Paper Group ANR 497
Fast View Synthesis with Deep Stereo Vision. ChestNet: A Deep Neural Network for Classification of Thoracic Diseases on Chest Radiography. Bayes-CPACE: PAC Optimal Exploration in Continuous Space Bayes-Adaptive Markov Decision Processes. Extractive Summarization of EHR Discharge Notes. Transforming Question Answering Datasets Into Natural Language …
Fast View Synthesis with Deep Stereo Vision
| Title | Fast View Synthesis with Deep Stereo Vision |
| Authors | Tewodros Habtegebrial, Kiran Varanasi, Christian Bailer, Didier Stricker |
| Abstract | Novel view synthesis is an important problem in computer vision and graphics. Over the years a large number of solutions have been put forward to solve the problem. However, the large-baseline novel view synthesis problem is far from being “solved”. Recent works have attempted to use Convolutional Neural Networks (CNNs) to solve view synthesis tasks. Due to the difficulty of learning scene geometry and interpreting camera motion, CNNs are often unable to generate realistic novel views. In this paper, we present a novel view synthesis approach based on stereo-vision and CNNs that decomposes the problem into two sub-tasks: view dependent geometry estimation and texture inpainting. Both tasks are structured prediction problems that could be effectively learned with CNNs. Experiments on the KITTI Odometry dataset show that our approach is more accurate and significantly faster than the current state-of-the-art. The code and supplementary material will be publicly available. Results could be found here https://youtu.be/5pzS9jc-5t0 |
| Tasks | Novel View Synthesis, Structured Prediction |
| Published | 2018-04-25 |
| URL | http://arxiv.org/abs/1804.09690v2 |
| http://arxiv.org/pdf/1804.09690v2.pdf | |
| PWC | https://paperswithcode.com/paper/fast-view-synthesis-with-deep-stereo-vision |
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ChestNet: A Deep Neural Network for Classification of Thoracic Diseases on Chest Radiography
| Title | ChestNet: A Deep Neural Network for Classification of Thoracic Diseases on Chest Radiography |
| Authors | Hongyu Wang, Yong Xia |
| Abstract | Computer-aided techniques may lead to more accurate and more acces-sible diagnosis of thorax diseases on chest radiography. Despite the success of deep learning-based solutions, this task remains a major challenge in smart healthcare, since it is intrinsically a weakly supervised learning problem. In this paper, we incorporate the attention mechanism into a deep convolutional neural network, and thus propose the ChestNet model to address effective diagnosis of thorax diseases on chest radiography. This model consists of two branches: a classification branch serves as a uniform feature extraction-classification network to free users from troublesome handcrafted feature extraction, and an attention branch exploits the correlation between class labels and the locations of patholog-ical abnormalities and allows the model to concentrate adaptively on the patholog-ically abnormal regions. We evaluated our model against three state-of-the-art deep learning models on the Chest X-ray 14 dataset using the official patient-wise split. The results indicate that our model outperforms other methods, which use no extra training data, in diagnosing 14 thorax diseases on chest radiography. |
| Tasks | |
| Published | 2018-07-09 |
| URL | http://arxiv.org/abs/1807.03058v1 |
| http://arxiv.org/pdf/1807.03058v1.pdf | |
| PWC | https://paperswithcode.com/paper/chestnet-a-deep-neural-network-for |
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Bayes-CPACE: PAC Optimal Exploration in Continuous Space Bayes-Adaptive Markov Decision Processes
| Title | Bayes-CPACE: PAC Optimal Exploration in Continuous Space Bayes-Adaptive Markov Decision Processes |
| Authors | Gilwoo Lee, Sanjiban Choudhury, Brian Hou, Siddhartha S. Srinivasa |
| Abstract | We present the first PAC optimal algorithm for Bayes-Adaptive Markov Decision Processes (BAMDPs) in continuous state and action spaces, to the best of our knowledge. The BAMDP framework elegantly addresses model uncertainty by incorporating Bayesian belief updates into long-term expected return. However, computing an exact optimal Bayesian policy is intractable. Our key insight is to compute a near-optimal value function by covering the continuous state-belief-action space with a finite set of representative samples and exploiting the Lipschitz continuity of the value function. We prove the near-optimality of our algorithm and analyze a number of schemes that boost the algorithm’s efficiency. Finally, we empirically validate our approach on a number of discrete and continuous BAMDPs and show that the learned policy has consistently competitive performance against baseline approaches. |
