pwc-archive/papers-with-abstracts huggingface dataset:
Stacking-Based Deep Neural Network: Deep Analytic Network for Pattern Classification
Authors: Cheng-Yaw Low, Jaewoo Park, Andrew Beng-Jin Teoh
Date: 2018-11-17 00:00:00
Tasks: CPU, Data Augmentation, General Classification, GPU

Stacking-based deep neural network (S-DNN) is aggregated with pluralities of basic learning modules, one after another, to synthesize a deep neural network (DNN) alternative for pattern classification. Contrary to the DNNs trained end to end by backpropagation (BP), each S-DNN layer, i.e., a self-learnable module, is to be trained decisively and independently without BP intervention. In this paper, a ridge regression-based S-DNN, dubbed deep analytic network (DAN), along with its kernelization (K-DAN), are devised for multilayer feature re-learning from the pre-extracted baseline features and the structured features. Our theoretical formulation demonstrates that DAN/K-DAN re-learn by perturbing the intra/inter-class variations, apart from diminishing the prediction errors. We scrutinize the DAN/K-DAN performance for pattern classification on datasets of varying domains - faces, handwritten digits, generic objects, to name a few. Unlike the typical BP-optimized DNNs to be trained from gigantic datasets by GPU, we disclose that DAN/K-DAN are trainable using only CPU even for small-scale training sets. Our experimental results disclose that DAN/K-DAN outperform the present S-DNNs and also the BP-trained DNNs, including multiplayer perceptron, deep belief network, etc., without data augmentation applied.
Link Prediction in Dynamic Graphs for Recommendation
Authors: Samuel G. Fadel, Ricardo da S. Torres
Date: 2018-11-17 00:00:00
Tasks: Link Prediction, Prediction

Recent advances in employing neural networks on graph domains helped push the
state of the art in link prediction tasks, particularly in recommendation
services. However, the use of temporal contextual information, often modeled as
dynamic graphs that encode the evolution of user-item relationships over time,
has been overlooked in link prediction problems. In this paper, we consider the
hypothesis that leveraging such information enables models to make better
predictions, proposing a new neural network approach for this. Our experiments,
performed on the widely used ML-100k and ML-1M datasets, show that our approach
produces better predictions in scenarios where the pattern of user-item
relationships change over time. In addition, they suggest that existing
approaches are significantly impacted by those changes.
Person Identification and Body Mass Index: A Deep Learning-Based Study on Micro-Dopplers
Authors: Sherif Abdulatif, Fady Aziz, Karim Armanious, Bernhard Kleiner, Bin Yang, Urs Schneider
Date: 2018-11-17 00:00:00
Tasks: Person Identification

Obtaining a smart surveillance requires a sensing system that can capture
accurate and detailed information for the human walking style. The radar
micro-Doppler ($\boldsymbol{\mu}$-D) analysis is proved to be a reliable metric
for studying human locomotions. Thus, $\boldsymbol{\mu}$-D signatures can be
used to identify humans based on their walking styles. Additionally, the
signatures contain information about the radar cross section (RCS) of the
moving subject. This paper investigates the effect of human body
characteristics on human identification based on their $\boldsymbol{\mu}$-D
signatures. In our proposed experimental setup, a treadmill is used to collect
$\boldsymbol{\mu}$-D signatures of 22 subjects with different genders and body
characteristics. Convolutional autoencoders (CAE) are then used to extract the
latent space representation from the $\boldsymbol{\mu}$-D signatures. It is
then interpreted in two dimensions using t-distributed stochastic neighbor
embedding (t-SNE). Our study shows that the body mass index (BMI) has a
correlation with the $\boldsymbol{\mu}$-D signature of the walking subject. A
50-layer deep residual network is then trained to identify the walking subject
based on the $\boldsymbol{\mu}$-D signature. We achieve an accuracy of 98% on
the test set with high signal-to-noise-ratio (SNR) and 84% in case of different
SNR levels.
Optical Flow Dataset and Benchmark for Visual Crowd Analysis
Authors: Gregory Schröder, Tobias Senst, Erik Bochinski, Thomas Sikora
Date: 2018-11-17 00:00:00
Tasks: Optical Flow Estimation, Visual Crowd Analysis

