pwc-archive/papers-with-abstracts huggingface dataset:
Defending Against Adversarial Attacks by Leveraging an Entire GAN
Authors: Gokula Krishnan Santhanam, Paulina Grnarova
Date: 2018-05-27 00:00:00

Recent work has shown that state-of-the-art models are highly vulnerable to
adversarial perturbations of the input. We propose cowboy, an approach to
detecting and defending against adversarial attacks by using both the
discriminator and generator of a GAN trained on the same dataset. We show that
the discriminator consistently scores the adversarial samples lower than the
real samples across multiple attacks and datasets. We provide empirical
evidence that adversarial samples lie outside of the data manifold learned by
the GAN. Based on this, we propose a cleaning method which uses both the
discriminator and generator of the GAN to project the samples back onto the
data manifold. This cleaning procedure is independent of the classifier and
type of attack and thus can be deployed in existing systems.
Hierarchical correlation reconstruction with missing data, for example for biology-inspired neuron
Authors: Jarek Duda
Date: 2018-04-17 00:00:00
Tasks: Density Estimation, Imputation

Machine learning often needs to model density from a multidimensional data
sample, including correlations between coordinates. Additionally, we often have
missing data case: that data points can miss values for some of coordinates.
This article adapts rapid parametric density estimation approach for this
purpose: modelling density as a linear combination of orthonormal functions,
for which $L^2$ optimization says that (independently) estimated coefficient
for a given function is just average over the sample of value of this function.
Hierarchical correlation reconstruction first models probability density for
each separate coordinate using all its appearances in data sample, then adds
corrections from independently modelled pairwise correlations using all samples
having both coordinates, and so on independently adding correlations for
growing numbers of variables using often decreasing evidence in data sample. A
basic application of such modelled multidimensional density can be imputation
of missing coordinates: by inserting known coordinates to the density, and
taking expected values for the missing coordinates, or even their entire joint
probability distribution. Presented method can be compared with cascade
correlations approach, offering several advantages in flexibility and accuracy.
It can be also used as artificial neuron: maximizing prediction capabilities
for only local behavior - modelling and predicting local connections.
Comparison of VCA and GAEE algorithms for Endmember Extraction
Authors: Douglas Winston. R. S., Gustavo T. Laureano, Celso G. Camilo Jr
Date: 2018-05-27 00:00:00
Tasks: General Classification, Hyperspectral image analysis

Endmember Extraction is a critical step in hyperspectral image analysis and
classification. It is an useful method to decompose a mixed spectrum into a
collection of spectra and their corresponding proportions. In this paper, we
solve a linear endmember extraction problem as an evolutionary optimization
task, maximizing the Simplex Volume in the endmember space. We propose a
standard genetic algorithm and a variation with In Vitro Fertilization module
(IVFm) to find the best solutions and compare the results with the state-of-art
Vertex Component Analysis (VCA) method and the traditional algorithms Pixel
Purity Index (PPI) and N-FINDR. The experimental results on real and synthetic
hyperspectral data confirms the overcome in performance and accuracy of the
proposed approaches over the mentioned algorithms.
Fast K-Means Clustering with Anderson Acceleration
Authors: Juyong Zhang, Yuxin Yao, Yue Peng, Hao Yu, Bailin Deng
Date: 2018-05-27 00:00:00
Tasks: Clustering

We propose a novel method to accelerate Lloyd's algorithm for K-Means
clustering. Unlike previous acceleration approaches that reduce computational
cost per iterations or improve initialization, our approach is focused on
reducing the number of iterations required for convergence. This is achieved by
treating the assignment step and the update step of Lloyd's algorithm as a
fixed-point iteration, and applying Anderson acceleration, a well-established
technique for accelerating fixed-point solvers. Classical Anderson acceleration
utilizes m previous iterates to find an accelerated iterate, and its
performance on K-Means clustering can be sensitive to choice of m and the
distribution of samples. We propose a new strategy to dynamically adjust the
value of m, which achieves robust and consistent speedups across different
problem instances. Our method complements existing acceleration techniques, and
can be combined with them to achieve state-of-the-art performance. We perform
extensive experiments to evaluate the performance of the proposed method, where
it outperforms other algorithms in 106 out of 120 test cases, and the mean
decrease ratio of computational time is more than 33%.
Contextual Graph Markov Model: A Deep and Generative Approach to Graph Processing
Authors: Davide Bacciu, Federico Errica, Alessio Micheli
Date: 2018-05-27 00:00:00
Proceeding: ICML 2018 7
Tasks: General Classification

