pwc-archive/papers-with-abstracts huggingface dataset:
Exploring the Space of Black-box Attacks on Deep Neural Networks
Authors: Arjun Nitin Bhagoji, Warren He, Bo Li, Dawn Song
Date: 2017-12-27 00:00:00
Proceeding: ICLR 2018 1

Existing black-box attacks on deep neural networks (DNNs) so far have largely
focused on transferability, where an adversarial instance generated for a
locally trained model can "transfer" to attack other learning models. In this
paper, we propose novel Gradient Estimation black-box attacks for adversaries
with query access to the target model's class probabilities, which do not rely
on transferability. We also propose strategies to decouple the number of
queries required to generate each adversarial sample from the dimensionality of
the input. An iterative variant of our attack achieves close to 100%
adversarial success rates for both targeted and untargeted attacks on DNNs. We
carry out extensive experiments for a thorough comparative evaluation of
black-box attacks and show that the proposed Gradient Estimation attacks
outperform all transferability based black-box attacks we tested on both MNIST
and CIFAR-10 datasets, achieving adversarial success rates similar to well
known, state-of-the-art white-box attacks. We also apply the Gradient
Estimation attacks successfully against a real-world Content Moderation
classifier hosted by Clarifai. Furthermore, we evaluate black-box attacks
against state-of-the-art defenses. We show that the Gradient Estimation attacks
are very effective even against these defenses.
NegBio: a high-performance tool for negation and uncertainty detection in radiology reports
Authors: Yifan Peng, Xiaosong Wang, Le Lu, Mohammadhadi Bagheri, Ronald Summers, Zhiyong Lu
Date: 2017-12-16 00:00:00
Tasks: Benchmarking, Negation

Negative and uncertain medical findings are frequent in radiology reports,
but discriminating them from positive findings remains challenging for
information extraction. Here, we propose a new algorithm, NegBio, to detect
negative and uncertain findings in radiology reports. Unlike previous
rule-based methods, NegBio utilizes patterns on universal dependencies to
identify the scope of triggers that are indicative of negation or uncertainty.
We evaluated NegBio on four datasets, including two public benchmarking corpora
of radiology reports, a new radiology corpus that we annotated for this work,
and a public corpus of general clinical texts. Evaluation on these datasets
demonstrates that NegBio is highly accurate for detecting negative and
uncertain findings and compares favorably to a widely-used state-of-the-art
system NegEx (an average of 9.5% improvement in precision and 5.1% in
F1-score).
Robust Loss Functions under Label Noise for Deep Neural Networks
Authors: Aritra Ghosh, Himanshu Kumar, P. S. Sastry
Date: 2017-12-27 00:00:00
Tasks: Binary Classification, Classification, General Classification

In many applications of classifier learning, training data suffers from label
noise. Deep networks are learned using huge training data where the problem of
noisy labels is particularly relevant. The current techniques proposed for
learning deep networks under label noise focus on modifying the network
architecture and on algorithms for estimating true labels from noisy labels. An
alternate approach would be to look for loss functions that are inherently
noise-tolerant. For binary classification there exist theoretical results on
loss functions that are robust to label noise. In this paper, we provide some
sufficient conditions on a loss function so that risk minimization under that
loss function would be inherently tolerant to label noise for multiclass
classification problems. These results generalize the existing results on
noise-tolerant loss functions for binary classification. We study some of the
widely used loss functions in deep networks and show that the loss function
based on mean absolute value of error is inherently robust to label noise. Thus
standard back propagation is enough to learn the true classifier even under
label noise. Through experiments, we illustrate the robustness of risk
minimization with such loss functions for learning neural networks.
An Online Ride-Sharing Path Planning Strategy for Public Vehicle Systems
Authors: Ming Zhu, Xiao-Yang Liu, Xiaodong Wang
Date: 2017-12-27 00:00:00
Tasks: Management, Scheduling

