pwc-archive/papers-with-abstracts huggingface dataset:
The Complexity of Campaigning
Authors: Cory Siler, Luke Harold Miles, Judy Goldsmith
Date: 2017-06-20 00:00:00

In "The Logic of Campaigning", Dean and Parikh consider a candidate making
campaign statements to appeal to the voters. They model these statements as
Boolean formulas over variables that represent stances on the issues, and study
optimal candidate strategies under three proposed models of voter preferences
based on the assignments that satisfy these formulas. We prove that voter
utility evaluation is computationally hard under these preference models (in
one case, #P-hard), along with certain problems related to candidate strategic
reasoning. Our results raise questions about the desirable characteristics of a
voter preference model and to what extent a polynomial-time-evaluable function
can capture them.
Incremental Boosting Convolutional Neural Network for Facial Action Unit Recognition
Authors: Shizhong Han, Zibo Meng, Ahmed Shehab Khan, Yan Tong
Date: 2017-07-17 00:00:00
Proceeding: NeurIPS 2016 12
Tasks: Facial Action Unit Detection, Incremental Learning

Recognizing facial action units (AUs) from spontaneous facial expressions is
still a challenging problem. Most recently, CNNs have shown promise on facial
AU recognition. However, the learned CNNs are often overfitted and do not
generalize well to unseen subjects due to limited AU-coded training images. We
proposed a novel Incremental Boosting CNN (IB-CNN) to integrate boosting into
the CNN via an incremental boosting layer that selects discriminative neurons
from the lower layer and is incrementally updated on successive mini-batches.
In addition, a novel loss function that accounts for errors from both the
incremental boosted classifier and individual weak classifiers was proposed to
fine-tune the IB-CNN. Experimental results on four benchmark AU databases have
demonstrated that the IB-CNN yields significant improvement over the
traditional CNN and the boosting CNN without incremental learning, as well as
outperforming the state-of-the-art CNN-based methods in AU recognition. The
improvement is more impressive for the AUs that have the lowest frequencies in
the databases.
Freehand Ultrasound Image Simulation with Spatially-Conditioned Generative Adversarial Networks
Authors: Yipeng Hu, Eli Gibson, Li-Lin Lee, Weidi Xie, Dean C. Barratt, Tom Vercauteren, J. Alison Noble
Date: 2017-07-17 00:00:00
Tasks: Anatomy, Image Registration, Medical Procedure

Sonography synthesis has a wide range of applications, including medical
procedure simulation, clinical training and multimodality image registration.
In this paper, we propose a machine learning approach to simulate ultrasound
images at given 3D spatial locations (relative to the patient anatomy), based
on conditional generative adversarial networks (GANs). In particular, we
introduce a novel neural network architecture that can sample anatomically
accurate images conditionally on spatial position of the (real or mock)
freehand ultrasound probe. To ensure an effective and efficient spatial
information assimilation, the proposed spatially-conditioned GANs take
calibrated pixel coordinates in global physical space as conditioning input,
and utilise residual network units and shortcuts of conditioning data in the
GANs' discriminator and generator, respectively. Using optically tracked B-mode
ultrasound images, acquired by an experienced sonographer on a fetus phantom,
we demonstrate the feasibility of the proposed method by two sets of
quantitative results: distances were calculated between corresponding
anatomical landmarks identified in the held-out ultrasound images and the
simulated data at the same locations unseen to the networks; a usability study
was carried out to distinguish the simulated data from the real images. In
summary, we present what we believe are state-of-the-art visually realistic
ultrasound images, simulated by the proposed GAN architecture that is stable to
train and capable of generating plausibly diverse image samples.
TensorLog: Deep Learning Meets Probabilistic DBs
Authors: William W. Cohen, Fan Yang, Kathryn Rivard Mazaitis
Date: 2017-07-17 00:00:00
Tasks: Deep Learning, Logical Reasoning

