pwc-archive/papers-with-abstracts huggingface dataset:
Invariance Matters: Exemplar Memory for Domain Adaptive Person Re-identification
Authors: Zhun Zhong, Liang Zheng, Zhiming Luo, Shaozi Li, Yi Yang
Date: 2019-04-03 00:00:00
Proceeding: CVPR 2019 6
Tasks: Domain Adaptation, Domain Adaptive Person Re-Identification, Person Re-Identification, Unsupervised Domain Adaptation

This paper considers the domain adaptive person re-identification (re-ID)
problem: learning a re-ID model from a labeled source domain and an unlabeled
target domain. Conventional methods are mainly to reduce feature distribution
gap between the source and target domains. However, these studies largely
neglect the intra-domain variations in the target domain, which contain
critical factors influencing the testing performance on the target domain. In
this work, we comprehensively investigate into the intra-domain variations of
the target domain and propose to generalize the re-ID model w.r.t three types
of the underlying invariance, i.e., exemplar-invariance, camera-invariance and
neighborhood-invariance. To achieve this goal, an exemplar memory is introduced
to store features of the target domain and accommodate the three invariance
properties. The memory allows us to enforce the invariance constraints over
global training batch without significantly increasing computation cost.
Experiment demonstrates that the three invariance properties and the proposed
memory are indispensable towards an effective domain adaptation system. Results
on three re-ID domains show that our domain adaptation accuracy outperforms the
state of the art by a large margin. Code is available at:
https://github.com/zhunzhong07/ECN
CAM-Convs: Camera-Aware Multi-Scale Convolutions for Single-View Depth
Authors: Jose M. Facil, Benjamin Ummenhofer, Huizhong Zhou, Luis Montesano, Thomas Brox, Javier Civera
Date: 2019-04-03 00:00:00
Proceeding: CVPR 2019 6
Tasks: 3D Depth Estimation, Depth Estimation, Depth Prediction, Domain Generalization

Single-view depth estimation suffers from the problem that a network trained
on images from one camera does not generalize to images taken with a different
camera model. Thus, changing the camera model requires collecting an entirely
new training dataset. In this work, we propose a new type of convolution that
can take the camera parameters into account, thus allowing neural networks to
learn calibration-aware patterns. Experiments confirm that this improves the
generalization capabilities of depth prediction networks considerably, and
clearly outperforms the state of the art when the train and test images are
acquired with different cameras.
Point Cloud Oversegmentation with Graph-Structured Deep Metric Learning
Authors: Loic Landrieu, Mohamed Boussaha
Date: 2019-04-03 00:00:00
Proceeding: CVPR 2019 6
Tasks: Semantic Segmentation

We propose a new supervized learning framework for oversegmenting 3D point
clouds into superpoints. We cast this problem as learning deep embeddings of
the local geometry and radiometry of 3D points, such that the border of objects
presents high contrasts. The embeddings are computed using a lightweight neural
network operating on the points' local neighborhood. Finally, we formulate
point cloud oversegmentation as a graph partition problem with respect to the
learned embeddings.
  This new approach allows us to set a new state-of-the-art in point cloud
oversegmentation by a significant margin, on a dense indoor dataset (S3DIS) and
a sparse outdoor one (vKITTI). Our best solution requires over five times fewer
superpoints to reach similar performance than previously published methods on
S3DIS. Furthermore, we show that our framework can be used to improve
superpoint-based semantic segmentation algorithms, setting a new
state-of-the-art for this task as well.
HopSkipJumpAttack: A Query-Efficient Decision-Based Attack
Authors: Jianbo Chen, Michael. I. Jordan, Martin J. Wainwright
Date: 2019-04-03 00:00:00
Tasks: Adversarial Attack

The goal of a decision-based adversarial attack on a trained model is to generate adversarial examples based solely on observing output labels returned by the targeted model. We develop HopSkipJumpAttack, a family of algorithms based on a novel estimate of the gradient direction using binary information at the decision boundary. The proposed family includes both untargeted and targeted attacks optimized for $\ell_2$ and $\ell_\infty$ similarity metrics respectively. Theoretical analysis is provided for the proposed algorithms and the gradient direction estimate. Experiments show HopSkipJumpAttack requires significantly fewer model queries than Boundary Attack. It also achieves competitive performance in attacking several widely-used defense mechanisms. (HopSkipJumpAttack was named Boundary Attack++ in a previous version of the preprint.)
Unsupervised Latent Tree Induction with Deep Inside-Outside Recursive Autoencoders
Authors: Andrew Drozdov, Pat Verga, Mohit Yadav, Mohit Iyyer, Andrew McCallum
Date: 2019-04-03 00:00:00
Tasks: Constituency Parsing, Sentence