The performance of optical flow algorithms greatly depends on the specifics
of the content and the application for which it is used. Existing and well
established optical flow datasets are limited to rather particular contents
from which none is close to crowd behavior analysis; whereas such applications
heavily utilize optical flow. We introduce a new optical flow dataset
exploiting the possibilities of a recent video engine to generate sequences
with ground-truth optical flow for large crowds in different scenarios. We
break with the development of the last decade of introducing ever increasing
displacements to pose new difficulties. Instead we focus on real-world
surveillance scenarios where numerous small, partly independent, non rigidly
moving objects observed over a long temporal range pose a challenge. By
evaluating different optical flow algorithms, we find that results of
established datasets can not be transferred to these new challenges. In
exhaustive experiments we are able to provide new insight into optical flow for
crowd analysis. Finally, the results have been validated on the real-world UCF
crowd tracking benchmark while achieving competitive results compared to more
sophisticated state-of-the-art crowd tracking approaches.
Edge-Based Blur Kernel Estimation Using Sparse Representation and Self-Similarity
Authors: Jing Yu, Zhenchun Chang, Chuangbai Xiao
Date: 2018-11-17 00:00:00
Tasks: Deblurring, Image Deconvolution

Blind image deconvolution is the problem of recovering the latent image from
the only observed blurry image when the blur kernel is unknown. In this paper,
we propose an edge-based blur kernel estimation method for blind motion
deconvolution. In our previous work, we incorporate both sparse representation
and self-similarity of image patches as priors into our blind deconvolution
model to regularize the recovery of the latent image. Since almost any natural
image has properties of sparsity and multi-scale self-similarity, we construct
a sparsity regularizer and a cross-scale non-local regularizer based on our
patch priors. It has been observed that our regularizers often favor sharp
images over blurry ones only for image patches of the salient edges and thus we
define an edge mask to locate salient edges that we want to apply our
regularizers. Experimental results on both simulated and real blurry images
demonstrate that our method outperforms existing state-of-the-art blind
deblurring methods even for handling of very large blurs, thanks to the use of
the edge mask.
Recurrent Convolutions for Causal 3D CNNs
Authors: Gurkirt Singh, Fabio Cuzzolin
Date: 2018-11-17 00:00:00
Tasks: Action Detection

Recently, three dimensional (3D) convolutional neural networks (CNNs) have emerged as dominant methods to capture spatiotemporal representations in videos, by adding to pre-existing 2D CNNs a third, temporal dimension. Such 3D CNNs, however, are anti-causal (i.e., they exploit information from both the past and the future frames to produce feature representations, thus preventing their use in online settings), constrain the temporal reasoning horizon to the size of the temporal convolution kernel, and are not temporal resolution-preserving for video sequence-to-sequence modelling, as, for instance, in action detection. To address these serious limitations, here we present a new 3D CNN architecture for the causal/online processing of videos. Namely, we propose a novel Recurrent Convolutional Network (RCN), which relies on recurrence to capture the temporal context across frames at each network level. Our network decomposes 3D convolutions into (1) a 2D spatial convolution component, and (2) an additional hidden state $1\times 1$ convolution, applied across time. The hidden state at any time $t$ is assumed to depend on the hidden state at $t-1$ and on the current output of the spatial convolution component. As a result, the proposed network: (i) produces causal outputs, (ii) provides flexible temporal reasoning, (iii) preserves temporal resolution. Our experiments on the large-scale large Kinetics and MultiThumos datasets show that the proposed method performs comparably to anti-causal 3D CNNs, while being causal and using fewer parameters.
Monotonic classification: an overview on algorithms, performance measures and data sets
Authors: José-Ramón Cano, Pedro Antonio Gutiérrez, Bartosz Krawczyk, Michał Woźniak, Salvador García
Date: 2018-11-17 00:00:00
Tasks: Decision Making, General Classification, Medical Diagnosis, valid

