October 16, 2019

2857 words 14 mins read

Paper Group ANR 1120

Novelty Detection Meets Collider Physics. Phrase Table as Recommendation Memory for Neural Machine Translation. Population Anomaly Detection through Deep Gaussianization. Piano Genie. Annotation Artifacts in Natural Language Inference Data. Towards Machine Learning Induction. Deep contextualized word representations for detecting sarcasm and irony. …

Novelty Detection Meets Collider Physics


Title	Novelty Detection Meets Collider Physics
Authors	Jan Hajer, Ying-Ying Li, Tao Liu, He Wang
Abstract	Novelty detection is the machine learning task to recognize data, which belong to an unknown pattern. Complementary to supervised learning, it allows to analyze data model-independently. We demonstrate the potential role of novelty detection in collider physics, using autoencoder-based deep neural network. Explicitly, we develop a set of density-based novelty evaluators, which are sensitive to the clustering of unknown-pattern testing data or new-physics signal events, for the design of detection algorithms. We also explore the influence of the known-pattern data fluctuations, arising from non-signal regions, on detection sensitivity. Strategies to address it are proposed. The algorithms are applied to detecting fermionic di-top partner and resonant di-top productions at LHC, and exotic Higgs decays of two specific modes at a $e^+e^-$ future collider. With parton-level analysis, we conclude that potentially the new-physics benchmarks can be recognized with high efficiency.
Tasks
Published	2018-07-26
URL	http://arxiv.org/abs/1807.10261v2
PDF	http://arxiv.org/pdf/1807.10261v2.pdf
PWC	https://paperswithcode.com/paper/novelty-detection-meets-collider-physics
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Phrase Table as Recommendation Memory for Neural Machine Translation


Title	Phrase Table as Recommendation Memory for Neural Machine Translation
Authors	Yang Zhao, Yining Wang, Jiajun Zhang, Chengqing Zong
Abstract	Neural Machine Translation (NMT) has drawn much attention due to its promising translation performance recently. However, several studies indicate that NMT often generates fluent but unfaithful translations. In this paper, we propose a method to alleviate this problem by using a phrase table as recommendation memory. The main idea is to add bonus to words worthy of recommendation, so that NMT can make correct predictions. Specifically, we first derive a prefix tree to accommodate all the candidate target phrases by searching the phrase translation table according to the source sentence. Then, we construct a recommendation word set by matching between candidate target phrases and previously translated target words by NMT. After that, we determine the specific bonus value for each recommendable word by using the attention vector and phrase translation probability. Finally, we integrate this bonus value into NMT to improve the translation results. The extensive experiments demonstrate that the proposed methods obtain remarkable improvements over the strong attentionbased NMT.
Tasks	Machine Translation
Published	2018-05-25
URL	http://arxiv.org/abs/1805.09960v1
PDF	http://arxiv.org/pdf/1805.09960v1.pdf
PWC	https://paperswithcode.com/paper/phrase-table-as-recommendation-memory-for
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Population Anomaly Detection through Deep Gaussianization


Title	Population Anomaly Detection through Deep Gaussianization
Authors	David Tolpin
Abstract	We introduce an algorithmic method for population anomaly detection based on gaussianization through an adversarial autoencoder. This method is applicable to detection of `soft’ anomalies in arbitrarily distributed highly-dimensional data. A soft, or population, anomaly is characterized by a shift in the distribution of the data set, where certain elements appear with higher probability than anticipated. Such anomalies must be detected by considering a sufficiently large sample set rather than a single sample. Applications include, but not limited to, payment fraud trends, data exfiltration, disease clusters and epidemics, and social unrests. We evaluate the method on several domains and obtain both quantitative results and qualitative insights. \|
Tasks	Anomaly Detection
Published	2018-05-05
URL	http://arxiv.org/abs/1805.02123v1
PDF	http://arxiv.org/pdf/1805.02123v1.pdf
PWC	https://paperswithcode.com/paper/population-anomaly-detection-through-deep
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Piano Genie