We introduce deep inside-outside recursive autoencoders (DIORA), a
fully-unsupervised method for discovering syntax that simultaneously learns
representations for constituents within the induced tree. Our approach predicts
each word in an input sentence conditioned on the rest of the sentence and uses
inside-outside dynamic programming to consider all possible binary trees over
the sentence. At test time the CKY algorithm extracts the highest scoring
parse. DIORA achieves a new state-of-the-art F1 in unsupervised binary
constituency parsing (unlabeled) in two benchmark datasets, WSJ and MultiNLI.
CAN-NER: Convolutional Attention Network for Chinese Named Entity Recognition
Authors: Yuying Zhu, Guoxin Wang, Börje F. Karlsson
Date: 2019-04-03 00:00:00
Proceeding: NAACL 2019 6
Tasks: Chinese Named Entity Recognition, named-entity-recognition, Named Entity Recognition, Named Entity Recognition (NER), NER, Segmentation, Sentence

Named entity recognition (NER) in Chinese is essential but difficult because of the lack of natural delimiters. Therefore, Chinese Word Segmentation (CWS) is usually considered as the first step for Chinese NER. However, models based on word-level embeddings and lexicon features often suffer from segmentation errors and out-of-vocabulary (OOV) words. In this paper, we investigate a Convolutional Attention Network called CAN for Chinese NER, which consists of a character-based convolutional neural network (CNN) with local-attention layer and a gated recurrent unit (GRU) with global self-attention layer to capture the information from adjacent characters and sentence contexts. Also, compared to other models, not depending on any external resources like lexicons and employing small size of char embeddings make our model more practical. Extensive experimental results show that our approach outperforms state-of-the-art methods without word embedding and external lexicon resources on different domain datasets including Weibo, MSRA and Chinese Resume NER dataset.
Normal Approximation for Stochastic Gradient Descent via Non-Asymptotic Rates of Martingale CLT
Authors: Andreas Anastasiou, Krishnakumar Balasubramanian, Murat A. Erdogdu
Date: 2019-04-03 00:00:00
Tasks: parameter estimation, valid

We provide non-asymptotic convergence rates of the Polyak-Ruppert averaged
stochastic gradient descent (SGD) to a normal random vector for a class of
twice-differentiable test functions. A crucial intermediate step is proving a
non-asymptotic martingale central limit theorem (CLT), i.e., establishing the
rates of convergence of a multivariate martingale difference sequence to a
normal random vector, which might be of independent interest. We obtain the
explicit rates for the multivariate martingale CLT using a combination of
Stein's method and Lindeberg's argument, which is then used in conjunction with
a non-asymptotic analysis of averaged SGD proposed in [PJ92]. Our results have
potentially interesting consequences for computing confidence intervals for
parameter estimation with SGD and constructing hypothesis tests with SGD that
are valid in a non-asymptotic sense.
Group-wise classification approach to improve Android malicious apps detection accuracy
Authors: Ashu Sharma, Sanjay K. Sahay
Date: 2019-04-03 00:00:00
Tasks: General Classification

