pwc-archive/papers-with-abstracts huggingface dataset:
Effective Quantization Approaches for Recurrent Neural Networks
Authors: Md Zahangir Alom, Adam T Moody, Naoya Maruyama, Brian C. Van Essen, Tarek M. Taha
Date: 2018-02-07 00:00:00
Tasks: Machine Translation, Quantization, Sentiment Analysis, Translation

Deep learning, and in particular Recurrent Neural Networks (RNN) have shown
superior accuracy in a large variety of tasks including machine translation,
language understanding, and movie frame generation. However, these deep
learning approaches are very expensive in terms of computation. In most cases,
Graphic Processing Units (GPUs) are in used for large scale implementations.
Meanwhile, energy efficient RNN approaches are proposed for deploying solutions
on special purpose hardware including Field Programming Gate Arrays (FPGAs) and
mobile platforms. In this paper, we propose an effective quantization approach
for Recurrent Neural Networks (RNN) techniques including Long Short Term Memory
(LSTM), Gated Recurrent Units (GRU), and Convolutional Long Short Term Memory
(ConvLSTM). We have implemented different quantization methods including Binary
Connect {-1, 1}, Ternary Connect {-1, 0, 1}, and Quaternary Connect {-1, -0.5,
0.5, 1}. These proposed approaches are evaluated on different datasets for
sentiment analysis on IMDB and video frame predictions on the moving MNIST
dataset. The experimental results are compared against the full precision
versions of the LSTM, GRU, and ConvLSTM. They show promising results for both
sentiment analysis and video frame prediction.
Deep Versus Wide Convolutional Neural Networks for Object Recognition on Neuromorphic System
Authors: Md Zahangir Alom, Theodore Josue, Md Nayim Rahman, Will Mitchell, Chris Yakopcic, Tarek M. Taha
Date: 2018-02-07 00:00:00
Tasks: Classification, General Classification, Object Recognition

In the last decade, special purpose computing systems, such as Neuromorphic
computing, have become very popular in the field of computer vision and machine
learning for classification tasks. In 2015, IBM's released the TrueNorth
Neuromorphic system, kick-starting a new era of Neuromorphic computing.
Alternatively, Deep Learning approaches such as Deep Convolutional Neural
Networks (DCNN) show almost human-level accuracies for detection and
classification tasks. IBM's 2016 release of a deep learning framework for
DCNNs, called Energy Efficient Deep Neuromorphic Networks (Eedn). Eedn shows
promise for delivering high accuracies across a number of different benchmarks,
while consuming very low power, using IBM's TrueNorth chip. However, there are
many things that remained undiscovered using the Eedn framework for
classification tasks on a Neuromorphic system. In this paper, we have
empirically evaluated the performance of different DCNN architectures
implemented within the Eedn framework. The goal of this work was discover the
most efficient way to implement DCNN models for object classification tasks
using the TrueNorth system. We performed our experiments using benchmark data
sets such as MNIST, COIL 20, and COIL 100. The experimental results show very
promising classification accuracies with very low power consumption on IBM's
NS1e Neurosynaptic system. The results show that for datasets with large
numbers of classes, wider networks perform better when compared to deep
networks comprised of nearly the same core complexity on IBM's TrueNorth
system.
Learning from Past Mistakes: Improving Automatic Speech Recognition Output via Noisy-Clean Phrase Context Modeling
Authors: Prashanth Gurunath Shivakumar, Haoqi Li, Kevin Knight, Panayiotis Georgiou
Date: 2018-02-07 00:00:00
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), Language Modeling, Language Modelling, speech-recognition, Speech Recognition