Title	Piano Genie
Authors	Chris Donahue, Ian Simon, Sander Dieleman
Abstract	We present Piano Genie, an intelligent controller which allows non-musicians to improvise on the piano. With Piano Genie, a user performs on a simple interface with eight buttons, and their performance is decoded into the space of plausible piano music in real time. To learn a suitable mapping procedure for this problem, we train recurrent neural network autoencoders with discrete bottlenecks: an encoder learns an appropriate sequence of buttons corresponding to a piano piece, and a decoder learns to map this sequence back to the original piece. During performance, we substitute a user’s input for the encoder output, and play the decoder’s prediction each time the user presses a button. To improve the intuitiveness of Piano Genie’s performance behavior, we impose musically meaningful constraints over the encoder’s outputs.
Tasks
Published	2018-10-11
URL	http://arxiv.org/abs/1810.05246v2
PDF	http://arxiv.org/pdf/1810.05246v2.pdf
PWC	https://paperswithcode.com/paper/piano-genie
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Annotation Artifacts in Natural Language Inference Data


Title	Annotation Artifacts in Natural Language Inference Data
Authors	Suchin Gururangan, Swabha Swayamdipta, Omer Levy, Roy Schwartz, Samuel R. Bowman, Noah A. Smith
Abstract	Large-scale datasets for natural language inference are created by presenting crowd workers with a sentence (premise), and asking them to generate three new sentences (hypotheses) that it entails, contradicts, or is logically neutral with respect to. We show that, in a significant portion of such data, this protocol leaves clues that make it possible to identify the label by looking only at the hypothesis, without observing the premise. Specifically, we show that a simple text categorization model can correctly classify the hypothesis alone in about 67% of SNLI (Bowman et. al, 2015) and 53% of MultiNLI (Williams et. al, 2017). Our analysis reveals that specific linguistic phenomena such as negation and vagueness are highly correlated with certain inference classes. Our findings suggest that the success of natural language inference models to date has been overestimated, and that the task remains a hard open problem.
Tasks	Natural Language Inference, Text Categorization
Published	2018-03-06
URL	http://arxiv.org/abs/1803.02324v2
PDF	http://arxiv.org/pdf/1803.02324v2.pdf
PWC	https://paperswithcode.com/paper/annotation-artifacts-in-natural-language
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Towards Machine Learning Induction


Title	Towards Machine Learning Induction
Authors	Yutaka Nagashima
Abstract	Induction lies at the heart of mathematics and computer science. However, automated theorem proving of inductive problems is still limited in its power. In this abstract, we first summarize our progress in automating inductive theorem proving for Isabelle/HOL. Then, we present MeLoId, our approach to suggesting promising applications of induction without completing a proof search.
Tasks	Automated Theorem Proving
Published	2018-12-04
URL	http://arxiv.org/abs/1812.04088v2
PDF	http://arxiv.org/pdf/1812.04088v2.pdf
PWC	https://paperswithcode.com/paper/towards-machine-learning-mathematical
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Deep contextualized word representations for detecting sarcasm and irony


Title	Deep contextualized word representations for detecting sarcasm and irony
Authors	Suzana Ilić, Edison Marrese-Taylor, Jorge A. Balazs, Yutaka Matsuo
Abstract	Predicting context-dependent and non-literal utterances like sarcastic and ironic expressions still remains a challenging task in NLP, as it goes beyond linguistic patterns, encompassing common sense and shared knowledge as crucial components. To capture complex morpho-syntactic features that can usually serve as indicators for irony or sarcasm across dynamic contexts, we propose a model that uses character-level vector representations of words, based on ELMo. We test our model on 7 different datasets derived from 3 different data sources, providing state-of-the-art performance in 6 of them, and otherwise offering competitive results.
Tasks	Common Sense Reasoning
Published	2018-09-26
URL	http://arxiv.org/abs/1809.09795v1
PDF	http://arxiv.org/pdf/1809.09795v1.pdf
PWC	https://paperswithcode.com/paper/deep-contextualized-word-representations-for
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Exploiting the Value of the Center-dark Channel Prior for Salient Object Detection