As efficient traffic-management platforms, public vehicle (PV) systems are
envisioned to be a promising approach to solving traffic congestions and
pollutions for future smart cities. PV systems provide online/dynamic
peer-to-peer ride-sharing services with the goal of serving sufficient number
of customers with minimum number of vehicles and lowest possible cost. A key
component of the PV system is the online ride-sharing scheduling strategy. In
this paper, we propose an efficient path planning strategy that focuses on a
limited potential search area for each vehicle by filtering out the requests
that violate passenger service quality level, so that the global search is
reduced to local search. We analyze the performance of the proposed solution
such as reduction ratio of computational complexity. Simulations based on the
Manhattan taxi data set show that, the computing time is reduced by 22%
compared with the exhaustive search method under the same service quality
performance.
Sketching for Kronecker Product Regression and P-splines
Authors: Huaian Diao, Zhao Song, Wen Sun, David P. Woodruff
Date: 2017-12-27 00:00:00
Tasks: regression

TensorSketch is an oblivious linear sketch introduced in Pagh'13 and later
used in Pham, Pagh'13 in the context of SVMs for polynomial kernels. It was
shown in Avron, Nguyen, Woodruff'14 that TensorSketch provides a subspace
embedding, and therefore can be used for canonical correlation analysis, low
rank approximation, and principal component regression for the polynomial
kernel. We take TensorSketch outside of the context of polynomials kernels, and
show its utility in applications in which the underlying design matrix is a
Kronecker product of smaller matrices. This allows us to solve Kronecker
product regression and non-negative Kronecker product regression, as well as
regularized spline regression. Our main technical result is then in extending
TensorSketch to other norms. That is, TensorSketch only provides input sparsity
time for Kronecker product regression with respect to the $2$-norm. We show how
to solve Kronecker product regression with respect to the $1$-norm in time
sublinear in the time required for computing the Kronecker product, as well as
for more general $p$-norms.
Multi-modal Geolocation Estimation Using Deep Neural Networks
Authors: Jesse M. Johns, Jeremiah Rounds, Michael J. Henry
Date: 2017-12-26 00:00:00

Estimating the location where an image was taken based solely on the contents
of the image is a challenging task, even for humans, as properly labeling an
image in such a fashion relies heavily on contextual information, and is not as
simple as identifying a single object in the image. Thus any methods which
attempt to do so must somehow account for these complexities, and no single
model to date is completely capable of addressing all challenges. This work
contributes to the state of research in image geolocation inferencing by
introducing a novel global meshing strategy, outlining a variety of training
procedures to overcome the considerable data limitations when training these
models, and demonstrating how incorporating additional information can be used
to improve the overall performance of a geolocation inference model. In this
work, it is shown that Delaunay triangles are an effective type of mesh for
geolocation in relatively low volume scenarios when compared to results from
state of the art models which use quad trees and an order of magnitude more
training data. In addition, the time of posting, learned user albuming, and
other meta data are easily incorporated to improve geolocation by up to 11% for
country-level (750 km) locality accuracy to 3% for city-level (25 km)
localities.
First Draft on the xInf Model for Universal Physical Computation and Reverse Engineering of Natural Intelligence
Authors: Hongbo Jia
Date: 2017-12-26 00:00:00

Turing Machines are universal computing machines in theory. It has been a
long debate whether Turing Machines can simulate the consciousness mind
behaviors in the materialistic universe. Three different hypotheses come out of
such debate, in short:(A) Can; (B) Cannot; (C) Super-Turing machines can.
Because Turing Machines or other kinds of theoretical computing models are
abstract objects while behaviors are real observables, this debate involves at
least three distinct fields of science and technology: physics, computer
engineering, and experimental neuroscience. However, the languages used in
these different fields are highly heterogeneous and not easily interpretable
for each other, making it very difficult to reach partial agreements regarding
this debate, Therefore, the main goal of this manuscript is to establish a
proper language that can translate among those different fields. First, I
propose a theoretical model for analyzing how theoretical computing machines
would physically run in physical time. This model, termed as the xInf, is at
first place Turing-complete in theory, and depending on the properties of
physical time, it can be either Turing-equivalent or Super-Turing in the
physical universe. The xInf Model is demonstrated to be a suitable universal
language to translate among physics, computer engineering, and neuroscience.
Finally, I propose a conjecture that there exists a Minimal Complete Set of
rules in the xInf Model that enables the construction of a physical machine
using inorganic materials that can pass the Turing Test in physical time. I
cannot demonstrate whether such a conjecture to be testified or falsified on
paper using finite-order logic, my only solution is physical time itself, i.e.
an evolutionary competition will eventually tell the conclusion.
Data-adaptive Active Sampling for Efficient Graph-Cognizant Classification
Authors: Dimitris Berberidis, Georgios B. Giannakis
Date: 2017-05-19 00:00:00
Tasks: Binary Classification, Classification, General Classification

