pwc-archive/papers-with-abstracts huggingface dataset:
Analysis of Bag-of-n-grams Representation's Properties Based on Textual Reconstruction
Authors: Qi Huang, Zhanghao Chen, Zijie Lu, Yuan Ye
Date: 2018-09-18 00:00:00
Tasks: Sentence

Despite its simplicity, bag-of-n-grams sen- tence representation has been
found to excel in some NLP tasks. However, it has not re- ceived much attention
in recent years and fur- ther analysis on its properties is necessary. We
propose a framework to investigate the amount and type of information captured
in a general- purposed bag-of-n-grams sentence represen- tation. We first use
sentence reconstruction as a tool to obtain bag-of-n-grams representa- tion
that contains general information of the sentence. We then run prediction tasks
(sen- tence length, word content, phrase content and word order) using the
obtained representation to look into the specific type of information captured
in the representation. Our analysis demonstrates that bag-of-n-grams
representa- tion does contain sentence structure level in- formation. However,
incorporating n-grams with higher order n empirically helps little with
encoding more information in general, except for phrase content information.
HashTran-DNN: A Framework for Enhancing Robustness of Deep Neural Networks against Adversarial Malware Samples
Authors: Deqiang Li, Ramesh Baral, Tao Li, Han Wang, Qianmu Li, Shouhuai Xu
Date: 2018-09-18 00:00:00
Tasks: BIG-bench Machine Learning, Denoising, Malware Classification, Malware Detection

Adversarial machine learning in the context of image processing and related
applications has received a large amount of attention. However, adversarial
machine learning, especially adversarial deep learning, in the context of
malware detection has received much less attention despite its apparent
importance. In this paper, we present a framework for enhancing the robustness
of Deep Neural Networks (DNNs) against adversarial malware samples, dubbed
Hashing Transformation Deep Neural Networks} (HashTran-DNN). The core idea is
to use hash functions with a certain locality-preserving property to transform
samples to enhance the robustness of DNNs in malware classification. The
framework further uses a Denoising Auto-Encoder (DAE) regularizer to
reconstruct the hash representations of samples, making the resulting DNN
classifiers capable of attaining the locality information in the latent space.
We experiment with two concrete instantiations of the HashTran-DNN framework to
classify Android malware. Experimental results show that four known attacks can
render standard DNNs useless in classifying Android malware, that known
defenses can at most defend three of the four attacks, and that HashTran-DNN
can effectively defend against all of the four attacks.
Triad-based Neural Network for Coreference Resolution
Authors: Yuanliang Meng, Anna Rumshisky
Date: 2018-09-18 00:00:00
Proceeding: COLING 2018 8
Tasks: Clustering, coreference-resolution, Coreference Resolution

We propose a triad-based neural network system that generates affinity scores
between entity mentions for coreference resolution. The system simultaneously
accepts three mentions as input, taking mutual dependency and logical
constraints of all three mentions into account, and thus makes more accurate
predictions than the traditional pairwise approach. Depending on system
choices, the affinity scores can be further used in clustering or mention
ranking. Our experiments show that a standard hierarchical clustering using the
scores produces state-of-art results with gold mentions on the English portion
of CoNLL 2012 Shared Task. The model does not rely on many handcrafted features
and is easy to train and use. The triads can also be easily extended to polyads
of higher orders. To our knowledge, this is the first neural network system to
model mutual dependency of more than two members at mention level.
In-Session Personalization for Talent Search
Authors: Sahin Cem Geyik, Vijay Dialani, Meng Meng, Ryan Smith
Date: 2018-09-18 00:00:00
Tasks: Clustering

