Paper Group ANR 520
An End-to-End Deep Learning Histochemical Scoring System for Breast Cancer Tissue Microarray. Writing Style Invariant Deep Learning Model for Historical Manuscripts Alignment. Anomaly detection with Wasserstein GAN. Probabilistic DL Reasoning with Pinpointing Formulas: A Prolog-based Approach. Learning sparse mixtures of rankings from noisy informa …
An End-to-End Deep Learning Histochemical Scoring System for Breast Cancer Tissue Microarray
| Title | An End-to-End Deep Learning Histochemical Scoring System for Breast Cancer Tissue Microarray |
| Authors | Jingxin Liu, Bolei Xu, Chi Zheng, Yuanhao Gong, Jon Garibaldi, Daniele Soria, Andew Green, Ian O. Ellis, Wenbin Zou, Guoping Qiu |
| Abstract | One of the methods for stratifying different molecular classes of breast cancer is the Nottingham Prognostic Index Plus (NPI+) which uses breast cancer relevant biomarkers to stain tumour tissues prepared on tissue microarray (TMA). To determine the molecular class of the tumour, pathologists will have to manually mark the nuclei activity biomarkers through a microscope and use a semi-quantitative assessment method to assign a histochemical score (H-Score) to each TMA core. Manually marking positively stained nuclei is a time consuming, imprecise and subjective process which will lead to inter-observer and intra-observer discrepancies. In this paper, we present an end-to-end deep learning system which directly predicts the H-Score automatically. Our system imitates the pathologists’ decision process and uses one fully convolutional network (FCN) to extract all nuclei region (tumour and non-tumour), a second FCN to extract tumour nuclei region, and a multi-column convolutional neural network which takes the outputs of the first two FCNs and the stain intensity description image as input and acts as the high-level decision making mechanism to directly output the H-Score of the input TMA image. To the best of our knowledge, this is the first end-to-end system that takes a TMA image as input and directly outputs a clinical score. We will present experimental results which demonstrate that the H-Scores predicted by our model have very high and statistically significant correlation with experienced pathologists’ scores and that the H-Score discrepancy between our algorithm and the pathologists is on par with the inter-subject discrepancy between the pathologists. |
| Tasks | Decision Making |
| Published | 2018-01-19 |
| URL | http://arxiv.org/abs/1801.06288v1 |
| http://arxiv.org/pdf/1801.06288v1.pdf | |
| PWC | https://paperswithcode.com/paper/an-end-to-end-deep-learning-histochemical |
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Writing Style Invariant Deep Learning Model for Historical Manuscripts Alignment
| Title | Writing Style Invariant Deep Learning Model for Historical Manuscripts Alignment |
| Authors | Majeed Kassis, Jumana Nassour, Jihad El-Sana |
| Abstract | Historical manuscript alignment is a widely known problem in document analysis. Finding the differences between manuscript editions is mostly done manually. In this paper, we present a writer independent deep learning model which is trained on several writing styles, and able to achieve high detection accuracy when tested on writing styles not present in training data. We test our model using cross validation, each time we train the model on five manuscripts, and test it on the other two manuscripts, never seen in the training data. We’ve applied cross validation on seven manuscripts, netting 21 different tests, achieving average accuracy of $%92.17$. We also present a new alignment algorithm based on dynamic sized sliding window, which is able to successfully handle complex cases. |
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| Published | 2018-06-07 |
| URL | http://arxiv.org/abs/1806.03987v1 |
| http://arxiv.org/pdf/1806.03987v1.pdf | |
| PWC | https://paperswithcode.com/paper/writing-style-invariant-deep-learning-model |
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Anomaly detection with Wasserstein GAN
| Title | Anomaly detection with Wasserstein GAN |
| Authors | Ilyass Haloui, Jayant Sen Gupta, Vincent Feuillard |
| Abstract | Generative adversarial networks are a class of generative algorithms that have been widely used to produce state-of-the-art samples. In this paper, we investigate GAN to perform anomaly detection on time series dataset. In order to achieve this goal, a bibliography is made focusing on theoretical properties of GAN and GAN used for anomaly detection. A Wasserstein GAN has been chosen to learn the representation of normal data distribution and a stacked encoder with the generator performs the anomaly detection. W-GAN with encoder seems to produce state of the art anomaly detection scores on MNIST dataset and we investigate its usage on multi-variate time series. |