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| Published | 2018-10-06 |
| URL | http://arxiv.org/abs/1810.03048v1 |
| http://arxiv.org/pdf/1810.03048v1.pdf | |
| PWC | https://paperswithcode.com/paper/bayes-cpace-pac-optimal-exploration-in |
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Extractive Summarization of EHR Discharge Notes
| Title | Extractive Summarization of EHR Discharge Notes |
| Authors | Emily Alsentzer, Anne Kim |
| Abstract | Patient summarization is essential for clinicians to provide coordinated care and practice effective communication. Automated summarization has the potential to save time, standardize notes, aid clinical decision making, and reduce medical errors. Here we provide an upper bound on extractive summarization of discharge notes and develop an LSTM model to sequentially label topics of history of present illness notes. We achieve an F1 score of 0.876, which indicates that this model can be employed to create a dataset for evaluation of extractive summarization methods. |
| Tasks | Decision Making |
| Published | 2018-10-26 |
| URL | http://arxiv.org/abs/1810.12085v1 |
| http://arxiv.org/pdf/1810.12085v1.pdf | |
| PWC | https://paperswithcode.com/paper/extractive-summarization-of-ehr-discharge |
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Transforming Question Answering Datasets Into Natural Language Inference Datasets
| Title | Transforming Question Answering Datasets Into Natural Language Inference Datasets |
| Authors | Dorottya Demszky, Kelvin Guu, Percy Liang |
| Abstract | Existing datasets for natural language inference (NLI) have propelled research on language understanding. We propose a new method for automatically deriving NLI datasets from the growing abundance of large-scale question answering datasets. Our approach hinges on learning a sentence transformation model which converts question-answer pairs into their declarative forms. Despite being primarily trained on a single QA dataset, we show that it can be successfully applied to a variety of other QA resources. Using this system, we automatically derive a new freely available dataset of over 500k NLI examples (QA-NLI), and show that it exhibits a wide range of inference phenomena rarely seen in previous NLI datasets. |
| Tasks | Natural Language Inference, Question Answering |
| Published | 2018-09-09 |
| URL | http://arxiv.org/abs/1809.02922v2 |
| http://arxiv.org/pdf/1809.02922v2.pdf | |
| PWC | https://paperswithcode.com/paper/transforming-question-answering-datasets-into |
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Renormalized Normalized Maximum Likelihood and Three-Part Code Criteria For Learning Gaussian Networks
| Title | Renormalized Normalized Maximum Likelihood and Three-Part Code Criteria For Learning Gaussian Networks |
| Authors | Borzou Alipourfard, Jean X. Gao |
| Abstract | Score based learning (SBL) is a promising approach for learning Bayesian networks in the discrete domain. However, when employing SBL in the continuous domain, one is either forced to move the problem to the discrete domain or use metrics such as BIC/AIC, and these approaches are often lacking. Discretization can have an undesired impact on the accuracy of the results, and BIC/AIC can fall short of achieving the desired accuracy. In this paper, we introduce two new scoring metrics for scoring Bayesian networks in the continuous domain: the three-part minimum description length and the renormalized normalized maximum likelihood metric. We rely on the minimum description length principle in formulating these metrics. The metrics proposed are free of hyperparameters, decomposable, and are asymptotically consistent. We evaluate our solution by studying the convergence rate of the learned graph to the generating network and, also, the structural hamming distance of the learned graph to the generating network. Our evaluations show that the proposed metrics outperform their competitors, the BIC/AIC metrics. Furthermore, using the proposed RNML metric, SBL will have the fastest rate of convergence with the smallest structural hamming distance to the generating network. |
| Tasks | |
| Published | 2018-10-20 |
| URL | http://arxiv.org/abs/1810.08749v1 |
| http://arxiv.org/pdf/1810.08749v1.pdf | |
| PWC | https://paperswithcode.com/paper/renormalized-normalized-maximum-likelihood |
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Weight-importance sparse training in keyword spotting
| Title | Weight-importance sparse training in keyword spotting |
