pwc-archive/papers-with-abstracts huggingface dataset:
One-dimensional Deep Image Prior for Time Series Inverse Problems
Authors: Sriram Ravula, Alexandros G. Dimakis
Date: 2019-04-18 00:00:00
Tasks: compressed sensing, Denoising, Imputation, Time Series, Time Series Analysis

We extend the Deep Image Prior (DIP) framework to one-dimensional signals.
DIP is using a randomly initialized convolutional neural network (CNN) to solve
linear inverse problems by optimizing over weights to fit the observed
measurements. Our main finding is that properly tuned one-dimensional
convolutional architectures provide an excellent Deep Image Prior for various
types of temporal signals including audio, biological signals, and sensor
measurements. We show that our network can be used in a variety of recovery
tasks including missing value imputation, blind denoising, and compressed
sensing from random Gaussian projections. The key challenge is how to avoid
overfitting by carefully tuning early stopping, total variation, and weight
decay regularization. Our method requires up to 4 times fewer measurements than
Lasso and outperforms NLM-VAMP for random Gaussian measurements on audio
signals, has similar imputation performance to a Kalman state-space model on a
variety of data, and outperforms wavelet filtering in removing additive noise
from air-quality sensor readings.
Creative Procedural-Knowledge Extraction From Web Design Tutorials
Authors: Longqi Yang, Chen Fang, Hailin Jin, Walter Chang, Deborah Estrin
Date: 2019-04-18 00:00:00
Tasks: Knowledge Base Construction, text-classification, Text Classification

Complex design tasks often require performing diverse actions in a specific
order. To (semi-)autonomously accomplish these tasks, applications need to
understand and learn a wide range of design procedures, i.e., Creative
Procedural-Knowledge (CPK). Prior knowledge base construction and mining have
not typically addressed the creative fields, such as design and arts. In this
paper, we formalize an ontology of CPK using five components: goal, workflow,
action, command and usage; and extract components' values from online design
tutorials. We scraped 19.6K tutorial-related webpages and built a web
application for professional designers to identify and summarize CPK
components. The annotated dataset consists of 819 unique commands, 47,491
actions, and 2,022 workflows and goals. Based on this dataset, we propose a
general CPK extraction pipeline and demonstrate that existing text
classification and sequence-to-sequence models are limited in identifying,
predicting and summarizing complex operations described in heterogeneous
styles. Through quantitative and qualitative error analysis, we discuss CPK
extraction challenges that need to be addressed by future research.
Road Crack Detection Using Deep Convolutional Neural Network and Adaptive Thresholding
Authors: Rui Fan, Mohammud Junaid Bocus, Yilong Zhu, Jianhao Jiao, Li Wang, Fulong Ma, Shanshan Cheng, Ming Liu
Date: 2019-04-18 00:00:00
Tasks: Image Segmentation, Semantic Segmentation

Crack is one of the most common road distresses which may pose road safety
hazards. Generally, crack detection is performed by either certified inspectors
or structural engineers. This task is, however, time-consuming, subjective and
labor-intensive. In this paper, we propose a novel road crack detection
algorithm based on deep learning and adaptive image segmentation. Firstly, a
deep convolutional neural network is trained to determine whether an image
contains cracks or not. The images containing cracks are then smoothed using
bilateral filtering, which greatly minimizes the number of noisy pixels.
Finally, we utilize an adaptive thresholding method to extract the cracks from
road surface. The experimental results illustrate that our network can classify
images with an accuracy of 99.92%, and the cracks can be successfully extracted
from the images using our proposed thresholding algorithm.
Semantic variation operators for multidimensional genetic programming
Authors: William La Cava, Jason H. Moore
Date: 2019-04-18 00:00:00
Tasks: Heuristic Search