Currently, knowledge discovery in databases is an essential step to identify
valid, novel and useful patterns for decision making. There are many real-world
scenarios, such as bankruptcy prediction, option pricing or medical diagnosis,
where the classification models to be learned need to fulfil restrictions of
monotonicity (i.e. the target class label should not decrease when input
attributes values increase). For instance, it is rational to assume that a
higher debt ratio of a company should never result in a lower level of
bankruptcy risk. Consequently, there is a growing interest from the data mining
research community concerning monotonic predictive models. This paper aims to
present an overview about the literature in the field, analyzing existing
techniques and proposing a taxonomy of the algorithms based on the type of
model generated. For each method, we review the quality metrics considered in
the evaluation and the different data sets and monotonic problems used in the
analysis. In this way, this paper serves as an overview of the research about
monotonic classification in specialized literature and can be used as a
functional guide of the field.
Robust Website Fingerprinting Through the Cache Occupancy Channel
Authors: Anatoly Shusterman, Lachlan Kang, Yarden Haskal, Yosef Meltser, Prateek Mittal, Yossi Oren, Yuval Yarom
Date: 2018-11-17 00:00:00
Tasks: Website Fingerprinting Attacks

Website fingerprinting attacks, which use statistical analysis on network
traffic to compromise user privacy, have been shown to be effective even if the
traffic is sent over anonymity-preserving networks such as Tor. The classical
attack model used to evaluate website fingerprinting attacks assumes an on-path
adversary, who can observe all traffic traveling between the user's computer
and the Tor network. In this work we investigate these attacks under a
different attack model, in which the adversary is capable of running a small
amount of unprivileged code on the target user's computer. Under this model,
the attacker can mount cache side-channel attacks, which exploit the effects of
contention on the CPU's cache, to identify the website being browsed. In an
important special case of this attack model, a JavaScript attack is launched
when the target user visits a website controlled by the attacker. The
effectiveness of this attack scenario has never been systematically analyzed,
especially in the open-world model which assumes that the user is visiting a
mix of both sensitive and non-sensitive sites. In this work we show that cache
website fingerprinting attacks in JavaScript are highly feasible, even when
they are run from highly restrictive environments, such as the Tor Browser.
Specifically, we use machine learning techniques to classify traces of cache
activity. Unlike prior works, which try to identify cache conflicts, our work
measures the overall occupancy of the last-level cache. We show that our
approach achieves high classification accuracy in both the open-world and the
closed-world models. We further show that our techniques are resilient both to
network-based defenses and to side-channel countermeasures introduced to modern
browsers as a response to the Spectre attack.
The Impatient May Use Limited Optimism to Minimize Regret
Authors: Michaël Cadilhac, Guillermo A. Pérez, Marie van den Bogaard
Date: 2018-11-17 00:00:00
Tasks: Reinforcement Learning

Discounted-sum games provide a formal model for the study of reinforcement
learning, where the agent is enticed to get rewards early since later rewards
are discounted. When the agent interacts with the environment, she may regret
her actions, realizing that a previous choice was suboptimal given the behavior
of the environment. The main contribution of this paper is a PSPACE algorithm
for computing the minimum possible regret of a given game. To this end, several
results of independent interest are shown. (1) We identify a class of
regret-minimizing and admissible strategies that first assume that the
environment is collaborating, then assume it is adversarial---the precise
timing of the switch is key here. (2) Disregarding the computational cost of
numerical analysis, we provide an NP algorithm that checks that the regret
entailed by a given time-switching strategy exceeds a given value. (3) We show
that determining whether a strategy minimizes regret is decidable in PSPACE.
High Quality Prediction of Protein Q8 Secondary Structure by Diverse Neural Network Architectures
Authors: Iddo Drori, Isht Dwivedi, Pranav Shrestha, Jeffrey Wan, Yueqi Wang, Yunchu He, Anthony Mazza, Hugh Krogh-Freeman, Dimitri Leggas, Kendal Sandridge, Linyong Nan, Kaveri Thakoor, Chinmay Joshi, Sonam Goenka, Chen Keasar, Itsik Pe'er
Date: 2018-11-17 00:00:00
Tasks: Protein Secondary Structure Prediction