In the fast-growing smart devices, Android is the most popular OS, and due to
its attractive features, mobility, ease of use, these devices hold sensitive
information such as personal data, browsing history, shopping history,
financial details, etc. Therefore, any security gap in these devices means that
the information stored or accessing the smart devices are at high risk of being
breached by the malware. These malware are continuously growing and are also
used for military espionage, disrupting the industry, power grids, etc. To
detect these malware, traditional signature matching techniques are widely
used. However, such strategies are not capable to detect the advanced Android
malicious apps because malware developer uses several obfuscation techniques.
Hence, researchers are continuously addressing the security issues in the
Android based smart devices. Therefore, in this paper using Drebin benchmark
malware dataset we experimentally demonstrate how to improve the detection
accuracy by analyzing the apps after grouping the collected data based on the
permissions and achieved 97.15% overall average accuracy. Our results
outperform the accuracy obtained without grouping data (79.27%, 2017), Arp, et
al. (94%, 2014), Annamalai et al. (84.29%, 2016), Bahman Rashidi et al. (82%,
2017)) and Ali Feizollah, et al. (95.5%, 2017). The analysis also shows that
among the groups, Microphone group detection accuracy is least while Calendar
group apps are detected with the highest accuracy, and with the highest
accuracy, and for the best performance, one shall take 80-100 features.
Target-Tailored Source-Transformation for Scene Graph Generation
Authors: Wentong Liao, Cuiling Lan, Wen-Jun Zeng, Michael Ying Yang, Bodo Rosenhahn
Date: 2019-04-03 00:00:00
Tasks: graph construction, Graph Generation, Object, object-detection, Object Detection, Scene Graph Generation, TAR, Visual Relationship Detection

Scene graph generation aims to provide a semantic and structural description of an image, denoting the objects (with nodes) and their relationships (with edges). The best performing works to date are based on exploiting the context surrounding objects or relations,e.g., by passing information among objects. In these approaches, to transform the representation of source objects is a critical process for extracting information for the use by target objects. In this work, we argue that a source object should give what tar-get object needs and give different objects different information rather than contributing common information to all targets. To achieve this goal, we propose a Target-TailoredSource-Transformation (TTST) method to efficiently propagate information among object proposals and relations. Particularly, for a source object proposal which will contribute information to other target objects, we transform the source object feature to the target object feature domain by simultaneously taking both the source and target into account. We further explore more powerful representations by integrating language prior with the visual context in the transformation for the scene graph generation. By doing so the target object is able to extract target-specific information from the source object and source relation accordingly to refine its representation. Our framework is validated on the Visual Genome bench-mark and demonstrated its state-of-the-art performance for the scene graph generation. The experimental results show that the performance of object detection and visual relation-ship detection are promoted mutually by our method.
75 Languages, 1 Model: Parsing Universal Dependencies Universally
Authors: Dan Kondratyuk, Milan Straka
Date: 2019-04-03 00:00:00
Proceeding: IJCNLP 2019 11
Tasks: Dependency Parsing, model, Zero-Shot Learning

We present UDify, a multilingual multi-task model capable of accurately predicting universal part-of-speech, morphological features, lemmas, and dependency trees simultaneously for all 124 Universal Dependencies treebanks across 75 languages. By leveraging a multilingual BERT self-attention model pretrained on 104 languages, we found that fine-tuning it on all datasets concatenated together with simple softmax classifiers for each UD task can result in state-of-the-art UPOS, UFeats, Lemmas, UAS, and LAS scores, without requiring any recurrent or language-specific components. We evaluate UDify for multilingual learning, showing that low-resource languages benefit the most from cross-linguistic annotations. We also evaluate for zero-shot learning, with results suggesting that multilingual training provides strong UD predictions even for languages that neither UDify nor BERT have ever been trained on. Code for UDify is available at https://github.com/hyperparticle/udify.
The Medical Deconfounder: Assessing Treatment Effects with Electronic Health Records
Authors: Linying Zhang, Yixin Wang, Anna Ostropolets, Jami J. Mulgrave, David M. Blei, George Hripcsak
Date: 2019-04-03 00:00:00

The treatment effects of medications play a key role in guiding medical prescriptions. They are usually assessed with randomized controlled trials (RCTs), which are expensive. Recently, large-scale electronic health records (EHRs) have become available, opening up new opportunities for more cost-effective assessments. However, assessing a treatment effect from EHRs is challenging: it is biased by unobserved confounders, unmeasured variables that affect both patients' medical prescription and their outcome, e.g. the patients' social economic status. To adjust for unobserved confounders, we develop the medical deconfounder, a machine learning algorithm that unbiasedly estimates treatment effects from EHRs. The medical deconfounder first constructs a substitute confounder by modeling which medications were prescribed to each patient; this substitute confounder is guaranteed to capture all multi-medication confounders, observed or unobserved (arXiv:1805.06826). It then uses this substitute confounder to adjust for the confounding bias in the analysis. We validate the medical deconfounder on two simulated and two real medical data sets. Compared to classical approaches, the medical deconfounder produces closer-to-truth treatment effect estimates; it also identifies effective medications that are more consistent with the findings in the medical literature.
Interpretable Deep Learning for Two-Prong Jet Classification with Jet Spectra
Authors: Amit Chakraborty, Sung Hak Lim, Mihoko M. Nojiri
Date: 2019-04-03 00:00:00
Tasks: Classification, General Classification, Vocal Bursts Valence Prediction