The present work deals with active sampling of graph nodes representing
training data for binary classification. The graph may be given or constructed
using similarity measures among nodal features. Leveraging the graph for
classification builds on the premise that labels across neighboring nodes are
correlated according to a categorical Markov random field (MRF). This model is
further relaxed to a Gaussian (G)MRF with labels taking continuous values - an
approximation that not only mitigates the combinatorial complexity of the
categorical model, but also offers optimal unbiased soft predictors of the
unlabeled nodes. The proposed sampling strategy is based on querying the node
whose label disclosure is expected to inflict the largest change on the GMRF,
and in this sense it is the most informative on average. Such a strategy
subsumes several measures of expected model change, including uncertainty
sampling, variance minimization, and sampling based on the $\Sigma-$optimality
criterion. A simple yet effective heuristic is also introduced for increasing
the exploration capabilities of the sampler, and reducing bias of the resultant
classifier, by taking into account the confidence on the model label
predictions. The novel sampling strategies are based on quantities that are
readily available without the need for model retraining, rendering them
computationally efficient and scalable to large graphs. Numerical tests using
synthetic and real data demonstrate that the proposed methods achieve accuracy
that is comparable or superior to the state-of-the-art even at reduced runtime.
Large-scale Kernel-based Feature Extraction via Budgeted Nonlinear Subspace Tracking
Authors: Fatemeh Sheikholeslami, Dimitris Berberidis, Georgios B. Giannakis
Date: 2016-01-28 00:00:00
Tasks: General Classification, regression

Kernel-based methods enjoy powerful generalization capabilities in handling a
variety of learning tasks. When such methods are provided with sufficient
training data, broadly-applicable classes of nonlinear functions can be
approximated with desired accuracy. Nevertheless, inherent to the nonparametric
nature of kernel-based estimators are computational and memory requirements
that become prohibitive with large-scale datasets. In response to this
formidable challenge, the present work puts forward a low-rank, kernel-based,
feature extraction approach that is particularly tailored for online operation,
where data streams need not be stored in memory. A novel generative model is
introduced to approximate high-dimensional (possibly infinite) features via a
low-rank nonlinear subspace, the learning of which leads to a direct kernel
function approximation. Offline and online solvers are developed for the
subspace learning task, along with affordable versions, in which the number of
stored data vectors is confined to a predefined budget. Analytical results
provide performance bounds on how well the kernel matrix as well as
kernel-based classification and regression tasks can be approximated by
leveraging budgeted online subspace learning and feature extraction schemes.
Tests on synthetic and real datasets demonstrate and benchmark the efficiency
of the proposed method when linear classification and regression is applied to
the extracted features.
Horn-ICE Learning for Synthesizing Invariants and Contracts
Authors: Deepak D'Souza, P. Ezudheen, Pranav Garg, P. Madhusudan, Daniel Neider
Date: 2017-12-26 00:00:00

We design learning algorithms for synthesizing invariants using Horn
implication counterexamples (Horn-ICE), extending the ICE-learning model. In
particular, we describe a decision-tree learning algorithm that learns from
Horn-ICE samples, works in polynomial time, and uses statistical heuristics to
learn small trees that satisfy the samples. Since most verification proofs can
be modeled using Horn clauses, Horn-ICE learning is a more robust technique to
learn inductive annotations that prove programs correct. Our experiments show
that an implementation of our algorithm is able to learn adequate inductive
invariants and contracts efficiently for a variety of sequential and concurrent
programs.
Advances in Pre-Training Distributed Word Representations
Authors: Tomas Mikolov, Edouard Grave, Piotr Bojanowski, Christian Puhrsch, Armand Joulin
Date: 2017-12-26 00:00:00
Proceeding: LREC 2018 5