| Authors | Sihao Xue, Zhenyi Ying, Fan Mo, Min Wang, Jue Sun |
| Abstract | Large size models are implemented in recently ASR system to deal with complex speech recognition problems. The num- ber of parameters in these models makes them hard to deploy, especially on some resource-short devices such as car tablet. Besides this, at most of time, ASR system is used to deal with real-time problem such as keyword spotting (KWS). It is contradictory to the fact that large model requires long com- putation time. To deal with this problem, we apply some sparse algo- rithms to reduces number of parameters in some widely used models, Deep Neural Network (DNN) KWS, which requires real short computation time. We can prune more than 90 % even 95% of parameters in the model with tiny effect decline. And the sparse model performs better than baseline models which has same order number of parameters. Besides this, sparse algorithm can lead us to find rational model size au- tomatically for certain problem without concerning choosing an original model size. |
| Tasks | Keyword Spotting, Speech Recognition |
| Published | 2018-07-02 |
| URL | http://arxiv.org/abs/1807.00560v3 |
| http://arxiv.org/pdf/1807.00560v3.pdf | |
| PWC | https://paperswithcode.com/paper/weight-importance-sparse-training-in-keyword |
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Convolutional sparse coding for capturing high speed video content
| Title | Convolutional sparse coding for capturing high speed video content |
| Authors | Ana Serrano, Elena Garces, Diego Gutierrez, Belen Masia |
| Abstract | Video capture is limited by the trade-off between spatial and temporal resolution: when capturing videos of high temporal resolution, the spatial resolution decreases due to bandwidth limitations in the capture system. Achieving both high spatial and temporal resolution is only possible with highly specialized and very expensive hardware, and even then the same basic trade-off remains. The recent introduction of compressive sensing and sparse reconstruction techniques allows for the capture of single-shot high-speed video, by coding the temporal information in a single frame, and then reconstructing the full video sequence from this single coded image and a trained dictionary of image patches. In this paper, we first analyze this approach, and find insights that help improve the quality of the reconstructed videos. We then introduce a novel technique, based on convolutional sparse coding (CSC), and show how it outperforms the state-of-the-art, patch-based approach in terms of flexibility and efficiency, due to the convolutional nature of its filter banks. The key idea for CSC high-speed video acquisition is extending the basic formulation by imposing an additional constraint in the temporal dimension, which enforces sparsity of the first-order derivatives over time. |
| Tasks | Compressive Sensing |
| Published | 2018-06-13 |
| URL | http://arxiv.org/abs/1806.04935v1 |
| http://arxiv.org/pdf/1806.04935v1.pdf | |
| PWC | https://paperswithcode.com/paper/convolutional-sparse-coding-for-capturing |
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Causal Generative Domain Adaptation Networks
| Title | Causal Generative Domain Adaptation Networks |
| Authors | Mingming Gong, Kun Zhang, Biwei Huang, Clark Glymour, Dacheng Tao, Kayhan Batmanghelich |
| Abstract | An essential problem in domain adaptation is to understand and make use of distribution changes across domains. For this purpose, we first propose a flexible Generative Domain Adaptation Network (G-DAN) with specific latent variables to capture changes in the generating process of features across domains. By explicitly modeling the changes, one can even generate data in new domains using the generating process with new values for the latent variables in G-DAN. In practice, the process to generate all features together may involve high-dimensional latent variables, requiring dealing with distributions in high dimensions and making it difficult to learn domain changes from few source domains. Interestingly, by further making use of the causal representation of joint distributions, we then decompose the joint distribution into separate modules, each of which involves different low-dimensional latent variables and can be learned separately, leading to a Causal G-DAN (CG-DAN). This improves both statistical and computational efficiency of the learning procedure. Finally, by matching the feature distribution in the target domain, we can recover the target-domain joint distribution and derive the learning machine for the target domain. We demonstrate the efficacy of both G-DAN and CG-DAN in domain generation and cross-domain prediction on both synthetic and real data experiments. |
