pwc-archive/papers-with-abstracts huggingface dataset:
Latent Variable Time-varying Network Inference
Authors: Federico Tomasi, Veronica Tozzo, Saverio Salzo, Alessandro Verri
Date: 2018-02-12 00:00:00
Tasks: Sociology, Time Series, Time Series Analysis

In many applications of finance, biology and sociology, complex systems
involve entities interacting with each other. These processes have the
peculiarity of evolving over time and of comprising latent factors, which
influence the system without being explicitly measured. In this work we present
latent variable time-varying graphical lasso (LTGL), a method for multivariate
time-series graphical modelling that considers the influence of hidden or
unmeasurable factors. The estimation of the contribution of the latent factors
is embedded in the model which produces both sparse and low-rank components for
each time point. In particular, the first component represents the connectivity
structure of observable variables of the system, while the second represents
the influence of hidden factors, assumed to be few with respect to the observed
variables. Our model includes temporal consistency on both components,
providing an accurate evolutionary pattern of the system. We derive a tractable
optimisation algorithm based on alternating direction method of multipliers,
and develop a scalable and efficient implementation which exploits proximity
operators in closed form. LTGL is extensively validated on synthetic data,
achieving optimal performance in terms of accuracy, structure learning and
scalability with respect to ground truth and state-of-the-art methods for
graphical inference. We conclude with the application of LTGL to real case
studies, from biology and finance, to illustrate how our method can be
successfully employed to gain insights on multivariate time-series data.
Spectral Filtering for General Linear Dynamical Systems
Authors: Elad Hazan, Holden Lee, Karan Singh, Cyril Zhang, Yi Zhang
Date: 2018-02-12 00:00:00
Proceeding: NeurIPS 2018 12

We give a polynomial-time algorithm for learning latent-state linear
dynamical systems without system identification, and without assumptions on the
spectral radius of the system's transition matrix. The algorithm extends the
recently introduced technique of spectral filtering, previously applied only to
systems with a symmetric transition matrix, using a novel convex relaxation to
allow for the efficient identification of phases.
Reinforcement Learning with Wasserstein Distance Regularisation, with Applications to Multipolicy Learning
Authors: Mohammed Amin Abdullah, Aldo Pacchiano, Moez Draief
Date: 2018-02-12 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

We describe an application of Wasserstein distance to Reinforcement Learning. The Wasserstein distance in question is between the distribution of mappings of trajectories of a policy into some metric space, and some other fixed distribution (which may, for example, come from another policy). Different policies induce different distributions, so given an underlying metric, the Wasserstein distance quantifies how different policies are. This can be used to learn multiple polices which are different in terms of such Wasserstein distances by using a Wasserstein regulariser. Changing the sign of the regularisation parameter, one can learn a policy for which its trajectory mapping distribution is attracted to a given fixed distribution.
Email Classification into Relevant Category Using Neural Networks
Authors: Deepak Kumar Gupta, Shruti Goyal
Date: 2018-02-12 00:00:00
Tasks: Classification, General Classification, text-classification, Text Classification

In the real world, many online shopping websites or service provider have
single email-id where customers can send their query, concern etc. At the
back-end service provider receive million of emails every week, how they can
identify which email is belonged of a particular department? This paper
presents an artificial neural network (ANN) model that is used to solve this
problem and experiments are carried out on user personal Gmail emails datasets.
This problem can be generalised as typical Text Classification or
Categorization.
The Shape of Art History in the Eyes of the Machine
Authors: Ahmed Elgammal, Marian Mazzone, Bingchen Liu, Diana Kim, Mohamed Elhoseiny
Date: 2018-01-23 00:00:00