Previous efforts in recommendation of candidates for talent search followed
the general pattern of receiving an initial search criteria and generating a
set of candidates utilizing a pre-trained model. Traditionally, the generated
recommendations are final, that is, the list of potential candidates is not
modified unless the user explicitly changes his/her search criteria. In this
paper, we are proposing a candidate recommendation model which takes into
account the immediate feedback of the user, and updates the candidate
recommendations at each step. This setting also allows for very uninformative
initial search queries, since we pinpoint the user's intent due to the feedback
during the search session. To achieve our goal, we employ an intent clustering
method based on topic modeling which separates the candidate space into
meaningful, possibly overlapping, subsets (which we call intent clusters) for
each position. On top of the candidate segments, we apply a multi-armed bandit
approach to choose which intent cluster is more appropriate for the current
session. We also present an online learning scheme which updates the intent
clusters within the session, due to user feedback, to achieve further
personalization. Our offline experiments as well as the results from the online
deployment of our solution demonstrate the benefits of our proposed
methodology.
Talent Search and Recommendation Systems at LinkedIn: Practical Challenges and Lessons Learned
Authors: Sahin Cem Geyik, Qi Guo, Bo Hu, Cagri Ozcaglar, Ketan Thakkar, Xianren Wu, Krishnaram Kenthapadi
Date: 2018-09-18 00:00:00
Tasks: Information Retrieval, Recommendation Systems, Retrieval

LinkedIn Talent Solutions business contributes to around 65% of LinkedIn's
annual revenue, and provides tools for job providers to reach out to potential
candidates and for job seekers to find suitable career opportunities.
LinkedIn's job ecosystem has been designed as a platform to connect job
providers and job seekers, and to serve as a marketplace for efficient matching
between potential candidates and job openings. A key mechanism to help achieve
these goals is the LinkedIn Recruiter product, which enables recruiters to
search for relevant candidates and obtain candidate recommendations for their
job postings. In this work, we highlight a set of unique information retrieval,
system, and modeling challenges associated with talent search and
recommendation systems.
Active Anomaly Detection via Ensembles
Authors: Shubhomoy Das, Md. Rakibul Islam, Nitthilan Kannappan Jayakodi, Janardhan Rao Doppa
Date: 2018-09-17 00:00:00
Tasks: Active Learning, Anomaly Detection, Computer Security, Drift Detection

In critical applications of anomaly detection including computer security and
fraud prevention, the anomaly detector must be configurable by the analyst to
minimize the effort on false positives. One important way to configure the
anomaly detector is by providing true labels for a few instances. We study the
problem of label-efficient active learning to automatically tune anomaly
detection ensembles and make four main contributions. First, we present an
important insight into how anomaly detector ensembles are naturally suited for
active learning. This insight allows us to relate the greedy querying strategy
to uncertainty sampling, with implications for label-efficiency. Second, we
present a novel formalism called compact description to describe the discovered
anomalies and show that it can also be employed to improve the diversity of the
instances presented to the analyst without loss in the anomaly discovery rate.
Third, we present a novel data drift detection algorithm that not only detects
the drift robustly, but also allows us to take corrective actions to adapt the
detector in a principled manner. Fourth, we present extensive experiments to
evaluate our insights and algorithms in both batch and streaming settings. Our
results show that in addition to discovering significantly more anomalies than
state-of-the-art unsupervised baselines, our active learning algorithms under
the streaming-data setup are competitive with the batch setup.
Zeroth-order Nonconvex Stochastic Optimization: Handling Constraints, High-Dimensionality and Saddle-Points
Authors: Krishnakumar Balasubramanian, Saeed Ghadimi
Date: 2018-09-17 00:00:00
Proceeding: NeurIPS 2018
Tasks: Stochastic Optimization, Vocal Bursts Intensity Prediction