Automatic speech recognition (ASR) systems often make unrecoverable errors
due to subsystem pruning (acoustic, language and pronunciation models); for
example pruning words due to acoustics using short-term context, prior to
rescoring with long-term context based on linguistics. In this work we model
ASR as a phrase-based noisy transformation channel and propose an error
correction system that can learn from the aggregate errors of all the
independent modules constituting the ASR and attempt to invert those. The
proposed system can exploit long-term context using a neural network language
model and can better choose between existing ASR output possibilities as well
as re-introduce previously pruned or unseen (out-of-vocabulary) phrases. It
provides corrections under poorly performing ASR conditions without degrading
any accurate transcriptions; such corrections are greater on top of
out-of-domain and mismatched data ASR. Our system consistently provides
improvements over the baseline ASR, even when baseline is further optimized
through recurrent neural network language model rescoring. This demonstrates
that any ASR improvements can be exploited independently and that our proposed
system can potentially still provide benefits on highly optimized ASR. Finally,
we present an extensive analysis of the type of errors corrected by our system.
Generating Triples with Adversarial Networks for Scene Graph Construction
Authors: Matthew Klawonn, Eric Heim
Date: 2018-02-07 00:00:00
Tasks: Attribute, graph construction, Graph Generation, Image Captioning, image-classification, Image Classification, object-detection, Object Detection, Question Answering, Scene Graph Generation, Visual Question Answering, Visual Question Answering (VQA)

Driven by successes in deep learning, computer vision research has begun to
move beyond object detection and image classification to more sophisticated
tasks like image captioning or visual question answering. Motivating such
endeavors is the desire for models to capture not only objects present in an
image, but more fine-grained aspects of a scene such as relationships between
objects and their attributes. Scene graphs provide a formal construct for
capturing these aspects of an image. Despite this, there have been only a few
recent efforts to generate scene graphs from imagery. Previous works limit
themselves to settings where bounding box information is available at train
time and do not attempt to generate scene graphs with attributes. In this paper
we propose a method, based on recent advancements in Generative Adversarial
Networks, to overcome these deficiencies. We take the approach of first
generating small subgraphs, each describing a single statement about a scene
from a specific region of the input image chosen using an attention mechanism.
By doing so, our method is able to produce portions of the scene graphs with
attribute information without the need for bounding box labels. Then, the
complete scene graph is constructed from these subgraphs. We show that our
model improves upon prior work in scene graph generation on state-of-the-art
data sets and accepted metrics. Further, we demonstrate that our model is
capable of handling a larger vocabulary size than prior work has attempted.
Unsupervised Typography Transfer
Authors: Hanfei Sun, Yiming Luo, Ziang Lu
Date: 2018-02-07 00:00:00

Traditional methods in Chinese typography synthesis view characters as an
assembly of radicals and strokes, but they rely on manual definition of the key
points, which is still time-costing. Some recent work on computer vision
proposes a brand new approach: to treat every Chinese character as an
independent and inseparable image, so the pre-processing and post-processing of
each character can be avoided. Then with a combination of a transfer network
and a discriminating network, one typography can be well transferred to
another. Despite the quite satisfying performance of the model, the training
process requires to be supervised, which means in the training data each
character in the source domain and the target domain needs to be perfectly
paired. Sometimes the pairing is time-costing, and sometimes there is no
perfect pairing, such as the pairing between traditional Chinese and simplified
Chinese characters. In this paper, we proposed an unsupervised typography
transfer method which doesn't need pairing.
Near-linear time approximation algorithms for optimal transport via Sinkhorn iteration
Authors: Jason Altschuler, Jonathan Weed, Philippe Rigollet
Date: 2017-05-26 00:00:00
Proceeding: NeurIPS 2017 12
Tasks: BIG-bench Machine Learning

Computing optimal transport distances such as the earth mover's distance is a
fundamental problem in machine learning, statistics, and computer vision.
Despite the recent introduction of several algorithms with good empirical
performance, it is unknown whether general optimal transport distances can be
approximated in near-linear time. This paper demonstrates that this ambitious
goal is in fact achieved by Cuturi's Sinkhorn Distances. This result relies on
a new analysis of Sinkhorn iteration, which also directly suggests a new greedy
coordinate descent algorithm, Greenkhorn, with the same theoretical guarantees.
Numerical simulations illustrate that Greenkhorn significantly outperforms the
classical Sinkhorn algorithm in practice.
VISER: Visual Self-Regularization
Authors: Hamid Izadinia, Pierre Garrigues
Date: 2018-02-07 00:00:00
Tasks: Object Categorization, Retrieval

