arbitrage
Authors	Christopher Mohri
Abstract	Statistical arbitrage is a class of financial trading strategies using mean reversion models. The corresponding techniques rely on a number of assumptions which may not hold for general non-stationary stochastic processes. This paper presents an alternative technique for statistical arbitrage based on online learning which does not require such assumptions and which benefits from strong learning guarantees.
Tasks
Published	2018-11-01
URL	http://arxiv.org/abs/1811.00200v1
PDF	http://arxiv.org/pdf/1811.00200v1.pdf
PWC	https://paperswithcode.com/paper/online-learning-algorithms-for-statistical
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Generalized Logical Operations among Conditional Events


Title	Generalized Logical Operations among Conditional Events
Authors	Angelo Gilio, Giuseppe Sanfilippo
Abstract	We generalize, by a progressive procedure, the notions of conjunction and disjunction of two conditional events to the case of $n$ conditional events. In our coherence-based approach, conjunctions and disjunctions are suitable conditional random quantities. We define the notion of negation, by verifying De Morgan’s Laws. We also show that conjunction and disjunction satisfy the associative and commutative properties, and a monotonicity property. Then, we give some results on coherence of prevision assessments for some families of compounded conditionals; in particular we examine the Fr'echet-Hoeffding bounds. Moreover, we study the reverse probabilistic inference from the conjunction $\mathcal{C}{n+1}$ of $n+1$ conditional events to the family ${\mathcal{C}{n},E_{n+1}H_{n+1}}$. We consider the relation with the notion of quasi-conjunction and we examine in detail the coherence of the prevision assessments related with the conjunction of three conditional events. Based on conjunction, we also give a characterization of p-consistency and of p-entailment, with applications to several inference rules in probabilistic nonmonotonic reasoning. Finally, we examine some non p-valid inference rules; then, we illustrate by an example two methods which allow to suitably modify non p-valid inference rules in order to get inferences which are p-valid.
Tasks
Published	2018-04-27
URL	http://arxiv.org/abs/1804.10447v1
PDF	http://arxiv.org/pdf/1804.10447v1.pdf
PWC	https://paperswithcode.com/paper/generalized-logical-operations-among
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Waveform to Single Sinusoid Regression to Estimate the F0 Contour from Noisy Speech Using Recurrent Deep Neural Networks


Title	Waveform to Single Sinusoid Regression to Estimate the F0 Contour from Noisy Speech Using Recurrent Deep Neural Networks
Authors	Akihiro Kato, Tomi Kinnunen
Abstract	The fundamental frequency (F0) represents pitch in speech that determines prosodic characteristics of speech and is needed in various tasks for speech analysis and synthesis. Despite decades of research on this topic, F0 estimation at low signal-to-noise ratios (SNRs) in unexpected noise conditions remains difficult. This work proposes a new approach to noise robust F0 estimation using a recurrent neural network (RNN) trained in a supervised manner. Recent studies employ deep neural networks (DNNs) for F0 tracking as a frame-by-frame classification task into quantised frequency states but we propose waveform-to-sinusoid regression instead to achieve both noise robustness and accurate estimation with increased frequency resolution. Experimental results with PTDB-TUG corpus contaminated by additive noise (NOISEX-92) demonstrate that the proposed method improves gross pitch error (GPE) rate and fine pitch error (FPE) by more than 35 % at SNRs between -10 dB and +10 dB compared with well-known noise robust F0 tracker, PEFAC. Furthermore, the proposed method also outperforms state-of-the-art DNN-based approaches by more than 15 % in terms of both FPE and GPE rate over the preceding SNR range.
Tasks
Published	2018-07-02
URL	http://arxiv.org/abs/1807.00752v1
PDF	http://arxiv.org/pdf/1807.00752v1.pdf
PWC	https://paperswithcode.com/paper/waveform-to-single-sinusoid-regression-to
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A Review on The Use of Deep Learning in Android Malware Detection


Title	A Review on The Use of Deep Learning in Android Malware Detection
Authors	Abdelmonim Naway, Yuancheng LI
Abstract	Android is the predominant mobile operating system for the past few years. The prevalence of devices that can be powered by Android magnetized not merely application developers but also malware developers with criminal intention to design and spread malicious applications that can affect the normal work of Android phones and tablets, steal personal information and credential data, or even worse lock the phone and ask for ransom. Researchers persistently devise countermeasures strategies to fight back malware. One of these strategies applied in the past five years is the use of deep learning methods in Android malware detection. This necessitates a review to inspect the accomplished work in order to know where the endeavors have been established, identify unresolved problems, and motivate future research directions. In this work, an extensive survey of static analysis, dynamic analysis, and hybrid analysis that utilized deep learning methods are reviewed with an elaborated discussion on their key concepts, contributions, and limitations.
Tasks	Android Malware Detection, Malware Detection
Published	2018-12-26
URL	http://arxiv.org/abs/1812.10360v1
PDF	http://arxiv.org/pdf/1812.10360v1.pdf
PWC	https://paperswithcode.com/paper/a-review-on-the-use-of-deep-learning-in
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PCN: Part and Context Information for Pedestrian Detection with CNNs


