pwc-archive/papers-with-abstracts huggingface dataset:
MagNet: a Two-Pronged Defense against Adversarial Examples
Authors: Dongyu Meng, Hao Chen
Date: 2017-05-25 00:00:00
Tasks: Deep Learning, Vocal Bursts Valence Prediction

Deep learning has shown promising results on hard perceptual problems in
recent years. However, deep learning systems are found to be vulnerable to
small adversarial perturbations that are nearly imperceptible to human. Such
specially crafted perturbations cause deep learning systems to output incorrect
decisions, with potentially disastrous consequences. These vulnerabilities
hinder the deployment of deep learning systems where safety or security is
important. Attempts to secure deep learning systems either target specific
attacks or have been shown to be ineffective.
  In this paper, we propose MagNet, a framework for defending neural network
classifiers against adversarial examples. MagNet does not modify the protected
classifier or know the process for generating adversarial examples. MagNet
includes one or more separate detector networks and a reformer network.
Different from previous work, MagNet learns to differentiate between normal and
adversarial examples by approximating the manifold of normal examples. Since it
does not rely on any process for generating adversarial examples, it has
substantial generalization power. Moreover, MagNet reconstructs adversarial
examples by moving them towards the manifold, which is effective for helping
classify adversarial examples with small perturbation correctly. We discuss the
intrinsic difficulty in defending against whitebox attack and propose a
mechanism to defend against graybox attack. Inspired by the use of randomness
in cryptography, we propose to use diversity to strengthen MagNet. We show
empirically that MagNet is effective against most advanced state-of-the-art
attacks in blackbox and graybox scenarios while keeping false positive rate on
normal examples very low.
Deep Feature Consistent Deep Image Transformations: Downscaling, Decolorization and HDR Tone Mapping
Authors: Xianxu Hou, Jiang Duan, Guoping Qiu
Date: 2017-07-29 00:00:00
Tasks: Tone Mapping

Building on crucial insights into the determining factors of the visual
integrity of an image and the property of deep convolutional neural network
(CNN), we have developed the Deep Feature Consistent Deep Image Transformation
(DFC-DIT) framework which unifies challenging one-to-many mapping image
processing problems such as image downscaling, decolorization (colour to
grayscale conversion) and high dynamic range (HDR) image tone mapping. We train
one CNN as a non-linear mapper to transform an input image to an output image
following what we term the deep feature consistency principle which is enforced
through another pretrained and fixed deep CNN. This is the first work that uses
deep learning to solve and unify these three common image processing tasks. We
present experimental results to demonstrate the effectiveness of the DFC-DIT
technique and its state of the art performances.
Recurrent neural networks based Indic word-wise script identification using character-wise training
Authors: Rohun Tripathi, Aman Gill, Riccha Tripati
Date: 2017-09-11 00:00:00
Tasks: Prediction

This paper presents a novel methodology of Indic handwritten script
recognition using Recurrent Neural Networks and addresses the problem of script
recognition in poor data scenarios, such as when only character level online
data is available. It is based on the hypothesis that curves of online
character data comprise sufficient information for prediction at the word
level. Online character data is used to train RNNs using BLSTM architecture
which are then used to make predictions of online word level data. These
prediction results on the test set are at par with prediction results of models
trained with online word data, while the training of the character level model
is much less data intensive and takes much less time. Performance for
binary-script models and then 5 Indic script models are reported, along with
comparison with HMM models.The system is extended for offline data prediction.
Raw offline data lacks the temporal information available in online data and
required for prediction using models trained with online data. To overcome
this, stroke recovery is implemented and the strokes are utilized for
predicting using the online character level models. The performance on
character and word level offline data is reported.
Semi-Supervised Active Clustering with Weak Oracles
Authors: Taewan Kim, Joydeep Ghosh
Date: 2017-09-11 00:00:00
Tasks: Clustering

