Paper Group ANR 314
Collaborative Filtering and Multi-Label Classification with Matrix Factorization. Neighborhood Cognition Consistent Multi-Agent Reinforcement Learning. An Adaptive Stochastic Nesterov Accelerated Quasi Newton Method for Training RNNs. Deep Multi Label Classification in Affine Subspaces. Deep Learning for UL/DL Channel Calibration in Generic Massive …
Collaborative Filtering and Multi-Label Classification with Matrix Factorization
| Title | Collaborative Filtering and Multi-Label Classification with Matrix Factorization |
| Authors | Vikas Kumar |
| Abstract | Machine learning techniques for Recommendation System (RS) and Classification has become a prime focus of research to tackle the problem of information overload. RS are software tools that aim at making informed decisions about the services that a user may like. On the other hand, classification technique deals with the categorization of a data object into one of the several predefined classes. In the multi-label classification problem, unlike the traditional multi-class classification setting, each instance can be simultaneously associated with a subset of labels. The focus of thesis is on the development of novel techniques for collaborative filtering and multi-label classification. We propose a novel method of constructing a hierarchical bi-level maximum margin matrix factorization to handle matrix completion of ordinal rating matrix. Taking the cue from the alternative formulation of support vector machines, a novel loss function is derived by considering proximity as an alternative criterion instead of margin maximization criterion for matrix factorization framework. We extended the concept of matrix factorization for yet another important problem of machine learning namely multi-label classification which deals with the classification of data with multiple labels. We propose a novel piecewise-linear embedding method with a low-rank constraint on parametrization to capture nonlinear intrinsic relationships that exist in the original feature and label space. We also study the embedding of labels together with the group information with an objective to build an efficient multi-label classifier. We assume the existence of a low-dimensional space onto which the feature vectors and label vectors can be embedded. We ensure that labels belonging to the same group share the same sparsity pattern in their low-rank representations. |
| Tasks | Matrix Completion, Multi-Label Classification |
| Published | 2019-07-23 |
| URL | https://arxiv.org/abs/1907.12365v1 |
| https://arxiv.org/pdf/1907.12365v1.pdf | |
| PWC | https://paperswithcode.com/paper/collaborative-filtering-and-multi-label |
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Neighborhood Cognition Consistent Multi-Agent Reinforcement Learning
| Title | Neighborhood Cognition Consistent Multi-Agent Reinforcement Learning |
| Authors | Hangyu Mao, Wulong Liu, Jianye Hao, Jun Luo, Dong Li, Zhengchao Zhang, Jun Wang, Zhen Xiao |
| Abstract | Social psychology and real experiences show that cognitive consistency plays an important role to keep human society in order: if people have a more consistent cognition about their environments, they are more likely to achieve better cooperation. Meanwhile, only cognitive consistency within a neighborhood matters because humans only interact directly with their neighbors. Inspired by these observations, we take the first step to introduce \emph{neighborhood cognitive consistency} (NCC) into multi-agent reinforcement learning (MARL). Our NCC design is quite general and can be easily combined with existing MARL methods. As examples, we propose neighborhood cognition consistent deep Q-learning and Actor-Critic to facilitate large-scale multi-agent cooperations. Extensive experiments on several challenging tasks (i.e., packet routing, wifi configuration, and Google football player control) justify the superior performance of our methods compared with state-of-the-art MARL approaches. |
| Tasks | Multi-agent Reinforcement Learning, Q-Learning |
| Published | 2019-12-03 |
| URL | https://arxiv.org/abs/1912.01160v2 |
| https://arxiv.org/pdf/1912.01160v2.pdf | |
| PWC | https://paperswithcode.com/paper/neighborhood-cognition-consistent-multi-agent |
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An Adaptive Stochastic Nesterov Accelerated Quasi Newton Method for Training RNNs
| Title | An Adaptive Stochastic Nesterov Accelerated Quasi Newton Method for Training RNNs |
| Authors | S. Indrapriyadarsini, Shahrzad Mahboubi, Hiroshi Ninomiya, Hideki Asai |
| Abstract | A common problem in training neural networks is the vanishing and/or exploding gradient problem which is more prominently seen in training of Recurrent Neural Networks (RNNs). Thus several algorithms have been proposed for training RNNs. This paper proposes a novel adaptive stochastic Nesterov accelerated quasiNewton (aSNAQ) method for training RNNs. The proposed method aSNAQ is an accelerated method that uses the Nesterov’s gradient term along with second order curvature information. The performance of the proposed method is evaluated in Tensorflow on benchmark sequence modeling problems. The results show an improved performance while maintaining a low per-iteration cost and thus can be effectively used to train RNNs. |
