pwc-archive/papers-with-abstracts huggingface dataset:
Grammatical Error Correction in Low Error Density Domains: A New Benchmark and Analyses
Authors: Simon Flachs, Ophélie Lacroix, Helen Yannakoudakis, Marek Rei, Anders Søgaard
Date: 2020-10-15 00:00:00
Proceeding: EMNLP 2020 11
Tasks: Grammatical Error Correction, Language Modeling, Language Modelling

Evaluation of grammatical error correction (GEC) systems has primarily focused on essays written by non-native learners of English, which however is only part of the full spectrum of GEC applications. We aim to broaden the target domain of GEC and release CWEB, a new benchmark for GEC consisting of website text generated by English speakers of varying levels of proficiency. Website data is a common and important domain that contains far fewer grammatical errors than learner essays, which we show presents a challenge to state-of-the-art GEC systems. We demonstrate that a factor behind this is the inability of systems to rely on a strong internal language model in low error density domains. We hope this work shall facilitate the development of open-domain GEC models that generalize to different topics and genres.
Multi-view Hierarchical Clustering
Authors: Qinghai Zheng, Jihua Zhu, Shuangxun Ma
Date: 2020-10-15 00:00:00
Tasks: Clustering

This paper focuses on the multi-view clustering, which aims to promote clustering results with multi-view data. Usually, most existing works suffer from the issues of parameter selection and high computational complexity. To overcome these limitations, we propose a Multi-view Hierarchical Clustering (MHC), which partitions multi-view data recursively at multiple levels of granularity. Specifically, MHC consists of two important components: the cosine distance integration (CDI) and the nearest neighbor agglomeration (NNA). The CDI can explore the underlying complementary information of multi-view data so as to learn an essential distance matrix, which is utilized in NNA to obtain the clustering results. Significantly, the proposed MHC can be easily and effectively employed in real-world applications without parameter selection. Experiments on nine benchmark datasets illustrate the superiority of our method comparing to several state-of-the-art multi-view clustering methods.
Revisiting Projection-free Online Learning: the Strongly Convex Case
Authors: Dan Garber, Ben Kretzu
Date: 2020-10-15 00:00:00

Projection-free optimization algorithms, which are mostly based on the classical Frank-Wolfe method, have gained significant interest in the machine learning community in recent years due to their ability to handle convex constraints that are popular in many applications, but for which computing projections is often computationally impractical in high-dimensional settings, and hence prohibit the use of most standard projection-based methods. In particular, a significant research effort was put on projection-free methods for online learning. In this paper we revisit the Online Frank-Wolfe (OFW) method suggested by Hazan and Kale \cite{Hazan12} and fill a gap that has been left unnoticed for several years: OFW achieves a faster rate of $O(T^{2/3})$ on strongly convex functions (as opposed to the standard $O(T^{3/4})$ for convex but not strongly convex functions), where $T$ is the sequence length. This is somewhat surprising since it is known that for offline optimization, in general, strong convexity does not lead to faster rates for Frank-Wolfe. We also revisit the bandit setting under strong convexity and prove a similar bound of $\tilde O(T^{2/3})$ (instead of $O(T^{3/4})$ without strong convexity). Hence, in the current state-of-affairs, the best projection-free upper-bounds for the full-information and bandit settings with strongly convex and nonsmooth functions match up to logarithmic factors in $T$.
Bi-GCN: Binary Graph Convolutional Network
Authors: Junfu Wang, Yunhong Wang, Zhen Yang, Liang Yang, Yuanfang Guo
Date: 2020-10-15 00:00:00
Proceeding: CVPR 2021 1
Tasks: Binarization, Graph Representation Learning, Representation Learning