Semi-supervised active clustering (SSAC) utilizes the knowledge of a domain
expert to cluster data points by interactively making pairwise "same-cluster"
queries. However, it is impractical to ask human oracles to answer every
pairwise query. In this paper, we study the influence of allowing "not-sure"
answers from a weak oracle and propose algorithms to efficiently handle
uncertainties. Different types of model assumptions are analyzed to cover
realistic scenarios of oracle abstraction. In the first model, random-weak
oracle, an oracle randomly abstains with a certain probability. We also
proposed two distance-weak oracle models which simulate the case of getting
confused based on the distance between two points in a pairwise query. For each
weak oracle model, we show that a small query complexity is adequate for the
effective $k$ means clustering with high probability. Sufficient conditions for
the guarantee include a $\gamma$-margin property of the data, and an existence
of a point close to each cluster center. Furthermore, we provide a sample
complexity with a reduced effect of the cluster's margin and only a logarithmic
dependency on the data dimension. Our results allow significantly less number
of same-cluster queries if the margin of the clusters is tight, i.e. $\gamma
\approx 1$. Experimental results on synthetic data show the effective
performance of our approach in overcoming uncertainties.
Deep learning for undersampled MRI reconstruction
Authors: Chang Min Hyun, Hwa Pyung Kim, Sung Min Lee, Sungchul Lee, Jin Keun Seo
Date: 2017-09-08 00:00:00
Tasks: Deep Learning, MRI Reconstruction

This paper presents a deep learning method for faster magnetic resonance imaging (MRI) by reducing k-space data with sub-Nyquist sampling strategies and provides a rationale for why the proposed approach works well. Uniform subsampling is used in the time-consuming phase-encoding direction to capture high-resolution image information, while permitting the image-folding problem dictated by the Poisson summation formula. To deal with the localization uncertainty due to image folding, very few low-frequency k-space data are added. Training the deep learning net involves input and output images that are pairs of Fourier transforms of the subsampled and fully sampled k-space data. Numerous experiments show the remarkable performance of the proposed method; only 29% of k-space data can generate images of high quality as effectively as standard MRI reconstruction with fully sampled data.
Data-Driven Dialogue Systems for Social Agents
Authors: Kevin K. Bowden, Shereen Oraby, Amita Misra, Jiaqi Wu, Stephanie Lukin
Date: 2017-09-10 00:00:00
Tasks: Retrieval

In order to build dialogue systems to tackle the ambitious task of holding
social conversations, we argue that we need a data driven approach that
includes insight into human conversational chit chat, and which incorporates
different natural language processing modules. Our strategy is to analyze and
index large corpora of social media data, including Twitter conversations,
online debates, dialogues between friends, and blog posts, and then to couple
this data retrieval with modules that perform tasks such as sentiment and style
analysis, topic modeling, and summarization. We aim for personal assistants
that can learn more nuanced human language, and to grow from task-oriented
agents to more personable social bots.
Applying ACO To Large Scale TSP Instances
Authors: Darren M. Chitty
Date: 2017-09-10 00:00:00
Tasks: CPU

Ant Colony Optimisation (ACO) is a well known metaheuristic that has proven
successful at solving Travelling Salesman Problems (TSP). However, ACO suffers
from two issues; the first is that the technique has significant memory
requirements for storing pheromone levels on edges between cities and second,
the iterative probabilistic nature of choosing which city to visit next at
every step is computationally expensive. This restricts ACO from solving larger
TSP instances. This paper will present a methodology for deploying ACO on
larger TSP instances by removing the high memory requirements, exploiting
parallel CPU hardware and introducing a significant efficiency saving measure.
The approach results in greater accuracy and speed. This enables the proposed
ACO approach to tackle TSP instances of up to 200K cities within reasonable
timescales using a single CPU. Speedups of as much as 1200 fold are achieved by
the technique.
Rates of Convergence of Spectral Methods for Graphon Estimation
Authors: Jiaming Xu
Date: 2017-09-10 00:00:00
Proceeding: ICML 2018 7
Tasks: Community Detection, Graphon Estimation, Recommendation Systems, Stochastic Block Model

