pwc-archive/papers-with-abstracts huggingface dataset:
Using anomaly detection to support classification of fast running (packaging) processes
Authors: Tilman Klaeger, Andre Schult, Lukas Oehm
Date: 2019-06-06 00:00:00
Tasks: Anomaly Detection, General Classification, Model Selection, Self-Learning

In this paper we propose a new method to assist in labeling data arriving from fast running processes using anomaly detection. A result is the possibility to manually classify data arriving at a high rates to train machine learning models. To circumvent the problem of not having a real ground truth we propose specific metrics for model selection and validation of the results. The use case is taken from the food packaging industry, where processes are affected by regular but short breakdowns causing interruptions in the production process. Fast production rates make it hard for machine operators to identify the source and thus the cause of the breakdown. Self learning assistance systems can help them finding the root cause of the problem and assist the machine operator in applying lasting solutions. These learning systems need to be trained to identify reoccurring problems using data analytics. Training is not easy as the process is too fast to be manually monitored to add specific classifications on the single data points.
StyleNAS: An Empirical Study of Neural Architecture Search to Uncover Surprisingly Fast End-to-End Universal Style Transfer Networks
Authors: Jie An, Haoyi Xiong, Jinwen Ma, Jiebo Luo, Jun Huan
Date: 2019-06-06 00:00:00
Tasks: image-classification, Image Classification, Neural Architecture Search, object-detection, Object Detection, Semantic Segmentation, Style Transfer

Neural Architecture Search (NAS) has been widely studied for designing discriminative deep learning models such as image classification, object detection, and semantic segmentation. As a large number of priors have been obtained through the manual design of architectures in the fields, NAS is usually considered as a supplement approach. In this paper, we have significantly expanded the application areas of NAS by performing an empirical study of NAS to search generative models, or specifically, auto-encoder based universal style transfer, which lacks systematic exploration, if any, from the architecture search aspect. In our work, we first designed a search space where common operators for image style transfer such as VGG-based encoders, whitening and coloring transforms (WCT), convolution kernels, instance normalization operators, and skip connections were searched in a combinatorial approach. With a simple yet effective parallel evolutionary NAS algorithm with multiple objectives, we derived the first group of end-to-end deep networks for universal photorealistic style transfer. Comparing to random search, a NAS method that is gaining popularity recently, we demonstrated that carefully designed search strategy leads to much better architecture design. Finally compared to existing universal style transfer networks for photorealistic rendering such as PhotoWCT that stacks multiple well-trained auto-encoders and WCT transforms in a non-end-to-end manner, the architectures designed by StyleNAS produce better style-transferred images with details preserving, using a tiny number of operators/parameters, and enjoying around 500x inference time speed-up.
ActivityNet-QA: A Dataset for Understanding Complex Web Videos via Question Answering
Authors: Zhou Yu, Dejing Xu, Jun Yu, Ting Yu, Zhou Zhao, Yueting Zhuang, DaCheng Tao
Date: 2019-06-06 00:00:00
Tasks: Question Answering, Video Question Answering, Visual Question Answering (VQA), Zero-Shot Video Question Answer

Recent developments in modeling language and vision have been successfully applied to image question answering. It is both crucial and natural to extend this research direction to the video domain for video question answering (VideoQA). Compared to the image domain where large scale and fully annotated benchmark datasets exists, VideoQA datasets are limited to small scale and are automatically generated, etc. These limitations restrict their applicability in practice. Here we introduce ActivityNet-QA, a fully annotated and large scale VideoQA dataset. The dataset consists of 58,000 QA pairs on 5,800 complex web videos derived from the popular ActivityNet dataset. We present a statistical analysis of our ActivityNet-QA dataset and conduct extensive experiments on it by comparing existing VideoQA baselines. Moreover, we explore various video representation strategies to improve VideoQA performance, especially for long videos. The dataset is available at https://github.com/MILVLG/activitynet-qa
Unsupervised Pivot Translation for Distant Languages
Authors: Yichong Leng, Xu Tan, Tao Qin, Xiang-Yang Li, Tie-Yan Liu
Date: 2019-06-06 00:00:00
Proceeding: ACL 2019 7
Tasks: Machine Translation, NMT, Translation