Title	Exploiting the Value of the Center-dark Channel Prior for Salient Object Detection
Authors	Chunbiao Zhu, Wenhao Zhang, Thomas H. Li, Ge Li
Abstract	Saliency detection aims to detect the most attractive objects in images and is widely used as a foundation for various applications. In this paper, we propose a novel salient object detection algorithm for RGB-D images using center-dark channel priors. First, we generate an initial saliency map based on a color saliency map and a depth saliency map of a given RGB-D image. Then, we generate a center-dark channel map based on center saliency and dark channel priors. Finally, we fuse the initial saliency map with the center dark channel map to generate the final saliency map. Extensive evaluations over four benchmark datasets demonstrate that our proposed method performs favorably against most of the state-of-the-art approaches. Besides, we further discuss the application of the proposed algorithm in small target detection and demonstrate the universal value of center-dark channel priors in the field of object detection.
Tasks	Object Detection, Saliency Detection, Salient Object Detection
Published	2018-05-14
URL	http://arxiv.org/abs/1805.05132v1
PDF	http://arxiv.org/pdf/1805.05132v1.pdf
PWC	https://paperswithcode.com/paper/exploiting-the-value-of-the-center-dark
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Exact Passive-Aggressive Algorithms for Learning to Rank Using Interval Labels


Title	Exact Passive-Aggressive Algorithms for Learning to Rank Using Interval Labels
Authors	Naresh Manwani, Mohit Chandra
Abstract	In this paper, we propose exact passive-aggressive (PA) online algorithms for learning to rank. The proposed algorithms can be used even when we have interval labels instead of actual labels for examples. The proposed algorithms solve a convex optimization problem at every trial. We find exact solution to those optimization problems to determine the updated parameters. We propose support class algorithm (SCA) which finds the active constraints using the KKT conditions of the optimization problems. These active constrains form support set which determines the set of thresholds that need to be updated. We derive update rules for PA, PA-I and PA-II. We show that the proposed algorithms maintain the ordering of the thresholds after every trial. We provide the mistake bounds of the proposed algorithms in both ideal and general settings. We also show experimentally that the proposed algorithms successfully learn accurate classifiers using interval labels as well as exact labels. Proposed algorithms also do well compared to other approaches.
Tasks	Learning-To-Rank
Published	2018-08-18
URL	http://arxiv.org/abs/1808.06107v1
PDF	http://arxiv.org/pdf/1808.06107v1.pdf
PWC	https://paperswithcode.com/paper/exact-passive-aggressive-algorithms-for
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Learning-based attacks in cyber-physical systems


Title	Learning-based attacks in cyber-physical systems
Authors	Mohammad Javad Khojasteh, Anatoly Khina, Massimo Franceschetti, Tara Javidi
Abstract	We introduce the problem of learning-based attacks in a simple abstraction of cyber-physical systems—the case of a discrete-time, linear, time-invariant plant that may be subject to an attack that overrides the sensor readings and the controller actions. The attacker attempts to learn the dynamics of the plant and subsequently override the controller’s actuation signal, to destroy the plant without being detected. The attacker can feed fictitious sensor readings to the controller using its estimate of the plant dynamics and mimic the legitimate plant operation. The controller, on the other hand, is constantly on the lookout for an attack; once the controller detects an attack, it immediately shuts the plant off. In the case of scalar plants, we derive an upper bound on the attacker’s deception probability for any measurable control policy when the attacker uses an arbitrary learning algorithm to estimate the system dynamics. We then derive lower bounds for the attacker’s deception probability for both scalar and vector plants by assuming a specific authentication test that inspects the empirical variance of the system disturbance. We also show how the controller can improve the security of the system by superimposing a carefully crafted privacy-enhancing signal on top of the “nominal control policy.” Finally, for nonlinear scalar dynamics that belong to the Reproducing Kernel Hilbert Space (RKHS), we investigate the performance of attacks based on nonlinear Gaussian-processes (GP) learning algortihms.
Tasks	Gaussian Processes
Published	2018-09-17
URL	https://arxiv.org/abs/1809.06023v6
PDF	https://arxiv.org/pdf/1809.06023v6.pdf
PWC	https://paperswithcode.com/paper/authentication-of-cyber-physical-systems
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Diffeomorphic brain shape modelling using Gauss-Newton optimisation