Multidimensional genetic programming represents candidate solutions as sets
of programs, and thereby provides an interesting framework for exploiting
building block identification. Towards this goal, we investigate the use of
machine learning as a way to bias which components of programs are promoted,
and propose two semantic operators to choose where useful building blocks are
placed during crossover. A forward stagewise crossover operator we propose
leads to significant improvements on a set of regression problems, and produces
state-of-the-art results in a large benchmark study. We discuss this
architecture and others in terms of their propensity for allowing heuristic
search to utilize information during the evolutionary process. Finally, we look
at the collinearity and complexity of the data representations that result from
these architectures, with a view towards disentangling factors of variation in
application.
On Low-rank Trace Regression under General Sampling Distribution
Authors: Nima Hamidi, Mohsen Bayati
Date: 2019-04-18 00:00:00
Tasks: Matrix Completion, Multi-Task Learning, regression

In this paper, we study the trace regression when a matrix of parameters B* is estimated via the convex relaxation of a rank-regularized regression or via regularized non-convex optimization. It is known that these estimators satisfy near-optimal error bounds under assumptions on the rank, coherence, and spikiness of B*. We start by introducing a general notion of spikiness for B* that provides a generic recipe to prove the restricted strong convexity of the sampling operator of the trace regression and obtain near-optimal and non-asymptotic error bounds for the estimation error. Similar to the existing literature, these results require the regularization parameter to be above a certain theory-inspired threshold that depends on observation noise that may be unknown in practice. Next, we extend the error bounds to cases where the regularization parameter is chosen via cross-validation. This result is significant in that existing theoretical results on cross-validated estimators (Kale et al., 2011; Kumar et al., 2013; Abou-Moustafa and Szepesvari, 2017) do not apply to our setting since the estimators we study are not known to satisfy their required notion of stability. Finally, using simulations on synthetic and real data, we show that the cross-validated estimator selects a near-optimal penalty parameter and outperforms the theory-inspired approach of selecting the parameter.
SPONGE: A generalized eigenproblem for clustering signed networks
Authors: Mihai Cucuringu, Peter Davies, Aldo Glielmo, Hemant Tyagi
Date: 2019-04-18 00:00:00
Tasks: Clustering, Constrained Clustering, Stochastic Block Model

We introduce a principled and theoretically sound spectral method for $k$-way clustering in signed graphs, where the affinity measure between nodes takes either positive or negative values. Our approach is motivated by social balance theory, where the task of clustering aims to decompose the network into disjoint groups, such that individuals within the same group are connected by as many positive edges as possible, while individuals from different groups are connected by as many negative edges as possible. Our algorithm relies on a generalized eigenproblem formulation inspired by recent work on constrained clustering. We provide theoretical guarantees for our approach in the setting of a signed stochastic block model, by leveraging tools from matrix perturbation theory and random matrix theory. An extensive set of numerical experiments on both synthetic and real data shows that our approach compares favorably with state-of-the-art methods for signed clustering, especially for large number of clusters and sparse measurement graphs.
Fast Single Image Dehazing via Multilevel Wavelet Transform based Optimization
Authors: Jiaxi He, Frank Z. Xing, Ran Yang, Cishen Zhang
Date: 2019-04-18 00:00:00
Tasks: Computational Efficiency, Dimensionality Reduction, Image Dehazing, Image Reconstruction, Image Segmentation, object-detection, Object Detection, Semantic Segmentation, Single Image Dehazing

The quality of images captured in outdoor environments can be affected by
poor weather conditions such as fog, dust, and atmospheric scattering of other
particles. This problem can bring extra challenges to high-level computer
vision tasks like image segmentation and object detection. However, previous
studies on image dehazing suffer from a huge computational workload and
corruption of the original image, such as over-saturation and halos. In this
paper, we present a novel image dehazing approach based on the optical model
for haze images and regularized optimization. Specifically, we convert the
non-convex, bilinear problem concerning the unknown haze-free image and light
transmission distribution to a convex, linear optimization problem by
estimating the atmosphere light constant. Our method is further accelerated by
introducing a multilevel Haar wavelet transform. The optimization, instead, is
applied to the low frequency sub-band decomposition of the original image. This
dimension reduction significantly improves the processing speed of our method
and exhibits the potential for real-time applications. Experimental results
show that our approach outperforms state-of-the-art dehazing algorithms in
terms of both image reconstruction quality and computational efficiency. For
implementation details, source code can be publicly accessed via
http://github.com/JiaxiHe/Image-and-Video-Dehazing.
node2bits: Compact Time- and Attribute-aware Node Representations for User Stitching
Authors: Di Jin, Mark Heimann, Ryan Rossi, Danai Koutra
Date: 2019-04-18 00:00:00
Tasks: Attribute, Blocking