In this paper, we propose and analyze zeroth-order stochastic approximation
algorithms for nonconvex and convex optimization, with a focus on addressing
constrained optimization, high-dimensional setting and saddle-point avoiding.
To handle constrained optimization, we first propose generalizations of the
conditional gradient algorithm achieving rates similar to the standard
stochastic gradient algorithm using only zeroth-order information. To
facilitate zeroth-order optimization in high-dimensions, we explore the
advantages of structural sparsity assumptions. Specifically, (i) we highlight
an implicit regularization phenomenon where the standard stochastic gradient
algorithm with zeroth-order information adapts to the sparsity of the problem
at hand by just varying the step-size and (ii) propose a truncated stochastic
gradient algorithm with zeroth-order information, whose rate of convergence
depends only poly-logarithmically on the dimensionality. We next focus on
avoiding saddle-points in non-convex setting. Towards that, we interpret the
Gaussian smoothing technique for estimating gradient based on zeroth-order
information as an instantiation of first-order Stein's identity. Based on this,
we provide a novel linear-(in dimension) time estimator of the Hessian matrix
of a function using only zeroth-order information, which is based on
second-order Stein's identity. We then provide an algorithm for avoiding
saddle-points, which is based on a zeroth-order cubic regularization Newton's
method and discuss its convergence rates.
Towards Deep and Representation Learning for Talent Search at LinkedIn
Authors: Rohan Ramanath, Hakan Inan, Gungor Polatkan, Bo Hu, Qi Guo, Cagri Ozcaglar, Xianren Wu, Krishnaram Kenthapadi, Sahin Cem Geyik
Date: 2018-09-17 00:00:00
Tasks: Learning Semantic Representations, Learning-To-Rank, Recommendation Systems, Representation Learning

Talent search and recommendation systems at LinkedIn strive to match the
potential candidates to the hiring needs of a recruiter or a hiring manager
expressed in terms of a search query or a job posting. Recent work in this
domain has mainly focused on linear models, which do not take complex
relationships between features into account, as well as ensemble tree models,
which introduce non-linearity but are still insufficient for exploring all the
potential feature interactions, and strictly separate feature generation from
modeling. In this paper, we present the results of our application of deep and
representation learning models on LinkedIn Recruiter. Our key contributions
include: (i) Learning semantic representations of sparse entities within the
talent search domain, such as recruiter ids, candidate ids, and skill entity
ids, for which we utilize neural network models that take advantage of LinkedIn
Economic Graph, and (ii) Deep models for learning recruiter engagement and
candidate response in talent search applications. We also explore learning to
rank approaches applied to deep models, and show the benefits for the talent
search use case. Finally, we present offline and online evaluation results for
LinkedIn talent search and recommendation systems, and discuss potential
challenges along the path to a fully deep model architecture. The challenges
and approaches discussed generalize to any multi-faceted search engine.
Self Configuration in Machine Learning
Authors: Eugene Wong
Date: 2018-09-17 00:00:00
Tasks: BIG-bench Machine Learning, Self-Organized Clustering

In this paper we first present a class of algorithms for training multi-level
neural networks with a quadratic cost function one layer at a time starting
from the input layer. The algorithm is based on the fact that for any layer to
be trained, the effect of a direct connection to an optimized linear output
layer can be computed without the connection being made. Thus, starting from
the input layer, we can train each layer in succession in isolation from the
other layers. Once trained, the weights are kept fixed and the outputs of the
trained layer then serve as the inputs to the next layer to be trained. The
result is a very fast algorithm. The simplicity of this training arrangement
allows the activation function and step size in weight adjustment to be
adaptive and self-adjusting. Furthermore, the stability of the training process
allows relatively large steps to be taken and thereby achieving in even greater
speeds. Finally, in our context configuring the network means determining the
number of outputs for each layer. By decomposing the overall cost function into
separate components related to approximation and estimation, we obtain an
optimization formula for determining the number of outputs for each layer. With
the ability to self-configure and set parameters, we now have more than a fast
training algorithm, but the ability to build automatically a fully trained deep
neural network starting with nothing more than data.
Mask Editor : an Image Annotation Tool for Image Segmentation Tasks
Authors: Chuanhai Zhang, Kurt Loken, Zhiyu Chen, Zhiyong Xiao, Gary Kunkel
Date: 2018-09-17 00:00:00
Tasks: Image Cropping, Image Segmentation, Semantic Segmentation