| Tasks | Domain Adaptation |
| Published | 2018-04-12 |
| URL | http://arxiv.org/abs/1804.04333v3 |
| http://arxiv.org/pdf/1804.04333v3.pdf | |
| PWC | https://paperswithcode.com/paper/causal-generative-domain-adaptation-networks |
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Multi-Level Structured Self-Attentions for Distantly Supervised Relation Extraction
| Title | Multi-Level Structured Self-Attentions for Distantly Supervised Relation Extraction |
| Authors | Jinhua Du, Jingguang Han, Andy Way, Dadong Wan |
| Abstract | Attention mechanisms are often used in deep neural networks for distantly supervised relation extraction (DS-RE) to distinguish valid from noisy instances. However, traditional 1-D vector attention models are insufficient for the learning of different contexts in the selection of valid instances to predict the relationship for an entity pair. To alleviate this issue, we propose a novel multi-level structured (2-D matrix) self-attention mechanism for DS-RE in a multi-instance learning (MIL) framework using bidirectional recurrent neural networks. In the proposed method, a structured word-level self-attention mechanism learns a 2-D matrix where each row vector represents a weight distribution for different aspects of an instance regarding two entities. Targeting the MIL issue, the structured sentence-level attention learns a 2-D matrix where each row vector represents a weight distribution on selection of different valid in-stances. Experiments conducted on two publicly available DS-RE datasets show that the proposed framework with a multi-level structured self-attention mechanism significantly outperform state-of-the-art baselines in terms of PR curves, P@N and F1 measures. |
| Tasks | Relation Extraction |
| Published | 2018-09-03 |
| URL | http://arxiv.org/abs/1809.00699v1 |
| http://arxiv.org/pdf/1809.00699v1.pdf | |
| PWC | https://paperswithcode.com/paper/multi-level-structured-self-attentions-for |
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Not Just Depressed: Bipolar Disorder Prediction on Reddit
| Title | Not Just Depressed: Bipolar Disorder Prediction on Reddit |
| Authors | Ivan Sekulić, Matej Gjurković, Jan Šnajder |
| Abstract | Bipolar disorder, an illness characterized by manic and depressive episodes, affects more than 60 million people worldwide. We present a preliminary study on bipolar disorder prediction from user-generated text on Reddit, which relies on users’ self-reported labels. Our benchmark classifiers for bipolar disorder prediction outperform the baselines and reach accuracy and F1-scores of above 86%. Feature analysis shows interesting differences in language use between users with bipolar disorders and the control group, including differences in the use of emotion-expressive words. |
| Tasks | |
| Published | 2018-11-12 |
| URL | http://arxiv.org/abs/1811.04655v2 |
| http://arxiv.org/pdf/1811.04655v2.pdf | |
| PWC | https://paperswithcode.com/paper/not-just-depressed-bipolar-disorder |
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A Predictive Model for Notional Anaphora in English
| Title | A Predictive Model for Notional Anaphora in English |
| Authors | Amir Zeldes |
| Abstract | Notional anaphors are pronouns which disagree with their antecedents’ grammatical categories for notional reasons, such as plural to singular agreement in: ‘the government … they’. Since such cases are rare and conflict with evidence from strictly agreeing cases (‘the government … it’), they present a substantial challenge to both coreference resolution and referring expression generation. Using the OntoNotes corpus, this paper takes an ensemble approach to predicting English notional anaphora in context on the basis of the largest empirical data to date. In addition to state of the art prediction accuracy, the results suggest that theoretical approaches positing a plural construal at the antecedent’s utterance are insufficient, and that circumstances at the anaphor’s utterance location, as well as global factors such as genre, have a strong effect on the choice of referring expression. |
| Tasks | Coreference Resolution |
| Published | 2018-04-19 |
| URL | http://arxiv.org/abs/1804.07375v1 |
| http://arxiv.org/pdf/1804.07375v1.pdf | |
| PWC | https://paperswithcode.com/paper/a-predictive-model-for-notional-anaphora-in |
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Large Data and Zero Noise Limits of Graph-Based Semi-Supervised Learning Algorithms