| Tasks | Anomaly Detection, Time Series |
| Published | 2018-12-06 |
| URL | http://arxiv.org/abs/1812.02463v2 |
| http://arxiv.org/pdf/1812.02463v2.pdf | |
| PWC | https://paperswithcode.com/paper/anomaly-detection-with-wasserstein-gan |
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Probabilistic DL Reasoning with Pinpointing Formulas: A Prolog-based Approach
| Title | Probabilistic DL Reasoning with Pinpointing Formulas: A Prolog-based Approach |
| Authors | Riccardo Zese, Giuseppe Cota, Evelina Lamma, Elena Bellodi, Fabrizio Riguzzi |
| Abstract | When modeling real world domains we have to deal with information that is incomplete or that comes from sources with different trust levels. This motivates the need for managing uncertainty in the Semantic Web. To this purpose, we introduced a probabilistic semantics, named DISPONTE, in order to combine description logics with probability theory. The probability of a query can be then computed from the set of its explanations by building a Binary Decision Diagram (BDD). The set of explanations can be found using the tableau algorithm, which has to handle non-determinism. Prolog, with its efficient handling of non-determinism, is suitable for implementing the tableau algorithm. TRILL and TRILLP are systems offering a Prolog implementation of the tableau algorithm. TRILLP builds a pinpointing formula, that compactly represents the set of explanations and can be directly translated into a BDD. Both reasoners were shown to outperform state-of-the-art DL reasoners. In this paper, we present an improvement of TRILLP, named TORNADO, in which the BDD is directly built during the construction of the tableau, further speeding up the overall inference process. An experimental comparison shows the effectiveness of TORNADO. All systems can be tried online in the TRILL on SWISH web application at http://trill.ml.unife.it/. |
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| Published | 2018-09-17 |
| URL | http://arxiv.org/abs/1809.06180v3 |
| http://arxiv.org/pdf/1809.06180v3.pdf | |
| PWC | https://paperswithcode.com/paper/probabilistic-dl-reasoning-with-pinpointing |
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Learning sparse mixtures of rankings from noisy information
| Title | Learning sparse mixtures of rankings from noisy information |
| Authors | Anindya De, Ryan O’Donnell, Rocco Servedio |
| Abstract | We study the problem of learning an unknown mixture of $k$ rankings over $n$ elements, given access to noisy samples drawn from the unknown mixture. We consider a range of different noise models, including natural variants of the “heat kernel” noise framework and the Mallows model. For each of these noise models we give an algorithm which, under mild assumptions, learns the unknown mixture to high accuracy and runs in $n^{O(\log k)}$ time. The best previous algorithms for closely related problems have running times which are exponential in $k$. |
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| Published | 2018-11-03 |
| URL | http://arxiv.org/abs/1811.01216v1 |
| http://arxiv.org/pdf/1811.01216v1.pdf | |
| PWC | https://paperswithcode.com/paper/learning-sparse-mixtures-of-rankings-from |
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IDSGAN: Generative Adversarial Networks for Attack Generation against Intrusion Detection
| Title | IDSGAN: Generative Adversarial Networks for Attack Generation against Intrusion Detection |
| Authors | Zilong Lin, Yong Shi, Zhi Xue |
| Abstract | As an important tool in security, the intrusion detection system bears the responsibility of the defense to network attacks performed by malicious traffic. Nowadays, with the help of machine learning algorithms, the intrusion detection system develops rapidly. However, the robustness of this system is questionable when it faces the adversarial attacks. To improve the detection system, more potential attack approaches should be researched. In this paper, a framework of the generative adversarial networks, IDSGAN, is proposed to generate the adversarial attacks, which can deceive and evade the intrusion detection system. Considering that the internal structure of the detection system is unknown to attackers, adversarial attack examples perform the black-box attacks against the detection system. IDSGAN leverages a generator to transform original malicious traffic into adversarial malicious traffic. A discriminator classifies traffic examples and simulates the black-box detection system. More significantly, we only modify part of the attacks’ nonfunctional features to guarantee the validity of the intrusion. Based on the dataset NSL-KDD, the feasibility of the model is demonstrated to attack many detection systems with different attacks and the excellent results are achieved. Moreover, the robustness of IDSGAN is verified by changing the amount of the unmodified features. |