We present an implementation of a probabilistic first-order logic called
TensorLog, in which classes of logical queries are compiled into differentiable
functions in a neural-network infrastructure such as Tensorflow or Theano. This
leads to a close integration of probabilistic logical reasoning with
deep-learning infrastructure: in particular, it enables high-performance deep
learning frameworks to be used for tuning the parameters of a probabilistic
logic. Experimental results show that TensorLog scales to problems involving
hundreds of thousands of knowledge-base triples and tens of thousands of
examples.
Make Your Bone Great Again : A study on Osteoporosis Classification
Authors: Rahul Paul, Saeed Alahamri, Sulav Malla, Ghulam Jilani Quadri
Date: 2017-07-17 00:00:00
Tasks: Classification, General Classification

Osteoporosis can be identified by looking at 2D x-ray images of the bone. The
high degree of similarity between images of a healthy bone and a diseased one
makes classification a challenge. A good bone texture characterization
technique is essential for identifying osteoporosis cases. Standard texture
feature extraction techniques like Local Binary Pattern (LBP), Gray Level
Co-occurrence Matrix (GLCM) have been used for this purpose. In this paper, we
draw a comparison between deep features extracted from convolution neural
network against these traditional features. Our results show that deep features
have more discriminative power as classifiers trained on them always outperform
the ones trained on traditional features.
Houdini: Fooling Deep Structured Prediction Models
Authors: Moustapha Cisse, Yossi Adi, Natalia Neverova, Joseph Keshet
Date: 2017-07-17 00:00:00
Tasks: General Classification, Pose Estimation, Prediction, Semantic Segmentation, speech-recognition, Speech Recognition, Structured Prediction

Generating adversarial examples is a critical step for evaluating and
improving the robustness of learning machines. So far, most existing methods
only work for classification and are not designed to alter the true performance
measure of the problem at hand. We introduce a novel flexible approach named
Houdini for generating adversarial examples specifically tailored for the final
performance measure of the task considered, be it combinatorial and
non-decomposable. We successfully apply Houdini to a range of applications such
as speech recognition, pose estimation and semantic segmentation. In all cases,
the attacks based on Houdini achieve higher success rate than those based on
the traditional surrogates used to train the models while using a less
perceptible adversarial perturbation.
Remote sensing of forests using discrete return airborne LiDAR
Authors: Hamid Hamraz, Marco A. Contreras
Date: 2017-07-17 00:00:00
Tasks: Distributed Computing, Segmentation

Airborne discrete return light detection and ranging (LiDAR) point clouds
covering forested areas can be processed to segment individual trees and
retrieve their morphological attributes. Segmenting individual trees in natural
deciduous forests however remained a challenge because of the complex and
multi-layered canopy. In this chapter, we present (i) a robust segmentation
method that avoids a priori assumptions about the canopy structure, (ii) a
vertical canopy stratification procedure that improves segmentation of
understory trees, (iii) an occlusion model for estimating the point density of
each canopy stratum, and (iv) a distributed computing approach for efficient
processing at the forest level. When applied to the University of Kentucky
Robinson Forest, the segmentation method detected about 90% of overstory and
47% of understory trees with over-segmentation rates of 14% and 2%. Stratifying
the canopy improved the detection rate of understory trees to 68% at the cost
of increasing their over-segmentations to 16%. According to our occlusion
model, a point density of ~170 pt/m-sqr is needed to segment understory trees
as accurately as overstory trees. Lastly, using the distributed approach, we
segmented about two million trees in the 7,440-ha forest in 2.5 hours using 192
processors, which is 167 times faster than using a single processor. Keywords:
individual tree segmentation, multi-layered stand, vertical canopy
stratification, segmentation evaluation, point density, canopy occlusion
effect, big data, distributed computing.
Efficient Online Learning for Optimizing Value of Information: Theory and Application to Interactive Troubleshooting
Authors: Yuxin Chen, Jean-Michel Renders, Morteza Haghir Chehreghani, Andreas Krause
Date: 2017-03-16 00:00:00