| Title | Large Data and Zero Noise Limits of Graph-Based Semi-Supervised Learning Algorithms |
| Authors | Matthew M. Dunlop, Dejan Slepčev, Andrew M. Stuart, Matthew Thorpe |
| Abstract | Scalings in which the graph Laplacian approaches a differential operator in the large graph limit are used to develop understanding of a number of algorithms for semi-supervised learning; in particular the extension, to this graph setting, of the probit algorithm, level set and kriging methods, are studied. Both optimization and Bayesian approaches are considered, based around a regularizing quadratic form found from an affine transformation of the Laplacian, raised to a, possibly fractional, exponent. Conditions on the parameters defining this quadratic form are identified under which well-defined limiting continuum analogues of the optimization and Bayesian semi-supervised learning problems may be found, thereby shedding light on the design of algorithms in the large graph setting. The large graph limits of the optimization formulations are tackled through $\Gamma-$convergence, using the recently introduced $TL^p$ metric. The small labelling noise limits of the Bayesian formulations are also identified, and contrasted with pre-existing harmonic function approaches to the problem. |
| Tasks | |
| Published | 2018-05-23 |
| URL | http://arxiv.org/abs/1805.09450v2 |
| http://arxiv.org/pdf/1805.09450v2.pdf | |
| PWC | https://paperswithcode.com/paper/large-data-and-zero-noise-limits-of-graph |
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Deterministic Stretchy Regression
| Title | Deterministic Stretchy Regression |
| Authors | Kar-Ann Toh, Lei Sun, Zhiping Lin |
| Abstract | An extension of the regularized least-squares in which the estimation parameters are stretchable is introduced and studied in this paper. The solution of this ridge regression with stretchable parameters is given in primal and dual spaces and in closed-form. Essentially, the proposed solution stretches the covariance computation by a power term, thereby compressing or amplifying the estimation parameters. To maintain the computation of power root terms within the real space, an input transformation is proposed. The results of an empirical evaluation in both synthetic and real-world data illustrate that the proposed method is effective for compressive learning with high-dimensional data. |
| Tasks | |
| Published | 2018-06-09 |
| URL | http://arxiv.org/abs/1806.03404v1 |
| http://arxiv.org/pdf/1806.03404v1.pdf | |
| PWC | https://paperswithcode.com/paper/deterministic-stretchy-regression |
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You are your Metadata: Identification and Obfuscation of Social Media Users using Metadata Information
| Title | You are your Metadata: Identification and Obfuscation of Social Media Users using Metadata Information |
| Authors | Beatrice Perez, Mirco Musolesi, Gianluca Stringhini |
| Abstract | Metadata are associated to most of the information we produce in our daily interactions and communication in the digital world. Yet, surprisingly, metadata are often still catergorized as non-sensitive. Indeed, in the past, researchers and practitioners have mainly focused on the problem of the identification of a user from the content of a message. In this paper, we use Twitter as a case study to quantify the uniqueness of the association between metadata and user identity and to understand the effectiveness of potential obfuscation strategies. More specifically, we analyze atomic fields in the metadata and systematically combine them in an effort to classify new tweets as belonging to an account using different machine learning algorithms of increasing complexity. We demonstrate that through the application of a supervised learning algorithm, we are able to identify any user in a group of 10,000 with approximately 96.7% accuracy. Moreover, if we broaden the scope of our search and consider the 10 most likely candidates we increase the accuracy of the model to 99.22%. We also found that data obfuscation is hard and ineffective for this type of data: even after perturbing 60% of the training data, it is still possible to classify users with an accuracy higher than 95%. These results have strong implications in terms of the design of metadata obfuscation strategies, for example for data set release, not only for Twitter, but, more generally, for most social media platforms. |
| Tasks | |
| Published | 2018-03-27 |
| URL | http://arxiv.org/abs/1803.10133v2 |
| http://arxiv.org/pdf/1803.10133v2.pdf | |
| PWC | https://paperswithcode.com/paper/you-are-your-metadata-identification-and |
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