This paper studies the problem of estimating the grahpon model - the
underlying generating mechanism of a network. Graphon estimation arises in many
applications such as predicting missing links in networks and learning user
preferences in recommender systems. The graphon model deals with a random graph
of $n$ vertices such that each pair of two vertices $i$ and $j$ are connected
independently with probability $\rho \times f(x_i,x_j)$, where $x_i$ is the
unknown $d$-dimensional label of vertex $i$, $f$ is an unknown symmetric
function, and $\rho$ is a scaling parameter characterizing the graph sparsity.
Recent studies have identified the minimax error rate of estimating the graphon
from a single realization of the random graph. However, there exists a wide gap
between the known error rates of computationally efficient estimation
procedures and the minimax optimal error rate.
  Here we analyze a spectral method, namely universal singular value
thresholding (USVT) algorithm, in the relatively sparse regime with the average
vertex degree $n\rho=\Omega(\log n)$. When $f$ belongs to H\"{o}lder or Sobolev
space with smoothness index $\alpha$, we show the error rate of USVT is at most
$(n\rho)^{ -2 \alpha / (2\alpha+d)}$, approaching the minimax optimal error
rate $\log (n\rho)/(n\rho)$ for $d=1$ as $\alpha$ increases. Furthermore, when
$f$ is analytic, we show the error rate of USVT is at most $\log^d
(n\rho)/(n\rho)$. In the special case of stochastic block model with $k$
blocks, the error rate of USVT is at most $k/(n\rho)$, which is larger than the
minimax optimal error rate by at most a multiplicative factor $k/\log k$. This
coincides with the computational gap observed for community detection. A key
step of our analysis is to derive the eigenvalue decaying rate of the edge
probability matrix using piecewise polynomial approximations of the graphon
function $f$.
An Iterative Regression Approach for Face Pose Estimation from RGB Images
Authors: Wenye He
Date: 2017-09-10 00:00:00
Tasks: feature selection, Pose Estimation, regression

This paper presents a iterative optimization method, explicit shape
regression, for face pose detection and localization. The regression function
is learnt to find out the entire facial shape and minimize the alignment
errors. A cascaded learning framework is employed to enhance shape constraint
during detection. A combination of a two-level boosted regression, shape
indexed features and a correlation-based feature selection method is used to
improve the performance. In this paper, we have explain the advantage of ESR
for deformable object like face pose estimation and reveal its generic
applications of the method. In the experiment, we compare the results with
different work and demonstrate the accuracy and robustness in different
scenarios.
Debbie, the Debate Bot of the Future
Authors: Geetanjali Rakshit, Kevin K. Bowden, Lena Reed, Amita Misra, Marilyn Walker
Date: 2017-09-10 00:00:00

Chatbots are a rapidly expanding application of dialogue systems with
companies switching to bot services for customer support, and new applications
for users interested in casual conversation. One style of casual conversation
is argument, many people love nothing more than a good argument. Moreover,
there are a number of existing corpora of argumentative dialogues, annotated
for agreement and disagreement, stance, sarcasm and argument quality. This
paper introduces Debbie, a novel arguing bot, that selects arguments from
conversational corpora, and aims to use them appropriately in context. We
present an initial working prototype of Debbie, with some preliminary
evaluation and describe future work.
Variational inference for the multi-armed contextual bandit
Authors: Iñigo Urteaga, Chris H. Wiggins
Date: 2017-09-10 00:00:00
Tasks: Multi-Armed Bandits, Reinforcement Learning, Thompson Sampling, Variational Inference

