Paper Group ANR 1217
Pneumothorax Segmentation: Deep Learning Image Segmentation to predict Pneumothorax. Supervised Discrete Hashing with Relaxation. Mining Uncertain Event Data in Process Mining. On the Learnability of Deep Random Networks. Aff-Wild Database and AffWildNet. Adaptive Model Refinement with Batch Bayesian Sampling for Optimization of Bio-inspired Flow T …
Pneumothorax Segmentation: Deep Learning Image Segmentation to predict Pneumothorax
| Title | Pneumothorax Segmentation: Deep Learning Image Segmentation to predict Pneumothorax |
| Authors | Karan Jakhar, Rohit Bajaj, Ruchika Gupta |
| Abstract | Computer vision has shown promising results in medical image processing. Pneumothorax is a deadly condition and if not diagnosed and treated at time then it causes death. It can be diagnosed with chest X-ray images. We need an expert and experienced radiologist to predict whether a person is suffering from pneumothorax or not by looking at the chest X-ray images. Everyone does not have access to such a facility. Moreover, in some cases, we need quick diagnoses. So we propose an image segmentation model to predict and give the output a mask that will assist the doctor in taking this crucial decision. Deep Learning has proved their worth in many areas and outperformed man state-of-the-art models. We want to use the power of these deep learning model to solve this problem. We have used U-net [13] architecture with ResNet [17] as a backbone and achieved promising results. U-net [13] performs very well in medical image processing and semantic segmentation. Our problem falls in the semantic segmentation category. |
| Tasks | Semantic Segmentation |
| Published | 2019-12-16 |
| URL | https://arxiv.org/abs/1912.07329v1 |
| https://arxiv.org/pdf/1912.07329v1.pdf | |
| PWC | https://paperswithcode.com/paper/pneumothorax-segmentation-deep-learning-image |
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Supervised Discrete Hashing with Relaxation
| Title | Supervised Discrete Hashing with Relaxation |
| Authors | Jie Gui, Tongliang Liu, Zhenan Sun, Dacheng Tao, Tieniu Tan |
| Abstract | Data-dependent hashing has recently attracted attention due to being able to support efficient retrieval and storage of high-dimensional data such as documents, images, and videos. In this paper, we propose a novel learning-based hashing method called “Supervised Discrete Hashing with Relaxation” (SDHR) based on “Supervised Discrete Hashing” (SDH). SDH uses ordinary least squares regression and traditional zero-one matrix encoding of class label information as the regression target (code words), thus fixing the regression target. In SDHR, the regression target is instead optimized. The optimized regression target matrix satisfies a large margin constraint for correct classification of each example. Compared with SDH, which uses the traditional zero-one matrix, SDHR utilizes the learned regression target matrix and, therefore, more accurately measures the classification error of the regression model and is more flexible. As expected, SDHR generally outperforms SDH. Experimental results on two large-scale image datasets (CIFAR-10 and MNIST) and a large-scale and challenging face dataset (FRGC) demonstrate the effectiveness and efficiency of SDHR. |
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| Published | 2019-04-07 |
| URL | http://arxiv.org/abs/1904.03549v1 |
| http://arxiv.org/pdf/1904.03549v1.pdf | |
| PWC | https://paperswithcode.com/paper/supervised-discrete-hashing-with-relaxation |
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Mining Uncertain Event Data in Process Mining
| Title | Mining Uncertain Event Data in Process Mining |
| Authors | Marco Pegoraro, Wil M. P. van der Aalst |
| Abstract | Nowadays, more and more process data are automatically recorded by information systems, and made available in the form of event logs. Process mining techniques enable process-centric analysis of data, including automatically discovering process models and checking if event data conform to a certain model. In this paper we analyze the previously unexplored setting of uncertain event logs: logs where quantified uncertainty is recorded together with the corresponding data. We define a taxonomy of uncertain event logs and models, and we examine the challenges that uncertainty poses on process discovery and conformance checking. Finally, we show how upper and lower bounds for conformance can be obtained aligning an uncertain trace onto a regular process model. |
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| Published | 2019-09-20 |
| URL | https://arxiv.org/abs/1910.00089v3 |
| https://arxiv.org/pdf/1910.00089v3.pdf | |
| PWC | https://paperswithcode.com/paper/mining-uncertain-event-data-in-process-mining |
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On the Learnability of Deep Random Networks
| Title | On the Learnability of Deep Random Networks |
| Authors | Abhimanyu Das, Sreenivas Gollapudi, Ravi Kumar, Rina Panigrahy |
| Abstract | In this paper we study the learnability of deep random networks from both theoretical and practical points of view. On the theoretical front, we show that the learnability of random deep networks with sign activation drops exponentially with its depth. On the practical front, we find that the learnability drops sharply with depth even with the state-of-the-art training methods, suggesting that our stylized theoretical results are closer to reality. |
