Paper Group NANR 232
X-ray Computed Tomography Through Scatter. Negotiable Reinforcement Learning for Pareto Optimal Sequential Decision-Making. Exploring Sentence Vectors Through Automatic Summarization. Adversarially Occluded Samples for Person Re-Identification. JDCFC: A Japanese Dialogue Corpus with Feature Changes. Convolutional Neural Networks for Soft Matching N …
X-ray Computed Tomography Through Scatter
| Title | X-ray Computed Tomography Through Scatter |
| Authors | Adam Geva, Yoav Y. Schechner, Yonatan Chernyak, Rajiv Gupta |
| Abstract | In current Xray CT scanners, tomographic reconstruction relies only on directly transmitted photons. The models used for reconstruction have regarded photons scattered by the body as noise or disturbance to be disposed of, either by acquisition hardware (an anti-scatter grid) or by the reconstruction software. This increases the radiation dose delivered to the patient. Treating these scattered photons as a source of information, we solve an inverse problem based on a 3D radiative transfer model that includes both elastic (Rayleigh) and inelastic (Compton) scattering. We further present ways to make the solution numerically efficient. The resulting tomographic reconstruction is more accurate than traditional CT, while enabling significant dose reduction and chemical decomposition. Demonstrations include both simulations based on a standard medical phantom and a real scattering tomography experiment. |
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| Published | 2018-09-01 |
| URL | http://openaccess.thecvf.com/content_ECCV_2018/html/Adam_Geva_X-ray_Computational_Tomography_ECCV_2018_paper.html |
| http://openaccess.thecvf.com/content_ECCV_2018/papers/Adam_Geva_X-ray_Computational_Tomography_ECCV_2018_paper.pdf | |
| PWC | https://paperswithcode.com/paper/x-ray-computed-tomography-through-scatter |
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Negotiable Reinforcement Learning for Pareto Optimal Sequential Decision-Making
| Title | Negotiable Reinforcement Learning for Pareto Optimal Sequential Decision-Making |
| Authors | Nishant Desai, Andrew Critch, Stuart J. Russell |
| Abstract | It is commonly believed that an agent making decisions on behalf of two or more principals who have different utility functions should adopt a Pareto optimal policy, i.e. a policy that cannot be improved upon for one principal without making sacrifices for another. Harsanyi’s theorem shows that when the principals have a common prior on the outcome distributions of all policies, a Pareto optimal policy for the agent is one that maximizes a fixed, weighted linear combination of the principals’ utilities. In this paper, we derive a more precise generalization for the sequential decision setting in the case of principals with different priors on the dynamics of the environment. We refer to this generalization as the Negotiable Reinforcement Learning (NRL) framework. In this more general case, the relative weight given to each principal’s utility should evolve over time according to how well the agent’s observations conform with that principal’s prior. To gain insight into the dynamics of this new framework, we implement a simple NRL agent and empirically examine its behavior in a simple environment. |
| Tasks | Decision Making |
| Published | 2018-12-01 |
| URL | http://papers.nips.cc/paper/7721-negotiable-reinforcement-learning-for-pareto-optimal-sequential-decision-making |
| http://papers.nips.cc/paper/7721-negotiable-reinforcement-learning-for-pareto-optimal-sequential-decision-making.pdf | |
| PWC | https://paperswithcode.com/paper/negotiable-reinforcement-learning-for-pareto |
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Exploring Sentence Vectors Through Automatic Summarization
| Title | Exploring Sentence Vectors Through Automatic Summarization |
| Authors | Adly Templeton, Jugal Kalita |
| Abstract | Vector semantics, especially sentence vectors, have recently been used successfully in many areas of natural language processing. However, relatively little work has explored the internal structure and properties of spaces of sentence vectors. In this paper, we will explore the properties of sentence vectors by studying a particular real-world application: Automatic Summarization. In particular, we show that cosine similarity between sentence vectors and document vectors is strongly correlated with sentence importance and that vector semantics can identify and correct gaps between the sentences chosen so far and the document. In addition, we identify specific dimensions which are linked to effective summaries. To our knowledge, this is the first time specific dimensions of sentence embeddings have been connected to sentence properties. We also compare the features of different methods of sentence embeddings. Many of these insights have applications in uses of sentence embeddings far beyond summarization. |