Unsupervised neural machine translation (NMT) has attracted a lot of attention recently. While state-of-the-art methods for unsupervised translation usually perform well between similar languages (e.g., English-German translation), they perform poorly between distant languages, because unsupervised alignment does not work well for distant languages. In this work, we introduce unsupervised pivot translation for distant languages, which translates a language to a distant language through multiple hops, and the unsupervised translation on each hop is relatively easier than the original direct translation. We propose a learning to route (LTR) method to choose the translation path between the source and target languages. LTR is trained on language pairs whose best translation path is available and is applied on the unseen language pairs for path selection. Experiments on 20 languages and 294 distant language pairs demonstrate the advantages of the unsupervised pivot translation for distant languages, as well as the effectiveness of the proposed LTR for path selection. Specifically, in the best case, LTR achieves an improvement of 5.58 BLEU points over the conventional direct unsupervised method.
Clustered Reinforcement Learning
Authors: Xiao Ma, Shen-Yi Zhao, Wu-Jun Li
Date: 2019-06-06 00:00:00
Tasks: Atari Games, Clustering, continuous-control, Continuous Control, Efficient Exploration, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Exploration strategy design is one of the challenging problems in reinforcement learning~(RL), especially when the environment contains a large state space or sparse rewards. During exploration, the agent tries to discover novel areas or high reward~(quality) areas. In most existing methods, the novelty and quality in the neighboring area of the current state are not well utilized to guide the exploration of the agent. To tackle this problem, we propose a novel RL framework, called \underline{c}lustered \underline{r}einforcement \underline{l}earning~(CRL), for efficient exploration in RL. CRL adopts clustering to divide the collected states into several clusters, based on which a bonus reward reflecting both novelty and quality in the neighboring area~(cluster) of the current state is given to the agent. Experiments on a continuous control task and several \emph{Atari 2600} games show that CRL can outperform other state-of-the-art methods to achieve the best performance in most cases.
Bridging the Gap between Training and Inference for Neural Machine Translation
Authors: Wen Zhang, Yang Feng, Fandong Meng, Di You, Qun Liu
Date: 2019-06-06 00:00:00
Proceeding: ACL 2019 7
Tasks: Machine Translation, NMT, Sentence, Translation

Neural Machine Translation (NMT) generates target words sequentially in the way of predicting the next word conditioned on the context words. At training time, it predicts with the ground truth words as context while at inference it has to generate the entire sequence from scratch. This discrepancy of the fed context leads to error accumulation among the way. Furthermore, word-level training requires strict matching between the generated sequence and the ground truth sequence which leads to overcorrection over different but reasonable translations. In this paper, we address these issues by sampling context words not only from the ground truth sequence but also from the predicted sequence by the model during training, where the predicted sequence is selected with a sentence-level optimum. Experiment results on Chinese->English and WMT'14 English->German translation tasks demonstrate that our approach can achieve significant improvements on multiple datasets.
Evolution of Hierarchical Structure & Reuse in iGEM Synthetic DNA Sequences
Authors: Payam Siyari, Bistra Dilkina, Constantine Dovrolis
Date: 2019-06-06 00:00:00
Tasks: Time Series, Time Series Analysis

Many complex systems, both in technology and nature, exhibit hierarchical modularity: smaller modules, each of them providing a certain function, are used within larger modules that perform more complex functions. Previously, we have proposed a modeling framework, referred to as Evo-Lexis, that provides insight to some fundamental questions about evolving hierarchical systems. The predictions of the Evo-Lexis model should be tested using real data from evolving systems in which the outputs can be well represented by sequences. In this paper, we investigate the time series of iGEM synthetic DNA dataset sequences, and whether the resulting iGEM hierarchies exhibit the qualitative properties predicted by the Evo-Lexis framework. Contrary to Evo-Lexis, in iGEM the amount of reuse decreases during the timeline of the dataset. Although this results in development of less cost-efficient and less deep Lexis-DAGs, the dataset exhibits a bias in reusing specific nodes more often than others. This results in the Lexis-DAGs to take the shape of an hourglass with relatively high H-score values and stable set of core nodes. Despite the reuse bias and stability of the core set, the dataset presents a high amount of diversity among the targets which is in line with modeling of Evo-Lexis.
Fault Diagnosis of Rotary Machines using Deep Convolutional Neural Network with three axis signal input
Authors: Davor Kolar, Dragutin Lisjak, Michal Pajak, Danijel Pavkovic
Date: 2019-06-06 00:00:00
Tasks: Deep Learning, Fault Diagnosis