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| Published | 2019-09-09 |
| URL | https://arxiv.org/abs/1909.03620v1 |
| https://arxiv.org/pdf/1909.03620v1.pdf | |
| PWC | https://paperswithcode.com/paper/an-adaptive-stochastic-nesterov-accelerated |
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Deep Multi Label Classification in Affine Subspaces
| Title | Deep Multi Label Classification in Affine Subspaces |
| Authors | Thomas Kurmann, Pablo Marquez Neila, Sebastian Wolf, Raphael Sznitman |
| Abstract | Multi-label classification (MLC) problems are becoming increasingly popular in the context of medical imaging. This has in part been driven by the fact that acquiring annotations for MLC is far less burdensome than for semantic segmentation and yet provides more expressiveness than multi-class classification. However, to train MLCs, most methods have resorted to similar objective functions as with traditional multi-class classification settings. We show in this work that such approaches are not optimal and instead propose a novel deep MLC classification method in affine subspace. At its core, the method attempts to pull features of class-labels towards different affine subspaces while maximizing the distance between them. We evaluate the method using two MLC medical imaging datasets and show a large performance increase compared to previous multi-label frameworks. This method can be seen as a plug-in replacement loss function and is trainable in an end-to-end fashion. |
| Tasks | Multi-Label Classification, Semantic Segmentation |
| Published | 2019-07-10 |
| URL | https://arxiv.org/abs/1907.04563v1 |
| https://arxiv.org/pdf/1907.04563v1.pdf | |
| PWC | https://paperswithcode.com/paper/deep-multi-label-classification-in-affine |
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Deep Learning for UL/DL Channel Calibration in Generic Massive MIMO Systems
| Title | Deep Learning for UL/DL Channel Calibration in Generic Massive MIMO Systems |
| Authors | Chongwen Huang, George C. Alexandropoulos, Alessio Zappone, Chau Yuen, Mérouane Debbah |
| Abstract | One of the fundamental challenges to realize massive Multiple-Input Multiple-Output (MIMO) communications is the accurate acquisition of channel state information for a plurality of users at the base station. This is usually accomplished in the UpLink (UL) direction profiting from the time division duplexing mode. In practical base station transceivers, there exist inevitably nonlinear hardware components, like signal amplifiers and various analog filters, which complicates the calibration task. To deal with this challenge, we design a deep neural network for channel calibration between the UL and DownLink (DL) directions. During the initial training phase, the deep neural network is trained from both UL and DL channel measurements. We then leverage the trained deep neural network with the instantaneously estimated UL channel to calibrate the DL one, which is not observable during the UL transmission phase. Our numerical results confirm the merits of the proposed approach, and show that it can achieve performance comparable to conventional approaches, like the Agros method and methods based on least squares, that however assume linear hardware behavior models. More importantly, considering generic nonlinear relationships between the UL and DL channels, it is demonstrated that our deep neural network approach exhibits robust performance, even when the number of training sequences is limited. |
| Tasks | Calibration |
| Published | 2019-03-07 |
| URL | https://arxiv.org/abs/1903.02875v2 |
| https://arxiv.org/pdf/1903.02875v2.pdf | |
| PWC | https://paperswithcode.com/paper/deep-learning-for-uldl-channel-calibration-in |
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Coupled Network for Robust Pedestrian Detection with Gated Multi-Layer Feature Extraction and Deformable Occlusion Handling
| Title | Coupled Network for Robust Pedestrian Detection with Gated Multi-Layer Feature Extraction and Deformable Occlusion Handling |
| Authors | Tianrui Liu, Wenhan Luo, Lin Ma, Jun-Jie Huang, Tania Stathaki, Tianhong Dai |
| Abstract | Pedestrian detection methods have been significantly improved with the development of deep convolutional neural networks. Nevertheless, detecting small-scaled pedestrians and occluded pedestrians remains a challenging problem. In this paper, we propose a pedestrian detection method with a couple-network to simultaneously address these two issues. One of the sub-networks, the gated multi-layer feature extraction sub-network, aims to adaptively generate discriminative features for pedestrian candidates in order to robustly detect pedestrians with large variations on scales. The second sub-network targets in handling the occlusion problem of pedestrian detection by using deformable regional RoI-pooling. We investigate two different gate units for the gated sub-network, namely, the channel-wise gate unit and the spatio-wise gate unit, which can enhance the representation ability of the regional convolutional features among the channel dimensions or across the spatial domain, repetitively. Ablation studies have validated the effectiveness of both the proposed gated multi-layer feature extraction sub-network and the deformable occlusion handling sub-network. With the coupled framework, our proposed pedestrian detector achieves state-of-the-art results on the Caltech and the CityPersons pedestrian detection benchmarks. |