We introduce the Contextual Graph Markov Model, an approach combining ideas from generative models and neural networks for the processing of graph data. It founds on a constructive methodology to build a deep architecture comprising layers of probabilistic models that learn to encode the structured information in an incremental fashion. Context is diffused in an efficient and scalable way across the graph vertexes and edges. The resulting graph encoding is used in combination with discriminative models to address structure classification benchmarks.
Unsupervised Learning of GMM with a Uniform Background Component
Authors: Sida Liu, Adrian Barbu
Date: 2018-04-08 00:00:00
Tasks: Clustering

Gaussian Mixture Models are one of the most studied and mature models in unsupervised learning. However, outliers are often present in the data and could influence the cluster estimation. In this paper, we study a new model that assumes that data comes from a mixture of a number of Gaussians as well as a uniform ``background'' component assumed to contain outliers and other non-interesting observations. We develop a novel method based on robust loss minimization that performs well in clustering such GMM with a uniform background. We give theoretical guarantees for our clustering algorithm to obtain best clustering results with high probability. Besides, we show that the result of our algorithm does not depend on initialization or local optima, and the parameter tuning is an easy task. By numeric simulations, we demonstrate that our algorithm enjoys high accuracy and achieves the best clustering results given a large enough sample size. Finally, experimental comparisons with typical clustering methods on real datasets witness the potential of our algorithm in real applications.
A Simple Riemannian Manifold Network for Image Set Classification
Authors: Rui Wang, Xiao-Jun Wu, Josef Kittler
Date: 2018-05-27 00:00:00
Tasks: Classification, Face Recognition, General Classification, Object Categorization

In the domain of image-set based classification, a considerable advance has
been made by representing original image sets as covariance matrices which
typical lie in a Riemannian manifold. Specifically, it is a Symmetric Positive
Definite (SPD) manifold. Traditional manifold learning methods inevitably have
the property of high computational complexity or weak performance of the
feature representation. In order to overcome these limitations, we propose a
very simple Riemannian manifold network for image set classification. Inspired
by deep learning architectures, we design a fully connected layer to generate
more novel, more powerful SPD matrices. However we exploit the rectifying layer
to prevent the input SPD matrices from being singular. We also introduce a
non-linear learning of the proposed network with an innovative objective
function. Furthermore we devise a pooling layer to further reduce the
redundancy of the input SPD matrices, and the log-map layer to project the SPD
manifold to the Euclidean space. For learning the connection weights between
the input layer and the fully connected layer, we use Two-directional
two-dimensional Principal Component Analysis ((2D)2PCA) algorithm. The proposed
Riemannian manifold network (RieMNet) avoids complex computing and can be built
and trained extremely easy and efficient. We have also developed a deep version
of RieMNet, named as DRieMNet. The proposed RieMNet and DRieMNet are evaluated
on three tasks: video-based face recognition, set-based object categorization,
and set-based cell identification. Extensive experimental results show the
superiority of our method over the state-of-the-art.
Reliability and Learnability of Human Bandit Feedback for Sequence-to-Sequence Reinforcement Learning
Authors: Julia Kreutzer, Joshua Uyheng, Stefan Riezler
Date: 2018-05-27 00:00:00
Proceeding: ACL 2018 7
Tasks: Machine Translation, NMT, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL), Translation