Graph Neural Networks (GNNs) have achieved tremendous success in graph representation learning. Unfortunately, current GNNs usually rely on loading the entire attributed graph into network for processing. This implicit assumption may not be satisfied with limited memory resources, especially when the attributed graph is large. In this paper, we pioneer to propose a Binary Graph Convolutional Network (Bi-GCN), which binarizes both the network parameters and input node features. Besides, the original matrix multiplications are revised to binary operations for accelerations. According to the theoretical analysis, our Bi-GCN can reduce the memory consumption by an average of ~30x for both the network parameters and input data, and accelerate the inference speed by an average of ~47x, on the citation networks. Meanwhile, we also design a new gradient approximation based back-propagation method to train our Bi-GCN well. Extensive experiments have demonstrated that our Bi-GCN can give a comparable performance compared to the full-precision baselines. Besides, our binarization approach can be easily applied to other GNNs, which has been verified in the experiments.
A Robust Deep Unfolded Network for Sparse Signal Recovery from Noisy Binary Measurements
Authors: Y. Yang, P. Xiao, B. Liao, N. Deligiannis
Date: 2020-10-15 00:00:00
Tasks: compressed sensing

We propose a novel deep neural network, coined DeepFPC-$\ell_2$, for solving the 1-bit compressed sensing problem. The network is designed by unfolding the iterations of the fixed-point continuation (FPC) algorithm with one-sided $\ell_2$-norm (FPC-$\ell_2$). The DeepFPC-$\ell_2$ method shows higher signal reconstruction accuracy and convergence speed than the traditional FPC-$\ell_2$ algorithm. Furthermore, we compare its robustness to noise with the previously proposed DeepFPC network---which stemmed from unfolding the FPC-$\ell_1$ algorithm---for different signal to noise ratio (SNR) and sign-flipped ratio (flip ratio) scenarios. We show that the proposed network has better noise immunity than the previous DeepFPC method. This result indicates that the robustness of a deep-unfolded neural network is related with that of the algorithm it stems from.
Melody Classification based on Performance Event Vector and BRNN
Authors: Jinyue Guo, Aozhi Liu, Jing Xiao
Date: 2020-10-15 00:00:00
Tasks: Classification, General Classification

We proposed a model for the Conference of Music and Technology (CSMT2020) data challenge of melody classification. Our model used the Performance Event Vector as the input sequence to build a Bidirectional RNN network for classfication. The model achieved a satisfying performance on the development dataset and Wikifonia dataset. We also discussed the effect of several hyper-parameters, and created multiple prediction outputs for the evaluation dataset.
Token Sequence Labeling vs. Clause Classification for English Emotion Stimulus Detection
Authors: Laura Oberländer, Roman Klinger
Date: 2020-10-15 00:00:00
Proceeding: Joint Conference on Lexical and Computational Semantics 2020
Tasks: Classification, General Classification, Sentiment Analysis, text-classification, Text Classification

Emotion stimulus detection is the task of finding the cause of an emotion in a textual description, similar to target or aspect detection for sentiment analysis. Previous work approached this in three ways, namely (1) as text classification into an inventory of predefined possible stimuli ("Is the stimulus category A or B?"), (2) as sequence labeling of tokens ("Which tokens describe the stimulus?"), and (3) as clause classification ("Does this clause contain the emotion stimulus?"). So far, setting (3) has been evaluated broadly on Mandarin and (2) on English, but no comparison has been performed. Therefore, we aim to answer whether clause classification or sequence labeling is better suited for emotion stimulus detection in English. To accomplish that, we propose an integrated framework which enables us to evaluate the two different approaches comparably, implement models inspired by state-of-the-art approaches in Mandarin, and test them on four English data sets from different domains. Our results show that sequence labeling is superior on three out of four datasets, in both clause-based and sequence-based evaluation. The only case in which clause classification performs better is one data set with a high density of clause annotations. Our error analysis further confirms quantitatively and qualitatively that clauses are not the appropriate stimulus unit in English.
Encoder-decoder semantic segmentation models for electroluminescence images of thin-film photovoltaic modules
Authors: Evgenii Sovetkin, Elbert Jan Achterberg, Thomas Weber, Bart E. Pieters
Date: 2020-10-15 00:00:00
Tasks: Decoder, Image Segmentation, Segmentation, Semantic Segmentation