How does the machine classify styles in art? And how does it relate to art
historians' methods for analyzing style? Several studies have shown the ability
of the machine to learn and predict style categories, such as Renaissance,
Baroque, Impressionism, etc., from images of paintings. This implies that the
machine can learn an internal representation encoding discriminative features
through its visual analysis. However, such a representation is not necessarily
interpretable. We conducted a comprehensive study of several of the
state-of-the-art convolutional neural networks applied to the task of style
classification on 77K images of paintings, and analyzed the learned
representation through correlation analysis with concepts derived from art
history. Surprisingly, the networks could place the works of art in a smooth
temporal arrangement mainly based on learning style labels, without any a
priori knowledge of time of creation, the historical time and context of
styles, or relations between styles. The learned representations showed that
there are few underlying factors that explain the visual variations of style in
art. Some of these factors were found to correlate with style patterns
suggested by Heinrich W\"olfflin (1846-1945). The learned representations also
consistently highlighted certain artists as the extreme distinctive
representative of their styles, which quantitatively confirms art historian
observations.
Blind Source Separation Using Mixtures of Alpha-Stable Distributions
Authors: Nicolas Keriven, Antoine Deleforge, Antoine Liutkus
Date: 2017-11-13 00:00:00
Tasks: blind source separation

We propose a new blind source separation algorithm based on mixtures of
alpha-stable distributions. Complex symmetric alpha-stable distributions have
been recently showed to better model audio signals in the time-frequency domain
than classical Gaussian distributions thanks to their larger dynamic range.
However, inference of these models is notoriously hard to perform because their
probability density functions do not have a closed-form expression in general.
Here, we introduce a novel method for estimating mixture of alpha-stable
distributions based on characteristic function matching. We apply this to the
blind estimation of binary masks in individual frequency bands from
multichannel convolutive audio mixes. We show that the proposed method yields
better separation performance than Gaussian-based binary-masking methods.
Temporal and volumetric denoising via quantile sparse image prior
Authors: Franziska Schirrmacher, Thomas Köhler, Tobias Lindenberger, Lennart Husvogt, Jürgen Endres, James G. Fujimoto, Joachim Hornegger, Arnd Dörfler, Philip Hoelter, Andreas K. Maier
Date: 2018-02-12 00:00:00
Tasks: Computed Tomography (CT), Denoising

This paper introduces an universal and structure-preserving regularization term, called quantile sparse image (QuaSI) prior. The prior is suitable for denoising images from various medical imaging modalities. We demonstrate its effectiveness on volumetric optical coherence tomography (OCT) and computed tomography (CT) data, which show different noise and image characteristics. OCT offers high-resolution scans of the human retina but is inherently impaired by speckle noise. CT on the other hand has a lower resolution and shows high-frequency noise. For the purpose of denoising, we propose a variational framework based on the QuaSI prior and a Huber data fidelity model that can handle 3-D and 3-D+t data. Efficient optimization is facilitated through the use of an alternating direction method of multipliers (ADMM) scheme and the linearization of the quantile filter. Experiments on multiple datasets emphasize the excellent performance of the proposed method.
Revisiting the Vector Space Model: Sparse Weighted Nearest-Neighbor Method for Extreme Multi-Label Classification
Authors: Tatsuhiro Aoshima, Kei Kobayashi, Mihoko Minami
Date: 2018-02-12 00:00:00
Tasks: BIG-bench Machine Learning, Extreme Multi-Label Classification, General Classification, Information Retrieval, Multi-Label Classification, MUlTI-LABEL-ClASSIFICATION, Retrieval

Machine learning has played an important role in information retrieval (IR)
in recent times. In search engines, for example, query keywords are accepted
and documents are returned in order of relevance to the given query; this can
be cast as a multi-label ranking problem in machine learning. Generally, the
number of candidate documents is extremely large (from several thousand to
several million); thus, the classifier must handle many labels. This problem is
referred to as extreme multi-label classification (XMLC). In this paper, we
propose a novel approach to XMLC termed the Sparse Weighted Nearest-Neighbor
Method. This technique can be derived as a fast implementation of
state-of-the-art (SOTA) one-versus-rest linear classifiers for very sparse
datasets. In addition, we show that the classifier can be written as a sparse
generalization of a representer theorem with a linear kernel. Furthermore, our
method can be viewed as the vector space model used in IR. Finally, we show
that the Sparse Weighted Nearest-Neighbor Method can process data points in
real time on XMLC datasets with equivalent performance to SOTA models, with a
single thread and smaller storage footprint. In particular, our method exhibits
superior performance to the SOTA models on a dataset with 3 million labels.
On the Needs for Rotations in Hypercubic Quantization Hashing
Authors: Anne Morvan, Antoine Souloumiac, Krzysztof Choromanski, Cédric Gouy-Pailler, Jamal Atif
Date: 2018-02-12 00:00:00
Tasks: Dimensionality Reduction, Quantization