| Tasks | Sentence Embeddings |
| Published | 2018-01-01 |
| URL | https://openreview.net/forum?id=S1347ot3b |
| https://openreview.net/pdf?id=S1347ot3b | |
| PWC | https://paperswithcode.com/paper/exploring-sentence-vectors-through-automatic |
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Adversarially Occluded Samples for Person Re-Identification
| Title | Adversarially Occluded Samples for Person Re-Identification |
| Authors | Houjing Huang, Dangwei Li, Zhang Zhang, Xiaotang Chen, Kaiqi Huang |
| Abstract | Person re-identification (ReID) is the task of retrieving particular persons across different cameras. Despite its great progress in recent years, it is still confronted with challenges like pose variation, occlusion, and similar appearance among different persons. The large gap between training and testing performance with existing models implies the insufficiency of generalization. Considering this fact, we propose to augment the variation of training data by introducing Adversarially Occluded Samples. These special samples are both a) meaningful in that they resemble real-scene occlusions, and b) effective in that they are tough for the original model and thus provide the momentum to jump out of local optimum. We mine these samples based on a trained ReID model and with the help of network visualization techniques. Extensive experiments show that the proposed samples help the model discover new discriminative clues on the body and generalize much better at test time. Our strategy makes significant improvement over strong baselines on three large-scale ReID datasets, Market1501, CUHK03 and DukeMTMC-reID. |
| Tasks | Person Re-Identification |
| Published | 2018-06-01 |
| URL | http://openaccess.thecvf.com/content_cvpr_2018/html/Huang_Adversarially_Occluded_Samples_CVPR_2018_paper.html |
| http://openaccess.thecvf.com/content_cvpr_2018/papers/Huang_Adversarially_Occluded_Samples_CVPR_2018_paper.pdf | |
| PWC | https://paperswithcode.com/paper/adversarially-occluded-samples-for-person-re |
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JDCFC: A Japanese Dialogue Corpus with Feature Changes
| Title | JDCFC: A Japanese Dialogue Corpus with Feature Changes |
| Authors | Tetsuaki Nakamura, Daisuke Kawahara |
| Abstract | |
| Tasks | Dialogue Understanding |
| Published | 2018-05-01 |
| URL | https://www.aclweb.org/anthology/L18-1461/ |
| https://www.aclweb.org/anthology/L18-1461 | |
| PWC | https://paperswithcode.com/paper/jdcfc-a-japanese-dialogue-corpus-with-feature |
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Convolutional Neural Networks for Soft Matching N-Grams in Ad-hoc Search
| Title | Convolutional Neural Networks for Soft Matching N-Grams in Ad-hoc Search |
| Authors | Zhuyun Dai, Chenyan Xiong, Jamie Callan, Zhiyuan Liu |
| Abstract | This paper presents Conv-KNRM, a Convolutional Kernel-based Neural Ranking Model that models n-gram soft matches for ad-hoc search. Instead of exact matching query and document n-grams, Conv-KNRM uses Convolutional Neural Networks to represent ngrams of various lengths and soft matches them in a unied embedding space. The n-gram soft matches are then utilized by the kernel pooling and learning-to-rank layers to generate the final ranking score. Conv-KNRM can be learned end-to-end and fully optimized from user feedback. The learned model’s generalizability is investigated by testing how well it performs in a related domain with small amounts of training data. Experiments on English search logs, Chinese search logs, and TREC Web track tasks demonstrated consistent advantages of Conv-KNRM over prior neural IR methods and feature-based methods. |
| Tasks | Learning-To-Rank |
| Published | 2018-02-02 |
| URL | http://www.cs.cmu.edu/~./callan/Papers/wsdm18-zhuyun-dai.pdf |
| http://www.cs.cmu.edu/~./callan/Papers/wsdm18-zhuyun-dai.pdf | |
| PWC | https://paperswithcode.com/paper/convolutional-neural-networks-for-soft |
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Discriminating between Similar Languages on Imbalanced Conversational Texts
| Title | Discriminating between Similar Languages on Imbalanced Conversational Texts |
| Authors | Junqing He, Xian Huang, Xuemin Zhao, Yan Zhang, Yonghong Yan |
| Abstract | |
| Tasks | Language Identification |
| Published | 2018-05-01 |
| URL | https://www.aclweb.org/anthology/L18-1497/ |
| https://www.aclweb.org/anthology/L18-1497 | |
| PWC | https://paperswithcode.com/paper/discriminating-between-similar-languages-on |