Deep convolutional neural network (DCNN) is the state-of-the-art method for
image segmentation, which is one of key challenging computer vision tasks.
However, DCNN requires a lot of training images with corresponding image masks
to get a good segmentation result. Image annotation software which is easy to
use and allows fast image mask generation is in great demand. To the best of
our knowledge, all existing image annotation software support only drawing
bounding polygons, bounding boxes, or bounding ellipses to mark target objects.
These existing software are inefficient when targeting objects that have
irregular shapes (e.g., defects in fabric images or tire images). In this paper
we design an easy-to-use image annotation software called Mask Editor for image
mask generation. Mask Editor allows drawing any bounding curve to mark objects
and improves efficiency to mark objects with irregular shapes. Mask Editor also
supports drawing bounding polygons, drawing bounding boxes, drawing bounding
ellipses, painting, erasing, super-pixel-marking, image cropping, multi-class
masks, mask loading, and mask modifying.
Robustness Guarantees for Bayesian Inference with Gaussian Processes
Authors: Luca Cardelli, Marta Kwiatkowska, Luca Laurenti, Andrea Patane
Date: 2018-09-17 00:00:00
Tasks: Bayesian Inference, Gaussian Processes

Bayesian inference and Gaussian processes are widely used in applications
ranging from robotics and control to biological systems. Many of these
applications are safety-critical and require a characterization of the
uncertainty associated with the learning model and formal guarantees on its
predictions. In this paper we define a robustness measure for Bayesian
inference against input perturbations, given by the probability that, for a
test point and a compact set in the input space containing the test point, the
prediction of the learning model will remain $\delta-$close for all the points
in the set, for $\delta>0.$ Such measures can be used to provide formal
guarantees for the absence of adversarial examples. By employing the theory of
Gaussian processes, we derive tight upper bounds on the resulting robustness by
utilising the Borell-TIS inequality, and propose algorithms for their
computation. We evaluate our techniques on two examples, a GP regression
problem and a fully-connected deep neural network, where we rely on weak
convergence to GPs to study adversarial examples on the MNIST dataset.
Robust Spoken Language Understanding via Paraphrasing
Authors: Avik Ray, Yilin Shen, Hongxia Jin
Date: 2018-09-17 00:00:00
Tasks: Spoken Language Understanding

Learning intents and slot labels from user utterances is a fundamental step
in all spoken language understanding (SLU) and dialog systems. State-of-the-art
neural network based methods, after deployment, often suffer from performance
degradation on encountering paraphrased utterances, and out-of-vocabulary
words, rarely observed in their training set. We address this challenging
problem by introducing a novel paraphrasing based SLU model which can be
integrated with any existing SLU model in order to improve their overall
performance. We propose two new paraphrase generators using RNN and
sequence-to-sequence based neural networks, which are suitable for our
application. Our experiments on existing benchmark and in house datasets
demonstrate the robustness of our models to rare and complex paraphrased
utterances, even under adversarial test distributions.
LMap: Shape-Preserving Local Mappings for Biomedical Visualization
Authors: Saad Nadeem, Xianfeng GU, Arie Kaufman
Date: 2018-09-17 00:00:00

Visualization of medical organs and biological structures is a challenging
task because of their complex geometry and the resultant occlusions. Global
spherical and planar mapping techniques simplify the complex geometry and
resolve the occlusions to aid in visualization. However, while resolving the
occlusions these techniques do not preserve the geometric context, making them
less suitable for mission-critical biomedical visualization tasks. In this
paper, we present a shape-preserving local mapping technique for resolving
occlusions locally while preserving the overall geometric context. More
specifically, we present a novel visualization algorithm, LMap, for conformally
parameterizing and deforming a selected local region-of-interest (ROI) on an
arbitrary surface. The resultant shape-preserving local mappings help to
visualize complex surfaces while preserving the overall geometric context. The
algorithm is based on the robust and efficient extrinsic Ricci flow technique,
and uses the dynamic Ricci flow algorithm to guarantee the existence of a local
map for a selected ROI on an arbitrary surface. We show the effectiveness and
efficacy of our method in three challenging use cases: (1) multimodal brain
visualization, (2) optimal coverage of virtual colonoscopy centerline
flythrough, and (3) molecular surface visualization.
Toward Bridging the Simulated-to-Real Gap: Benchmarking Super-Resolution on Real Data
Authors: Thomas Köhler, Michel Bätz, Farzad Naderi, André Kaup, Andreas Maier, Christian Riess
Date: 2018-09-17 00:00:00
Tasks: Benchmarking, Super-Resolution