Identity stitching, the task of identifying and matching various online references (e.g., sessions over different devices and timespans) to the same user in real-world web services, is crucial for personalization and recommendations. However, traditional user stitching approaches, such as grouping or blocking, require quadratic pairwise comparisons between a massive number of user activities, thus posing both computational and storage challenges. Recent works, which are often application-specific, heuristically seek to reduce the amount of comparisons, but they suffer from low precision and recall. To solve the problem in an application-independent way, we take a heterogeneous network-based approach in which users (nodes) interact with content (e.g., sessions, websites), and may have attributes (e.g., location). We propose node2bits, an efficient framework that represents multi-dimensional features of node contexts with binary hashcodes. node2bits leverages feature-based temporal walks to encapsulate short- and long-term interactions between nodes in heterogeneous web networks, and adopts SimHash to obtain compact, binary representations and avoid the quadratic complexity for similarity search. Extensive experiments on large-scale real networks show that node2bits outperforms traditional techniques and existing works that generate real-valued embeddings by up to 5.16% in F1 score on user stitching, while taking only up to 1.56% as much storage.
Design of Communication Systems using Deep Learning: A Variational Inference Perspective
Authors: Vishnu Raj, Sheetal Kalyani
Date: 2019-04-18 00:00:00
Tasks: Decoder, Variational Inference

Recent research in the design of end to end communication system using deep learning has produced models which can outperform traditional communication schemes. Most of these architectures leveraged autoencoders to design the encoder at the transmitter and decoder at the receiver and train them jointly by modeling transmit symbols as latent codes from the encoder. However, in communication systems, the receiver has to work with noise corrupted versions of transmit symbols. Traditional autoencoders are not designed to work with latent codes corrupted with noise. In this work, we provide a framework to design end to end communication systems which accounts for the existence of noise corrupted transmit symbols. The proposed method uses deep neural architecture. An objective function for optimizing these models is derived based on the concepts of variational inference. Further, domain knowledge such as channel type can be systematically integrated into the objective. Through numerical simulation, the proposed method is shown to consistently produce models with better packing density and achieving it faster in multiple popular channel models as compared to the previous works leveraging deep learning models.
Inpatient2Vec: Medical Representation Learning for Inpatients
Authors: Ying Wang, Xiao Xu, Tao Jin, Xiang Li, Guotong Xie, Jian-Min Wang
Date: 2019-04-18 00:00:00
Tasks: Representation Learning, Semantic Similarity, Semantic Textual Similarity

Representation learning (RL) plays an important role in extracting proper representations from complex medical data for various analyzing tasks, such as patient grouping, clinical endpoint prediction and medication recommendation. Medical data can be divided into two typical categories, outpatient and inpatient, that have different data characteristics. However, few of existing RL methods are specially designed for inpatients data, which have strong temporal relations and consistent diagnosis. In addition, for unordered medical activity set, existing medical RL methods utilize a simple pooling strategy, which would result in indistinguishable contributions among the activities for learning. In this work, weproposeInpatient2Vec, anovelmodel for learning three kinds of representations for inpatient, including medical activity, hospital day and diagnosis. A multi-layer self-attention mechanism with two training tasks is designed to capture the inpatient data characteristics and process the unordered set. Using a real-world dataset, we demonstrate that the proposed approach outperforms the competitive baselines on semantic similarity measurement and clinical events prediction tasks.
Gotta Catch 'Em All: Using Honeypots to Catch Adversarial Attacks on Neural Networks
Authors: Shawn Shan, Emily Wenger, Bolun Wang, Bo Li, Hai-Tao Zheng, Ben Y. Zhao
Date: 2019-04-18 00:00:00
Tasks: Adversarial Attack, Adversarial Attack Detection, Adversarial Defense, All, Face Recognition, General Classification, Traffic Sign Recognition