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Out-of-the-box Universal Romanization Tool uroman
| Title | Out-of-the-box Universal Romanization Tool uroman |
| Authors | Ulf Hermjakob, Jonathan May, Kevin Knight |
| Abstract | We present uroman, a tool for converting text in myriads of languages and scripts such as Chinese, Arabic and Cyrillic into a common Latin-script representation. The tool relies on Unicode data and other tables, and handles nearly all character sets, including some that are quite obscure such as Tibetan and Tifinagh. uroman converts digital numbers in various scripts to Western Arabic numerals. Romanization enables the application of string-similarity metrics to texts from different scripts without the need and complexity of an intermediate phonetic representation. The tool is freely and publicly available as a Perl script suitable for inclusion in data processing pipelines and as an interactive demo web page. |
| Tasks | Machine Translation |
| Published | 2018-07-01 |
| URL | https://www.aclweb.org/anthology/P18-4003/ |
| https://www.aclweb.org/anthology/P18-4003 | |
| PWC | https://paperswithcode.com/paper/out-of-the-box-universal-romanization-tool |
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Enhanced network anomaly detection based on deep neural networks
| Title | Enhanced network anomaly detection based on deep neural networks |
| Authors | Naseer, Sheraz; Saleem, Yasir; Khalid, Shehzad; Bashir, Muhammad Khawar; Han, Jihun; Iqbal, Muhammad Munwar; Han, Kijun |
| Abstract | Due to the monumental growth of Internet applications in the last decade, the need for security of information network has increased manifolds. As a primary defense of network infrastructure, an intrusion detection system is expected to adapt to dynamically changing threat landscape. Many supervised and unsupervised techniques have been devised by researchers from the discipline of machine learning and data mining to achieve reliable detection of anomalies. Deep learning is an area of machine learning which applies neuron-like structure for learning tasks. Deep learning has profoundly changed the way we approach learning tasks by delivering monumental progress in different disciplines like speech processing, computer vision, and natural language processing to name a few. It is only relevant that this new technology must be investigated for information security applications. The aim of this paper is to investigate the suitability of deep learning approaches for anomaly-based intrusion detection system. For this research, we developed anomaly detection models based on different deep neural network structures, including convolutional neural networks, autoencoders, and recurrent neural networks. These deep models were trained on NSLKDD training data set and evaluated on both test data sets provided by NSLKDD, namely NSLKDDTest+ and NSLKDDTest21. All experiments in this paper are performed by authors on a GPU-based test bed. Conventional machine learning-based intrusion detection models were implemented using well-known classification techniques, including extreme learning machine, nearest neighbor, decision-tree, random-forest, support vector machine, naive-bays, and quadratic discriminant analysis. Both deep and conventional machine learning models were evaluated using well-known classification metrics, including receiver operating characteristics, area under curve, precision-recall curve, mean average precision and accuracy of classification. Experimental results of deep IDS models showed promising results for real-world application in anomaly detection systems. |
| Tasks | Anomaly Detection, Intrusion Detection |
| Published | 2018-08-17 |
| URL | https://ieeexplore.ieee.org/abstract/document/8438865 |
| https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8438865 | |
| PWC | https://paperswithcode.com/paper/enhanced-network-anomaly-detection-based-on |
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A Bandit Approach to Sequential Experimental Design with False Discovery Control
| Title | A Bandit Approach to Sequential Experimental Design with False Discovery Control |
| Authors | Kevin G. Jamieson, Lalit Jain |