We present a study on reinforcement learning (RL) from human bandit feedback
for sequence-to-sequence learning, exemplified by the task of bandit neural
machine translation (NMT). We investigate the reliability of human bandit
feedback, and analyze the influence of reliability on the learnability of a
reward estimator, and the effect of the quality of reward estimates on the
overall RL task. Our analysis of cardinal (5-point ratings) and ordinal
(pairwise preferences) feedback shows that their intra- and inter-annotator
$\alpha$-agreement is comparable. Best reliability is obtained for standardized
cardinal feedback, and cardinal feedback is also easiest to learn and
generalize from. Finally, improvements of over 1 BLEU can be obtained by
integrating a regression-based reward estimator trained on cardinal feedback
for 800 translations into RL for NMT. This shows that RL is possible even from
small amounts of fairly reliable human feedback, pointing to a great potential
for applications at larger scale.
Question Answering over Freebase via Attentive RNN with Similarity Matrix based CNN
Authors: Yingqi Qu, Jie Liu, Liangyi Kang, Qinfeng Shi, Dan Ye
Date: 2018-04-10 00:00:00
Tasks: Question Answering

With the rapid growth of knowledge bases (KBs), question answering over
knowledge base, a.k.a. KBQA has drawn huge attention in recent years. Most of
the existing KBQA methods follow so called encoder-compare framework. They map
the question and the KB facts to a common embedding space, in which the
similarity between the question vector and the fact vectors can be conveniently
computed. This, however, inevitably loses original words interaction
information. To preserve more original information, we propose an attentive
recurrent neural network with similarity matrix based convolutional neural
network (AR-SMCNN) model, which is able to capture comprehensive hierarchical
information utilizing the advantages of both RNN and CNN. We use RNN to capture
semantic-level correlation by its sequential modeling nature, and use an
attention mechanism to keep track of the entities and relations simultaneously.
Meanwhile, we use a similarity matrix based CNN with two-directions pooling to
extract literal-level words interaction matching utilizing CNNs strength of
modeling spatial correlation among data. Moreover, we have developed a new
heuristic extension method for entity detection, which significantly decreases
the effect of noise. Our method has outperformed the state-of-the-arts on
SimpleQuestion benchmark in both accuracy and efficiency.
Anomaly Detection and Localization in Crowded Scenes by Motion-field Shape Description and Similarity-based Statistical Learning
Authors: Xinfeng Zhang, Su Yang, Xinjian Zhang, Weishan Zhang, Jiulong Zhang
Date: 2018-05-27 00:00:00
Tasks: Anomaly Detection, Clustering, Semantic Segmentation

In crowded scenes, detection and localization of abnormal behaviors is
challenging in that high-density people make object segmentation and tracking
extremely difficult. We associate the optical flows of multiple frames to
capture short-term trajectories and introduce the histogram-based shape
descriptor referred to as shape contexts to describe such short-term
trajectories. Furthermore, we propose a K-NN similarity-based statistical model
to detect anomalies over time and space, which is an unsupervised one-class
learning algorithm requiring no clustering nor any prior assumption. Firstly,
we retrieve the K-NN samples from the training set in regard to the testing
sample, and then use the similarities between every pair of the K-NN samples to
construct a Gaussian model. Finally, the probabilities of the similarities from
the testing sample to the K-NN samples under the Gaussian model are calculated
in the form of a joint probability. Abnormal events can be detected by judging
whether the joint probability is below predefined thresholds in terms of time
and space, separately. Such a scheme can adapt to the whole scene, since the
probability computed as such is not affected by motion distortions arising from
perspective distortion. We conduct experiments on real-world surveillance
videos, and the results demonstrate that the proposed method can reliably
detect and locate the abnormal events in the video sequences, outperforming the
state-of-the-art approaches.
A Local Information Criterion for Dynamical Systems
Authors: Arash Mehrjou, Friedrich Solowjow, Sebastian Trimpe, Bernhard Schölkopf
Date: 2018-05-27 00:00:00
Tasks: Model Selection

Encoding a sequence of observations is an essential task with many
applications. The encoding can become highly efficient when the observations
are generated by a dynamical system. A dynamical system imposes regularities on
the observations that can be leveraged to achieve a more efficient code. We
propose a method to encode a given or learned dynamical system. Apart from its
application for encoding a sequence of observations, we propose to use the
compression achieved by this encoding as a criterion for model selection. Given
a dataset, different learning algorithms result in different models. But not
all learned models are equally good. We show that the proposed encoding
approach can be used to choose the learned model which is closer to the true
underlying dynamics. We provide experiments for both encoding and model
selection, and theoretical results that shed light on why the approach works.
Dual Skipping Networks
Authors: Changmao Cheng, Yanwei Fu, Yu-Gang Jiang, Wei Liu, Wenlian Lu, Jianfeng Feng, xiangyang xue
Date: 2017-10-28 00:00:00
Proceeding: CVPR 2018 6
Tasks: General Classification, Object, Object Categorization