Many Natural Language Processing applications nowadays rely on pre-trained
word representations estimated from large text corpora such as news
collections, Wikipedia and Web Crawl. In this paper, we show how to train
high-quality word vector representations by using a combination of known tricks
that are however rarely used together. The main result of our work is the new
set of publicly available pre-trained models that outperform the current state
of the art by a large margin on a number of tasks.
Robust Minutiae Extractor: Integrating Deep Networks and Fingerprint Domain Knowledge
Authors: Dinh-Luan Nguyen, Kai Cao, Anil K. Jain
Date: 2017-12-26 00:00:00

We propose a fully automatic minutiae extractor, called MinutiaeNet, based on
deep neural networks with compact feature representation for fast comparison of
minutiae sets. Specifically, first a network, called CoarseNet, estimates the
minutiae score map and minutiae orientation based on convolutional neural
network and fingerprint domain knowledge (enhanced image, orientation field,
and segmentation map). Subsequently, another network, called FineNet, refines
the candidate minutiae locations based on score map. We demonstrate the
effectiveness of using the fingerprint domain knowledge together with the deep
networks. Experimental results on both latent (NIST SD27) and plain (FVC 2004)
public domain fingerprint datasets provide comprehensive empirical support for
the merits of our method. Further, our method finds minutiae sets that are
better in terms of precision and recall in comparison with state-of-the-art on
these two datasets. Given the lack of annotated fingerprint datasets with
minutiae ground truth, the proposed approach to robust minutiae detection will
be useful to train network-based fingerprint matching algorithms as well as for
evaluating fingerprint individuality at scale. MinutiaeNet is implemented in
Tensorflow: https://github.com/luannd/MinutiaeNet
RaspiReader: Open Source Fingerprint Reader
Authors: Joshua J. Engelsma, Kai Cao, Anil K. Jain
Date: 2017-12-26 00:00:00

We open source an easy to assemble, spoof resistant, high resolution, optical
fingerprint reader, called RaspiReader, using ubiquitous components. By using
our open source STL files and software, RaspiReader can be built in under one
hour for only US $175. As such, RaspiReader provides the fingerprint research
community a seamless and simple method for quickly prototyping new ideas
involving fingerprint reader hardware. In particular, we posit that this open
source fingerprint reader will facilitate the exploration of novel fingerprint
spoof detection techniques involving both hardware and software. We demonstrate
one such spoof detection technique by specially customizing RaspiReader with
two cameras for fingerprint image acquisition. One camera provides high
contrast, frustrated total internal reflection (FTIR) fingerprint images, and
the other outputs direct images of the finger in contact with the platen. Using
both of these image streams, we extract complementary information which, when
fused together and used for spoof detection, results in marked performance
improvement over previous methods relying only on grayscale FTIR images
provided by COTS optical readers. Finally, fingerprint matching experiments
between images acquired from the FTIR output of RaspiReader and images acquired
from a COTS reader verify the interoperability of the RaspiReader with existing
COTS optical readers.
Mapping to Declarative Knowledge for Word Problem Solving
Authors: Subhro Roy, Dan Roth
Date: 2017-12-26 00:00:00
Proceeding: TACL 2018 1
Tasks: Math, Translation

Math word problems form a natural abstraction to a range of quantitative
reasoning problems, such as understanding financial news, sports results, and
casualties of war. Solving such problems requires the understanding of several
mathematical concepts such as dimensional analysis, subset relationships, etc.
In this paper, we develop declarative rules which govern the translation of
natural language description of these concepts to math expressions. We then
present a framework for incorporating such declarative knowledge into word
problem solving. Our method learns to map arithmetic word problem text to math
expressions, by learning to select the relevant declarative knowledge for each
operation of the solution expression. This provides a way to handle multiple
concepts in the same problem while, at the same time, support interpretability
of the answer expression. Our method models the mapping to declarative
knowledge as a latent variable, thus removing the need for expensive
annotations. Experimental evaluation suggests that our domain knowledge based
solver outperforms all other systems, and that it generalizes better in the
realistic case where the training data it is exposed to is biased in a
different way than the test data.
IHT dies hard: Provable accelerated Iterative Hard Thresholding
Authors: Rajiv Khanna, Anastasios Kyrillidis
Date: 2017-12-26 00:00:00