| Abstract | We propose a new adaptive sampling approach to multiple testing which aims to maximize statistical power while ensuring anytime false discovery control. We consider $n$ distributions whose means are partitioned by whether they are below or equal to a baseline (nulls), versus above the baseline (true positives). In addition, each distribution can be sequentially and repeatedly sampled. Using techniques from multi-armed bandits, we provide an algorithm that takes as few samples as possible to exceed a target true positive proportion (i.e. proportion of true positives discovered) while giving anytime control of the false discovery proportion (nulls predicted as true positives). Our sample complexity results match known information theoretic lower bounds and through simulations we show a substantial performance improvement over uniform sampling and an adaptive elimination style algorithm. Given the simplicity of the approach, and its sample efficiency, the method has promise for wide adoption in the biological sciences, clinical testing for drug discovery, and maximization of click through in A/B/n testing problems. |
| Tasks | Drug Discovery, Multi-Armed Bandits |
| Published | 2018-12-01 |
| URL | http://papers.nips.cc/paper/7624-a-bandit-approach-to-sequential-experimental-design-with-false-discovery-control |
| http://papers.nips.cc/paper/7624-a-bandit-approach-to-sequential-experimental-design-with-false-discovery-control.pdf | |
| PWC | https://paperswithcode.com/paper/a-bandit-approach-to-sequential-experimental |
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A Certifiably Globally Optimal Solution to the Non-Minimal Relative Pose Problem
| Title | A Certifiably Globally Optimal Solution to the Non-Minimal Relative Pose Problem |
| Authors | Jesus Briales, Laurent Kneip, Javier Gonzalez-Jimenez |
| Abstract | Finding the relative pose between two calibrated views ranks among the most fundamental geometric vision problems. It therefore appears as somewhat a surprise that a globally optimal solver that minimizes a properly defined energy over non-minimal correspondence sets and in the original space of relative transformations has yet to be discovered. This, notably, is the contribution of the present paper. We formulate the problem as a Quadratically Constrained Quadratic Program (QCQP), which can be converted into a Semidefinite Program (SDP) using Shor’s convex relaxation. While a theoretical proof for the tightness of this relaxation remains open, we prove through exhaustive validation on both simulated and real experiments that our approach always finds and certifies (a-posteriori) the global optimum of the cost function. |
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| Published | 2018-06-01 |
| URL | http://openaccess.thecvf.com/content_cvpr_2018/html/Briales_A_Certifiably_Globally_CVPR_2018_paper.html |
| http://openaccess.thecvf.com/content_cvpr_2018/papers/Briales_A_Certifiably_Globally_CVPR_2018_paper.pdf | |
| PWC | https://paperswithcode.com/paper/a-certifiably-globally-optimal-solution-to |
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QuickEdit: Editing Text & Translations by Crossing Words Out
| Title | QuickEdit: Editing Text & Translations by Crossing Words Out |
| Authors | David Grangier, Michael Auli |
| Abstract | We propose a framework for computer-assisted text editing. It applies to translation post-editing and to paraphrasing. Our proposal relies on very simple interactions: a human editor modifies a sentence by marking tokens they would like the system to change. Our model then generates a new sentence which reformulates the initial sentence by avoiding marked words. The approach builds upon neural sequence-to-sequence modeling and introduces a neural network which takes as input a sentence along with change markers. Our model is trained on translation bitext by simulating post-edits. We demonstrate the advantage of our approach for translation post-editing through simulated post-edits. We also evaluate our model for paraphrasing through a user study. |
| Tasks | Machine Translation |
| Published | 2018-06-01 |
| URL | https://www.aclweb.org/anthology/N18-1025/ |
| https://www.aclweb.org/anthology/N18-1025 | |
| PWC | https://paperswithcode.com/paper/quickedit-editing-text-translations-by-1 |
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Generative Adversarial Learning Towards Fast Weakly Supervised Detection
| Title | Generative Adversarial Learning Towards Fast Weakly Supervised Detection |
| Authors | Yunhan Shen, Rongrong Ji, Shengchuan Zhang, Wangmeng Zuo, Yan Wang |
| Abstract | Weakly supervised object detection has attracted extensive research efforts in recent years. Without the need of annotating bounding boxes, the existing methods usually follow a two/multi-stage pipeline with an online compulsive stage to extract object proposals, which is an order of magnitude slower than fast fully supervised object detectors such as SSD [31] and YOLO [34]. In this paper, we speedup online weakly supervised object detectors by orders of magnitude by proposing a novel generative adversarial learning paradigm. In the proposed paradigm, the generator is a one-stage object detector to generate bounding boxes from images. To guide the learning of object-level generator, a surrogator is introduced to mine high-quality bounding boxes for training. We further adapt a structural similarity loss in combination with an adversarial loss into the training objective, which solves the challenge that the bounding boxes produced by the surrogator may not well capture their ground truth. Our one-stage detector outperforms all existing schemes in terms of detection accuracy, running at 118 frames per second, which is up to 438x faster than the state-of-the-art weakly supervised detectors [8, 30, 15, 27, 45]. The code will be available publicly soon. |
