pwc-archive/papers-with-abstracts huggingface dataset:
Concentration for matrix martingales in continuous time and microscopic activity of social networks
Authors: Emmanuel Bacry, Stéphane Gaïffas, Jean-François Muzy
Date: 2014-12-24 00:00:00

This paper gives new concentration inequalities for the spectral norm of a
wide class of matrix martingales in continuous time. These results extend
previously established Freedman and Bernstein inequalities for series of random
matrices to the class of continuous time processes. Our analysis relies on a
new supermartingale property of the trace exponential proved within the
framework of stochastic calculus. We provide also several examples that
illustrate the fact that our results allow us to recover easily several
formerly obtained sharp bounds for discrete time matrix martingales.
Anomaly Detection with the Voronoi Diagram Evolutionary Algorithm
Authors: Marti Luis, Fansi-Tchango Arsene, Navarro Laurent, Marc Schoenauer
Date: 2016-10-27 00:00:00
Tasks: Anomaly Detection, General Classification

This paper presents the Voronoi diagram-based evolutionary algorithm
(VorEAl). VorEAl partitions input space in abnormal/normal subsets using
Voronoi diagrams. Diagrams are evolved using a multi-objective bio-inspired
approach in order to conjointly optimize classification metrics while also
being able to represent areas of the data space that are not present in the
training dataset. As part of the paper VorEAl is experimentally validated and
contrasted with similar approaches.
Statistical Inference for Model Parameters in Stochastic Gradient Descent
Authors: Xi Chen, Jason D. Lee, Xin T. Tong, Yichen Zhang
Date: 2016-10-27 00:00:00
Tasks: regression

The stochastic gradient descent (SGD) algorithm has been widely used in statistical estimation for large-scale data due to its computational and memory efficiency. While most existing works focus on the convergence of the objective function or the error of the obtained solution, we investigate the problem of statistical inference of true model parameters based on SGD when the population loss function is strongly convex and satisfies certain smoothness conditions. Our main contributions are two-fold. First, in the fixed dimension setup, we propose two consistent estimators of the asymptotic covariance of the average iterate from SGD: (1) a plug-in estimator, and (2) a batch-means estimator, which is computationally more efficient and only uses the iterates from SGD. Both proposed estimators allow us to construct asymptotically exact confidence intervals and hypothesis tests. Second, for high-dimensional linear regression, using a variant of the SGD algorithm, we construct a debiased estimator of each regression coefficient that is asymptotically normal. This gives a one-pass algorithm for computing both the sparse regression coefficients and confidence intervals, which is computationally attractive and applicable to online data.
Regret Bounds for Lifelong Learning
Authors: Pierre Alquier, The Tien Mai, Massimiliano Pontil
Date: 2016-10-27 00:00:00
Tasks: Dictionary Learning, Lifelong learning, Transfer Learning

We consider the problem of transfer learning in an online setting. Different
tasks are presented sequentially and processed by a within-task algorithm. We
propose a lifelong learning strategy which refines the underlying data
representation used by the within-task algorithm, thereby transferring
information from one task to the next. We show that when the within-task
algorithm comes with some regret bound, our strategy inherits this good
property. Our bounds are in expectation for a general loss function, and
uniform for a convex loss. We discuss applications to dictionary learning and
finite set of predictors. In the latter case, we improve previous
$O(1/\sqrt{m})$ bounds to $O(1/m)$ where $m$ is the per task sample size.
Estimation of Bandlimited Grayscale Images From the Single Bit Observations of Pixels Affected by Additive Gaussian Noise
Authors: Abhinav Kumar, Animesh Kumar
Date: 2016-10-27 00:00:00

The estimation of grayscale images using their single-bit zero mean Gaussian
noise-affected pixels is presented in this paper. The images are assumed to be
bandlimited in the Fourier Cosine transform (FCT) domain. The images are
oversampled over their Nyquist rate in the FCT domain. We propose a
non-recursive approach based on first order approximation of Cumulative
Distribution Function (CDF) to estimate the image from single bit pixels which
itself is based on Banach's contraction theorem. The decay rate for mean
squared error of estimating such images is found to be independent of the
precision of the quantizer and it varies as $O(1/N)$ where $N$ is the
"effective" oversampling ratio with respect to the Nyquist rate in the FCT
domain.
Mask-off: Synthesizing Face Images in the Presence of Head-mounted Displays
Authors: Yajie Zhao, Qingguo Xu, Xinyu Huang, Ruigang Yang
Date: 2016-10-26 00:00:00
Tasks: Colorization, Face Alignment, Face Generation