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| Published | 2019-04-08 |
| URL | http://arxiv.org/abs/1904.03866v1 |
| http://arxiv.org/pdf/1904.03866v1.pdf | |
| PWC | https://paperswithcode.com/paper/on-the-learnability-of-deep-random-networks |
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Aff-Wild Database and AffWildNet
| Title | Aff-Wild Database and AffWildNet |
| Authors | Mengyao Liu, Dimitrios Kollias |
| Abstract | In the context of HCI, building an automatic system to recognize affect of human facial expression in real-world condition is very crucial to make machine interact naturallisticaly with a man. However, existing facial emotion databases usually contain expression in the limited scenario under well-controlled condition. Aff-Wild is currently the largest database consisting of spontaneous facial expression in the wild annotated with valence and arousal. The first contribution of this project is the completion of extending Aff-Wild database which is fulfilled by collecting videos from YouTube on which the videos have spontaneous facial expressions in the wild, annotating videos with valence and arousal ranging in [-1,1], detecting faces in frames using FFLD2 detector and partitioning the whole data set into train, validate and test set, with 527056, 94223 and 135145 frames. The diversity is guaranteed regarding age, ethnicity and values of valence and arousal. The ratio of male to female is close to 1. Regarding the techniques used to build the automatic system, deep learning is outstanding since almost all winning methods in emotion challenges adopt DNN techniques. The second contribution of this project is that an end-to-end DNN is constructed to have joint CNN and RNN block and gives the estimation on valence and arousal for each frame in sequential data. VGGFace, ResNet, DenseNet with the corresponding pre-trained model for CNN block and LSTM, GRU, IndRNN, Attention mechanism for RNN block are experimented aiming to find the best combination. Fine tuning and transfer learning techniques are also tried out. By comparing the CCC evaluation value on test data, the best model is found to be pre-trained VGGFace connected with 2 layers GRU with attention mechanism. The models test performance is 0.555 CCC for valence with sequence length 80 and 0.499 CCC for arousal with sequence length 70. |
| Tasks | Transfer Learning |
| Published | 2019-10-11 |
| URL | https://arxiv.org/abs/1910.05318v2 |
| https://arxiv.org/pdf/1910.05318v2.pdf | |
| PWC | https://paperswithcode.com/paper/aff-wild-database-and-affwildnet |
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Adaptive Model Refinement with Batch Bayesian Sampling for Optimization of Bio-inspired Flow Tailoring
| Title | Adaptive Model Refinement with Batch Bayesian Sampling for Optimization of Bio-inspired Flow Tailoring |
| Authors | Payam Ghassemi, Sumeet Sanjay Lulekar, Souma Chowdhury |
| Abstract | This paper presents an advancement to an approach for model-independent surrogate-based optimization with adaptive batch sampling, known as Adaptive Model Refinement (AMR). While the original AMR method provides unique decisions with regards to “when” to sample and “how many” samples to add (to preserve the credibility of the optimization search process), it did not provide specific direction towards “where” to sample in the design variable space. This paper thus introduces the capability to identify optimum location to add new samples. The location of the infill points is decided by integrating a Gaussian Process-based criteria (“q-EI”), adopted from Bayesian optimization. The consideration of a penalization term to mitigate interaction among samples (in a batch) is crucial to effective integration of the q-EI criteria into AMR. The new AMR method, called AMR with Penalized Batch Bayesian Sampling (AMR-PBS) is tested on benchmark functions, demonstrating better performance compared to Bayesian EGO. In addition, it is successfully applied to design surface riblets for bio-inspired passive flow control (where high-fidelity samples are given by costly RANS CFD simulations), leading to a 10% drag reduction over the corresponding baseline (i.e., riblet-free aerodynamic surface). |
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| Published | 2019-05-31 |
| URL | https://arxiv.org/abs/1906.00793v1 |
| https://arxiv.org/pdf/1906.00793v1.pdf | |
| PWC | https://paperswithcode.com/paper/190600793 |
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Regressing Word and Sentence Embeddings for Regularization of Neural Machine Translation
| Title | Regressing Word and Sentence Embeddings for Regularization of Neural Machine Translation |
| Authors | Inigo Jauregi Unanue, Ehsan Zare Borzeshi, Massimo Piccardi |