In this work, we propose the use of large set of unlabeled images as a source
of regularization data for learning robust visual representation. Given a
visual model trained by a labeled dataset in a supervised fashion, we augment
our training samples by incorporating large number of unlabeled data and train
a semi-supervised model. We demonstrate that our proposed learning approach
leverages an abundance of unlabeled images and boosts the visual recognition
performance which alleviates the need to rely on large labeled datasets for
learning robust representation. To increment the number of image instances
needed to learn robust visual models in our approach, each labeled image
propagates its label to its nearest unlabeled image instances. These retrieved
unlabeled images serve as local perturbations of each labeled image to perform
Visual Self-Regularization (VISER). To retrieve such visual self regularizers,
we compute the cosine similarity in a semantic space defined by the penultimate
layer in a fully convolutional neural network. We use the publicly available
Yahoo Flickr Creative Commons 100M dataset as the source of our unlabeled image
set and propose a distributed approximate nearest neighbor algorithm to make
retrieval practical at that scale. Using the labeled instances and their
regularizer samples we show that we significantly improve object categorization
and localization performance on the MS COCO and Visual Genome datasets where
objects appear in context.
Applying Cooperative Machine Learning to Speed Up the Annotation of Social Signals in Large Multi-modal Corpora
Authors: Johannes Wagner, Tobias Baur, Yue Zhang, Michel F. Valstar, Björn Schuller, Elisabeth André
Date: 2018-02-07 00:00:00

Scientific disciplines, such as Behavioural Psychology, Anthropology and
recently Social Signal Processing are concerned with the systematic exploration
of human behaviour. A typical work-flow includes the manual annotation (also
called coding) of social signals in multi-modal corpora of considerable size.
For the involved annotators this defines an exhausting and time-consuming task.
In the article at hand we present a novel method and also provide the tools to
speed up the coding procedure. To this end, we suggest and evaluate the use of
Cooperative Machine Learning (CML) techniques to reduce manual labelling
efforts by combining the power of computational capabilities and human
intelligence. The proposed CML strategy starts with a small number of labelled
instances and concentrates on predicting local parts first. Afterwards, a
session-independent classification model is created to finish the remaining
parts of the database. Confidence values are computed to guide the manual
inspection and correction of the predictions. To bring the proposed approach
into application we introduce NOVA - an open-source tool for collaborative and
machine-aided annotations. In particular, it gives labellers immediate access
to CML strategies and directly provides visual feedback on the results. Our
experiments show that the proposed method has the potential to significantly
reduce human labelling efforts.
Focal Loss for Dense Object Detection
Authors: Tsung-Yi Lin, Priya Goyal, Ross Girshick, Kaiming He, Piotr Dollár
Date: 2017-08-07 00:00:00
Proceeding: ICCV 2017 10
Tasks: 2D Object Detection, Dense Object Detection, Knowledge Distillation, Long-tail Learning, Object, Object Detection, Pedestrian Detection, Real-Time Object Detection, Region Proposal

The highest accuracy object detectors to date are based on a two-stage
approach popularized by R-CNN, where a classifier is applied to a sparse set of
candidate object locations. In contrast, one-stage detectors that are applied
over a regular, dense sampling of possible object locations have the potential
to be faster and simpler, but have trailed the accuracy of two-stage detectors
thus far. In this paper, we investigate why this is the case. We discover that
the extreme foreground-background class imbalance encountered during training
of dense detectors is the central cause. We propose to address this class
imbalance by reshaping the standard cross entropy loss such that it
down-weights the loss assigned to well-classified examples. Our novel Focal
Loss focuses training on a sparse set of hard examples and prevents the vast
number of easy negatives from overwhelming the detector during training. To
evaluate the effectiveness of our loss, we design and train a simple dense
detector we call RetinaNet. Our results show that when trained with the focal
loss, RetinaNet is able to match the speed of previous one-stage detectors
while surpassing the accuracy of all existing state-of-the-art two-stage
detectors. Code is at: https://github.com/facebookresearch/Detectron.
Polisis: Automated Analysis and Presentation of Privacy Policies Using Deep Learning
Authors: Hamza Harkous, Kassem Fawaz, Rémi Lebret, Florian Schaub, Kang G. Shin, Karl Aberer
Date: 2018-02-07 00:00:00
Tasks: Language Modeling, Language Modelling, Question Answering

