pwc-archive/papers-with-abstracts huggingface dataset:
Mastering the Dungeon: Grounded Language Learning by Mechanical Turker Descent
Authors: Zhilin Yang, Saizheng Zhang, Jack Urbanek, Will Feng, Alexander H. Miller, Arthur Szlam, Douwe Kiela, Jason Weston
Date: 2017-11-21 00:00:00
Proceeding: ICLR 2018 1
Tasks: Grounded language learning

Contrary to most natural language processing research, which makes use of
static datasets, humans learn language interactively, grounded in an
environment. In this work we propose an interactive learning procedure called
Mechanical Turker Descent (MTD) and use it to train agents to execute natural
language commands grounded in a fantasy text adventure game. In MTD, Turkers
compete to train better agents in the short term, and collaborate by sharing
their agents' skills in the long term. This results in a gamified, engaging
experience for the Turkers and a better quality teaching signal for the agents
compared to static datasets, as the Turkers naturally adapt the training data
to the agent's abilities.
Decision Provenance: Harnessing data flow for accountable systems
Authors: Jatinder Singh, Jennifer Cobbe, Chris Norval
Date: 2018-04-16 00:00:00
Tasks: Decision Making

Demand is growing for more accountability regarding the technological systems that increasingly occupy our world. However, the complexity of many of these systems - often systems-of-systems - poses accountability challenges. A key reason for this is because the details and nature of the information flows that interconnect and drive systems, which often occur across technical and organisational boundaries, tend to be invisible or opaque. This paper argues that data provenance methods show much promise as a technical means for increasing the transparency of these interconnected systems. Specifically, given the concerns regarding ever-increasing levels of automated and algorithmic decision-making, and so-called 'algorithmic systems' in general, we propose decision provenance as a concept showing much promise. Decision provenance entails using provenance methods to provide information exposing decision pipelines: chains of inputs to, the nature of, and the flow-on effects from the decisions and actions taken (at design and run-time) throughout systems. This paper introduces the concept of decision provenance, and takes an interdisciplinary (tech-legal) exploration into its potential for assisting accountability in algorithmic systems. We argue that decision provenance can help facilitate oversight, audit, compliance, risk mitigation, and user empowerment, and we also indicate the implementation considerations and areas for research necessary for realising its vision. More generally, we make the case that considerations of data flow, and systems more broadly, are important to discussions of accountability, and complement the considerable attention already given to algorithmic specifics.
Learning How to Self-Learn: Enhancing Self-Training Using Neural Reinforcement Learning
Authors: Chenhua Chen, Yue Zhang
Date: 2018-04-16 00:00:00
Tasks: Deep Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Self-training is a useful strategy for semi-supervised learning, leveraging
raw texts for enhancing model performances. Traditional self-training methods
depend on heuristics such as model confidence for instance selection, the
manual adjustment of which can be expensive. To address these challenges, we
propose a deep reinforcement learning method to learn the self-training
strategy automatically. Based on neural network representation of sentences,
our model automatically learns an optimal policy for instance selection.
Experimental results show that our approach outperforms the baseline solutions
in terms of better tagging performances and stability.
Analysis of Extremely Obese Individuals Using Deep Learning Stacked Autoencoders and Genome-Wide Genetic Data
Authors: Casimiro A. Curbelo Montañez, Paul Fergus, Carl Chalmers, Jade Hind
Date: 2018-04-16 00:00:00
Tasks: General Classification

The aetiology of polygenic obesity is multifactorial, which indicates that
life-style and environmental factors may influence multiples genes to aggravate
this disorder. Several low-risk single nucleotide polymorphisms (SNPs) have
been associated with BMI. However, identified loci only explain a small
proportion of the variation ob-served for this phenotype. The linear nature of
genome wide association studies (GWAS) used to identify associations between
genetic variants and the phenotype have had limited success in explaining the
heritability variation of BMI and shown low predictive capacity in
classification studies. GWAS ignores the epistatic interactions that less
significant variants have on the phenotypic outcome. In this paper we utilise a
novel deep learning-based methodology to reduce the high dimensional space in
GWAS and find epistatic interactions between SNPs for classification purposes.
SNPs were filtered based on the effects associations have with BMI. Since
Bonferroni adjustment for multiple testing is highly conservative, an important
proportion of SNPs involved in SNP-SNP interactions are ignored. Therefore,
only SNPs with p-values < 1x10-2 were considered for subsequent epistasis
analysis using stacked auto encoders (SAE). This allows the nonlinearity
present in SNP-SNP interactions to be discovered through progressively smaller
hidden layer units and to initialise a multi-layer feedforward artificial
neural network (ANN) classifier. The classifier is fine-tuned to classify
extremely obese and non-obese individuals. The best results were obtained with
2000 compressed units (SE=0.949153, SP=0.933014, Gini=0.949936,
Lo-gloss=0.1956, AUC=0.97497 and MSE=0.054057). Using 50 compressed units it
was possible to achieve (SE=0.785311, SP=0.799043, Gini=0.703566,
Logloss=0.476864, AUC=0.85178 and MSE=0.156315).
What does 2D geometric information really tell us about 3D face shape?
Authors: Anil Bas, William A. P. Smith
Date: 2017-08-22 00:00:00
Tasks: 3D Reconstruction