We consider a series of image segmentation methods based on the deep neural networks in order to perform semantic segmentation of electroluminescence (EL) images of thin-film modules. We utilize the encoder-decoder deep neural network architecture. The framework is general such that it can easily be extended to other types of images (e.g. thermography) or solar cell technologies (e.g. crystalline silicon modules). The networks are trained and tested on a sample of images from a database with 6000 EL images of Copper Indium Gallium Diselenide (CIGS) thin film modules. We selected two types of features to extract, shunts and so called "droplets". The latter feature is often observed in the set of images. Several models are tested using various combinations of encoder-decoder layers, and a procedure is proposed to select the best model. We show exemplary results with the best selected model. Furthermore, we applied the best model to the full set of 6000 images and demonstrate that the automated segmentation of EL images can reveal many subtle features which cannot be inferred from studying a small sample of images. We believe these features can contribute to process optimization and quality control.
Solar Coronal Magnetic Field Extrapolation from Synchronic Data with AI-generated Farside
Authors: Hyun-Jin Jeong, Yong-Jae Moon, Eunsu Park, Harim Lee
Date: 2020-10-15 00:00:00

Solar magnetic fields play a key role in understanding the nature of the coronal phenomena. Global coronal magnetic fields are usually extrapolated from photospheric fields, for which farside data is taken when it was at the frontside, about two weeks earlier. For the first time we have constructed the extrapolations of global magnetic fields using frontside and artificial intelligence (AI)-generated farside magnetic fields at a near-real time basis. We generate the farside magnetograms from three channel farside observations of Solar Terrestrial Relations Observatory (STEREO) Ahead (A) and Behind (B) by our deep learning model trained with frontside Solar Dynamics Observatory extreme ultraviolet images and magnetograms. For frontside testing data sets, we demonstrate that the generated magnetic field distributions are consistent with the real ones; not only active regions (ARs), but also quiet regions of the Sun. We make global magnetic field synchronic maps in which conventional farside data are replaced by farside ones generated by our model. The synchronic maps show much better not only the appearance of ARs but also the disappearance of others on the solar surface than before. We use these synchronized magnetic data to extrapolate the global coronal fields using Potential Field Source Surface (PFSS) model. We show that our results are much more consistent with coronal observations than those of the conventional method in view of solar active regions and coronal holes. We present several positive prospects of our new methodology for the study of solar corona, heliosphere, and space weather.
MOTChallenge: A Benchmark for Single-Camera Multiple Target Tracking
Authors: Patrick Dendorfer, Aljoša Ošep, Anton Milan, Konrad Schindler, Daniel Cremers, Ian Reid, Stefan Roth, Laura Leal-Taixé
Date: 2020-10-15 00:00:00
Tasks: Multiple Object Tracking, Multiple People Tracking, Object, Object Tracking, Robot Navigation, Self-Driving Cars

Standardized benchmarks have been crucial in pushing the performance of computer vision algorithms, especially since the advent of deep learning. Although leaderboards should not be over-claimed, they often provide the most objective measure of performance and are therefore important guides for research. We present MOTChallenge, a benchmark for single-camera Multiple Object Tracking (MOT) launched in late 2014, to collect existing and new data, and create a framework for the standardized evaluation of multiple object tracking methods. The benchmark is focused on multiple people tracking, since pedestrians are by far the most studied object in the tracking community, with applications ranging from robot navigation to self-driving cars. This paper collects the first three releases of the benchmark: (i) MOT15, along with numerous state-of-the-art results that were submitted in the last years, (ii) MOT16, which contains new challenging videos, and (iii) MOT17, that extends MOT16 sequences with more precise labels and evaluates tracking performance on three different object detectors. The second and third release not only offers a significant increase in the number of labeled boxes but also provide labels for multiple object classes beside pedestrians, as well as the level of visibility for every single object of interest. We finally provide a categorization of state-of-the-art trackers and a broad error analysis. This will help newcomers understand the related work and research trends in the MOT community, and hopefully shed some light on potential future research directions.
Deep Learning of Koopman Representation for Control
Authors: Yiqiang Han, Wenjian Hao, Umesh Vaidya
Date: 2020-10-15 00:00:00
Tasks: Deep Learning, OpenAI Gym, reinforcement-learning, Reinforcement Learning (RL)