| Tasks | Adversarial Attack, Intrusion Detection |
| Published | 2018-09-06 |
| URL | https://arxiv.org/abs/1809.02077v3 |
| https://arxiv.org/pdf/1809.02077v3.pdf | |
| PWC | https://paperswithcode.com/paper/idsgan-generative-adversarial-networks-for |
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An Interpretable Model with Globally Consistent Explanations for Credit Risk
| Title | An Interpretable Model with Globally Consistent Explanations for Credit Risk |
| Authors | Chaofan Chen, Kangcheng Lin, Cynthia Rudin, Yaron Shaposhnik, Sijia Wang, Tong Wang |
| Abstract | We propose a possible solution to a public challenge posed by the Fair Isaac Corporation (FICO), which is to provide an explainable model for credit risk assessment. Rather than present a black box model and explain it afterwards, we provide a globally interpretable model that is as accurate as other neural networks. Our “two-layer additive risk model” is decomposable into subscales, where each node in the second layer represents a meaningful subscale, and all of the nonlinearities are transparent. We provide three types of explanations that are simpler than, but consistent with, the global model. One of these explanation methods involves solving a minimum set cover problem to find high-support globally-consistent explanations. We present a new online visualization tool to allow users to explore the global model and its explanations. |
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| Published | 2018-11-30 |
| URL | http://arxiv.org/abs/1811.12615v1 |
| http://arxiv.org/pdf/1811.12615v1.pdf | |
| PWC | https://paperswithcode.com/paper/an-interpretable-model-with-globally |
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BDD100K: A Diverse Driving Video Database with Scalable Annotation Tooling
| Title | BDD100K: A Diverse Driving Video Database with Scalable Annotation Tooling |
| Authors | Fisher Yu, Wenqi Xian, Yingying Chen, Fangchen Liu, Mike Liao, Vashisht Madhavan, Trevor Darrell |
| Abstract | Datasets drive vision progress and autonomous driving is a critical vision application, yet existing driving datasets are impoverished in terms of visual content. Driving imagery is becoming plentiful, but annotation is slow and expensive, as annotation tools have not kept pace with the flood of data. Our first contribution is the design and implementation of a scalable annotation system that can provide a comprehensive set of image labels for large-scale driving datasets. Our second contribution is a new driving dataset, facilitated by our tooling, which is an order of magnitude larger than previous efforts, and is comprised of over 100K videos with diverse kinds of annotations including image level tagging, object bounding boxes, drivable areas, lane markings, and full-frame instance segmentation. The dataset possesses geographic, environmental, and weather diversity, which is useful for training models so that they are less likely to be surprised by new conditions. The dataset can be requested at http://bdd-data.berkeley.edu. |
| Tasks | Autonomous Driving, Instance Segmentation, Semantic Segmentation |
| Published | 2018-05-12 |
| URL | http://arxiv.org/abs/1805.04687v1 |
| http://arxiv.org/pdf/1805.04687v1.pdf | |
| PWC | https://paperswithcode.com/paper/bdd100k-a-diverse-driving-video-database-with |
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A Matrix Approach for Weighted Argumentation Frameworks: a Preliminary Report
| Title | A Matrix Approach for Weighted Argumentation Frameworks: a Preliminary Report |
| Authors | Stefano Bistarelli, Alessandra Tappini, Carlo Taticchi |
| Abstract | The assignment of weights to attacks in a classical Argumentation Framework allows to compute semantics by taking into account the different importance of each argument. We represent a Weighted Argumentation Framework by a non-binary matrix, and we characterize the basic extensions (such as w-admissible, w- stable, w-complete) by analysing sub-blocks of this matrix. Also, we show how to reduce the matrix into another one of smaller size, that is equivalent to the original one for the determination of extensions. Furthermore, we provide two algorithms that allow to build incrementally w-grounded and w-preferred extensions starting from a w-admissible extension. |