Deep neural networks (DNN) are known to be vulnerable to adversarial attacks. Numerous efforts either try to patch weaknesses in trained models, or try to make it difficult or costly to compute adversarial examples that exploit them. In our work, we explore a new "honeypot" approach to protect DNN models. We intentionally inject trapdoors, honeypot weaknesses in the classification manifold that attract attackers searching for adversarial examples. Attackers' optimization algorithms gravitate towards trapdoors, leading them to produce attacks similar to trapdoors in the feature space. Our defense then identifies attacks by comparing neuron activation signatures of inputs to those of trapdoors. In this paper, we introduce trapdoors and describe an implementation of a trapdoor-enabled defense. First, we analytically prove that trapdoors shape the computation of adversarial attacks so that attack inputs will have feature representations very similar to those of trapdoors. Second, we experimentally show that trapdoor-protected models can detect, with high accuracy, adversarial examples generated by state-of-the-art attacks (PGD, optimization-based CW, Elastic Net, BPDA), with negligible impact on normal classification. These results generalize across classification domains, including image, facial, and traffic-sign recognition. We also present significant results measuring trapdoors' robustness against customized adaptive attacks (countermeasures).
A New Class of Time Dependent Latent Factor Models with Applications
Authors: Sinead A. Williamson, Michael Minyi Zhang, Paul Damien
Date: 2019-04-18 00:00:00
Tasks: Marketing

In many applications, observed data are influenced by some combination of
latent causes. For example, suppose sensors are placed inside a building to
record responses such as temperature, humidity, power consumption and noise
levels. These random, observed responses are typically affected by many
unobserved, latent factors (or features) within the building such as the number
of individuals, the turning on and off of electrical devices, power surges,
etc. These latent factors are usually present for a contiguous period of time
before disappearing; further, multiple factors could be present at a time. This
paper develops new probabilistic methodology and inference methods for random
object generation influenced by latent features exhibiting temporal
persistence. Every datum is associated with subsets of a potentially infinite
number of hidden, persistent features that account for temporal dynamics in an
observation. The ensuing class of dynamic models constructed by adapting the
Indian Buffet Process --- a probability measure on the space of random,
unbounded binary matrices --- finds use in a variety of applications arising in
operations, signal processing, biomedicine, marketing, image analysis, etc.
Illustrations using synthetic and real data are provided.
Deep Representation Learning for Social Network Analysis
Authors: Qiaoyu Tan, Ninghao Liu, Xia Hu
Date: 2019-04-18 00:00:00
Tasks: Anomaly Detection, Attribute, Clustering, Link Prediction, Representation Learning

Social network analysis is an important problem in data mining. A fundamental
step for analyzing social networks is to encode network data into
low-dimensional representations, i.e., network embeddings, so that the network
topology structure and other attribute information can be effectively
preserved. Network representation leaning facilitates further applications such
as classification, link prediction, anomaly detection and clustering. In
addition, techniques based on deep neural networks have attracted great
interests over the past a few years. In this survey, we conduct a comprehensive
review of current literature in network representation learning utilizing
neural network models. First, we introduce the basic models for learning node
representations in homogeneous networks. Meanwhile, we will also introduce some
extensions of the base models in tackling more complex scenarios, such as
analyzing attributed networks, heterogeneous networks and dynamic networks.
Then, we introduce the techniques for embedding subgraphs. After that, we
present the applications of network representation learning. At the end, we
discuss some promising research directions for future work.
Memory-Sample Tradeoffs for Linear Regression with Small Error
Authors: Vatsal Sharan, Aaron Sidford, Gregory Valiant
Date: 2019-04-18 00:00:00
Tasks: regression