We develop a data-driven, model-free approach for the optimal control of the dynamical system. The proposed approach relies on the Deep Neural Network (DNN) based learning of Koopman operator for the purpose of control. In particular, DNN is employed for the data-driven identification of basis function used in the linear lifting of nonlinear control system dynamics. The controller synthesis is purely data-driven and does not rely on a priori domain knowledge. The OpenAI Gym environment, employed for Reinforcement Learning-based control design, is used for data generation and learning of Koopman operator in control setting. The method is applied to two classic dynamical systems on OpenAI Gym environment to demonstrate the capability.
FOSS: Multi-Person Age Estimation with Focusing on Objects and Still Seeing Surroundings
Authors: Masakazu Yoshimura, Satoshi Ogata
Date: 2020-10-15 00:00:00
Tasks: Age Estimation

Age estimation from images can be used in many practical scenes. Most of the previous works targeted on the estimation from images in which only one face exists. Also, most of the open datasets for age estimation contain images like that. However, in some situations, age estimation in the wild and for multi-person is needed. Usually, such situations were solved by two separate models; one is a face detector model which crops facial regions and the other is an age estimation model which estimates from cropped images. In this work, we propose a method that can detect and estimate the age of multi-person with a single model which estimates age with focusing on faces and still seeing surroundings. Also, we propose a training method which enables the model to estimate multi-person well despite trained with images in which only one face is photographed. In the experiments, we evaluated our proposed method compared with the traditional approach using two separate models. As the result, the accuracy could be enhanced with our proposed method. We also adapted our proposed model to commonly used single person photographed age estimation datasets and it is proved that our method is also effective to those images and outperforms the state of the art accuracy.
Improving Constituency Parsing with Span Attention
Authors: Yuanhe Tian, Yan Song, Fei Xia, Tong Zhang
Date: 2020-10-15 00:00:00
Proceeding: Findings of the Association for Computational Linguistics 2020
Tasks: Constituency Parsing, Natural Language Understanding, Sentence

Constituency parsing is a fundamental and important task for natural language understanding, where a good representation of contextual information can help this task. N-grams, which is a conventional type of feature for contextual information, have been demonstrated to be useful in many tasks, and thus could also be beneficial for constituency parsing if they are appropriately modeled. In this paper, we propose span attention for neural chart-based constituency parsing to leverage n-gram information. Considering that current chart-based parsers with Transformer-based encoder represent spans by subtraction of the hidden states at the span boundaries, which may cause information loss especially for long spans, we incorporate n-grams into span representations by weighting them according to their contributions to the parsing process. Moreover, we propose categorical span attention to further enhance the model by weighting n-grams within different length categories, and thus benefit long-sentence parsing. Experimental results on three widely used benchmark datasets demonstrate the effectiveness of our approach in parsing Arabic, Chinese, and English, where state-of-the-art performance is obtained by our approach on all of them.
Back to the Future: Cycle Encoding Prediction for Self-supervised Contrastive Video Representation Learning
Authors: Xinyu Yang, Majid Mirmehdi, Tilo Burghardt
Date: 2020-10-14 00:00:00
Tasks: Action Classification, Action Recognition, Representation Learning