Privacy policies are the primary channel through which companies inform users
about their data collection and sharing practices. These policies are often
long and difficult to comprehend. Short notices based on information extracted
from privacy policies have been shown to be useful but face a significant
scalability hurdle, given the number of policies and their evolution over time.
Companies, users, researchers, and regulators still lack usable and scalable
tools to cope with the breadth and depth of privacy policies. To address these
hurdles, we propose an automated framework for privacy policy analysis
(Polisis). It enables scalable, dynamic, and multi-dimensional queries on
natural language privacy policies. At the core of Polisis is a privacy-centric
language model, built with 130K privacy policies, and a novel hierarchy of
neural-network classifiers that accounts for both high-level aspects and
fine-grained details of privacy practices. We demonstrate Polisis' modularity
and utility with two applications supporting structured and free-form querying.
The structured querying application is the automated assignment of privacy
icons from privacy policies. With Polisis, we can achieve an accuracy of 88.4%
on this task. The second application, PriBot, is the first freeform
question-answering system for privacy policies. We show that PriBot can produce
a correct answer among its top-3 results for 82% of the test questions. Using
an MTurk user study with 700 participants, we show that at least one of
PriBot's top-3 answers is relevant to users for 89% of the test questions.
A Game-Theoretic Approach to Design Secure and Resilient Distributed Support Vector Machines
Authors: Rui Zhang, Quanyan Zhu
Date: 2018-02-07 00:00:00

Distributed Support Vector Machines (DSVM) have been developed to solve
large-scale classification problems in networked systems with a large number of
sensors and control units. However, the systems become more vulnerable as
detection and defense are increasingly difficult and expensive. This work aims
to develop secure and resilient DSVM algorithms under adversarial environments
in which an attacker can manipulate the training data to achieve his objective.
We establish a game-theoretic framework to capture the conflicting interests
between an adversary and a set of distributed data processing units. The Nash
equilibrium of the game allows predicting the outcome of learning algorithms in
adversarial environments, and enhancing the resilience of the machine learning
through dynamic distributed learning algorithms. We prove that the convergence
of the distributed algorithm is guaranteed without assumptions on the training
data or network topologies. Numerical experiments are conducted to corroborate
the results. We show that network topology plays an important role in the
security of DSVM. Networks with fewer nodes and higher average degrees are more
secure. Moreover, a balanced network is found to be less vulnerable to attacks.
Blind Pre-Processing: A Robust Defense Method Against Adversarial Examples
Authors: Adnan Siraj Rakin, Zhezhi He, Boqing Gong, Deliang Fan
Date: 2018-02-05 00:00:00
Tasks: Adversarial Attack

Deep learning algorithms and networks are vulnerable to perturbed inputs
which is known as the adversarial attack. Many defense methodologies have been
investigated to defend against such adversarial attack. In this work, we
propose a novel methodology to defend the existing powerful attack model. We
for the first time introduce a new attacking scheme for the attacker and set a
practical constraint for white box attack. Under this proposed attacking
scheme, we present the best defense ever reported against some of the recent
strong attacks. It consists of a set of nonlinear function to process the input
data which will make it more robust over the adversarial attack. However, we
make this processing layer completely hidden from the attacker. Blind
pre-processing improves the white box attack accuracy of MNIST from 94.3\% to
98.7\%. Even with increasing defense when others defenses completely fail,
blind pre-processing remains one of the strongest ever reported. Another
strength of our defense is that it eliminates the need for adversarial training
as it can significantly increase the MNIST accuracy without adversarial
training as well. Additionally, blind pre-processing can also increase the
inference accuracy in the face of a powerful attack on CIFAR-10 and SVHN data
set as well without much sacrificing clean data accuracy.
Learning One Convolutional Layer with Overlapping Patches
Authors: Surbhi Goel, Adam Klivans, Raghu Meka
Date: 2018-02-07 00:00:00
Proceeding: ICML 2018 7