We consider the problem of performing linear regression over a stream of $d$-dimensional examples, and show that any algorithm that uses a subquadratic amount of memory exhibits a slower rate of convergence than can be achieved without memory constraints. Specifically, consider a sequence of labeled examples $(a_1,b_1), (a_2,b_2)\ldots,$ with $a_i$ drawn independently from a $d$-dimensional isotropic Gaussian, and where $b_i = \langle a_i, x\rangle + \eta_i,$ for a fixed $x \in \mathbb{R}^d$ with $\|x\|_2 = 1$ and with independent noise $\eta_i$ drawn uniformly from the interval $[-2^{-d/5},2^{-d/5}].$ We show that any algorithm with at most $d^2/4$ bits of memory requires at least $\Omega(d \log \log \frac{1}{\epsilon})$ samples to approximate $x$ to $\ell_2$ error $\epsilon$ with probability of success at least $2/3$, for $\epsilon$ sufficiently small as a function of $d$. In contrast, for such $\epsilon$, $x$ can be recovered to error $\epsilon$ with probability $1-o(1)$ with memory $O\left(d^2 \log(1/\epsilon)\right)$ using $d$ examples. This represents the first nontrivial lower bounds for regression with super-linear memory, and may open the door for strong memory/sample tradeoffs for continuous optimization.
Generative Model for Zero-Shot Sketch-Based Image Retrieval
Authors: Vinay Kumar Verma, Aakansha Mishra, Ashish Mishra, Piyush Rai
Date: 2019-04-18 00:00:00
Tasks: Image Generation, Image Retrieval, Retrieval, Sketch-Based Image Retrieval

We present a probabilistic model for Sketch-Based Image Retrieval (SBIR)
where, at retrieval time, we are given sketches from novel classes, that were
not present at training time. Existing SBIR methods, most of which rely on
learning class-wise correspondences between sketches and images, typically work
well only for previously seen sketch classes, and result in poor retrieval
performance on novel classes. To address this, we propose a generative model
that learns to generate images, conditioned on a given novel class sketch. This
enables us to reduce the SBIR problem to a standard image-to-image search
problem. Our model is based on an inverse auto-regressive flow based
variational autoencoder, with a feedback mechanism to ensure robust image
generation. We evaluate our model on two very challenging datasets, Sketchy,
and TU Berlin, with novel train-test split. The proposed approach significantly
outperforms various baselines on both the datasets.
Matrix Completion With Selective Sampling
Authors: Christian Parkinson, Kevin Huynh, Deanna Needell
Date: 2019-04-17 00:00:00
Tasks: Matrix Completion

Matrix completion is a classical problem in data science wherein one attempts
to reconstruct a low-rank matrix while only observing some subset of the
entries. Previous authors have phrased this problem as a nuclear norm
minimization problem. Almost all previous work assumes no explicit structure of
the matrix and uses uniform sampling to decide the observed entries. We suggest
methods for selective sampling in the case where we have some knowledge about
the structure of the matrix and are allowed to design the observation set.
Material Segmentation of Multi-View Satellite Imagery
Authors: Matthew Purri, Jia Xue, Kristin Dana, Matthew Leotta, Dan Lipsa, Zhixin Li, Bo Xu, Jie Shan
Date: 2019-04-17 00:00:00
Tasks: Material Recognition, Material Segmentation, Segmentation, Semantic Segmentation

Material recognition methods use image context and local cues for pixel-wise
classification. In many cases only a single image is available to make a
material prediction. Image sequences, routinely acquired in applications such
as mutliview stereo, can provide a sampling of the underlying reflectance
functions that reveal pixel-level material attributes. We investigate
multi-view material segmentation using two datasets generated for building
material segmentation and scene material segmentation from the SpaceNet
Challenge satellite image dataset. In this paper, we explore the impact of
multi-angle reflectance information by introducing the \textit{reflectance
residual encoding}, which captures both the multi-angle and multispectral
information present in our datasets. The residuals are computed by differencing
the sparse-sampled reflectance function with a dictionary of pre-defined
dense-sampled reflectance functions. Our proposed reflectance residual features
improves material segmentation performance when integrated into pixel-wise and
semantic segmentation architectures. At test time, predictions from individual
segmentations are combined through softmax fusion and refined by building
segment voting. We demonstrate robust and accurate pixelwise segmentation
results using the proposed material segmentation pipeline.