Recent trends focusing on Industry 4.0 concept and smart manufacturing arise a data-driven fault diagnosis as key topic in condition-based maintenance. Fault diagnosis is considered as an essential task in rotary machinery since possibility of an early detection and diagnosis of the faulty condition can save both time and money. Traditional data-driven techniques of fault diagnosis require signal processing for feature extraction, as they are unable to work with raw signal data, consequently leading to need for expert knowledge and human work. The emergence of deep learning architectures in condition-based maintenance promises to ensure high performance fault diagnosis while lowering necessity for expert knowledge and human work. This paper presents developed technique for deep learning-based data-driven fault diagnosis of rotary machinery. The proposed technique input raw three axis accelerometer signal as high-definition image into deep learning layers which automatically extract signal features, enabling high classification accuracy.
Robust Neural Machine Translation with Doubly Adversarial Inputs
Authors: Yong Cheng, Lu Jiang, Wolfgang Macherey
Date: 2019-06-06 00:00:00
Proceeding: ACL 2019 7
Tasks: Machine Translation, NMT, Translation

Neural machine translation (NMT) often suffers from the vulnerability to noisy perturbations in the input. We propose an approach to improving the robustness of NMT models, which consists of two parts: (1) attack the translation model with adversarial source examples; (2) defend the translation model with adversarial target inputs to improve its robustness against the adversarial source inputs.For the generation of adversarial inputs, we propose a gradient-based method to craft adversarial examples informed by the translation loss over the clean inputs.Experimental results on Chinese-English and English-German translation tasks demonstrate that our approach achieves significant improvements ($2.8$ and $1.6$ BLEU points) over Transformer on standard clean benchmarks as well as exhibiting higher robustness on noisy data.
Should Adversarial Attacks Use Pixel p-Norm?
Authors: Ayon Sen, Xiaojin Zhu, Liam Marshall, Robert Nowak
Date: 2019-06-06 00:00:00
Tasks: Adversarial Attack, BIG-bench Machine Learning, General Classification, image-classification, Image Classification

Adversarial attacks aim to confound machine learning systems, while remaining virtually imperceptible to humans. Attacks on image classification systems are typically gauged in terms of $p$-norm distortions in the pixel feature space. We perform a behavioral study, demonstrating that the pixel $p$-norm for any $0\le p \le \infty$, and several alternative measures including earth mover's distance, structural similarity index, and deep net embedding, do not fit human perception. Our result has the potential to improve the understanding of adversarial attack and defense strategies.
GCDT: A Global Context Enhanced Deep Transition Architecture for Sequence Labeling
Authors: Yijin Liu, Fandong Meng, Jinchao Zhang, Jinan Xu, Yufeng Chen, Jie zhou
Date: 2019-06-06 00:00:00
Proceeding: ACL 2019 7
Tasks: Chunking, Named Entity Recognition (NER), NER, Sentence, Word Embeddings

Current state-of-the-art systems for sequence labeling are typically based on the family of Recurrent Neural Networks (RNNs). However, the shallow connections between consecutive hidden states of RNNs and insufficient modeling of global information restrict the potential performance of those models. In this paper, we try to address these issues, and thus propose a Global Context enhanced Deep Transition architecture for sequence labeling named GCDT. We deepen the state transition path at each position in a sentence, and further assign every token with a global representation learned from the entire sentence. Experiments on two standard sequence labeling tasks show that, given only training data and the ubiquitous word embeddings (Glove), our GCDT achieves 91.96 F1 on the CoNLL03 NER task and 95.43 F1 on the CoNLL2000 Chunking task, which outperforms the best reported results under the same settings. Furthermore, by leveraging BERT as an additional resource, we establish new state-of-the-art results with 93.47 F1 on NER and 97.30 F1 on Chunking.
Primal-Dual Block Frank-Wolfe
Authors: Qi Lei, Jiacheng Zhuo, Constantine Caramanis, Inderjit S. Dhillon, Alexandros G. Dimakis
Date: 2019-06-06 00:00:00
Tasks: General Classification, Multi-class Classification, Retrieval