The aim of this paper is to endow the well-known family of hypercubic
quantization hashing methods with theoretical guarantees. In hypercubic
quantization, applying a suitable (random or learned) rotation after
dimensionality reduction has been experimentally shown to improve the results
accuracy in the nearest neighbors search problem. We prove in this paper that
the use of these rotations is optimal under some mild assumptions: getting
optimal binary sketches is equivalent to applying a rotation uniformizing the
diagonal of the covariance matrix between data points. Moreover, for two closed
points, the probability to have dissimilar binary sketches is upper bounded by
a factor of the initial distance between the data points. Relaxing these
assumptions, we obtain a general concentration result for random matrices. We
also provide some experiments illustrating these theoretical points and compare
a set of algorithms in both the batch and online settings.
BOOK: Storing Algorithm-Invariant Episodes for Deep Reinforcement Learning
Authors: Simyung Chang, Youngjoon Yoo, Jae-Seok Choi, Nojun Kwak
Date: 2017-09-05 00:00:00
Tasks: Deep Reinforcement Learning, Imitation Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

We introduce a novel method to train agents of reinforcement learning (RL) by
sharing knowledge in a way similar to the concept of using a book. The recorded
information in the form of a book is the main means by which humans learn
knowledge. Nevertheless, the conventional deep RL methods have mainly focused
either on experiential learning where the agent learns through interactions
with the environment from the start or on imitation learning that tries to
mimic the teacher. Contrary to these, our proposed book learning shares key
information among different agents in a book-like manner by delving into the
following two characteristic features: (1) By defining the linguistic function,
input states can be clustered semantically into a relatively small number of
core clusters, which are forwarded to other RL agents in a prescribed manner.
(2) By defining state priorities and the contents for recording, core
experiences can be selected and stored in a small container. We call this
container as `BOOK'. Our method learns hundreds to thousand times faster than
the conventional methods by learning only a handful of core cluster
information, which shows that deep RL agents can effectively learn through the
shared knowledge from other agents.
Object Detection with Mask-based Feature Encoding
Authors: Xiaochuan Fan, Hao Guo, Kang Zheng, Wei Feng, Song Wang
Date: 2018-02-12 00:00:00
Tasks: Object, object-detection, Object Detection

Region-based Convolutional Neural Networks (R-CNNs) have achieved great
success in the field of object detection. The existing R-CNNs usually divide a
Region-of-Interest (ROI) into grids, and then localize objects by utilizing the
spatial information reflected by the relative position of each grid in the ROI.
In this paper, we propose a novel feature-encoding approach, where spatial
information is represented through the spatial distributions of visual
patterns. In particular, we design a Mask Weight Network (MWN) to learn a set
of masks and then apply channel-wise masking operations to ROI feature map,
followed by a global pooling and a cheap fully-connected layer. We integrate
the newly designed feature encoder into the Faster R-CNN architecture. The
resulting new Faster R-CNNs can preserve the object-detection accuracy of the
standard Faster R-CNNs by using substantially fewer parameters. Compared to
R-FCNs using state-of-art PS ROI pooling and deformable PS ROI pooling, the new
Faster R-CNNs can produce higher object-detection accuracy with good run-time
efficiency. We also show that a specifically designed and learned MWN can
capture global contextual information and further improve the object-detection
accuracy. Validation experiments are conducted on both PASCAL VOC and MS COCO
datasets.
Deep feature compression for collaborative object detection
Authors: Hyomin Choi, Ivan V. Bajic
Date: 2018-02-12 00:00:00
Tasks: Feature Compression, Object, object-detection, Object Detection

Recent studies have shown that the efficiency of deep neural networks in
mobile applications can be significantly improved by distributing the
computational workload between the mobile device and the cloud. This paradigm,
termed collaborative intelligence, involves communicating feature data between
the mobile and the cloud. The efficiency of such approach can be further
improved by lossy compression of feature data, which has not been examined to
date. In this work we focus on collaborative object detection and study the
impact of both near-lossless and lossy compression of feature data on its
accuracy. We also propose a strategy for improving the accuracy under lossy
feature compression. Experiments indicate that using this strategy, the
communication overhead can be reduced by up to 70% without sacrificing
accuracy.
Safe Triplet Screening for Distance Metric Learning
Authors: Tomoki Yoshida, Ichiro Takeuchi, Masayuki Karasuyama
Date: 2018-02-12 00:00:00
Tasks: Metric Learning, Triplet