| Tasks | Pedestrian Detection |
| Published | 2019-12-18 |
| URL | https://arxiv.org/abs/1912.08661v1 |
| https://arxiv.org/pdf/1912.08661v1.pdf | |
| PWC | https://paperswithcode.com/paper/coupled-network-for-robust-pedestrian |
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Gated Multi-layer Convolutional Feature Extraction Network for Robust Pedestrian Detection
| Title | Gated Multi-layer Convolutional Feature Extraction Network for Robust Pedestrian Detection |
| Authors | Tianrui Liu, Jun-Jie Huang, Tianhong Dai, Guangyu Ren, Tania Stathaki |
| Abstract | Pedestrian detection methods have been significantly improved with the development of deep convolutional neural networks. Nevertheless, robustly detecting pedestrians with a large variant on sizes and with occlusions remains a challenging problem. In this paper, we propose a gated multi-layer convolutional feature extraction method which can adaptively generate discriminative features for candidate pedestrian regions. The proposed gated feature extraction framework consists of squeeze units, gate units and a concatenation layer which perform feature dimension squeezing, feature elements manipulation and convolutional features combination from multiple CNN layers, respectively. We proposed two different gate models which can manipulate the regional feature maps in a channel-wise selection manner and a spatial-wise selection manner, respectively. Experiments on the challenging CityPersons dataset demonstrate the effectiveness of the proposed method, especially on detecting those small-size and occluded pedestrians. |
| Tasks | Pedestrian Detection |
| Published | 2019-10-25 |
| URL | https://arxiv.org/abs/1910.11761v2 |
| https://arxiv.org/pdf/1910.11761v2.pdf | |
| PWC | https://paperswithcode.com/paper/gated-multi-layer-convolutional-feature |
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A Bayesian nonparametric test for conditional independence
| Title | A Bayesian nonparametric test for conditional independence |
| Authors | Onur Teymur, Sarah Filippi |
| Abstract | This article introduces a Bayesian nonparametric method for quantifying the relative evidence in a dataset in favour of the dependence or independence of two variables conditional on a third. The approach uses Polya tree priors on spaces of conditional probability densities, accounting for uncertainty in the form of the underlying distributions in a nonparametric way. The Bayesian perspective provides an inherently symmetric probability measure of conditional dependence or independence, a feature particularly advantageous in causal discovery and not employed by any previous procedure of this type. |
| Tasks | Causal Discovery |
| Published | 2019-10-24 |
| URL | https://arxiv.org/abs/1910.11219v1 |
| https://arxiv.org/pdf/1910.11219v1.pdf | |
| PWC | https://paperswithcode.com/paper/a-bayesian-nonparametric-test-for-conditional |
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EdgeNet: Balancing Accuracy and Performance for Edge-based Convolutional Neural Network Object Detectors
| Title | EdgeNet: Balancing Accuracy and Performance for Edge-based Convolutional Neural Network Object Detectors |
| Authors | George Plastiras, Christos Kyrkou, Theocharis Theocharides |
| Abstract | Visual intelligence at the edge is becoming a growing necessity for low latency applications and situations where real-time decision is vital. Object detection, the first step in visual data analytics, has enjoyed significant improvements in terms of state-of-the-art accuracy due to the emergence of Convolutional Neural Networks (CNNs) and Deep Learning. However, such complex paradigms intrude increasing computational demands and hence prevent their deployment on resource-constrained devices. In this work, we propose a hierarchical framework that enables to detect objects in high-resolution video frames, and maintain the accuracy of state-of-the-art CNN-based object detectors while outperforming existing works in terms of processing speed when targeting a low-power embedded processor using an intelligent data reduction mechanism. Moreover, a use-case for pedestrian detection from Unmanned-Areal-Vehicle (UAV) is presented showing the impact that the proposed approach has on sensitivity, average processing time and power consumption when is implemented on different platforms. Using the proposed selection process our framework manages to reduce the processed data by 100x leading to under 4W power consumption on different edge devices. |
| Tasks | Object Detection, Pedestrian Detection |
| Published | 2019-11-14 |
| URL | https://arxiv.org/abs/1911.06091v1 |
| https://arxiv.org/pdf/1911.06091v1.pdf | |
| PWC | https://paperswithcode.com/paper/edgenet-balancing-accuracy-and-performance |
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Linear Constrained Rayleigh Quotient Optimization: Theory and Algorithms
| Title | Linear Constrained Rayleigh Quotient Optimization: Theory and Algorithms |
| Authors | Yunshen Zhou, Zhaojun Bai, Ren-Cang Li |