A head-mounted display (HMD) could be an important component of augmented
reality system. However, as the upper face region is seriously occluded by the
device, the user experience could be affected in applications such as
telecommunication and multi-player video games. In this paper, we first present
a novel experimental setup that consists of two near-infrared (NIR) cameras to
point to the eye regions and one visible-light RGB camera to capture the
visible face region. The main purpose of this paper is to synthesize realistic
face images without occlusions based on the images captured by these cameras.
To this end, we propose a novel synthesis framework that contains four modules:
3D head reconstruction, face alignment and tracking, face synthesis, and eye
synthesis. In face synthesis, we propose a novel algorithm that can robustly
align and track a personalized 3D head model given a face that is severely
occluded by the HMD. In eye synthesis, in order to generate accurate eye
movements and dynamic wrinkle variations around eye regions, we propose another
novel algorithm to colorize the NIR eye images and further remove the "red eye"
effects caused by the colorization. Results show that both hardware setup and
system framework are robust to synthesize realistic face images in video
sequences.
PCM and APCM Revisited: An Uncertainty Perspective
Authors: Peixin Hou, Hao Deng, Jiguang Yue, Shuguang Liu
Date: 2016-10-27 00:00:00
Tasks: Clustering

In this paper, we take a new look at the possibilistic c-means (PCM) and
adaptive PCM (APCM) clustering algorithms from the perspective of uncertainty.
This new perspective offers us insights into the clustering process, and also
provides us greater degree of flexibility. We analyze the clustering behavior
of PCM-based algorithms and introduce parameters $\sigma_v$ and $\alpha$ to
characterize uncertainty of estimated bandwidth and noise level of the dataset
respectively. Then uncertainty (fuzziness) of membership values caused by
uncertainty of the estimated bandwidth parameter is modeled by a conditional
fuzzy set, which is a new formulation of the type-2 fuzzy set. Experiments show
that parameters $\sigma_v$ and $\alpha$ make the clustering process more easy
to control, and main features of PCM and APCM are unified in this new
clustering framework (UPCM). More specifically, UPCM reduces to PCM when we set
a small $\alpha$ or a large $\sigma_v$, and UPCM reduces to APCM when clusters
are confined in their physical clusters and possible cluster elimination are
ensured. Finally we present further researches of this paper.
Joint Target Detection and Tracking in Multipath Environment: A Variational Bayesian Approach
Authors: Hua Lan, Shuai Sun, Zengfu Wang, Quan Pan, Zhishan Zhang
Date: 2016-10-27 00:00:00
Tasks: Bayesian Inference, State Estimation

We consider multitarget detection and tracking problem for a class of
multipath detection system where one target may generate multiple measurements
via multiple propagation paths, and the association relationship among targets,
measurements and propagation paths is unknown. In order to effectively utilize
multipath measurements from one target to improve detection and tracking
performance, a tracker has to handle high-dimensional estimation of latent
variables including target active/dormant meta-state, target kinematic state,
and multipath data association. Based on variational Bayesian inference, we
propose a novel joint detection and tracking algorithm that incorporates
multipath data association, target detection and target state estimation in a
unified Bayesian framework. The posterior probabilities of these latent
variables are derived in a closed-form iterative manner, which is effective for
reducing the performance deterioration caused by the coupling between
estimation errors and identification errors. Loopy belief propagation is
exploited to approximately calculate the probability of multipath data
association, saving the computational cost significantly. Simulation results of
over-the-horizon radar multitarget tracking show that the proposed algorithm
outperforms multihypothesis multipath track fusion and multi-detection
(hypothesis-oriented) multiple hypothesis tracker, especially under low
signal-to-noise ratio circumstance.
Causal Network Learning from Multiple Interventions of Unknown Manipulated Targets
Authors: Yango He, Zhi Geng
Date: 2016-10-27 00:00:00