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| Published | 2018-02-23 |
| URL | http://arxiv.org/abs/1802.08445v1 |
| http://arxiv.org/pdf/1802.08445v1.pdf | |
| PWC | https://paperswithcode.com/paper/a-matrix-approach-for-weighted-argumentation |
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A Scale Invariant Approach for Sparse Signal Recovery
| Title | A Scale Invariant Approach for Sparse Signal Recovery |
| Authors | Yaghoub Rahimi, Chao Wang, Hongbo Dong, Yifei Lou |
| Abstract | In this paper, we study the ratio of the $L_1 $ and $L_2 $ norms, denoted as $L_1/L_2$, to promote sparsity. Due to the non-convexity and non-linearity, there has been little attention to this scale-invariant model. Compared to popular models in the literature such as the $L_p$ model for $p\in(0,1)$ and the transformed $L_1$ (TL1), this ratio model is parameter free. Theoretically, we present a strong null space property (sNSP) and prove that any sparse vector is a local minimizer of the $L_1 /L_2 $ model provided with this sNSP condition. Computationally, we focus on a constrained formulation that can be solved via the alternating direction method of multipliers (ADMM). Experiments show that the proposed approach is comparable to the state-of-the-art methods in sparse recovery. In addition, a variant of the $L_1/L_2$ model to apply on the gradient is also discussed with a proof-of-concept example of the MRI reconstruction. |
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| Published | 2018-12-20 |
| URL | https://arxiv.org/abs/1812.08852v4 |
| https://arxiv.org/pdf/1812.08852v4.pdf | |
| PWC | https://paperswithcode.com/paper/a-scale-invariant-approach-for-sparse-signal |
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Active Learning of Uniformly Accurate Inter-atomic Potentials for Materials Simulation
| Title | Active Learning of Uniformly Accurate Inter-atomic Potentials for Materials Simulation |
| Authors | Linfeng Zhang, De-Ye Lin, Han Wang, Roberto Car, Weinan E |
| Abstract | An active learning procedure called Deep Potential Generator (DP-GEN) is proposed for the construction of accurate and transferable machine learning-based models of the potential energy surface (PES) for the molecular modeling of materials. This procedure consists of three main components: exploration, generation of accurate reference data, and training. Application to the sample systems of Al, Mg and Al-Mg alloys demonstrates that DP-GEN can produce uniformly accurate PES models with a minimal number of reference data. |
| Tasks | Active Learning |
| Published | 2018-10-28 |
| URL | http://arxiv.org/abs/1810.11890v2 |
| http://arxiv.org/pdf/1810.11890v2.pdf | |
| PWC | https://paperswithcode.com/paper/active-learning-of-uniformly-accurate-inter |
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A comprehensive study of sparse representation techniques for offline signature verification
| Title | A comprehensive study of sparse representation techniques for offline signature verification |
| Authors | Elias N. Zois, Dimitrios Tsourounis, Ilias Theodorakopoulos, Anastasios Kesidis, George Economou |
| Abstract | In this work, a feature extraction method for offline signature verification is presented that harnesses the power of sparse representation in order to deliver state-of-the-art verification performance in several signature datasets like CEDAR, MCYT-75, GPDS and UTSIG. Beyond the accuracy improvements, several major parameters associated with sparse representation; such as selected configuration, dictionary size, sparsity level and positivity priors are investigated. Besides, it is evinced that 2nd order statistics of the sparse codes is a powerful pooling function for the formation of the global signature descriptor. Also, a thorough evaluation of the effects of preprocessing is introduced by an automated algorithm in order to select the optimum thinning level. Finally, a segmentation strategy which employs a special form of spatial pyramid tailored to the problem of sparse representation is presented along with the enhancing of the produced descriptor on meaningful areas of the signature as emerged from the BRISK key-point detection mechanism. The obtained state-of-the-art results on the most challenging signature datasets provide a strong indication towards the benefits of learned features, even in writer dependent (WD) scenarios with a unique model for each writer and only a few available reference samples of him/her. |
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| Published | 2018-07-13 |
| URL | http://arxiv.org/abs/1807.05039v4 |
| http://arxiv.org/pdf/1807.05039v4.pdf | |
| PWC | https://paperswithcode.com/paper/a-comprehensive-study-of-sparse |