We propose a variant of the Frank-Wolfe algorithm for solving a class of sparse/low-rank optimization problems. Our formulation includes Elastic Net, regularized SVMs and phase retrieval as special cases. The proposed Primal-Dual Block Frank-Wolfe algorithm reduces the per-iteration cost while maintaining linear convergence rate. The per iteration cost of our method depends on the structural complexity of the solution (i.e. sparsity/low-rank) instead of the ambient dimension. We empirically show that our algorithm outperforms the state-of-the-art methods on (multi-class) classification tasks.
Complete Dictionary Learning via $\ell^4$-Norm Maximization over the Orthogonal Group
Authors: Yuexiang Zhai, Zitong Yang, Zhenyu Liao, John Wright, Yi Ma
Date: 2019-06-06 00:00:00
Tasks: Dictionary Learning

This paper considers the fundamental problem of learning a complete (orthogonal) dictionary from samples of sparsely generated signals. Most existing methods solve the dictionary (and sparse representations) based on heuristic algorithms, usually without theoretical guarantees for either optimality or complexity. The recent $\ell^1$-minimization based methods do provide such guarantees but the associated algorithms recover the dictionary one column at a time. In this work, we propose a new formulation that maximizes the $\ell^4$-norm over the orthogonal group, to learn the entire dictionary. We prove that under a random data model, with nearly minimum sample complexity, the global optima of the $\ell^4$ norm are very close to signed permutations of the ground truth. Inspired by this observation, we give a conceptually simple and yet effective algorithm based on "matching, stretching, and projection" (MSP). The algorithm provably converges locally at a superlinear (cubic) rate and cost per iteration is merely an SVD. In addition to strong theoretical guarantees, experiments show that the new algorithm is significantly more efficient and effective than existing methods, including KSVD and $\ell^1$-based methods. Preliminary experimental results on mixed real imagery data clearly demonstrate advantages of so learned dictionary over classic PCA bases.
Nonconvex Approach for Sparse and Low-Rank Constrained Models with Dual Momentum
Authors: Cho-Ying Wu, Jian-Jiun Ding
Date: 2019-06-06 00:00:00
Tasks: Clustering, Denoising, Outlier Detection

In this manuscript, we research on the behaviors of surrogates for the rank function on different image processing problems and their optimization algorithms. We first propose a novel nonconvex rank surrogate on the general rank minimization problem and apply this to the corrupted image completion problem. Then, we propose that nonconvex rank surrogates can be introduced into two well-known sparse and low-rank models: Robust Principal Component Analysis (RPCA) and Low-Rank Representation (LRR). For optimization, we use alternating direction method of multipliers (ADMM) and propose a trick, which is called the dual momentum. We add the difference of the dual variable between the current and the last iteration with a weight. This trick can avoid the local minimum problem and make the algorithm converge to a solution with smaller recovery error in the nonconvex optimization problem. Also, it can boost the convergence when the variable updates too slowly. We also give a severe proof and verify that the proposed algorithms are convergent. Then, several experiments are conducted, including image completion, denoising, and spectral clustering with outlier detection. These experiments show that the proposed methods are effective in image and signal processing applications, and have the best performance compared with state-of-the-art methods.
Shift-of-Perspective Identification Within Legal Cases
Authors: Gathika Ratnayaka, Thejan Rupasinghe, Nisansa de Silva, Viraj Salaka Gamage, Menuka Warushavithana, Amal Shehan Perera
Date: 2019-06-06 00:00:00
Tasks: Open Information Extraction, Sentiment Analysis