A face image contains geometric cues in the form of configurational information and contours that can be used to estimate 3D face shape. While it is clear that 3D reconstruction from 2D points is highly ambiguous if no further constraints are enforced, one might expect that the face-space constraint solves this problem. We show that this is not the case and that geometric information is an ambiguous cue. There are two sources for this ambiguity. The first is that, within the space of 3D face shapes, there are flexibility modes that remain when some parts of the face are fixed. The second occurs only under perspective projection and is a result of perspective transformation as camera distance varies. Two different faces, when viewed at different distances, can give rise to the same 2D geometry. To demonstrate these ambiguities, we develop new algorithms for fitting a 3D morphable model to 2D landmarks or contours under either orthographic or perspective projection and show how to compute flexibility modes for both cases. We show that both fitting problems can be posed as a separable nonlinear least squares problem and solved efficiently. We demonstrate both quantitatively and qualitatively that the ambiguity is present in reconstructions from geometric information alone but also in reconstructions from a state-of-the-art CNN-based method.
Training convolutional neural networks with megapixel images
Authors: Hans Pinckaers, Geert Litjens
Date: 2018-04-16 00:00:00

To train deep convolutional neural networks, the input data and the
intermediate activations need to be kept in memory to calculate the gradient
descent step. Given the limited memory available in the current generation
accelerator cards, this limits the maximum dimensions of the input data. We
demonstrate a method to train convolutional neural networks holding only parts
of the image in memory while giving equivalent results. We quantitatively
compare this new way of training convolutional neural networks with
conventional training. In addition, as a proof of concept, we train a
convolutional neural network with 64 megapixel images, which requires 97% less
memory than the conventional approach.
Application of the Ranking Relative Principal Component Attributes Network Model (REL-PCANet) for the Inclusive Development Index Estimation
Authors: Anwar Irmatov, Elnura Irmatova
Date: 2018-04-16 00:00:00
Tasks: Learning-To-Rank

In 2018, at the World Economic Forum in Davos it was presented a new
countries' economic performance metric named the Inclusive Development Index
(IDI) composed of 12 indicators. The new metric implies that countries might
need to realize structural reforms for improving both economic expansion and
social inclusion performance. That is why, it is vital for the IDI calculation
method to have strong statistical and mathematical basis, so that results are
accurate and transparent for public purposes. In the current work, we propose a
novel approach for the IDI estimation - the Ranking Relative Principal
Component Attributes Network Model (REL-PCANet). The model is based on RELARM
and RankNet principles and combines elements of PCA, techniques applied in
image recognition and learning to rank mechanisms. Also, we define a new
approach for estimation of target probabilities matrix to reflect dynamic
changes in countries' inclusive development. Empirical study proved that
REL-PCANet ensures reliable and robust scores and rankings, thus is recommended
for practical implementation.
KBGAN: Adversarial Learning for Knowledge Graph Embeddings
Authors: Liwei Cai, William Yang Wang
Date: 2017-11-11 00:00:00
Proceeding: NAACL 2018 6
Tasks: Graph Embedding, Knowledge Base Completion, Knowledge Graph Embedding, Knowledge Graph Embeddings, Knowledge Graphs, Link Prediction