Inspired by the recent neuroscience studies on the left-right asymmetry of
the human brain in processing low and high spatial frequency information, this
paper introduces a dual skipping network which carries out coarse-to-fine
object categorization. Such a network has two branches to simultaneously deal
with both coarse and fine-grained classification tasks. Specifically, we
propose a layer-skipping mechanism that learns a gating network to predict
which layers to skip in the testing stage. This layer-skipping mechanism endows
the network with good flexibility and capability in practice. Evaluations are
conducted on several widely used coarse-to-fine object categorization
benchmarks, and promising results are achieved by our proposed network model.
Robust Hypothesis Testing Using Wasserstein Uncertainty Sets
Authors: Rui Gao, Liyan Xie, Yao Xie, Huan Xu
Date: 2018-05-27 00:00:00
Proceeding: NeurIPS 2018 12
Tasks: Two-sample testing

We develop a novel computationally efficient and general framework for robust
hypothesis testing. The new framework features a new way to construct
uncertainty sets under the null and the alternative distributions, which are
sets centered around the empirical distribution defined via Wasserstein metric,
thus our approach is data-driven and free of distributional assumptions. We
develop a convex safe approximation of the minimax formulation and show that
such approximation renders a nearly-optimal detector among the family of all
possible tests. By exploiting the structure of the least favorable
distribution, we also develop a tractable reformulation of such approximation,
with complexity independent of the dimension of observation space and can be
nearly sample-size-independent in general. Real-data example using human
activity data demonstrated the excellent performance of the new robust
detector.
Distributed Deep Forest and its Application to Automatic Detection of Cash-out Fraud
Authors: Ya-Lin Zhang, Jun Zhou, Wenhao Zheng, Ji Feng, Longfei Li, Ziqi Liu, Ming Li, Zhiqiang Zhang, Chaochao Chen, Xiaolong Li, Zhi-Hua Zhou, YUAN, QI
Date: 2018-05-11 00:00:00
Tasks: BIG-bench Machine Learning, feature selection

Internet companies are facing the need for handling large-scale machine learning applications on a daily basis and distributed implementation of machine learning algorithms which can handle extra-large scale tasks with great performance is widely needed. Deep forest is a recently proposed deep learning framework which uses tree ensembles as its building blocks and it has achieved highly competitive results on various domains of tasks. However, it has not been tested on extremely large scale tasks. In this work, based on our parameter server system, we developed the distributed version of deep forest. To meet the need for real-world tasks, many improvements are introduced to the original deep forest model, including MART (Multiple Additive Regression Tree) as base learners for efficiency and effectiveness consideration, the cost-based method for handling prevalent class-imbalanced data, MART based feature selection for high dimension data and different evaluation metrics for automatically determining of the cascade level. We tested the deep forest model on an extra-large scale task, i.e., automatic detection of cash-out fraud, with more than 100 millions of training samples. Experimental results showed that the deep forest model has the best performance according to the evaluation metrics from different perspectives even with very little effort for parameter tuning. This model can block fraud transactions in a large amount of money each day. Even compared with the best-deployed model, the deep forest model can additionally bring into a significant decrease in economic loss each day.
Reduction of the Pareto Set in Bicriteria Asymmetric Traveling Salesman Problem
Authors: Aleksey O. Zakharov, Yulia V. Kovalenko
Date: 2018-05-27 00:00:00
Tasks: Traveling Salesman Problem

We consider the bicriteria asymmetric traveling salesman problem (bi-ATSP).
Optimal solution to a multicriteria problem is usually supposed to be the
Pareto set, which is rather wide in real-world problems. We apply to the
bi-ATSP the axiomatic approach of the Pareto set reduction proposed by V.
Noghin. We identify series of "quanta of information" that guarantee the
reduction of the Pareto set for particular cases of the bi-ATSP. An
approximation of the Pareto set to the bi-ATSP is constructed by a new
multi-objective genetic algorithm. The experimental evaluation carried out in
this paper shows the degree of reduction of the Pareto set approximation for
various "quanta of information" and various structures of the bi-ATSP instances
generated randomly.
Deployment of Customized Deep Learning based Video Analytics On Surveillance Cameras
Authors: Pratik Dubal, Rohan Mahadev, Suraj Kothawade, Kunal Dargan, Rishabh Iyer
Date: 2018-05-27 00:00:00
Tasks: Attribute, Deep Learning, Face Detection, object-detection, Object Detection