Arguments, counter-arguments, facts, and evidence obtained via documents related to previous court cases are of essential need for legal professionals. Therefore, the process of automatic information extraction from documents containing legal opinions related to court cases can be considered to be of significant importance. This study is focused on the identification of sentences in legal opinion texts which convey different perspectives on a certain topic or entity. We combined several approaches based on semantic analysis, open information extraction, and sentiment analysis to achieve our objective. Then, our methodology was evaluated with the help of human judges. The outcomes of the evaluation demonstrate that our system is successful in detecting situations where two sentences deliver different opinions on the same topic or entity. The proposed methodology can be used to facilitate other information extraction tasks related to the legal domain. One such task is the automated detection of counter arguments for a given argument. Another is the identification of opponent parties in a court case.
Occluded Face Recognition Using Low-rank Regression with Generalized Gradient Direction
Authors: Cho-Ying Wu, Jian-Jiun Ding
Date: 2019-06-06 00:00:00
Tasks: Dictionary Learning, Face Recognition, regression

In this paper, a very effective method to solve the contiguous face occlusion recognition problem is proposed. It utilizes the robust image gradient direction features together with a variety of mapping functions and adopts a hierarchical sparse and low-rank regression model. This model unites the sparse representation in dictionary learning and the low-rank representation on the error term that is usually messy in the gradient domain. We call it the "weak low-rankness" optimization problem, which can be efficiently solved by the framework of Alternating Direction Method of Multipliers (ADMM). The optimum of the error term has a similar weak low-rank structure as the reference error map and the recognition performance can be enhanced by leaps and bounds using weak low-rankness optimization. Extensive experiments are conducted on real-world disguise / occlusion data and synthesized contiguous occlusion data. These experiments show that the proposed gradient direction-based hierarchical adaptive sparse and low-rank (GD-HASLR) algorithm has the best performance compared to state-of-the-art methods, including popular convolutional neural network-based methods.
Amortized Inference of Variational Bounds for Learning Noisy-OR
Authors: Yiming Yan, Melissa Ailem, Fei Sha
Date: 2019-06-06 00:00:00
Tasks: Variational Inference

Classical approaches for approximate inference depend on cleverly designed variational distributions and bounds. Modern approaches employ amortized variational inference, which uses a neural network to approximate any posterior without leveraging the structures of the generative models. In this paper, we propose Amortized Conjugate Posterior (ACP), a hybrid approach taking advantages of both types of approaches. Specifically, we use the classical methods to derive specific forms of posterior distributions and then learn the variational parameters using amortized inference. We study the effectiveness of the proposed approach on the noisy-or model and compare to both the classical and the modern approaches for approximate inference and parameter learning. Our results show that the proposed method outperforms or are at par with other approaches.
One-shot Information Extraction from Document Images using Neuro-Deductive Program Synthesis
Authors: Vishal Sunder, Ashwin Srinivasan, Lovekesh Vig, Gautam Shroff, Rohit Rahul
Date: 2019-06-06 00:00:00
Tasks: Program Synthesis

Our interest in this paper is in meeting a rapidly growing industrial demand for information extraction from images of documents such as invoices, bills, receipts etc. In practice users are able to provide a very small number of example images labeled with the information that needs to be extracted. We adopt a novel two-level neuro-deductive, approach where (a) we use pre-trained deep neural networks to populate a relational database with facts about each document-image; and (b) we use a form of deductive reasoning, related to meta-interpretive learning of transition systems to learn extraction programs: Given task-specific transitions defined using the entities and relations identified by the neural detectors and a small number of instances (usually 1, sometimes 2) of images and the desired outputs, a resource-bounded meta-interpreter constructs proofs for the instance(s) via logical deduction; a set of logic programs that extract each desired entity is easily synthesized from such proofs. In most cases a single training example together with a noisy-clone of itself suffices to learn a program-set that generalizes well on test documents, at which time the value of each entity is determined by a majority vote across its program-set. We demonstrate our two-level neuro-deductive approach on publicly available datasets ("Patent" and "Doctor's Bills") and also describe its use in a real-life industrial problem.