| Abstract | In recent years, neural machine translation (NMT) has become the dominant approach in automated translation. However, like many other deep learning approaches, NMT suffers from overfitting when the amount of training data is limited. This is a serious issue for low-resource language pairs and many specialized translation domains that are inherently limited in the amount of available supervised data. For this reason, in this paper we propose regressing word (ReWE) and sentence (ReSE) embeddings at training time as a way to regularize NMT models and improve their generalization. During training, our models are trained to jointly predict categorical (words in the vocabulary) and continuous (word and sentence embeddings) outputs. An extensive set of experiments over four language pairs of variable training set size has showed that ReWE and ReSE can outperform strong state-of-the-art baseline models, with an improvement that is larger for smaller training sets (e.g., up to +5:15 BLEU points in Basque-English translation). Visualizations of the decoder’s output space show that the proposed regularizers improve the clustering of unique words, facilitating correct predictions. In a final experiment on unsupervised NMT, we show that ReWE and ReSE are also able to improve the quality of machine translation when no parallel data are available. |
| Tasks | Machine Translation, Sentence Embeddings |
| Published | 2019-09-30 |
| URL | https://arxiv.org/abs/1909.13466v1 |
| https://arxiv.org/pdf/1909.13466v1.pdf | |
| PWC | https://paperswithcode.com/paper/regressing-word-and-sentence-embeddings-for |
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Explainable Machine Learning for Scientific Insights and Discoveries
| Title | Explainable Machine Learning for Scientific Insights and Discoveries |
| Authors | Ribana Roscher, Bastian Bohn, Marco F. Duarte, Jochen Garcke |
| Abstract | Machine learning methods have been remarkably successful for a wide range of application areas in the extraction of essential information from data. An exciting and relatively recent development is the uptake of machine learning in the natural sciences, where the major goal is to obtain novel scientific insights and discoveries from observational or simulated data. A prerequisite for obtaining a scientific outcome is domain knowledge, which is needed to gain explainability, but also to enhance scientific consistency. In this article we review explainable machine learning in view of applications in the natural sciences and discuss three core elements which we identified as relevant in this context: transparency, interpretability, and explainability. With respect to these core elements, we provide a survey of recent scientific works that incorporate machine learning and the way that explainable machine learning is used in combination with domain knowledge from the application areas. |
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| Published | 2019-05-21 |
| URL | https://arxiv.org/abs/1905.08883v3 |
| https://arxiv.org/pdf/1905.08883v3.pdf | |
| PWC | https://paperswithcode.com/paper/explainable-machine-learning-for-scientific |
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C-RPNs: Promoting Object Detection in real world via a Cascade Structure of Region Proposal Networks
| Title | C-RPNs: Promoting Object Detection in real world via a Cascade Structure of Region Proposal Networks |
| Authors | Dongming Yang, YueXian Zou, Jian Zhang, Ge Li |
| Abstract | Recently, significant progresses have been made in object detection on common benchmarks (i.e., Pascal VOC). However, object detection in real world is still challenging due to the serious data imbalance. Images in real world are dominated by easy samples like the wide range of background and some easily recognizable objects, for example. Although two-stage detectors like Faster R-CNN achieved big successes in object detection due to the strategy of extracting region proposals by region proposal network, they show their poor adaption in real-world object detection as a result of without considering mining hard samples during extracting region proposals. To address this issue, we propose a Cascade framework of Region Proposal Networks, referred to as C-RPNs. The essence of C-RPNs is adopting multiple stages to mine hard samples while extracting region proposals and learn stronger classifiers. Meanwhile, a feature chain and a score chain are proposed to help learning more discriminative representations for proposals. Moreover, a loss function of cascade stages is designed to train cascade classifiers through backpropagation. Our proposed method has been evaluated on Pascal VOC and several challenging datasets like BSBDV 2017, CityPersons, etc. Our method achieves competitive results compared with the current state-of-the-arts and all-sided improvements in error analysis, validating its efficacy for detection in real world. |
| Tasks | Object Detection |
| Published | 2019-08-19 |
| URL | https://arxiv.org/abs/1908.06665v1 |
| https://arxiv.org/pdf/1908.06665v1.pdf | |
| PWC | https://paperswithcode.com/paper/c-rpns-promoting-object-detection-in-real |
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Defeats GAN: A Simpler Model Outperforms in Knowledge Representation Learning
| Title | Defeats GAN: A Simpler Model Outperforms in Knowledge Representation Learning |
| Authors | Heng Wang, Mingzhi Mao |