We tackle the problem of protein secondary structure prediction using a
common task framework. This lead to the introduction of multiple ideas for
neural architectures based on state of the art building blocks, used in this
task for the first time. We take a principled machine learning approach, which
provides genuine, unbiased performance measures, correcting longstanding errors
in the application domain. We focus on the Q8 resolution of secondary
structure, an active area for continuously improving methods. We use an
ensemble of strong predictors to achieve accuracy of 70.7% (on the CB513 test
set using the CB6133filtered training set). These results are statistically
indistinguishable from those of the top existing predictors. In the spirit of
reproducible research we make our data, models and code available, aiming to
set a gold standard for purity of training and testing sets. Such good
practices lower entry barriers to this domain and facilitate reproducible,
extendable research.
Deep Discriminative Learning for Unsupervised Domain Adaptation
Authors: Rohith AP, Ambedkar Dukkipati, Gaurav Pandey
Date: 2018-11-17 00:00:00
Tasks: Domain Adaptation, General Classification, image-classification, Image Classification, Sentiment Analysis, Sentiment Classification, Unsupervised Domain Adaptation

The primary objective of domain adaptation methods is to transfer knowledge from a source domain to a target domain that has similar but different data distributions. Thus, in order to correctly classify the unlabeled target domain samples, the standard approach is to learn a common representation for both source and target domain, thereby indirectly addressing the problem of learning a classifier in the target domain. However, such an approach does not address the task of classification in the target domain directly. In contrast, we propose an approach that directly addresses the problem of learning a classifier in the unlabeled target domain. In particular, we train a classifier to correctly classify the training samples while simultaneously classifying the samples in the target domain in an unsupervised manner. The corresponding model is referred to as Discriminative Encoding for Domain Adaptation (DEDA). We show that this simple approach for performing unsupervised domain adaptation is indeed quite powerful. Our method achieves state of the art results in unsupervised adaptation tasks on various image classification benchmarks. We also obtained state of the art performance on domain adaptation in Amazon reviews sentiment classification dataset. We perform additional experiments when the source data has less labeled examples and also on zero-shot domain adaptation task where no target domain samples are used for training.
High SNR Consistent Compressive Sensing Without Signal and Noise Statistics
Authors: Sreejith Kallummil, Sheetal Kalyani
Date: 2018-11-17 00:00:00
Tasks: Compressive Sensing, regression, Vocal Bursts Intensity Prediction

Recovering the support of sparse vectors in underdetermined linear regression
models, \textit{aka}, compressive sensing is important in many signal
processing applications. High SNR consistency (HSC), i.e., the ability of a
support recovery technique to correctly identify the support with increasing
signal to noise ratio (SNR) is an increasingly popular criterion to qualify the
high SNR optimality of support recovery techniques. The HSC results available
in literature for support recovery techniques applicable to underdetermined
linear regression models like least absolute shrinkage and selection operator
(LASSO), orthogonal matching pursuit (OMP) etc. assume \textit{a priori}
knowledge of noise variance or signal sparsity. However, both these parameters
are unavailable in most practical applications. Further, it is extremely
difficult to estimate noise variance or signal sparsity in underdetermined
regression models. This limits the utility of existing HSC results. In this
article, we propose two techniques, \textit{viz.}, residual ratio minimization
(RRM) and residual ratio thresholding with adaptation (RRTA) to operate OMP
algorithm without the \textit{a priroi} knowledge of noise variance and signal
sparsity and establish their HSC analytically and numerically. To the best of
our knowledge, these are the first and only noise statistics oblivious
algorithms to report HSC in underdetermined regression models.
Batch DropBlock Network for Person Re-identification and Beyond
Authors: Zuozhuo Dai, Mingqiang Chen, Xiaodong Gu, Siyu Zhu, Ping Tan
Date: 2018-11-17 00:00:00
Proceeding: ICCV 2019 10
Tasks: Image Retrieval, Metric Learning, Person Re-Identification