This paper demonstrates the effectiveness of our customized deep learning
based video analytics system in various applications focused on security,
safety, customer analytics and process compliance. We describe our video
analytics system comprising of Search, Summarize, Statistics and real-time
alerting, and outline its building blocks. These building blocks include object
detection, tracking, face detection and recognition, human and face
sub-attribute analytics. In each case, we demonstrate how custom models trained
using data from the deployment scenarios provide considerably superior
accuracies than off-the-shelf models. Towards this end, we describe our data
processing and model training pipeline, which can train and fine-tune models
from videos with a quick turnaround time. Finally, since most of these models
are deployed on-site, it is important to have resource constrained models which
do not require GPUs. We demonstrate how we custom train resource constrained
models and deploy them on embedded devices without significant loss in
accuracy. To our knowledge, this is the first work which provides a
comprehensive evaluation of different deep learning models on various
real-world customer deployment scenarios of surveillance video analytics. By
sharing our implementation details and the experiences learned from deploying
customized deep learning models for various customers, we hope that customized
deep learning based video analytics is widely incorporated in commercial
products around the world.
Generative Adversarial Image Synthesis with Decision Tree Latent Controller
Authors: Takuhiro Kaneko, Kaoru Hiramatsu, Kunio Kashino
Date: 2018-05-27 00:00:00
Proceeding: CVPR 2018 6
Tasks: Generative Adversarial Network, Image Generation, Image Retrieval, Representation Learning, Retrieval

This paper proposes the decision tree latent controller generative
adversarial network (DTLC-GAN), an extension of a GAN that can learn
hierarchically interpretable representations without relying on detailed
supervision. To impose a hierarchical inclusion structure on latent variables,
we incorporate a new architecture called the DTLC into the generator input. The
DTLC has a multiple-layer tree structure in which the ON or OFF of the child
node codes is controlled by the parent node codes. By using this architecture
hierarchically, we can obtain the latent space in which the lower layer codes
are selectively used depending on the higher layer ones. To make the latent
codes capture salient semantic features of images in a hierarchically
disentangled manner in the DTLC, we also propose a hierarchical conditional
mutual information regularization and optimize it with a newly defined
curriculum learning method that we propose as well. This makes it possible to
discover hierarchically interpretable representations in a layer-by-layer
manner on the basis of information gain by only using a single DTLC-GAN model.
We evaluated the DTLC-GAN on various datasets, i.e., MNIST, CIFAR-10, Tiny
ImageNet, 3D Faces, and CelebA, and confirmed that the DTLC-GAN can learn
hierarchically interpretable representations with either unsupervised or weakly
supervised settings. Furthermore, we applied the DTLC-GAN to image-retrieval
tasks and showed its effectiveness in representation learning.
Expanding Abbreviations in a Strongly Inflected Language: Are Morphosyntactic Tags Sufficient?
Authors: Piotr Żelasko
Date: 2017-08-20 00:00:00
Tasks: TAG

In this paper, the problem of recovery of morphological information lost in
abbreviated forms is addressed with a focus on highly inflected languages.
Evidence is presented that the correct inflected form of an expanded
abbreviation can in many cases be deduced solely from the morphosyntactic tags
of the context. The prediction model is a deep bidirectional LSTM network with
tag embedding. The training and evaluation data are gathered by finding the
words which could have been abbreviated and using their corresponding
morphosyntactic tags as the labels, while the tags of the context words are
used as the input features for classification. The network is trained on over
10 million words from the Polish Sejm Corpus and achieves 74.2% prediction
accuracy on a smaller, but more general National Corpus of Polish. The analysis
of errors suggests that performance in this task may improve if some prior
knowledge about the abbreviated word is incorporated into the model.