| Abstract | The goal of knowledge representation learning is to embed entities and relations into a low-dimensional, continuous vector space. How to push a model to its limit and obtain better results is of great significance in knowledge graph’s applications. We propose a simple and elegant method, Trans-DLR, whose main idea is dynamic learning rate control during training. Our method achieves remarkable improvement, compared with recent GAN-based method. Moreover, we introduce a new negative sampling trick which corrupts not only entities, but also relations, in different probabilities. We also develop an efficient way, which fully utilizes multiprocessing and parallel computing, to speed up evaluation of the model in link prediction tasks. Experiments show that our method is effective. |
| Tasks | Link Prediction, Representation Learning |
| Published | 2019-04-03 |
| URL | http://arxiv.org/abs/1904.01777v1 |
| http://arxiv.org/pdf/1904.01777v1.pdf | |
| PWC | https://paperswithcode.com/paper/defeats-gan-a-simpler-model-outperforms-in |
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Elucidating image-to-set prediction: An analysis of models, losses and datasets
| Title | Elucidating image-to-set prediction: An analysis of models, losses and datasets |
| Authors | Luis Pineda, Amaia Salvador, Michal Drozdzal, Adriana Romero |
| Abstract | In recent years, we have experienced a flurry of contributions in the multi-label classification literature. This problem has been framed under different perspectives, from predicting independent labels, to modeling label co-occurrences via architectural and/or loss function design. Despite great progress, it is still unclear which modeling choices are best suited to address this task, partially due to the lack of well defined benchmarks. Therefore, in this paper, we provide an in-depth analysis on five different computer vision datasets of increasing task complexity that are suitable for multi-label clasification (VOC, COCO, NUS-WIDE, ADE20k and Recipe1M). Our results show that (1) modeling label co-occurrences and predicting the number of labels that appear in the image is important, especially in high-dimensional output spaces; (2) carefully tuning hyper-parameters for very simple baselines leads to significant improvements, comparable to previously reported results; and (3) as a consequence of our analysis, we achieve state-of-the-art results on 3 datasets for which a fair comparison to previously published methods is feasible. |
| Tasks | Multi-Label Classification |
| Published | 2019-04-11 |
| URL | http://arxiv.org/abs/1904.05709v1 |
| http://arxiv.org/pdf/1904.05709v1.pdf | |
| PWC | https://paperswithcode.com/paper/elucidating-image-to-set-prediction-an |
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Discovering Bands from Graphs
| Title | Discovering Bands from Graphs |
| Authors | Nikolaj Tatti |
| Abstract | Discovering the underlying structure of a given graph is one of the fundamental goals in graph mining. Given a graph, we can often order vertices in a way that neighboring vertices have a higher probability of being connected to each other. This implies that the edges form a band around the diagonal in the adjacency matrix. Such structure may rise for example if the graph was created over time: each vertex had an active time interval during which the vertex was connected with other active vertices. The goal of this paper is to model this phenomenon. To this end, we formulate an optimization problem: given a graph and an integer $K$, we want to order graph vertices and partition the ordered adjacency matrix into $K$ bands such that bands closer to the diagonal are more dense. We measure the goodness of a segmentation using the log-likelihood of a log-linear model, a flexible family of distributions containing many standard distributions. We divide the problem into two subproblems: finding the order and finding the bands. We show that discovering bands can be done in polynomial time with isotonic regression, and we also introduce a heuristic iterative approach. For discovering the order we use Fiedler order accompanied with a simple combinatorial refinement. We demonstrate empirically that our heuristic works well in practice. |
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| Published | 2019-04-09 |
| URL | http://arxiv.org/abs/1904.04403v1 |
| http://arxiv.org/pdf/1904.04403v1.pdf | |
| PWC | https://paperswithcode.com/paper/discovering-bands-from-graphs |
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Explanation in Human-AI Systems: A Literature Meta-Review, Synopsis of Key Ideas and Publications, and Bibliography for Explainable AI
| Title | Explanation in Human-AI Systems: A Literature Meta-Review, Synopsis of Key Ideas and Publications, and Bibliography for Explainable AI |
| Authors | Shane T. Mueller, Robert R. Hoffman, William Clancey, Abigail Emrey, Gary Klein |