We consider the optimal value of information (VoI) problem, where the goal is
to sequentially select a set of tests with a minimal cost, so that one can
efficiently make the best decision based on the observed outcomes. Existing
algorithms are either heuristics with no guarantees, or scale poorly (with
exponential run time in terms of the number of available tests). Moreover,
these methods assume a known distribution over the test outcomes, which is
often not the case in practice. We propose an efficient sampling-based online
learning framework to address the above issues. First, assuming the
distribution over hypotheses is known, we propose a dynamic hypothesis
enumeration strategy, which allows efficient information gathering with strong
theoretical guarantees. We show that with sufficient amount of samples, one can
identify a near-optimal decision with high probability. Second, when the
parameters of the hypotheses distribution are unknown, we propose an algorithm
which learns the parameters progressively via posterior sampling in an online
fashion. We further establish a rigorous bound on the expected regret. We
demonstrate the effectiveness of our approach on a real-world interactive
troubleshooting application and show that one can efficiently make high-quality
decisions with low cost.
A Simple Language Model based on PMI Matrix Approximations
Authors: Oren Melamud, Ido Dagan, Jacob Goldberger
Date: 2017-07-17 00:00:00
Proceeding: EMNLP 2017 9
Tasks: Language Modeling, Language Modelling, Word Embeddings

In this study, we introduce a new approach for learning language models by
training them to estimate word-context pointwise mutual information (PMI), and
then deriving the desired conditional probabilities from PMI at test time.
Specifically, we show that with minor modifications to word2vec's algorithm, we
get principled language models that are closely related to the well-established
Noise Contrastive Estimation (NCE) based language models. A compelling aspect
of our approach is that our models are trained with the same simple negative
sampling objective function that is commonly used in word2vec to learn word
embeddings.
Deep Variational Information Bottleneck
Authors: Alexander A. Alemi, Ian Fischer, Joshua V. Dillon, Kevin Murphy
Date: 2016-12-01 00:00:00
Tasks: Adversarial Attack

We present a variational approximation to the information bottleneck of Tishby et al. (1999). This variational approach allows us to parameterize the information bottleneck model using a neural network and leverage the reparameterization trick for efficient training. We call this method "Deep Variational Information Bottleneck", or Deep VIB. We show that models trained with the VIB objective outperform those that are trained with other forms of regularization, in terms of generalization performance and robustness to adversarial attack.
Exploring text datasets by visualizing relevant words
Authors: Franziska Horn, Leila Arras, Grégoire Montavon, Klaus-Robert Müller, Wojciech Samek
Date: 2017-07-17 00:00:00

When working with a new dataset, it is important to first explore and
familiarize oneself with it, before applying any advanced machine learning
algorithms. However, to the best of our knowledge, no tools exist that quickly
and reliably give insight into the contents of a selection of documents with
respect to what distinguishes them from other documents belonging to different
categories. In this paper we propose to extract `relevant words' from a
collection of texts, which summarize the contents of documents belonging to a
certain class (or discovered cluster in the case of unlabeled datasets), and
visualize them in word clouds to allow for a survey of salient features at a
glance. We compare three methods for extracting relevant words and demonstrate
the usefulness of the resulting word clouds by providing an overview of the
classes contained in a dataset of scientific publications as well as by
discovering trending topics from recent New York Times article snippets.
Aesthetic-Driven Image Enhancement by Adversarial Learning
Authors: Yubin Deng, Chen Change Loy, Xiaoou Tang
Date: 2017-07-17 00:00:00
Tasks: Image Cropping, Image Enhancement

We introduce EnhanceGAN, an adversarial learning based model that performs
automatic image enhancement. Traditional image enhancement frameworks typically
involve training models in a fully-supervised manner, which require expensive
annotations in the form of aligned image pairs. In contrast to these
approaches, our proposed EnhanceGAN only requires weak supervision (binary
labels on image aesthetic quality) and is able to learn enhancement operators
for the task of aesthetic-based image enhancement. In particular, we show the
effectiveness of a piecewise color enhancement module trained with weak
supervision, and extend the proposed EnhanceGAN framework to learning a deep
filtering-based aesthetic enhancer. The full differentiability of our image
enhancement operators enables the training of EnhanceGAN in an end-to-end
manner. We further demonstrate the capability of EnhanceGAN in learning
aesthetic-based image cropping without any groundtruth cropping pairs. Our
weakly-supervised EnhanceGAN reports competitive quantitative results on
aesthetic-based color enhancement as well as automatic image cropping, and a
user study confirms that our image enhancement results are on par with or even
preferred over professional enhancement.
Learning to select data for transfer learning with Bayesian Optimization
Authors: Sebastian Ruder, Barbara Plank
Date: 2017-07-17 00:00:00
Proceeding: EMNLP 2017 9
Tasks: Bayesian Optimization, Diversity, Part-Of-Speech Tagging, Sentiment Analysis, Transfer Learning