Title	Diffeomorphic brain shape modelling using Gauss-Newton optimisation
Authors	Yaël Balbastre, Mikael Brudfors, Kevin Bronik, John Ashburner
Abstract	Shape modelling describes methods aimed at capturing the natural variability of shapes and commonly relies on probabilistic interpretations of dimensionality reduction techniques such as principal component analysis. Due to their computational complexity when dealing with dense deformation models such as diffeomorphisms, previous attempts have focused on explicitly reducing their dimension, diminishing de facto their flexibility and ability to model complex shapes such as brains. In this paper, we present a generative model of shape that allows the covariance structure of deformations to be captured without squashing their domain, resulting in better normalisation. An efficient inference scheme based on Gauss-Newton optimisation is used, which enables processing of 3D neuroimaging data. We trained this algorithm on segmented brains from the OASIS database, generating physiologically meaningful deformation trajectories. To prove the model’s robustness, we applied it to unseen data, which resulted in equivalent fitting scores.
Tasks	Dimensionality Reduction
Published	2018-06-19
URL	http://arxiv.org/abs/1806.07109v1
PDF	http://arxiv.org/pdf/1806.07109v1.pdf
PWC	https://paperswithcode.com/paper/diffeomorphic-brain-shape-modelling-using
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Deep learning at the shallow end: Malware classification for non-domain experts


Title	Deep learning at the shallow end: Malware classification for non-domain experts
Authors	Quan Le, Oisín Boydell, Brian Mac Namee, Mark Scanlon
Abstract	Current malware detection and classification approaches generally rely on time consuming and knowledge intensive processes to extract patterns (signatures) and behaviors from malware, which are then used for identification. Moreover, these signatures are often limited to local, contiguous sequences within the data whilst ignoring their context in relation to each other and throughout the malware file as a whole. We present a Deep Learning based malware classification approach that requires no expert domain knowledge and is based on a purely data driven approach for complex pattern and feature identification.
Tasks	Malware Classification, Malware Detection
Published	2018-07-22
URL	http://arxiv.org/abs/1807.08265v1
PDF	http://arxiv.org/pdf/1807.08265v1.pdf
PWC	https://paperswithcode.com/paper/deep-learning-at-the-shallow-end-malware
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Multi-Cell Multi-Task Convolutional Neural Networks for Diabetic Retinopathy Grading


Title	Multi-Cell Multi-Task Convolutional Neural Networks for Diabetic Retinopathy Grading
Authors	Kang Zhou, Zaiwang Gu, Wen Liu, Weixin Luo, Jun Cheng, Shenghua Gao, Jiang Liu
Abstract	Diabetic Retinopathy (DR) is a non-negligible eye disease among patients with Diabetes Mellitus, and automatic retinal image analysis algorithm for the DR screening is in high demand. Considering the resolution of retinal image is very high, where small pathological tissues can be detected only with large resolution image and large local receptive field are required to identify those late stage disease, but directly training a neural network with very deep architecture and high resolution image is both time computational expensive and difficult because of gradient vanishing/exploding problem, we propose a \textbf{Multi-Cell} architecture which gradually increases the depth of deep neural network and the resolution of input image, which both boosts the training time but also improves the classification accuracy. Further, considering the different stages of DR actually progress gradually, which means the labels of different stages are related. To considering the relationships of images with different stages, we propose a \textbf{Multi-Task} learning strategy which predicts the label with both classification and regression. Experimental results on the Kaggle dataset show that our method achieves a Kappa of 0.841 on test set which is the 4-th rank of all state-of-the-arts methods. Further, our Multi-Cell Multi-Task Convolutional Neural Networks (M$^2$CNN) solution is a general framework, which can be readily integrated with many other deep neural network architectures.
Tasks	Multi-Task Learning
Published	2018-08-31
URL	http://arxiv.org/abs/1808.10564v2
PDF	http://arxiv.org/pdf/1808.10564v2.pdf
PWC	https://paperswithcode.com/paper/multi-cell-multi-task-convolutional-neural
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Modeling Language Vagueness in Privacy Policies using Deep Neural Networks