We give the first provably efficient algorithm for learning a one hidden
layer convolutional network with respect to a general class of (potentially
overlapping) patches. Additionally, our algorithm requires only mild conditions
on the underlying distribution. We prove that our framework captures commonly
used schemes from computer vision, including one-dimensional and
two-dimensional "patch and stride" convolutions.
  Our algorithm-- $Convotron$ -- is inspired by recent work applying isotonic
regression to learning neural networks. Convotron uses a simple, iterative
update rule that is stochastic in nature and tolerant to noise (requires only
that the conditional mean function is a one layer convolutional network, as
opposed to the realizable setting). In contrast to gradient descent, Convotron
requires no special initialization or learning-rate tuning to converge to the
global optimum.
  We also point out that learning one hidden convolutional layer with respect
to a Gaussian distribution and just $one$ disjoint patch $P$ (the other patches
may be arbitrary) is $easy$ in the following sense: Convotron can efficiently
recover the hidden weight vector by updating $only$ in the direction of $P$.
Yes, but Did It Work?: Evaluating Variational Inference
Authors: Yuling Yao, Aki Vehtari, Daniel Simpson, Andrew Gelman
Date: 2018-02-07 00:00:00
Proceeding: ICML 2018 7
Tasks: Diagnostic, Variational Inference

While it's always possible to compute a variational approximation to a
posterior distribution, it can be difficult to discover problems with this
approximation. We propose two diagnostic algorithms to alleviate this problem.
The Pareto-smoothed importance sampling (PSIS) diagnostic gives a goodness of
fit measurement for joint distributions, while simultaneously improving the
error in the estimate. The variational simulation-based calibration (VSBC)
assesses the average performance of point estimates.
Directly and Efficiently Optimizing Prediction Error and AUC of Linear Classifiers
Authors: Hiva Ghanbari, Katya Scheinberg
Date: 2018-02-07 00:00:00

The predictive quality of machine learning models is typically measured in
terms of their (approximate) expected prediction error or the so-called Area
Under the Curve (AUC) for a particular data distribution. However, when the
models are constructed by the means of empirical risk minimization, surrogate
functions such as the logistic loss are optimized instead. This is done because
the empirical approximations of the expected error and AUC functions are
nonconvex and nonsmooth, and more importantly have zero derivative almost
everywhere. In this work, we show that in the case of linear predictors, and
under the assumption that the data has normal distribution, the expected error
and the expected AUC are not only smooth, but have closed form expressions,
which depend on the first and second moments of the normal distribution. Hence,
we derive derivatives of these two functions and use these derivatives in an
optimization algorithm to directly optimize the expected error and the AUC. In
the case of real data sets, the derivatives can be approximated using empirical
moments. We show that even when data is not normally distributed, computed
derivatives are sufficiently useful to render an efficient optimization method
and high quality solutions. Thus, we propose a gradient-based optimization
method for direct optimization of the prediction error and AUC. Moreover, the
per-iteration complexity of the proposed algorithm has no dependence on the
size of the data set, unlike those for optimizing logistic regression and all
other well known empirical risk minimization problems.
Fair comparison of skin detection approaches on publicly available datasets
Authors: Alessandra Lumini, Loris Nanni
Date: 2018-02-07 00:00:00
Tasks: Face Detection

Skin detection is the process of discriminating skin and non-skin regions in a digital image and it is widely used in several applications ranging from hand gesture analysis to track body parts and face detection. Skin detection is a challenging problem which has drawn extensive attention from the research community, nevertheless a fair comparison among approaches is very difficult due to the lack of a common benchmark and a unified testing protocol. In this work, we investigate the most recent researches in this field and we propose a fair comparison among approaches using several different datasets. The major contributions of this work are an exhaustive literature review of skin color detection approaches, a framework to evaluate and combine different skin detector approaches, whose source code is made freely available for future research, and an extensive experimental comparison among several recent methods which have also been used to define an ensemble that works well in many different problems. Experiments are carried out in 10 different datasets including more than 10000 labelled images: experimental results confirm that the best method here proposed obtains a very good performance with respect to other stand-alone approaches, without requiring ad hoc parameter tuning. A MATLAB version of the framework for testing and of the methods proposed in this paper will be freely available from https://github.com/LorisNanni