Domain similarity measures can be used to gauge adaptability and select
suitable data for transfer learning, but existing approaches define ad hoc
measures that are deemed suitable for respective tasks. Inspired by work on
curriculum learning, we propose to \emph{learn} data selection measures using
Bayesian Optimization and evaluate them across models, domains and tasks. Our
learned measures outperform existing domain similarity measures significantly
on three tasks: sentiment analysis, part-of-speech tagging, and parsing. We
show the importance of complementing similarity with diversity, and that
learned measures are -- to some degree -- transferable across models, domains,
and even tasks.
Artificial Error Generation with Machine Translation and Syntactic Patterns
Authors: Marek Rei, Mariano Felice, Zheng Yuan, Ted Briscoe
Date: 2017-07-17 00:00:00
Proceeding: WS 2017 9
Tasks: Grammatical Error Detection, Machine Translation, Translation

Shortage of available training data is holding back progress in the area of
automated error detection. This paper investigates two alternative methods for
artificially generating writing errors, in order to create additional
resources. We propose treating error generation as a machine translation task,
where grammatically correct text is translated to contain errors. In addition,
we explore a system for extracting textual patterns from an annotated corpus,
which can then be used to insert errors into grammatically correct sentences.
Our experiments show that the inclusion of artificially generated errors
significantly improves error detection accuracy on both FCE and CoNLL 2014
datasets.
Detecting Off-topic Responses to Visual Prompts
Authors: Marek Rei
Date: 2017-07-17 00:00:00
Proceeding: WS 2017 9

Automated methods for essay scoring have made great progress in recent years,
achieving accuracies very close to human annotators. However, a known weakness
of such automated scorers is not taking into account the semantic relevance of
the submitted text. While there is existing work on detecting answer relevance
given a textual prompt, very little previous research has been done to
incorporate visual writing prompts. We propose a neural architecture and
several extensions for detecting off-topic responses to visual prompts and
evaluate it on a dataset of texts written by language learners.
Deep Learning for User Comment Moderation
Authors: John Pavlopoulos, Prodromos Malakasiotis, Ion Androutsopoulos
Date: 2017-05-28 00:00:00
Proceeding: WS 2017 8
Tasks: Deep Learning, General Classification

Experimenting with a new dataset of 1.6M user comments from a Greek news
portal and existing datasets of English Wikipedia comments, we show that an RNN
outperforms the previous state of the art in moderation. A deep,
classification-specific attention mechanism improves further the overall
performance of the RNN. We also compare against a CNN and a word-list baseline,
considering both fully automatic and semi-automatic moderation.
Auxiliary Objectives for Neural Error Detection Models
Authors: Marek Rei, Helen Yannakoudakis
Date: 2017-07-17 00:00:00
Proceeding: WS 2017 9
Tasks: Grammatical Error Detection

We investigate the utility of different auxiliary objectives and training
strategies within a neural sequence labeling approach to error detection in
learner writing. Auxiliary costs provide the model with additional linguistic
information, allowing it to learn general-purpose compositional features that
can then be exploited for other objectives. Our experiments show that a joint
learning approach trained with parallel labels on in-domain data improves
performance over the previous best error detection system. While the resulting
model has the same number of parameters, the additional objectives allow it to
be optimised more efficiently and achieve better performance.
Mesh Denoising based on Normal Voting Tensor and Binary Optimization
Authors: S. K. Yadav, U. Reitebuch, K. Polthier
Date: 2016-07-20 00:00:00
Tasks: Denoising

This paper presents a tensor multiplication based smoothing algorithm that
follows a two step denoising method. Unlike other traditional averaging
approaches, our approach uses an element based normal voting tensor to compute
smooth surfaces. By introducing a binary optimization on the proposed tensor
together with a local binary neighborhood concept, our algorithm better retains
sharp features and produces smoother umbilical regions than previous
approaches. On top of that, we provide a stochastic analysis on the different
kinds of noise based on the average edge length. The quantitative and visual
results demonstrate the performance our method is better compared to state of
the art smoothing approaches.
Merging MCMC Subposteriors through Gaussian-Process Approximations
Authors: Christopher Nemeth, Chris Sherlock
Date: 2016-05-27 00:00:00
Tasks: Bayesian Inference