Capturing ground truth data to benchmark super-resolution (SR) is challenging. Therefore, current quantitative studies are mainly evaluated on simulated data artificially sampled from ground truth images. We argue that such evaluations overestimate the actual performance of SR methods compared to their behavior on real images. Toward bridging this simulated-to-real gap, we introduce the Super-Resolution Erlangen (SupER) database, the first comprehensive laboratory SR database of all-real acquisitions with pixel-wise ground truth. It consists of more than 80k images of 14 scenes combining different facets: CMOS sensor noise, real sampling at four resolution levels, nine scene motion types, two photometric conditions, and lossy video coding at five levels. As such, the database exceeds existing benchmarks by an order of magnitude in quality and quantity. This paper also benchmarks 19 popular single-image and multi-frame algorithms on our data. The benchmark comprises a quantitative study by exploiting ground truth data and qualitative evaluations in a large-scale observer study. We also rigorously investigate agreements between both evaluations from a statistical perspective. One interesting result is that top-performing methods on simulated data may be surpassed by others on real data. Our insights can spur further algorithm development, and the publicy available dataset can foster future evaluations.
Radiative Transport Based Flame Volume Reconstruction from Videos
Authors: Liang Shen, Dengming Zhu, Saad Nadeem, Zhaoqi Wang, Arie Kaufman
Date: 2018-09-17 00:00:00

We introduce a novel approach for flame volume reconstruction from videos
using inexpensive charge-coupled device (CCD) consumer cameras. The approach
includes an economical data capture technique using inexpensive CCD cameras.
Leveraging the smear feature of the CCD chip, we present a technique for
synchronizing CCD cameras while capturing flame videos from different views.
Our reconstruction is based on the radiative transport equation which enables
complex phenomena such as emission, extinction, and scattering to be used in
the rendering process. Both the color intensity and temperature reconstructions
are implemented using the CUDA parallel computing framework, which provides
real-time performance and allows visualization of reconstruction results after
every iteration. We present the results of our approach using real captured
data and physically-based simulated data. Finally, we also compare our approach
against the other state-of-the-art flame volume reconstruction methods and
demonstrate the efficacy and efficiency of our approach in four different
applications: (1) rendering of reconstructed flames in virtual environments,
(2) rendering of reconstructed flames in augmented reality, (3) flame
stylization, and (4) reconstruction of other semitransparent phenomena.
DeClarE: Debunking Fake News and False Claims using Evidence-Aware Deep Learning
Authors: Kashyap Popat, Subhabrata Mukherjee, Andrew Yates, Gerhard Weikum
Date: 2018-09-17 00:00:00
Proceeding: EMNLP 2018 10
Tasks: Articles, Fact Checking, Misinformation

Misinformation such as fake news is one of the big challenges of our society.
Research on automated fact-checking has proposed methods based on supervised
learning, but these approaches do not consider external evidence apart from
labeled training instances. Recent approaches counter this deficit by
considering external sources related to a claim. However, these methods require
substantial feature modeling and rich lexicons. This paper overcomes these
limitations of prior work with an end-to-end model for evidence-aware
credibility assessment of arbitrary textual claims, without any human
intervention. It presents a neural network model that judiciously aggregates
signals from external evidence articles, the language of these articles and the
trustworthiness of their sources. It also derives informative features for
generating user-comprehensible explanations that makes the neural network
predictions transparent to the end-user. Experiments with four datasets and
ablation studies show the strength of our method.