We study safe screening for metric learning. Distance metric learning can
optimize a metric over a set of triplets, each one of which is defined by a
pair of same class instances and an instance in a different class. However, the
number of possible triplets is quite huge even for a small dataset. Our safe
triplet screening identifies triplets which can be safely removed from the
optimization problem without losing the optimality. Compared with existing safe
screening studies, triplet screening is particularly significant because of (1)
the huge number of possible triplets, and (2) the semi-definite constraint in
the optimization. We derive several variants of screening rules, and analyze
their relationships. Numerical experiments on benchmark datasets demonstrate
the effectiveness of safe triplet screening.
Assessing the Utility of Weather Data for Photovoltaic Power Prediction
Authors: Reza Zafarani, Sara Eftekharnejad, Urvi Patel
Date: 2018-02-12 00:00:00

Photovoltaic systems have been widely deployed in recent times to meet the
increased electricity demand as an environmental-friendly energy source. The
major challenge for integrating photovoltaic systems in power systems is the
unpredictability of the solar power generated. In this paper, we analyze the
impact of having access to weather information for solar power generation
prediction and find weather information that can help best predict photovoltaic
power.
Human Action Adverb Recognition: ADHA Dataset and A Three-Stream Hybrid Model
Authors: Bo Pang, Kaiwen Zha, Cewu Lu
Date: 2018-02-04 00:00:00
Tasks: Action Recognition, Image Captioning, Temporal Action Localization

We introduce the first benchmark for a new problem --- recognizing human
action adverbs (HAA): "Adverbs Describing Human Actions" (ADHA). This is the
first step for computer vision to change over from pattern recognition to real
AI. We demonstrate some key features of ADHA: a semantically complete set of
adverbs describing human actions, a set of common, describable human actions,
and an exhaustive labeling of simultaneously emerging actions in each video. We
commit an in-depth analysis on the implementation of current effective models
in action recognition and image captioning on adverb recognition, and the
results show that such methods are unsatisfactory. Moreover, we propose a novel
three-stream hybrid model to deal the HAA problem, which achieves a better
result.
Information-Theoretic Representation Learning for Positive-Unlabeled Classification
Authors: Tomoya Sakai, Gang Niu, Masashi Sugiyama
Date: 2017-10-15 00:00:00
Tasks: Classification, Dimensionality Reduction, General Classification, Representation Learning, Weakly Supervised Classification

Recent advances in weakly supervised classification allow us to train a classifier only from positive and unlabeled (PU) data. However, existing PU classification methods typically require an accurate estimate of the class-prior probability, which is a critical bottleneck particularly for high-dimensional data. This problem has been commonly addressed by applying principal component analysis in advance, but such unsupervised dimension reduction can collapse underlying class structure. In this paper, we propose a novel representation learning method from PU data based on the information-maximization principle. Our method does not require class-prior estimation and thus can be used as a preprocessing method for PU classification. Through experiments, we demonstrate that our method combined with deep neural networks highly improves the accuracy of PU class-prior estimation, leading to state-of-the-art PU classification performance.
Hardening Deep Neural Networks via Adversarial Model Cascades
Authors: Deepak Vijaykeerthy, Anshuman Suri, Sameep Mehta, Ponnurangam Kumaraguru
Date: 2018-02-02 00:00:00

Deep neural networks (DNNs) are vulnerable to malicious inputs crafted by an
adversary to produce erroneous outputs. Works on securing neural networks
against adversarial examples achieve high empirical robustness on simple
datasets such as MNIST. However, these techniques are inadequate when
empirically tested on complex data sets such as CIFAR-10 and SVHN. Further,
existing techniques are designed to target specific attacks and fail to
generalize across attacks. We propose the Adversarial Model Cascades (AMC) as a
way to tackle the above inadequacies. Our approach trains a cascade of models
sequentially where each model is optimized to be robust towards a mixture of
multiple attacks. Ultimately, it yields a single model which is secure against
a wide range of attacks; namely FGSM, Elastic, Virtual Adversarial
Perturbations and Madry. On an average, AMC increases the model's empirical
robustness against various attacks simultaneously, by a significant margin (of
6.225% for MNIST, 5.075% for SVHN and 2.65% for CIFAR10). At the same time, the
model's performance on non-adversarial inputs is comparable to the
state-of-the-art models.
Extrinsic Parameter Calibration for Line Scanning Cameras on Ground Vehicles with Navigation Systems Using a Calibration Pattern
Authors: Alexander Wendel, James Underwood
Date: 2017-09-04 00:00:00