In this paper, we discuss structure learning of causal networks from multiple
data sets obtained by external intervention experiments where we do not know
what variables are manipulated. For example, the conditions in these
experiments are changed by changing temperature or using drugs, but we do not
know what target variables are manipulated by the external interventions. From
such data sets, the structure learning becomes more difficult. For this case,
we first discuss the identifiability of causal structures. Next we present a
graph-merging method for learning causal networks for the case that the sample
sizes are large for these interventions. Then for the case that the sample
sizes of these interventions are relatively small, we propose a data-pooling
method for learning causal networks in which we pool all data sets of these
interventions together for the learning. Further we propose a re-sampling
approach to evaluate the edges of the causal network learned by the
data-pooling method. Finally we illustrate the proposed learning methods by
simulations.
VQA: Visual Question Answering
Authors: Aishwarya Agrawal, Jiasen Lu, Stanislaw Antol, Margaret Mitchell, C. Lawrence Zitnick, Dhruv Batra, Devi Parikh
Date: 2015-05-03 00:00:00
Proceeding: ICCV 2015 12
Tasks: Image Captioning, Multiple-choice, Visual Question Answering, Visual Question Answering (VQA)

We propose the task of free-form and open-ended Visual Question Answering
(VQA). Given an image and a natural language question about the image, the task
is to provide an accurate natural language answer. Mirroring real-world
scenarios, such as helping the visually impaired, both the questions and
answers are open-ended. Visual questions selectively target different areas of
an image, including background details and underlying context. As a result, a
system that succeeds at VQA typically needs a more detailed understanding of
the image and complex reasoning than a system producing generic image captions.
Moreover, VQA is amenable to automatic evaluation, since many open-ended
answers contain only a few words or a closed set of answers that can be
provided in a multiple-choice format. We provide a dataset containing ~0.25M
images, ~0.76M questions, and ~10M answers (www.visualqa.org), and discuss the
information it provides. Numerous baselines and methods for VQA are provided
and compared with human performance. Our VQA demo is available on CloudCV
(http://cloudcv.org/vqa).
Word Embeddings to Enhance Twitter Gang Member Profile Identification
Authors: Sanjaya Wijeratne, Lakshika Balasuriya, Derek Doran, Amit Sheth
Date: 2016-10-27 00:00:00
Tasks: General Classification, Word Embeddings

Gang affiliates have joined the masses who use social media to share thoughts
and actions publicly. Interestingly, they use this public medium to express
recent illegal actions, to intimidate others, and to share outrageous images
and statements. Agencies able to unearth these profiles may thus be able to
anticipate, stop, or hasten the investigation of gang-related crimes. This
paper investigates the use of word embeddings to help identify gang members on
Twitter. Building on our previous work, we generate word embeddings that
translate what Twitter users post in their profile descriptions, tweets,
profile images, and linked YouTube content to a real vector format amenable for
machine learning classification. Our experimental results show that pre-trained
word embeddings can boost the accuracy of supervised learning algorithms
trained over gang members social media posts.
Reshaped Wirtinger Flow and Incremental Algorithm for Solving Quadratic System of Equations
Authors: Huishuai Zhang, Yi Zhou, Yingbin Liang, Yuejie Chi
Date: 2016-05-25 00:00:00
Tasks: Retrieval