Title	PCN: Part and Context Information for Pedestrian Detection with CNNs
Authors	Shiguang Wang, Jian Cheng, Haijun Liu, Ming Tang
Abstract	Pedestrian detection has achieved great improvements in recent years, while complex occlusion handling is still one of the most important problems. To take advantage of the body parts and context information for pedestrian detection, we propose the part and context network (PCN) in this work. PCN specially utilizes two branches which detect the pedestrians through body parts semantic and context information, respectively. In the Part Branch, the semantic information of body parts can communicate with each other via recurrent neural networks. In the Context Branch, we adopt a local competition mechanism for adaptive context scale selection. By combining the outputs of all branches, we develop a strong complementary pedestrian detector with a lower miss rate and better localization accuracy, especially for occlusion pedestrian. Comprehensive evaluations on two challenging pedestrian detection datasets (i.e. Caltech and INRIA) well demonstrated the effectiveness of the proposed PCN.
Tasks	Pedestrian Detection
Published	2018-04-12
URL	http://arxiv.org/abs/1804.04483v1
PDF	http://arxiv.org/pdf/1804.04483v1.pdf
PWC	https://paperswithcode.com/paper/pcn-part-and-context-information-for
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Comparing and Integrating Constraint Programming and Temporal Planning for Quantum Circuit Compilation


Title	Comparing and Integrating Constraint Programming and Temporal Planning for Quantum Circuit Compilation
Authors	Kyle E. C. Booth, Minh Do, J. Christopher Beck, Eleanor Rieffel, Davide Venturelli, Jeremy Frank
Abstract	Recently, the makespan-minimization problem of compiling a general class of quantum algorithms into near-term quantum processors has been introduced to the AI community. The research demonstrated that temporal planning is a strong approach for a class of quantum circuit compilation (QCC) problems. In this paper, we explore the use of constraint programming (CP) as an alternative and complementary approach to temporal planning. We extend previous work by introducing two new problem variations that incorporate important characteristics identified by the quantum computing community. We apply temporal planning and CP to the baseline and extended QCC problems as both stand-alone and hybrid approaches. Our hybrid methods use solutions found by temporal planning to warm start CP, leveraging the ability of the former to find satisficing solutions to problems with a high degree of task optionality, an area that CP typically struggles with. The CP model, benefiting from inferred bounds on planning horizon length and task counts provided by the warm start, is then used to find higher quality solutions. Our empirical evaluation indicates that while stand-alone CP is only competitive for the smallest problems, CP in our hybridization with temporal planning out-performs stand-alone temporal planning in the majority of problem classes.
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Published	2018-03-19
URL	http://arxiv.org/abs/1803.06775v1
PDF	http://arxiv.org/pdf/1803.06775v1.pdf
PWC	https://paperswithcode.com/paper/comparing-and-integrating-constraint
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Binary Constrained Deep Hashing Network for Image Retrieval without Manual Annotation


Title	Binary Constrained Deep Hashing Network for Image Retrieval without Manual Annotation
Authors	Thanh-Toan Do, Tuan Hoang, Dang-Khoa Le Tan, Trung Pham, Huu Le, Ngai-Man Cheung, Ian Reid
Abstract	Learning compact binary codes for image retrieval task using deep neural networks has attracted increasing attention recently. However, training deep hashing networks for the task is challenging due to the binary constraints on the hash codes, the similarity preserving property, and the requirement for a vast amount of labelled images. To the best of our knowledge, none of the existing methods has tackled all of these challenges completely in a unified framework. In this work, we propose a novel end-to-end deep learning approach for the task, in which the network is trained to produce binary codes directly from image pixels without the need of manual annotation. In particular, to deal with the non-smoothness of binary constraints, we propose a novel pairwise constrained loss function, which simultaneously encodes the distances between pairs of hash codes, and the binary quantization error. In order to train the network with the proposed loss function, we propose an efficient parameter learning algorithm. In addition, to provide similar / dissimilar training images to train the network, we exploit 3D models reconstructed from unlabelled images for automatic generation of enormous training image pairs. The extensive experiments on image retrieval benchmark datasets demonstrate the improvements of the proposed method over the state-of-the-art compact representation methods on the image retrieval problem.
Tasks	Image Retrieval, Quantization
Published	2018-02-21
URL	http://arxiv.org/abs/1802.07437v7
PDF	http://arxiv.org/pdf/1802.07437v7.pdf
PWC	https://paperswithcode.com/paper/binary-constrained-deep-hashing-network-for
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Regularizing by the Variance of the Activations’ Sample-Variances