Classification of jets with deep learning has gained significant attention in recent times. However, the performance of deep neural networks is often achieved at the cost of interpretability. Here we propose an interpretable network trained on the jet spectrum $S_{2}(R)$ which is a two-point correlation function of the jet constituents. The spectrum can be derived from a functional Taylor series of an arbitrary jet classifier function of energy flows. An interpretable network can be obtained by truncating the series. The intermediate feature of the network is an infrared and collinear safe C-correlator which allows us to estimate the importance of a $S_{2}(R)$ deposit at an angular scale R in the classification. The performance of the architecture is comparable to that of a convolutional neural network (CNN) trained on jet images, although the number of inputs and complexity of architecture is significantly simpler than the CNN classifier. We consider two examples: one is the classification of two-prong jets which differ in color charge of the mother particle, and the other is a comparison between Pythia 8 and Herwig 7 generated jets.
FatSegNet : A Fully Automated Deep Learning Pipeline for Adipose Tissue Segmentation on Abdominal Dixon MRI
Authors: Santiago Estrada, Ran Lu, Sailesh Conjeti, Ximena Orozco-Ruiz, Joana Panos-Willuhn, Monique M. B Breteler, Martin Reuter
Date: 2019-04-03 00:00:00

Purpose: Development of a fast and fully automated deep learning pipeline (FatSegNet) to accurately identify, segment, and quantify abdominal adipose tissue on Dixon MRI from the Rhineland Study - a large prospective population-based study. Method: FatSegNet is composed of three stages: (i) consistent localization of the abdominal region using two 2D-Competitive Dense Fully Convolutional Networks (CDFNet), (ii) segmentation of adipose tissue on three views by independent CDFNets, and (iii) view aggregation. FatSegNet is trained with 33 manually annotated subjects, and validated by: 1) comparison of segmentation accuracy against a testingset covering a wide range of body mass index (BMI), 2) test-retest reliability, and 3) robustness in a large cohort study. Results: The CDFNet demonstrates increased robustness compared to traditional deep learning networks. FatSegNet dice score outperforms manual raters on the abdominal visceral adipose tissue (VAT, 0.828 vs. 0.788), and produces comparable results on subcutaneous adipose tissue (SAT, 0.973 vs. 0.982). The pipeline has very small test-retest absolute percentage difference and excellent agreement between scan sessions (VAT: APD = 2.957%, ICC=0.998 and SAT: APD= 3.254%, ICC=0.996). Conclusion: FatSegNet can reliably analyze a 3D Dixon MRI in1 min. It generalizes well to different body shapes, sensitively replicates known VAT and SAT volume effects in a large cohort study, and permits localized analysis of fat compartments.
Graph based Nearest Neighbor Search: Promises and Failures
Authors: Peng-Cheng Lin, Wan-Lei Zhao
Date: 2019-04-03 00:00:00
Tasks: Retrieval

Recently, graph based nearest neighbor search gets more and more popular on large-scale retrieval tasks. The attractiveness of this type of approaches lies in its superior performance over most of the known nearest neighbor search approaches as well as its genericness to various metrics. In this paper, the role of two strategies, namely hierarchical structure and graph diversification that are adopted as the key steps in the graph based approaches, is investigated. We find the hierarchical structure could not achieve "much better logarithmic complexity scaling" as it was claimed in the original paper, particularly on high dimensional cases. Moreover, we find that similar high search speed efficiency as the one with hierarchical structure could be achieved with the support of flat k-NN graph after graph diversification. Finally, we point out the difficulty, that is faced by most of the graph based search approaches, is directly linked to "curse of dimensionality".
Learning for Multi-Type Subspace Clustering
Authors: Xun Xu, Loong-Fah Cheong, Zhuwen Li
Date: 2019-04-03 00:00:00
Tasks: Clustering, Vocal Bursts Type Prediction