Markov chain Monte Carlo (MCMC) algorithms have become powerful tools for
Bayesian inference. However, they do not scale well to large-data problems.
Divide-and-conquer strategies, which split the data into batches and, for each
batch, run independent MCMC algorithms targeting the corresponding
subposterior, can spread the computational burden across a number of separate
workers. The challenge with such strategies is in recombining the subposteriors
to approximate the full posterior. By creating a Gaussian-process approximation
for each log-subposterior density we create a tractable approximation for the
full posterior. This approximation is exploited through three methodologies:
firstly a Hamiltonian Monte Carlo algorithm targeting the expectation of the
posterior density provides a sample from an approximation to the posterior;
secondly, evaluating the true posterior at the sampled points leads to an
importance sampler that, asymptotically, targets the true posterior
expectations; finally, an alternative importance sampler uses the full
Gaussian-process distribution of the approximation to the log-posterior density
to re-weight any initial sample and provide both an estimate of the posterior
expectation and a measure of the uncertainty in it.
Semantic Entity Retrieval Toolkit
Authors: Christophe Van Gysel, Maarten de Rijke, Evangelos Kanoulas
Date: 2017-06-12 00:00:00
Tasks: Clustering, Entity Retrieval, Representation Learning, Retrieval

Unsupervised learning of low-dimensional, semantic representations of words
and entities has recently gained attention. In this paper we describe the
Semantic Entity Retrieval Toolkit (SERT) that provides implementations of our
previously published entity representation models. The toolkit provides a
unified interface to different representation learning algorithms, fine-grained
parsing configuration and can be used transparently with GPUs. In addition,
users can easily modify existing models or implement their own models in the
framework. After model training, SERT can be used to rank entities according to
a textual query and extract the learned entity/word representation for use in
downstream algorithms, such as clustering or recommendation.
Trial without Error: Towards Safe Reinforcement Learning via Human Intervention
Authors: William Saunders, Girish Sastry, Andreas Stuhlmueller, Owain Evans
Date: 2017-07-17 00:00:00
Tasks: Atari Games, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL), Safe Reinforcement Learning

AI systems are increasingly applied to complex tasks that involve interaction
with humans. During training, such systems are potentially dangerous, as they
haven't yet learned to avoid actions that could cause serious harm. How can an
AI system explore and learn without making a single mistake that harms humans
or otherwise causes serious damage? For model-free reinforcement learning,
having a human "in the loop" and ready to intervene is currently the only way
to prevent all catastrophes. We formalize human intervention for RL and show
how to reduce the human labor required by training a supervised learner to
imitate the human's intervention decisions. We evaluate this scheme on Atari
games, with a Deep RL agent being overseen by a human for four hours. When the
class of catastrophes is simple, we are able to prevent all catastrophes
without affecting the agent's learning (whereas an RL baseline fails due to
catastrophic forgetting). However, this scheme is less successful when
catastrophes are more complex: it reduces but does not eliminate catastrophes
and the supervised learner fails on adversarial examples found by the agent.
Extrapolating to more challenging environments, we show that our implementation
would not scale (due to the infeasible amount of human labor required). We
outline extensions of the scheme that are necessary if we are to train
model-free agents without a single catastrophe.
A Meta-Learning Approach to One-Step Active Learning
Authors: Gabriella Contardo, Ludovic Denoyer, Thierry Artieres
Date: 2017-06-26 00:00:00
Tasks: Active Learning, Meta-Learning

We consider the problem of learning when obtaining the training labels is
costly, which is usually tackled in the literature using active-learning
techniques. These approaches provide strategies to choose the examples to label
before or during training. These strategies are usually based on heuristics or
even theoretical measures, but are not learned as they are directly used during
training. We design a model which aims at \textit{learning active-learning
strategies} using a meta-learning setting. More specifically, we consider a
pool-based setting, where the system observes all the examples of the dataset
of a problem and has to choose the subset of examples to label in a single
shot. Experiments show encouraging results.