| Tasks | Object Detection, Weakly Supervised Object Detection |
| Published | 2018-06-01 |
| URL | http://openaccess.thecvf.com/content_cvpr_2018/html/Shen_Generative_Adversarial_Learning_CVPR_2018_paper.html |
| http://openaccess.thecvf.com/content_cvpr_2018/papers/Shen_Generative_Adversarial_Learning_CVPR_2018_paper.pdf | |
| PWC | https://paperswithcode.com/paper/generative-adversarial-learning-towards-fast |
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Representing dynamically: An active process for describing sequential data
| Title | Representing dynamically: An active process for describing sequential data |
| Authors | Juan Sebastian Olier, Emilia Barakova, Matthias Rauterberg, Carlo Regazzoni |
| Abstract | We propose an unsupervised method for building dynamic representations of sequential data, particularly of observed interactions. The method simultaneously acquires representations of input data and its dynamics. It is based on a hierarchical generative model composed of two levels. In the first level, a model learns representations to generate observed data. In the second level, representational states encode the dynamics of the lower one. The model is designed as a Bayesian network with switching variables represented in the higher level, and which generates transition models. The method actively explores the latent space guided by its knowledge and the uncertainty about it. That is achieved by updating the latent variables from prediction error signals backpropagated to the latent space. So, no encoder or inference models are used since the generators also serve as their inverse transformations. The method is evaluated in two scenarios, with static images and with videos. The results show that the adaptation over time leads to better performance than with similar architectures without temporal dependencies, e.g., variational autoencoders. With videos, it is shown that the system extracts the dynamics of the data in states that highly correlate with the ground truth of the actions observed. |
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| Published | 2018-01-01 |
| URL | https://openreview.net/forum?id=Sk7cHb-C- |
| https://openreview.net/pdf?id=Sk7cHb-C- | |
| PWC | https://paperswithcode.com/paper/representing-dynamically-an-active-process |
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Soft Value Iteration Networks for Planetary Rover Path Planning
| Title | Soft Value Iteration Networks for Planetary Rover Path Planning |
| Authors | Max Pflueger, Ali Agha, Gaurav S. Sukhatme |
| Abstract | Value iteration networks are an approximation of the value iteration (VI) algorithm implemented with convolutional neural networks to make VI fully differentiable. In this work, we study these networks in the context of robot motion planning, with a focus on applications to planetary rovers. The key challenging task in learning-based motion planning is to learn a transformation from terrain observations to a suitable navigation reward function. In order to deal with complex terrain observations and policy learning, we propose a value iteration recurrence, referred to as the soft value iteration network (SVIN). SVIN is designed to produce more effective training gradients through the value iteration network. It relies on a soft policy model, where the policy is represented with a probability distribution over all possible actions, rather than a deterministic policy that returns only the best action. We demonstrate the effectiveness of the proposed method in robot motion planning scenarios. In particular, we study the application of SVIN to very challenging problems in planetary rover navigation and present early training results on data gathered by the Curiosity rover that is currently operating on Mars. |
| Tasks | Motion Planning |
| Published | 2018-01-01 |
| URL | https://openreview.net/forum?id=Sktm4zWRb |
| https://openreview.net/pdf?id=Sktm4zWRb | |
| PWC | https://paperswithcode.com/paper/soft-value-iteration-networks-for-planetary |
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