Since the person re-identification task often suffers from the problem of pose changes and occlusions, some attentive local features are often suppressed when training CNNs. In this paper, we propose the Batch DropBlock (BDB) Network which is a two branch network composed of a conventional ResNet-50 as the global branch and a feature dropping branch. The global branch encodes the global salient representations. Meanwhile, the feature dropping branch consists of an attentive feature learning module called Batch DropBlock, which randomly drops the same region of all input feature maps in a batch to reinforce the attentive feature learning of local regions. The network then concatenates features from both branches and provides a more comprehensive and spatially distributed feature representation. Albeit simple, our method achieves state-of-the-art on person re-identification and it is also applicable to general metric learning tasks. For instance, we achieve 76.4% Rank-1 accuracy on the CUHK03-Detect dataset and 83.0% Recall-1 score on the Stanford Online Products dataset, outperforming the existing works by a large margin (more than 6%).
Integrating domain knowledge: using hierarchies to improve deep classifiers
Authors: Clemens-Alexander Brust, Joachim Denzler
Date: 2018-11-17 00:00:00
Tasks: Data Augmentation, General Classification

One of the most prominent problems in machine learning in the age of deep learning is the availability of sufficiently large annotated datasets. For specific domains, e.g. animal species, a long-tail distribution means that some classes are observed and annotated insufficiently. Additional labels can be prohibitively expensive, e.g. because domain experts need to be involved. However, there is more information available that is to the best of our knowledge not exploited accordingly. In this paper, we propose to make use of preexisting class hierarchies like WordNet to integrate additional domain knowledge into classification. We encode the properties of such a class hierarchy into a probabilistic model. From there, we derive a novel label encoding and a corresponding loss function. On the ImageNet and NABirds datasets our method offers a relative improvement of 10.4% and 9.6% in accuracy over the baseline respectively. After less than a third of training time, it is already able to match the baseline's fine-grained recognition performance. Both results show that our suggested method is efficient and effective.
VommaNet: an End-to-End Network for Disparity Estimation from Reflective and Texture-less Light Field Images
Authors: Haoxin Ma, Haotian Li, Zhiwen Qian, Shengxian Shi, Tingting Mu
Date: 2018-11-17 00:00:00
Tasks: Disparity Estimation

The precise combination of image sensor and micro-lens array enables lenslet
light field cameras to record both angular and spatial information of incoming
light, therefore, one can calculate disparity and depth from light field
images. In turn, 3D models of the recorded objects can be recovered, which is a
great advantage over other imaging system. However, reflective and texture-less
areas in light field images have complicated conditions, making it hard to
correctly calculate disparity with existing algorithms. To tackle this problem,
we introduce a novel end-to-end network VommaNet to retrieve multi-scale
features from reflective and texture-less regions for accurate disparity
estimation. Meanwhile, our network has achieved similar or better performance
in other regions for both synthetic light field images and real-world data
compared to the state-of-the-art algorithms. Currently, we achieve the best
score for mean squared error (MSE) on HCI 4D Light Field Benchmark.
Explicit Spatiotemporal Joint Relation Learning for Tracking Human Pose
Authors: Xiao Sun, Chuankang Li, Stephen Lin
Date: 2018-11-17 00:00:00
Tasks: Optical Flow Estimation, Pose Estimation, Pose Tracking, Position, Relation

We present a method for human pose tracking that is based on learning
spatiotemporal relationships among joints. Beyond generating the heatmap of a
joint in a given frame, our system also learns to predict the offset of the
joint from a neighboring joint in the frame. Additionally, it is trained to
predict the displacement of the joint from its position in the previous frame,
in a manner that can account for possibly changing joint appearance, unlike
optical flow. These relational cues in the spatial domain and temporal domain
are inferred in a robust manner by attending only to relevant areas in the
video frames. By explicitly learning and exploiting these joint relationships,
our system achieves state-of-the-art performance on standard benchmarks for
various pose tracking tasks including 3D body pose tracking in RGB video, 3D
hand pose tracking in depth sequences, and 3D hand gesture tracking in RGB
video.