Title	Regularizing by the Variance of the Activations’ Sample-Variances
Authors	Etai Littwin, Lior Wolf
Abstract	Normalization techniques play an important role in supporting efficient and often more effective training of deep neural networks. While conventional methods explicitly normalize the activations, we suggest to add a loss term instead. This new loss term encourages the variance of the activations to be stable and not vary from one random mini-batch to the next. As we prove, this encourages the activations to be distributed around a few distinct modes. We also show that if the inputs are from a mixture of two Gaussians, the new loss would either join the two together, or separate between them optimally in the LDA sense, depending on the prior probabilities. Finally, we are able to link the new regularization term to the batchnorm method, which provides it with a regularization perspective. Our experiments demonstrate an improvement in accuracy over the batchnorm technique for both CNNs and fully connected networks.
Tasks
Published	2018-11-21
URL	http://arxiv.org/abs/1811.08764v1
PDF	http://arxiv.org/pdf/1811.08764v1.pdf
PWC	https://paperswithcode.com/paper/regularizing-by-the-variance-of-the
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Real-Time 6D Object Pose Estimation on CPU


Title	Real-Time 6D Object Pose Estimation on CPU
Authors	Yoshinori Konishi, Kosuke Hattori, Manabu Hashimoto
Abstract	We propose a fast and accurate 6D object pose estimation from a RGB-D image. Our proposed method is template matching based and consists of three main technical components, PCOF-MOD (multimodal PCOF), balanced pose tree (BPT) and optimum memory rearrangement for a coarse-to-fine search. Our model templates on densely sampled viewpoints and PCOF-MOD which explicitly handles a certain range of 3D object pose improve the robustness against background clutters. BPT which is an efficient tree-based data structures for a large number of templates and template matching on rearranged feature maps where nearby features are linearly aligned accelerate the pose estimation. The experimental evaluation on tabletop and bin-picking dataset showed that our method achieved higher accuracy and faster speed in comparison with state-of-the-art techniques including recent CNN based approaches. Moreover, our model templates can be trained only from 3D CAD in a few minutes and the pose estimation run in near real-time (23 fps) on CPU. These features are suitable for any real applications.
Tasks	6D Pose Estimation using RGB, Pose Estimation
Published	2018-11-21
URL	https://arxiv.org/abs/1811.08588v3
PDF	https://arxiv.org/pdf/1811.08588v3.pdf
PWC	https://paperswithcode.com/paper/real-time-6d-object-pose-estimation-on-cpu
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An Efficient ADMM-Based Algorithm to Nonconvex Penalized Support Vector Machines


Title	An Efficient ADMM-Based Algorithm to Nonconvex Penalized Support Vector Machines
Authors	Lei Guan, Linbo Qiao, Dongsheng Li, Tao Sun, Keshi Ge, Xicheng Lu
Abstract	Support vector machines (SVMs) with sparsity-inducing nonconvex penalties have received considerable attentions for the characteristics of automatic classification and variable selection. However, it is quite challenging to solve the nonconvex penalized SVMs due to their nondifferentiability, nonsmoothness and nonconvexity. In this paper, we propose an efficient ADMM-based algorithm to the nonconvex penalized SVMs. The proposed algorithm covers a large class of commonly used nonconvex regularization terms including the smooth clipped absolute deviation (SCAD) penalty, minimax concave penalty (MCP), log-sum penalty (LSP) and capped-$\ell_1$ penalty. The computational complexity analysis shows that the proposed algorithm enjoys low computational cost. Moreover, the convergence of the proposed algorithm is guaranteed. Extensive experimental evaluations on five benchmark datasets demonstrate the superior performance of the proposed algorithm to other three state-of-the-art approaches.
Tasks
Published	2018-09-11
URL	http://arxiv.org/abs/1809.03655v1
PDF	http://arxiv.org/pdf/1809.03655v1.pdf
PWC	https://paperswithcode.com/paper/an-efficient-admm-based-algorithm-to
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Improving Image Clustering With Multiple Pretrained CNN Feature Extractors