Title	Modeling Language Vagueness in Privacy Policies using Deep Neural Networks
Authors	Fei Liu, Nicole Lee Fella, Kexin Liao
Abstract	Website privacy policies are too long to read and difficult to understand. The over-sophisticated language makes privacy notices to be less effective than they should be. People become even less willing to share their personal information when they perceive the privacy policy as vague. This paper focuses on decoding vagueness from a natural language processing perspective. While thoroughly identifying the vague terms and their linguistic scope remains an elusive challenge, in this work we seek to learn vector representations of words in privacy policies using deep neural networks. The vector representations are fed to an interactive visualization tool (LSTMVis) to test on their ability to discover syntactically and semantically related vague terms. The approach holds promise for modeling and understanding language vagueness.
Tasks
Published	2018-05-25
URL	http://arxiv.org/abs/1805.10393v1
PDF	http://arxiv.org/pdf/1805.10393v1.pdf
PWC	https://paperswithcode.com/paper/modeling-language-vagueness-in-privacy
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Provably robust estimation of modulo 1 samples of a smooth function with applications to phase unwrapping


Title	Provably robust estimation of modulo 1 samples of a smooth function with applications to phase unwrapping
Authors	Mihai Cucuringu, Hemant Tyagi
Abstract	Consider an unknown smooth function $f: [0,1]^d \rightarrow \mathbb{R}$, and say we are given $n$ noisy mod 1 samples of $f$, i.e., $y_i = (f(x_i) + \eta_i)\mod 1$, for $x_i \in [0,1]^d$, where $\eta_i$ denotes the noise. Given the samples $(x_i,y_i)_{i=1}^{n}$, our goal is to recover smooth, robust estimates of the clean samples $f(x_i) \bmod 1$. We formulate a natural approach for solving this problem, which works with angular embeddings of the noisy mod 1 samples over the unit circle, inspired by the angular synchronization framework. This amounts to solving a smoothness regularized least-squares problem – a quadratically constrained quadratic program (QCQP) – where the variables are constrained to lie on the unit circle. Our approach is based on solving its relaxation, which is a trust-region sub-problem and hence solvable efficiently. We provide theoretical guarantees demonstrating its robustness to noise for adversarial, and random Gaussian and Bernoulli noise models. To the best of our knowledge, these are the first such theoretical results for this problem. We demonstrate the robustness and efficiency of our approach via extensive numerical simulations on synthetic data, along with a simple least-squares solution for the unwrapping stage, that recovers the original samples of $f$ (up to a global shift). It is shown to perform well at high levels of noise, when taking as input the denoised modulo $1$ samples. Finally, we also consider two other approaches for denoising the modulo 1 samples that leverage tools from Riemannian optimization on manifolds, including a Burer-Monteiro approach for a semidefinite programming relaxation of our formulation. For the two-dimensional version of the problem, which has applications in radar interferometry, we are able to solve instances of real-world data with a million sample points in under 10 seconds, on a personal laptop.
Tasks	Denoising
Published	2018-03-09
URL	https://arxiv.org/abs/1803.03669v2
PDF	https://arxiv.org/pdf/1803.03669v2.pdf
PWC	https://paperswithcode.com/paper/provably-robust-estimation-of-modulo-1
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