Line scanning cameras, which capture only a single line of pixels, have been
increasingly used in ground based mobile or robotic platforms. In applications
where it is advantageous to directly georeference the camera data to world
coordinates, an accurate estimate of the camera's 6D pose is required. This
paper focuses on the common case where a mobile platform is equipped with a
rigidly mounted line scanning camera, whose pose is unknown, and a navigation
system providing vehicle body pose estimates. We propose a novel method that
estimates the camera's pose relative to the navigation system. The approach
involves imaging and manually labelling a calibration pattern with distinctly
identifiable points, triangulating these points from camera and navigation
system data and reprojecting them in order to compute a likelihood, which is
maximised to estimate the 6D camera pose. Additionally, a Markov Chain Monte
Carlo (MCMC) algorithm is used to estimate the uncertainty of the offset.
Tested on two different platforms, the method was able to estimate the pose to
within 0.06 m / 1.05$^{\circ}$ and 0.18 m / 2.39$^{\circ}$. We also propose
several approaches to displaying and interpreting the 6D results in a human
readable way.
Unsupervised Anomaly Detection via Variational Auto-Encoder for Seasonal KPIs in Web Applications
Authors: Haowen Xu, Wenxiao Chen, Nengwen Zhao, Zeyan Li, Jiahao Bu, Zhihan Li, Ying Liu, Youjian Zhao, Dan Pei, Yang Feng, Jie Chen, Zhaogang Wang, Honglin Qiao
Date: 2018-02-12 00:00:00
Tasks: Anomaly Detection, Unsupervised Anomaly Detection

To ensure undisrupted business, large Internet companies need to closely
monitor various KPIs (e.g., Page Views, number of online users, and number of
orders) of its Web applications, to accurately detect anomalies and trigger
timely troubleshooting/mitigation. However, anomaly detection for these
seasonal KPIs with various patterns and data quality has been a great
challenge, especially without labels. In this paper, we proposed Donut, an
unsupervised anomaly detection algorithm based on VAE. Thanks to a few of our
key techniques, Donut greatly outperforms a state-of-arts supervised ensemble
approach and a baseline VAE approach, and its best F-scores range from 0.75 to
0.9 for the studied KPIs from a top global Internet company. We come up with a
novel KDE interpretation of reconstruction for Donut, making it the first
VAE-based anomaly detection algorithm with solid theoretical explanation.
Q-learning with Nearest Neighbors
Authors: Devavrat Shah, Qiaomin Xie
Date: 2018-02-12 00:00:00
Proceeding: NeurIPS 2018 12
Tasks: Q-Learning, Reinforcement Learning

We consider model-free reinforcement learning for infinite-horizon discounted
Markov Decision Processes (MDPs) with a continuous state space and unknown
transition kernel, when only a single sample path under an arbitrary policy of
the system is available. We consider the Nearest Neighbor Q-Learning (NNQL)
algorithm to learn the optimal Q function using nearest neighbor regression
method. As the main contribution, we provide tight finite sample analysis of
the convergence rate. In particular, for MDPs with a $d$-dimensional state
space and the discounted factor $\gamma \in (0,1)$, given an arbitrary sample
path with "covering time" $ L $, we establish that the algorithm is guaranteed
to output an $\varepsilon$-accurate estimate of the optimal Q-function using
$\tilde{O}\big(L/(\varepsilon^3(1-\gamma)^7)\big)$ samples. For instance, for a
well-behaved MDP, the covering time of the sample path under the purely random
policy scales as $ \tilde{O}\big(1/\varepsilon^d\big),$ so the sample
complexity scales as $\tilde{O}\big(1/\varepsilon^{d+3}\big).$ Indeed, we
establish a lower bound that argues that the dependence of $
\tilde{\Omega}\big(1/\varepsilon^{d+2}\big)$ is necessary.