In many biomedical, science, and engineering problems, one must sequentially decide which action to take next so as to maximize rewards. One general class of algorithms for optimizing interactions with the world, while simultaneously learning how the world operates, is the multi-armed bandit setting and, in particular, the contextual bandit case. In this setting, for each executed action, one observes rewards that are dependent on a given 'context', available at each interaction with the world. The Thompson sampling algorithm has recently been shown to enjoy provable optimality properties for this set of problems, and to perform well in real-world settings. It facilitates generative and interpretable modeling of the problem at hand. Nevertheless, the design and complexity of the model limit its application, since one must both sample from the distributions modeled and calculate their expected rewards. We here show how these limitations can be overcome using variational inference to approximate complex models, applying to the reinforcement learning case advances developed for the inference case in the machine learning community over the past two decades. We consider contextual multi-armed bandit applications where the true reward distribution is unknown and complex, which we approximate with a mixture model whose parameters are inferred via variational inference. We show how the proposed variational Thompson sampling approach is accurate in approximating the true distribution, and attains reduced regrets even with complex reward distributions. The proposed algorithm is valuable for practical scenarios where restrictive modeling assumptions are undesirable.
Bayesian bandits: balancing the exploration-exploitation tradeoff via double sampling
Authors: Iñigo Urteaga, Chris H. Wiggins
Date: 2017-09-10 00:00:00
Tasks: Reinforcement Learning, Thompson Sampling

Reinforcement learning studies how to balance exploration and exploitation in
real-world systems, optimizing interactions with the world while simultaneously
learning how the world operates. One general class of algorithms for such
learning is the multi-armed bandit setting. Randomized probability matching,
based upon the Thompson sampling approach introduced in the 1930s, has recently
been shown to perform well and to enjoy provable optimality properties. It
permits generative, interpretable modeling in a Bayesian setting, where prior
knowledge is incorporated, and the computed posteriors naturally capture the
full state of knowledge. In this work, we harness the information contained in
the Bayesian posterior and estimate its sufficient statistics via sampling. In
several application domains, for example in health and medicine, each
interaction with the world can be expensive and invasive, whereas drawing
samples from the model is relatively inexpensive. Exploiting this viewpoint, we
develop a double sampling technique driven by the uncertainty in the learning
process: it favors exploitation when certain about the properties of each arm,
exploring otherwise. The proposed algorithm does not make any distributional
assumption and it is applicable to complex reward distributions, as long as
Bayesian posterior updates are computable. Utilizing the estimated posterior
sufficient statistics, double sampling autonomously balances the
exploration-exploitation tradeoff to make better informed decisions. We
empirically show its reduced cumulative regret when compared to
state-of-the-art alternatives in representative bandit settings.
Neural Extractive Summarization with Side Information
Authors: Shashi Narayan, Nikos Papasarantopoulos, Shay B. Cohen, Mirella Lapata
Date: 2017-04-14 00:00:00
Tasks: Articles, Document Summarization, Extractive Summarization, Image Captioning, Informativeness

Most extractive summarization methods focus on the main body of the document
from which sentences need to be extracted. However, the gist of the document
may lie in side information, such as the title and image captions which are
often available for newswire articles. We propose to explore side information
in the context of single-document extractive summarization. We develop a
framework for single-document summarization composed of a hierarchical document
encoder and an attention-based extractor with attention over side information.
We evaluate our model on a large scale news dataset. We show that extractive
summarization with side information consistently outperforms its counterpart
that does not use any side information, in terms of both informativeness and
fluency.
R2N2: Residual Recurrent Neural Networks for Multivariate Time Series Forecasting
Authors: Hardik Goel, Igor Melnyk, Arindam Banerjee
Date: 2017-09-10 00:00:00
Tasks: Multivariate Time Series Forecasting, Time Series, Time Series Analysis, Time Series Forecasting