| Abstract | We consider the following constrained Rayleigh quotient optimization problem (CRQopt) $$ \min_{x\in \mathbb{R}^n} x^{T}Ax,,\mbox{subject to},, x^{T}x=1,\mbox{and},C^{T}x=b, $$ where $A$ is an $n\times n$ real symmetric matrix and $C$ is an $n\times m$ real matrix. Usually, $m\ll n$. The problem is also known as the constrained eigenvalue problem in the literature because it becomes an eigenvalue problem if the linear constraint $C^{T}x=b$ is removed. We start by equivalently transforming CRQopt into an optimization problem, called LGopt, of minimizing the Lagrangian multiplier of CRQopt, and then an problem, called QEPmin, of finding the smallest eigenvalue of a quadratic eigenvalue problem. Although such equivalences has been discussed in the literature, it appears to be the first time that these equivalences are rigorously justified. Then we propose to numerically solve LGopt and QEPmin by the Krylov subspace projection method via the Lanczos process. The basic idea, as the Lanczos method for the symmetric eigenvalue problem, is to first reduce LGopt and QEPmin by projecting them onto Krylov subspaces to yield problems of the same types but of much smaller sizes, and then solve the reduced problems by some direct methods, which is either a secular equation solver (in the case of LGopt) or an eigensolver (in the case of QEPmin). The resulting algorithm is called the Lanczos algorithm. We perform convergence analysis for the proposed method and obtain error bounds. The sharpness of the error bound is demonstrated by artificial examples, although in applications the method often converges much faster than the bounds suggest. Finally, we apply the Lanczos algorithm to semi-supervised learning in the context of constrained clustering. |
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| Published | 2019-11-07 |
| URL | https://arxiv.org/abs/1911.02770v1 |
| https://arxiv.org/pdf/1911.02770v1.pdf | |
| PWC | https://paperswithcode.com/paper/linear-constrained-rayleigh-quotient |
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Generative Modeling for Small-Data Object Detection
| Title | Generative Modeling for Small-Data Object Detection |
| Authors | Lanlan Liu, Michael Muelly, Jia Deng, Tomas Pfister, Li-Jia Li |
| Abstract | This paper explores object detection in the small data regime, where only a limited number of annotated bounding boxes are available due to data rarity and annotation expense. This is a common challenge today with machine learning being applied to many new tasks where obtaining training data is more challenging, e.g. in medical images with rare diseases that doctors sometimes only see once in their life-time. In this work we explore this problem from a generative modeling perspective by learning to generate new images with associated bounding boxes, and using these for training an object detector. We show that simply training previously proposed generative models does not yield satisfactory performance due to them optimizing for image realism rather than object detection accuracy. To this end we develop a new model with a novel unrolling mechanism that jointly optimizes the generative model and a detector such that the generated images improve the performance of the detector. We show this method outperforms the state of the art on two challenging datasets, disease detection and small data pedestrian detection, improving the average precision on NIH Chest X-ray by a relative 20% and localization accuracy by a relative 50%. |
| Tasks | Object Detection, Pedestrian Detection |
| Published | 2019-10-16 |
| URL | https://arxiv.org/abs/1910.07169v1 |
| https://arxiv.org/pdf/1910.07169v1.pdf | |
| PWC | https://paperswithcode.com/paper/generative-modeling-for-small-data-object |
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Learning-based estimation of dielectric properties and tissue density in head models for personalized radio-frequency dosimetry
| Title | Learning-based estimation of dielectric properties and tissue density in head models for personalized radio-frequency dosimetry |
| Authors | Essam A. Rashed, Yinliang Diao, Akimasa Hirata |
| Abstract | Radio-frequency dosimetry is an important process in human safety and for compliance of related products. Recently, computational human models generated from medical images have often been used for such assessment, especially to consider the inter-variability of subjects. However, the common procedure to develop personalized models is time consuming because it involves excessive segmentation of several components that represent different biological tissues, which limits the inter-variability assessment of radiation safety based on personalized dosimetry. Deep learning methods have been shown to be a powerful approach for pattern recognition and signal analysis. Convolutional neural networks with deep architecture are proven robust for feature extraction and image mapping in several biomedical applications. In this study, we develop a learning-based approach for fast and accurate estimation of the dielectric properties and density of tissues directly from magnetic resonance images in a single shot. The smooth distribution of the dielectric properties in head models, which is realized using a process without tissue segmentation, improves the smoothness of the specific absorption rate (SAR) distribution compared with that in the commonly used procedure. The estimated SAR distributions, as well as that averaged over 10-g of tissue in a cubic shape, are found to be highly consistent with those computed using the conventional methods that employ segmentation. |