Subspace clustering has been extensively studied from the
hypothesis-and-test, algebraic, and spectral clustering based perspectives.
Most assume that only a single type/class of subspace is present.
Generalizations to multiple types are non-trivial, plagued by challenges such
as choice of types and numbers of models, sampling imbalance and parameter
tuning. In this work, we formulate the multi-type subspace clustering problem
as one of learning non-linear subspace filters via deep multi-layer perceptrons
(mlps). The response to the learnt subspace filters serve as the feature
embedding that is clustering-friendly, i.e., points of the same clusters will
be embedded closer together through the network. For inference, we apply
K-means to the network output to cluster the data. Experiments are carried out
on both synthetic and real world multi-type fitting problems, producing
state-of-the-art results.
A Visual Neural Network for Robust Collision Perception in Vehicle Driving Scenarios
Authors: Qinbing Fu, Nicola Bellotto, Huatian Wang, F. Claire Rind, Hongxin Wang, Shigang Yue
Date: 2019-04-03 00:00:00
Tasks: Autonomous Vehicles

This research addresses the challenging problem of visual collision detection
in very complex and dynamic real physical scenes, specifically, the vehicle
driving scenarios. This research takes inspiration from a large-field looming
sensitive neuron, i.e., the lobula giant movement detector (LGMD) in the
locust's visual pathways, which represents high spike frequency to rapid
approaching objects. Building upon our previous models, in this paper we
propose a novel inhibition mechanism that is capable of adapting to different
levels of background complexity. This adaptive mechanism works effectively to
mediate the local inhibition strength and tune the temporal latency of local
excitation reaching the LGMD neuron. As a result, the proposed model is
effective to extract colliding cues from complex dynamic visual scenes. We
tested the proposed method using a range of stimuli including simulated
movements in grating backgrounds and shifting of a natural panoramic scene, as
well as vehicle crash video sequences. The experimental results demonstrate the
proposed method is feasible for fast collision perception in real-world
situations with potential applications in future autonomous vehicles.
Processing Tweets for Cybersecurity Threat Awareness
Authors: Fernando Alves, Aurélien Bettini, Pedro M. Ferreira, Alysson Bessani
Date: 2019-04-03 00:00:00
Tasks: Binary Classification, Clustering

Receiving timely and relevant security information is crucial for maintaining
a high-security level on an IT infrastructure. This information can be
extracted from Open Source Intelligence published daily by users, security
organisations, and researchers. In particular, Twitter has become an
information hub for obtaining cutting-edge information about many subjects,
including cybersecurity. This work proposes SYNAPSE, a Twitter-based streaming
threat monitor that generates a continuously updated summary of the threat
landscape related to a monitored infrastructure. Its tweet-processing pipeline
is composed of filtering, feature extraction, binary classification, an
innovative clustering strategy, and generation of Indicators of Compromise
(IoCs). A quantitative evaluation considering all tweets from 80 accounts over
more than 8 months (over 195.000 tweets), shows that our approach timely and
successfully finds the majority of security-related tweets concerning an
example IT infrastructure (true positive rate above 90%), incorrectly selects a
small number of tweets as relevant (false positive rate under 10%), and
summarises the results to very few IoCs per day. A qualitative evaluation of
the IoCs generated by SYNAPSE demonstrates their relevance (based on the CVSS
score and the availability of patches or exploits), and timeliness (based on
threat disclosure dates from NVD).
Minimum Volume Topic Modeling
Authors: Byoungwook Jang, Alfred Hero
Date: 2019-04-03 00:00:00
Tasks: Model Optimization

We propose a new topic modeling procedure that takes advantage of the fact
that the Latent Dirichlet Allocation (LDA) log likelihood function is
asymptotically equivalent to the logarithm of the volume of the topic simplex.
This allows topic modeling to be reformulated as finding the probability
simplex that minimizes its volume and encloses the documents that are
represented as distributions over words. A convex relaxation of the minimum
volume topic model optimization is proposed, and it is shown that the relaxed
problem has the same global minimum as the original problem under the
separability assumption and the sufficiently scattered assumption introduced by
Arora et al. (2013) and Huang et al. (2016). A locally convergent alternating
direction method of multipliers (ADMM) approach is introduced for solving the
relaxed minimum volume problem. Numerical experiments illustrate the benefits
of our approach in terms of computation time and topic recovery performance.