We study the phase retrieval problem, which solves quadratic system of
equations, i.e., recovers a vector $\boldsymbol{x}\in \mathbb{R}^n$ from its
magnitude measurements $y_i=|\langle \boldsymbol{a}_i, \boldsymbol{x}\rangle|,
i=1,..., m$. We develop a gradient-like algorithm (referred to as RWF
representing reshaped Wirtinger flow) by minimizing a nonconvex nonsmooth loss
function. In comparison with existing nonconvex Wirtinger flow (WF) algorithm
\cite{candes2015phase}, although the loss function becomes nonsmooth, it
involves only the second power of variable and hence reduces the complexity. We
show that for random Gaussian measurements, RWF enjoys geometric convergence to
a global optimal point as long as the number $m$ of measurements is on the
order of $n$, the dimension of the unknown $\boldsymbol{x}$. This improves the
sample complexity of WF, and achieves the same sample complexity as truncated
Wirtinger flow (TWF) \cite{chen2015solving}, but without truncation in gradient
loop. Furthermore, RWF costs less computationally than WF, and runs faster
numerically than both WF and TWF. We further develop the incremental
(stochastic) reshaped Wirtinger flow (IRWF) and show that IRWF converges
linearly to the true signal. We further establish performance guarantee of an
existing Kaczmarz method for the phase retrieval problem based on its
connection to IRWF. We also empirically demonstrate that IRWF outperforms
existing ITWF algorithm (stochastic version of TWF) as well as other batch
algorithms.
Residual Networks Behave Like Ensembles of Relatively Shallow Networks
Authors: Andreas Veit, Michael Wilber, Serge Belongie
Date: 2016-05-20 00:00:00
Proceeding: NeurIPS 2016 12

In this work we propose a novel interpretation of residual networks showing
that they can be seen as a collection of many paths of differing length.
Moreover, residual networks seem to enable very deep networks by leveraging
only the short paths during training. To support this observation, we rewrite
residual networks as an explicit collection of paths. Unlike traditional
models, paths through residual networks vary in length. Further, a lesion study
reveals that these paths show ensemble-like behavior in the sense that they do
not strongly depend on each other. Finally, and most surprising, most paths are
shorter than one might expect, and only the short paths are needed during
training, as longer paths do not contribute any gradient. For example, most of
the gradient in a residual network with 110 layers comes from paths that are
only 10-34 layers deep. Our results reveal one of the key characteristics that
seem to enable the training of very deep networks: Residual networks avoid the
vanishing gradient problem by introducing short paths which can carry gradient
throughout the extent of very deep networks.
Synthesis of Shared Control Protocols with Provable Safety and Performance Guarantees
Authors: Nils Jansen, Murat Cubuktepe, Ufuk Topcu
Date: 2016-10-26 00:00:00

We formalize synthesis of shared control protocols with correctness
guarantees for temporal logic specifications. More specifically, we introduce a
modeling formalism in which both a human and an autonomy protocol can issue
commands to a robot towards performing a certain task. These commands are
blended into a joint input to the robot. The autonomy protocol is synthesized
using an abstraction of possible human commands accounting for randomness in
decisions caused by factors such as fatigue or incomprehensibility of the
problem at hand. The synthesis is designed to ensure that the resulting robot
behavior satisfies given safety and performance specifications, e.g., in
temporal logic. Our solution is based on nonlinear programming and we address
the inherent scalability issue by presenting alternative methods. We assess the
feasibility and the scalability of the approach by an experimental evaluation.
Volumetric Light-field Encryption at the Microscopic Scale
Authors: Haoyu Li, Changliang Guo, Inbarasan Muniraj, Bryce C. Schroeder, John T. Sheridan, Shu Jia
Date: 2016-10-26 00:00:00

We report a light-field based method that allows the optical encryption of
three-dimensional (3D) volumetric information at the microscopic scale in a
single 2D light-field image. The system consists of a microlens array and an
array of random phase/amplitude masks. The method utilizes a wave optics model
to account for the dominant diffraction effect at this new scale, and the
system point-spread function (PSF) serves as the key for encryption and
decryption. We successfully developed and demonstrated a deconvolution
algorithm to retrieve spatially multiplexed discrete and continuous volumetric
data from 2D light-field images. Showing that the method is practical for data
transmission and storage, we obtained a faithful reconstruction of the 3D
volumetric information from a digital copy of the encrypted light-field image.
The method represents a new level of optical encryption, paving the way for
broad industrial and biomedical applications in processing and securing 3D data
at the microscopic scale.
Estimating the Size of a Large Network and its Communities from a Random Sample
Authors: Lin Chen, Amin Karbasi, Forrest W. Crawford
Date: 2016-10-26 00:00:00
Proceeding: NeurIPS 2016 12
Tasks: Epidemiology, Stochastic Block Model