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HUMBI: A Large Multiview Dataset of Human Body Expressions
| Title | HUMBI: A Large Multiview Dataset of Human Body Expressions |
| Authors | Zhixuan Yu, Jae Shin Yoon, In Kyu Lee, Prashanth Venkatesh, Jaesik Park, Jihun Yu, Hyun Soo Park |
| Abstract | This paper presents a new large multiview dataset called HUMBI for human body expressions with natural clothing. The goal of the HUMBI is to facilitate modeling view specific appearance and geometry of gaze, face, hand, body, and garment from assorted people. 107 synchronized high-definition cameras (70 cameras facing at the front body) are used to capture 772 distinctive subjects across gender, ethnicity, age, and physical condition. With the multiview image streams, we reconstruct high fidelity body expressions using 3D mesh models, which allows representing view specific appearance using their canonical atlas. We demonstrate that the HUMBI is highly effective in learning and reconstructing a complete human model and is complementary to the existing datasets of human expressions with sparse views or limited subjects such as MPII Gaze, Multi-PIE, Human 3.6M, and Panoptic Studio datasets. |
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| Published | 2018-12-01 |
| URL | https://arxiv.org/abs/1812.00281v2 |
| https://arxiv.org/pdf/1812.00281v2.pdf | |
| PWC | https://paperswithcode.com/paper/humbi-10-human-multiview-behavioral-imaging |
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Compressive Sampling Approach for Image Acquisition with Lensless Endoscope
| Title | Compressive Sampling Approach for Image Acquisition with Lensless Endoscope |
| Authors | Stéphanie Guérit, Siddharth Sivankutty, Camille Scotté, John Alto Lee, Hervé Rigneault, Laurent Jacques |
| Abstract | The lensless endoscope is a promising device designed to image tissues in vivo at the cellular scale. The traditional acquisition setup consists in raster scanning during which the focused light beam from the optical fiber illuminates sequentially each pixel of the field of view (FOV). The calibration step to focus the beam and the sampling scheme both take time. In this preliminary work, we propose a scanning method based on compressive sampling theory. The method does not rely on a focused beam but rather on the random illumination patterns generated by the single-mode fibers. Experiments are performed on synthetic data for different compression rates (from 10 to 100% of the FOV). |
| Tasks | Calibration |
| Published | 2018-10-29 |
| URL | http://arxiv.org/abs/1810.12286v2 |
| http://arxiv.org/pdf/1810.12286v2.pdf | |
| PWC | https://paperswithcode.com/paper/compressive-sampling-approach-for-image |
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EgoCoder: Intelligent Program Synthesis with Hierarchical Sequential Neural Network Model
| Title | EgoCoder: Intelligent Program Synthesis with Hierarchical Sequential Neural Network Model |
| Authors | Jiawei Zhang, Limeng Cui, Fisher B. Gouza |
| Abstract | Programming has been an important skill for researchers and practitioners in computer science and other related areas. To learn basic programing skills, a long-time systematic training is usually required for beginners. According to a recent market report, the computer software market is expected to continue expanding at an accelerating speed, but the market supply of qualified software developers can hardly meet such a huge demand. In recent years, the surge of text generation research works provides the opportunities to address such a dilemma through automatic program synthesis. In this paper, we propose to make our try to solve the program synthesis problem from a data mining perspective. To address the problem, a novel generative model, namely EgoCoder, will be introduced in this paper. EgoCoder effectively parses program code into abstract syntax trees (ASTs), where the tree nodes will contain the program code/comment content and the tree structure can capture the program logic flows. Based on a new unit model called Hsu, EgoCoder can effectively capture both the hierarchical and sequential patterns in the program ASTs. Extensive experiments will be done to compare EgoCoder with the state-of-the-art text generation methods, and the experimental results have demonstrated the effectiveness of EgoCoder in addressing the program synthesis problem. |
| Tasks | Program Synthesis, Text Generation |
| Published | 2018-05-22 |
| URL | http://arxiv.org/abs/1805.08747v1 |
| http://arxiv.org/pdf/1805.08747v1.pdf | |
| PWC | https://paperswithcode.com/paper/egocoder-intelligent-program-synthesis-with |
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