In this paper we show that learning video feature spaces in which temporal cycles are maximally predictable benefits action classification. In particular, we propose a novel learning approach termed Cycle Encoding Prediction (CEP) that is able to effectively represent high-level spatio-temporal structure of unlabelled video content. CEP builds a latent space wherein the concept of closed forward-backward as well as backward-forward temporal loops is approximately preserved. As a self-supervision signal, CEP leverages the bi-directional temporal coherence of the video stream and applies loss functions that encourage both temporal cycle closure as well as contrastive feature separation. Architecturally, the underpinning network structure utilises a single feature encoder for all video snippets, adding two predictive modules that learn temporal forward and backward transitions. We apply our framework for pretext training of networks for action recognition tasks. We report significantly improved results for the standard datasets UCF101 and HMDB51. Detailed ablation studies support the effectiveness of the proposed components. We publish source code for the CEP components in full with this paper.
Differential diagnosis and molecular stratification of gastrointestinal stromal tumors on CT images using a radiomics approach
Authors: Martijn P. A. Starmans, Milea J. M. Timbergen, Melissa Vos, Michel Renckens, Dirk J. Grünhagen, Geert J. L. H. van Leenders, Roy S. Dwarkasing, François E. J. A. Willemssen, Wiro J. Niessen, Cornelis Verhoef, Stefan Sleijfer, Jacob J. Visser, Stefan Klein
Date: 2020-10-14 00:00:00

Distinguishing gastrointestinal stromal tumors (GISTs) from other intra-abdominal tumors and GISTs molecular analysis is necessary for treatment planning, but challenging due to its rarity. The aim of this study was to evaluate radiomics for distinguishing GISTs from other intra-abdominal tumors, and in GISTs, predict the c-KIT, PDGFRA,BRAF mutational status and mitotic index (MI). All 247 included patients (125 GISTS, 122 non-GISTs) underwent a contrast-enhanced venous phase CT. The GIST vs. non-GIST radiomics model, including imaging, age, sex and location, had a mean area under the curve (AUC) of 0.82. Three radiologists had an AUC of 0.69, 0.76, and 0.84, respectively. The radiomics model had an AUC of 0.52 for c-KIT, 0.56 for c-KIT exon 11, and 0.52 for the MI. Hence, our radiomics model was able to distinguish GIST from non-GISTS with a performance similar to three radiologists, but was not able to predict the c-KIT mutation or MI.
TDRE: A Tensor Decomposition Based Approach for Relation Extraction
Authors: Bin-Bin Zhao, Liang Li, Hui-Dong Zhang
Date: 2020-10-15 00:00:00
Tasks: Relation, Relation Classification, Relation Extraction, Sentence, Tensor Decomposition, Triplet

Extracting entity pairs along with relation types from unstructured texts is a fundamental subtask of information extraction. Most existing joint models rely on fine-grained labeling scheme or focus on shared embedding parameters. These methods directly model the joint probability of multi-labeled triplets, which suffer from extracting redundant triplets with all relation types. However, each sentence may contain very few relation types. In this paper, we first model the final triplet extraction result as a three-order tensor of word-to-word pairs enriched with each relation type. And in order to obtain the sentence contained relations, we introduce an independent but joint training relation classification module. The tensor decomposition strategy is finally utilized to decompose the triplet tensor with predicted relational components which omits the calculations for unpredicted relation types. According to effective decomposition methods, we propose the Tensor Decomposition based Relation Extraction (TDRE) approach which is able to extract overlapping triplets and avoid detecting unnecessary entity pairs. Experiments on benchmark datasets NYT, CoNLL04 and ADE datasets demonstrate that the proposed method outperforms existing strong baselines.
Certifying Neural Network Robustness to Random Input Noise from Samples
Authors: Brendon G. Anderson, Somayeh Sojoudi
Date: 2020-10-15 00:00:00

Methods to certify the robustness of neural networks in the presence of input uncertainty are vital in safety-critical settings. Most certification methods in the literature are designed for adversarial input uncertainty, but researchers have recently shown a need for methods that consider random uncertainty. In this paper, we propose a novel robustness certification method that upper bounds the probability of misclassification when the input noise follows an arbitrary probability distribution. This bound is cast as a chance-constrained optimization problem, which is then reformulated using input-output samples to replace the optimization constraints. The resulting optimization reduces to a linear program with an analytical solution. Furthermore, we develop a sufficient condition on the number of samples needed to make the misclassification bound hold with overwhelming probability. Our case studies on MNIST classifiers show that this method is able to certify a uniform infinity-norm uncertainty region with a radius of nearly 50 times larger than what the current state-of-the-art method can certify.