Title	Improving Image Clustering With Multiple Pretrained CNN Feature Extractors
Authors	Joris Guérin, Byron Boots
Abstract	For many image clustering problems, replacing raw image data with features extracted by a pretrained convolutional neural network (CNN), leads to better clustering performance. However, the specific features extracted, and, by extension, the selected CNN architecture, can have a major impact on the clustering results. In practice, this crucial design choice is often decided arbitrarily due to the impossibility of using cross-validation with unsupervised learning problems. However, information contained in the different pretrained CNN architectures may be complementary, even when pretrained on the same data. To improve clustering performance, we rephrase the image clustering problem as a multi-view clustering (MVC) problem that considers multiple different pretrained feature extractors as different “views” of the same data. We then propose a multi-input neural network architecture that is trained end-to-end to solve the MVC problem effectively. Our experimental results, conducted on three different natural image datasets, show that: 1. using multiple pretrained CNNs jointly as feature extractors improves image clustering; 2. using an end-to-end approach improves MVC; and 3. combining both produces state-of-the-art results for the problem of image clustering.
Tasks	Image Clustering
Published	2018-07-20
URL	http://arxiv.org/abs/1807.07760v1
PDF	http://arxiv.org/pdf/1807.07760v1.pdf
PWC	https://paperswithcode.com/paper/improving-image-clustering-with-multiple
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Privacy Preserving Multi-Agent Planning with Provable Guarantees


Title	Privacy Preserving Multi-Agent Planning with Provable Guarantees
Authors	Amos Beimel, Ronen I. Brafman
Abstract	In privacy-preserving multi-agent planning, a group of agents attempt to cooperatively solve a multi-agent planning problem while maintaining private their data and actions. Although much work was carried out in this area in past years, its theoretical foundations have not been fully worked out. Specifically, although algorithms with precise privacy guarantees exist, even their most efficient implementations are not fast enough on realistic instances, whereas for practical algorithms no meaningful privacy guarantees exist. Secure-MAFS, a variant of the multi-agent forward search algorithm (MAFS) is the only practical algorithm to attempt to offer more precise guarantees, but only in very limited settings and with proof sketches only. In this paper we formulate a precise notion of secure computation for search-based algorithms and prove that Secure MAFS has this property in all domains.
Tasks
Published	2018-10-31
URL	http://arxiv.org/abs/1810.13354v2
PDF	http://arxiv.org/pdf/1810.13354v2.pdf
PWC	https://paperswithcode.com/paper/privacy-preserving-multi-agent-planning-with
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Predicting Adversarial Examples with High Confidence


Title	Predicting Adversarial Examples with High Confidence
Authors	Angus Galloway, Graham W. Taylor, Medhat Moussa
Abstract	It has been suggested that adversarial examples cause deep learning models to make incorrect predictions with high confidence. In this work, we take the opposite stance: an overly confident model is more likely to be vulnerable to adversarial examples. This work is one of the most proactive approaches taken to date, as we link robustness with non-calibrated model confidence on noisy images, providing a data-augmentation-free path forward. The adversarial examples phenomenon is most easily explained by the trend of increasing non-regularized model capacity, while the diversity and number of samples in common datasets has remained flat. Test accuracy has incorrectly been associated with true generalization performance, ignoring that training and test splits are often extremely similar in terms of the overall representation space. The transferability property of adversarial examples was previously used as evidence against overfitting arguments, a perceived random effect, but overfitting is not always random.
Tasks	Data Augmentation
Published	2018-02-13
URL	http://arxiv.org/abs/1802.04457v1
PDF	http://arxiv.org/pdf/1802.04457v1.pdf
PWC	https://paperswithcode.com/paper/predicting-adversarial-examples-with-high
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Facial Information Recovery from Heavily Damaged Images using Generative Adversarial Network- PART 1


Title	Facial Information Recovery from Heavily Damaged Images using Generative Adversarial Network- PART 1
Authors	Pushparaja Murugan
Abstract	Over the past decades, a large number of techniques have emerged in modern imaging systems to capture the exact information of the original scene regardless of shake, motion, lighting conditions and etc., These developments have progressively addressed the acquisition of images in high speed and high resolutions. However, the various ineradicable real-time factors cause the degradation of the information and the quality of the acquired images. The available techniques are not intelligent enough to generalize this complex phenomenon. Hence, it is necessary to develop an intellectual framework to recover the possible information presented in the original scene. In this article, we propose a kernel free framework based on conditional-GAN to recover the information from the heavily damaged images. The degradation of images is assumed to be occurred by the combination of a various blur. Learning parameter of the cGAN is optimized by multi-component loss function that includes improved wasserstein loss with regression loss function. The generator module of this network is developed by using U-Net architecture with local Residual connections and global skip connection. Local connections and a global skip connection are implemented for the utilization of all stages of features. Generated images show that the network has the potential to recover the probable information of blurred images from the learned features. This research work is carried out as a part of our IOP studio software ‘Facial recognition module’.
Tasks
Published	2018-08-27
URL	http://arxiv.org/abs/1808.08867v1
PDF	http://arxiv.org/pdf/1808.08867v1.pdf
PWC	https://paperswithcode.com/paper/facial-information-recovery-from-heavily
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