Most real-world networks are too large to be measured or studied directly and
there is substantial interest in estimating global network properties from
smaller sub-samples. One of the most important global properties is the number
of vertices/nodes in the network. Estimating the number of vertices in a large
network is a major challenge in computer science, epidemiology, demography, and
intelligence analysis. In this paper we consider a population random graph G =
(V;E) from the stochastic block model (SBM) with K communities/blocks. A sample
is obtained by randomly choosing a subset W and letting G(W) be the induced
subgraph in G of the vertices in W. In addition to G(W), we observe the total
degree of each sampled vertex and its block membership. Given this partial
information, we propose an efficient PopULation Size Estimation algorithm,
called PULSE, that correctly estimates the size of the whole population as well
as the size of each community. To support our theoretical analysis, we perform
an exhaustive set of experiments to study the effects of sample size, K, and
SBM model parameters on the accuracy of the estimates. The experimental results
also demonstrate that PULSE significantly outperforms a widely-used method
called the network scale-up estimator in a wide variety of scenarios. We
conclude with extensions and directions for future work.
Recurrent switching linear dynamical systems
Authors: Scott W. Linderman, Andrew C. Miller, Ryan P. Adams, David M. Blei, Liam Paninski, Matthew J. Johnson
Date: 2016-10-26 00:00:00
Tasks: Bayesian Inference, Time Series, Time Series Analysis

Many natural systems, such as neurons firing in the brain or basketball teams
traversing a court, give rise to time series data with complex, nonlinear
dynamics. We can gain insight into these systems by decomposing the data into
segments that are each explained by simpler dynamic units. Building on
switching linear dynamical systems (SLDS), we present a new model class that
not only discovers these dynamical units, but also explains how their switching
behavior depends on observations or continuous latent states. These "recurrent"
switching linear dynamical systems provide further insight by discovering the
conditions under which each unit is deployed, something that traditional SLDS
models fail to do. We leverage recent algorithmic advances in approximate
inference to make Bayesian inference in these models easy, fast, and scalable.
Bayesian latent structure discovery from multi-neuron recordings
Authors: Scott W. Linderman, Ryan P. Adams, Jonathan W. Pillow
Date: 2016-10-26 00:00:00
Proceeding: NeurIPS 2016 12
Tasks: Bayesian Inference, Clustering, Dimensionality Reduction

Neural circuits contain heterogeneous groups of neurons that differ in type,
location, connectivity, and basic response properties. However, traditional
methods for dimensionality reduction and clustering are ill-suited to
recovering the structure underlying the organization of neural circuits. In
particular, they do not take advantage of the rich temporal dependencies in
multi-neuron recordings and fail to account for the noise in neural spike
trains. Here we describe new tools for inferring latent structure from
simultaneously recorded spike train data using a hierarchical extension of a
multi-neuron point process model commonly known as the generalized linear model
(GLM). Our approach combines the GLM with flexible graph-theoretic priors
governing the relationship between latent features and neural connectivity
patterns. Fully Bayesian inference via P\'olya-gamma augmentation of the
resulting model allows us to classify neurons and infer latent dimensions of
circuit organization from correlated spike trains. We demonstrate the
effectiveness of our method with applications to synthetic data and
multi-neuron recordings in primate retina, revealing latent patterns of neural
types and locations from spike trains alone.
Distraction-Based Neural Networks for Document Summarization
Authors: Qian Chen, Xiaodan Zhu, Zhen-Hua Ling, Si Wei, Hui Jiang
Date: 2016-10-26 00:00:00
Tasks: Document Summarization

Distributed representation learned with neural networks has recently shown to
be effective in modeling natural languages at fine granularities such as words,
phrases, and even sentences. Whether and how such an approach can be extended
to help model larger spans of text, e.g., documents, is intriguing, and further
investigation would still be desirable. This paper aims to enhance neural
network models for such a purpose. A typical problem of document-level modeling
is automatic summarization, which aims to model documents in order to generate
summaries. In this paper, we propose neural models to train computers not just
to pay attention to specific regions and content of input documents with
attention models, but also distract them to traverse between different content
of a document so as to better grasp the overall meaning for summarization.
Without engineering any features, we train the models on two large datasets.
The models achieve the state-of-the-art performance, and they significantly
benefit from the distraction modeling, particularly when input documents are
long.