We study --both in theory and practice-- the use of momentum motions in classic iterative hard thresholding (IHT) methods. By simply modifying plain IHT, we investigate its convergence behavior on convex optimization criteria with non-convex constraints, under standard assumptions. In diverse scenaria, we observe that acceleration in IHT leads to significant improvements, compared to state of the art projected gradient descent and Frank-Wolfe variants. As a byproduct of our inspection, we study the impact of selecting the momentum parameter: similar to convex settings, two modes of behavior are observed --"rippling" and linear-- depending on the level of momentum.
HACS: Human Action Clips and Segments Dataset for Recognition and Temporal Localization
Authors: Hang Zhao, Antonio Torralba, Lorenzo Torresani, Zhicheng Yan
Date: 2017-12-26 00:00:00
Proceeding: ICCV 2019 10
Tasks: Action Classification, Action Localization, Action Recognition, Temporal Action Localization, Temporal Localization, Transfer Learning

This paper presents a new large-scale dataset for recognition and temporal localization of human actions collected from Web videos. We refer to it as HACS (Human Action Clips and Segments). We leverage both consensus and disagreement among visual classifiers to automatically mine candidate short clips from unlabeled videos, which are subsequently validated by human annotators. The resulting dataset is dubbed HACS Clips. Through a separate process we also collect annotations defining action segment boundaries. This resulting dataset is called HACS Segments. Overall, HACS Clips consists of 1.5M annotated clips sampled from 504K untrimmed videos, and HACS Seg-ments contains 139K action segments densely annotatedin 50K untrimmed videos spanning 200 action categories. HACS Clips contains more labeled examples than any existing video benchmark. This renders our dataset both a large scale action recognition benchmark and an excellent source for spatiotemporal feature learning. In our transferlearning experiments on three target datasets, HACS Clips outperforms Kinetics-600, Moments-In-Time and Sports1Mas a pretraining source. On HACS Segments, we evaluate state-of-the-art methods of action proposal generation and action localization, and highlight the new challenges posed by our dense temporal annotations.
Detecting Hate Speech in Social Media
Authors: Shervin Malmasi, Marcos Zampieri
Date: 2017-12-18 00:00:00
Proceeding: RANLP 2017 9
Tasks: General Classification

In this paper we examine methods to detect hate speech in social media, while
distinguishing this from general profanity. We aim to establish lexical
baselines for this task by applying supervised classification methods using a
recently released dataset annotated for this purpose. As features, our system
uses character n-grams, word n-grams and word skip-grams. We obtain results of
78% accuracy in identifying posts across three classes. Results demonstrate
that the main challenge lies in discriminating profanity and hate speech from
each other. A number of directions for future work are discussed.
Building Robust Deep Neural Networks for Road Sign Detection
Authors: Arkar Min Aung, Yousef Fadila, Radian Gondokaryono, Luis Gonzalez
Date: 2017-12-26 00:00:00

Deep Neural Networks are built to generalize outside of training set in mind
by using techniques such as regularization, early stopping and dropout. But
considerations to make them more resilient to adversarial examples are rarely
taken. As deep neural networks become more prevalent in mission-critical and
real-time systems, miscreants start to attack them by intentionally making deep
neural networks to misclassify an object of one type to be seen as another
type. This can be catastrophic in some scenarios where the classification of a
deep neural network can lead to a fatal decision by a machine. In this work, we
used GTSRB dataset to craft adversarial samples by Fast Gradient Sign Method
and Jacobian Saliency Method, used those crafted adversarial samples to attack
another Deep Convolutional Neural Network and built the attacked network to be
more resilient against adversarial attacks by making it more robust by
Defensive Distillation and Adversarial Training