Multivariate time-series modeling and forecasting is an important problem
with numerous applications. Traditional approaches such as VAR (vector
auto-regressive) models and more recent approaches such as RNNs (recurrent
neural networks) are indispensable tools in modeling time-series data. In many
multivariate time series modeling problems, there is usually a significant
linear dependency component, for which VARs are suitable, and a nonlinear
component, for which RNNs are suitable. Modeling such times series with only
VAR or only RNNs can lead to poor predictive performance or complex models with
large training times. In this work, we propose a hybrid model called R2N2
(Residual RNN), which first models the time series with a simple linear model
(like VAR) and then models its residual errors using RNNs. R2N2s can be trained
using existing algorithms for VARs and RNNs. Through an extensive empirical
evaluation on two real world datasets (aviation and climate domains), we show
that R2N2 is competitive, usually better than VAR or RNN, used alone. We also
show that R2N2 is faster to train as compared to an RNN, while requiring less
number of hidden units.
Fully Convolutional Neural Networks for Dynamic Object Detection in Grid Maps
Authors: Florian Piewak, Timo Rehfeld, Michael Weber, J. Marius Zöllner
Date: 2017-09-10 00:00:00
Tasks: object-detection, Object Detection

Grid maps are widely used in robotics to represent obstacles in the
environment and differentiating dynamic objects from static infrastructure is
essential for many practical applications. In this work, we present a methods
that uses a deep convolutional neural network (CNN) to infer whether grid cells
are covering a moving object or not. Compared to tracking approaches, that use
e.g. a particle filter to estimate grid cell velocities and then make a
decision for individual grid cells based on this estimate, our approach uses
the entire grid map as input image for a CNN that inspects a larger area around
each cell and thus takes the structural appearance in the grid map into account
to make a decision. Compared to our reference method, our concept yields a
performance increase from 83.9% to 97.2%. A runtime optimized version of our
approach yields similar improvements with an execution time of just 10
milliseconds.
Fully Convolutional Neural Networks for Dynamic Object Detection in Grid Maps (Masters Thesis)
Authors: Florian Piewak
Date: 2017-09-10 00:00:00
Tasks: Autonomous Vehicles, object-detection, Object Detection

One of the most important parts of environment perception is the detection of
obstacles in the surrounding of the vehicle. To achieve that, several sensors
like radars, LiDARs and cameras are installed in autonomous vehicles. The
produced sensor data is fused to a general representation of the surrounding.
In this thesis the dynamic occupancy grid map approach of Nuss et al. is used
while three goals are achieved. First, the approach of Nuss et al. to
distinguish between moving and non-moving obstacles is improved by using Fully
Convolutional Neural Networks to create a class prediction for each grid cell.
For this purpose, the network is initialized with public pre-trained network
models and the training is executed with a semi-automatic generated dataset.
The second goal is to provide orientation information for each detected moving
obstacle. This could improve tracking algorithms, which are based on the
dynamic occupancy grid map. The orientation extraction based on the
Convolutional Neural Network shows a better performance in comparison to an
orientation extraction directly over the velocity information of the dynamic
occupancy grid map. A general problem of developing machine learning approaches
like Neural Networks is the number of labeled data, which can always be
increased. For this reason, the last goal is to evaluate a semi-supervised
learning algorithm, to generate automatically more labeled data. The result of
this evaluation shows that the automated labeled data does not improve the
performance of the Convolutional Neural Network. All in all, the best results
are combined to compare the detection against the approach of Nuss et al. [36]
and a relative improvement of 34.8% is reached.
Computational Machines in a Coexistence with Concrete Universals and Data Streams
Authors: Vahid Moosavi
Date: 2017-09-10 00:00:00

We discuss that how the majority of traditional modeling approaches are
following the idealism point of view in scientific modeling, which follow the
set theoretical notions of models based on abstract universals. We show that
while successful in many classical modeling domains, there are fundamental
limits to the application of set theoretical models in dealing with complex
systems with many potential aspects or properties depending on the
perspectives. As an alternative to abstract universals, we propose a conceptual
modeling framework based on concrete universals that can be interpreted as a
category theoretical approach to modeling. We call this modeling framework
pre-specific modeling. We further, discuss how a certain group of mathematical
and computational methods, along with ever-growing data streams are able to
operationalize the concept of pre-specific modeling.