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| Published | 2019-11-04 |
| URL | https://arxiv.org/abs/1911.01220v3 |
| https://arxiv.org/pdf/1911.01220v3.pdf | |
| PWC | https://paperswithcode.com/paper/learning-based-estimation-of-dielectric |
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Bean Split Ratio for Dry Bean Canning Quality and Variety Analysis
| Title | Bean Split Ratio for Dry Bean Canning Quality and Variety Analysis |
| Authors | Yunfei Long, Amber Bassett, Karen Cichy, Addie Thompson, Daniel Morris |
| Abstract | Splits on canned beans appear in the process of preparation and canning. Researchers are studying how they are influenced by cooking environment and genotype. However, there is no existing method to automatically quantify or to characterize the severity of splits. To solve this, we propose two measures: the Bean Split Ratio (BSR) that quantifies the overall severity of splits, and the Bean Split Histogram (BSH) that characterizes the size distribution of splits. We create a pixel-wise segmentation method to automatically estimate these measures from images. We also present a bean dataset of recombinant inbred lines of two genotypes, use the BSR and BSH to assess canning quality, and explore heritability of these properties. |
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| Published | 2019-05-01 |
| URL | http://arxiv.org/abs/1905.00336v1 |
| http://arxiv.org/pdf/1905.00336v1.pdf | |
| PWC | https://paperswithcode.com/paper/bean-split-ratio-for-dry-bean-canning-quality |
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Integrating overlapping datasets using bivariate causal discovery
| Title | Integrating overlapping datasets using bivariate causal discovery |
| Authors | Anish Dhir, Ciarán M. Lee |
| Abstract | Causal knowledge is vital for effective reasoning in science, as causal relations, unlike correlations, allow one to reason about the outcomes of interventions. Algorithms that can discover causal relations from observational data are based on the assumption that all variables have been jointly measured in a single dataset. In many cases this assumption fails. Previous approaches to overcoming this shortcoming devised algorithms that returned all joint causal structures consistent with the conditional independence information contained in each individual dataset. But, as conditional independence tests only determine causal structure up to Markov equivalence, the number of consistent joint structures returned by these approaches can be quite large. The last decade has seen the development of elegant algorithms for discovering causal relations beyond conditional independence, which can distinguish among Markov equivalent structures. In this work we adapt and extend these so-called bivariate causal discovery algorithms to the problem of learning consistent causal structures from multiple datasets with overlapping variables belonging to the same generating process, providing a sound and complete algorithm that outperforms previous approaches on synthetic and real data. |
| Tasks | Causal Discovery |
| Published | 2019-10-24 |
| URL | https://arxiv.org/abs/1910.11356v2 |
| https://arxiv.org/pdf/1910.11356v2.pdf | |
| PWC | https://paperswithcode.com/paper/integrating-overlapping-datasets-using |
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Context-Aware Convolutional Neural Network for Grading of Colorectal Cancer Histology Images
| Title | Context-Aware Convolutional Neural Network for Grading of Colorectal Cancer Histology Images |
| Authors | Muhammad Shaban, Ruqayya Awan, Muhammad Moazam Fraz, Ayesha Azam, David Snead, Nasir M. Rajpoot |
| Abstract | Digital histology images are amenable to the application of convolutional neural network (CNN) for analysis due to the sheer size of pixel data present in them. CNNs are generally used for representation learning from small image patches (e.g. 224x224) extracted from digital histology images due to computational and memory constraints. However, this approach does not incorporate high-resolution contextual information in histology images. We propose a novel way to incorporate larger context by a context-aware neural network based on images with a dimension of 1,792x1,792 pixels. The proposed framework first encodes the local representation of a histology image into high dimensional features then aggregates the features by considering their spatial organization to make a final prediction. The proposed method is evaluated for colorectal cancer grading and breast cancer classification. A comprehensive analysis of some variants of the proposed method is presented. Our method outperformed the traditional patch-based approaches, problem-specific methods, and existing context-based methods quantitatively by a margin of 3.61%. Code and dataset related information is available at this link: https://tia-lab.github.io/Context-Aware-CNN |
| Tasks | Representation Learning |
| Published | 2019-07-22 |
| URL | https://arxiv.org/abs/1907.09478v1 |
| https://arxiv.org/pdf/1907.09478v1.pdf | |
| PWC | https://paperswithcode.com/paper/context-aware-convolutional-neural-network |
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