| Abstract | This is an integrative review that address the question, “What makes for a good explanation?” with reference to AI systems. Pertinent literatures are vast. Thus, this review is necessarily selective. That said, most of the key concepts and issues are expressed in this Report. The Report encapsulates the history of computer science efforts to create systems that explain and instruct (intelligent tutoring systems and expert systems). The Report expresses the explainability issues and challenges in modern AI, and presents capsule views of the leading psychological theories of explanation. Certain articles stand out by virtue of their particular relevance to XAI, and their methods, results, and key points are highlighted. It is recommended that AI/XAI researchers be encouraged to include in their research reports fuller details on their empirical or experimental methods, in the fashion of experimental psychology research reports: details on Participants, Instructions, Procedures, Tasks, Dependent Variables (operational definitions of the measures and metrics), Independent Variables (conditions), and Control Conditions. |
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| Published | 2019-02-05 |
| URL | http://arxiv.org/abs/1902.01876v1 |
| http://arxiv.org/pdf/1902.01876v1.pdf | |
| PWC | https://paperswithcode.com/paper/explanation-in-human-ai-systems-a-literature |
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Convolutional neural networks model improvements using demographics and image processing filters on chest x-rays
| Title | Convolutional neural networks model improvements using demographics and image processing filters on chest x-rays |
| Authors | Mir Muhammad Abdullah, Mir Muhammad Abdur Rahman, Mir Mohammed Assadullah |
| Abstract | Purpose: The purpose of this study was to observe change in accuracies of convolutional neural networks (CNN) models (ratio of correct classifications to total predictions) on thoracic radiological images by creating different binary classification models based on age, gender, and image pre-processing filters on 14 pathologies. Methodology: This is a quantitative research exploring variation in CNN model accuracies. Radiological thoracic images were divided by age and gender and pre-processed by various image processing filters. Findings: We found partial support for enhancement to model accuracies by segregating modeling images by age and gender and applying image processing filters even though image processing filters are sometimes thought of as information filters. Research limitations: This study may be biased because it is based on radiological images by another research that tagged the images using an automated process that was not checked by a human. Practical implications: Researchers may want to focus on creating models segregated by demographics and pre-process the modeling images using image processing filters. Practitioners developing assistive technologies for thoracic diagnoses may benefit from incorporating demographics and employing multiple models simultaneously with varying statistical likelihood. Originality/value: This study uses demographics in model creation and utilizes image processing filters to improve model performance. Keywords: Convolutional Neural Network (CNN), Chest X-Ray, ChestX-ray14, Lung, Atelectasis, Cardiomegaly, Consolidation, Edema, Effusion, Emphysema, Infiltration, Mass, Nodule, Pleural Thickening, Pneumonia, Pneumathorax |
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| Published | 2019-11-30 |
| URL | https://arxiv.org/abs/1912.00233v1 |
| https://arxiv.org/pdf/1912.00233v1.pdf | |
| PWC | https://paperswithcode.com/paper/convolutional-neural-networks-model |
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Estimating location parameters in entangled single-sample distributions
| Title | Estimating location parameters in entangled single-sample distributions |
| Authors | Ankit Pensia, Varun Jog, Po-Ling Loh |
| Abstract | We consider the problem of estimating the common mean of independently sampled data, where samples are drawn in a possibly non-identical manner from symmetric, unimodal distributions with a common mean. This generalizes the setting of Gaussian mixture modeling, since the number of distinct mixture components may diverge with the number of observations. We propose an estimator that adapts to the level of heterogeneity in the data, achieving near-optimality in both the i.i.d. setting and some heterogeneous settings, where the fraction of ``low-noise’’ points is as small as $\frac{\log n}{n}$. Our estimator is a hybrid of the modal interval, shorth, and median estimators from classical statistics; however, the key technical contributions rely on novel empirical process theory results that we derive for independent but non-i.i.d. data. In the multivariate setting, we generalize our theory to mean estimation for mixtures of radially symmetric distributions, and derive minimax lower bounds on the expected error of any estimator that is agnostic to the scales of individual data points. Finally, we describe an extension of our estimators applicable to linear regression. In the multivariate mean estimation and regression settings, we present computationally feasible versions of our estimators that run in time polynomial in the number of data points. | |
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| Published | 2019-07-06 |
| URL | https://arxiv.org/abs/1907.03087v1 |
| https://arxiv.org/pdf/1907.03087v1.pdf | |
| PWC | https://paperswithcode.com/paper/estimating-location-parameters-in-entangled |
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