We introduce KBGAN, an adversarial learning framework to improve the
performances of a wide range of existing knowledge graph embedding models.
Because knowledge graphs typically only contain positive facts, sampling useful
negative training examples is a non-trivial task. Replacing the head or tail
entity of a fact with a uniformly randomly selected entity is a conventional
method for generating negative facts, but the majority of the generated
negative facts can be easily discriminated from positive facts, and will
contribute little towards the training. Inspired by generative adversarial
networks (GANs), we use one knowledge graph embedding model as a negative
sample generator to assist the training of our desired model, which acts as the
discriminator in GANs. This framework is independent of the concrete form of
generator and discriminator, and therefore can utilize a wide variety of
knowledge graph embedding models as its building blocks. In experiments, we
adversarially train two translation-based models, TransE and TransD, each with
assistance from one of the two probability-based models, DistMult and ComplEx.
We evaluate the performances of KBGAN on the link prediction task, using three
knowledge base completion datasets: FB15k-237, WN18 and WN18RR. Experimental
results show that adversarial training substantially improves the performances
of target embedding models under various settings.
3D Human Pose Estimation in the Wild by Adversarial Learning
Authors: Wei Yang, Wanli Ouyang, Xiaolong Wang, Jimmy Ren, Hongsheng Li, Xiaogang Wang
Date: 2018-03-26 00:00:00
Proceeding: CVPR 2018 6
Tasks: 3D Human Pose Estimation, Monocular 3D Human Pose Estimation, Pose Estimation, valid

Recently, remarkable advances have been achieved in 3D human pose estimation
from monocular images because of the powerful Deep Convolutional Neural
Networks (DCNNs). Despite their success on large-scale datasets collected in
the constrained lab environment, it is difficult to obtain the 3D pose
annotations for in-the-wild images. Therefore, 3D human pose estimation in the
wild is still a challenge. In this paper, we propose an adversarial learning
framework, which distills the 3D human pose structures learned from the fully
annotated dataset to in-the-wild images with only 2D pose annotations. Instead
of defining hard-coded rules to constrain the pose estimation results, we
design a novel multi-source discriminator to distinguish the predicted 3D poses
from the ground-truth, which helps to enforce the pose estimator to generate
anthropometrically valid poses even with images in the wild. We also observe
that a carefully designed information source for the discriminator is essential
to boost the performance. Thus, we design a geometric descriptor, which
computes the pairwise relative locations and distances between body joints, as
a new information source for the discriminator. The efficacy of our adversarial
learning framework with the new geometric descriptor has been demonstrated
through extensive experiments on widely used public benchmarks. Our approach
significantly improves the performance compared with previous state-of-the-art
approaches.
A stigmergy-based analysis of city hotspots to discover trends and anomalies in urban transportation usage
Authors: Antonio L. Alfeo, Mario G. C. A. Cimino, Sara Egidi, Bruno Lepri, Gigliola Vaglini
Date: 2018-04-16 00:00:00

A key aspect of a sustainable urban transportation system is the
effectiveness of transportation policies. To be effective, a policy has to
consider a broad range of elements, such as pollution emission, traffic flow,
and human mobility. Due to the complexity and variability of these elements in
the urban area, to produce effective policies remains a very challenging task.
With the introduction of the smart city paradigm, a widely available amount of
data can be generated in the urban spaces. Such data can be a fundamental
source of knowledge to improve policies because they can reflect the
sustainability issues underlying the city. In this context, we propose an
approach to exploit urban positioning data based on stigmergy, a bio-inspired
mechanism providing scalar and temporal aggregation of samples. By employing
stigmergy, samples in proximity with each other are aggregated into a
functional structure called trail. The trail summarizes relevant dynamics in
data and allows matching them, providing a measure of their similarity.
Moreover, this mechanism can be specialized to unfold specific dynamics.
Specifically, we identify high-density urban areas (i.e hotspots), analyze
their activity over time, and unfold anomalies. Moreover, by matching activity
patterns, a continuous measure of the dissimilarity with respect to the typical
activity pattern is provided. This measure can be used by policy makers to
evaluate the effect of policies and change them dynamically. As a case study,
we analyze taxi trip data gathered in Manhattan from 2013 to 2015.
The Relevance of Text and Speech Features in Automatic Non-native English Accent Identification
Authors: Sowmya Vajjala, Ziwei Zhou
Date: 2018-04-16 00:00:00
Tasks: General Classification, Phoneme Recognition

This paper describes our experiments with automatically identifying native
accents from speech samples of non-native English speakers using low level
audio features, and n-gram features from manual transcriptions. Using a
publicly available non-native speech corpus and simple audio feature
representations that do not perform word/phoneme recognition, we show that it
is possible to achieve close to 90% classification accuracy for this task.
While character n-grams perform similar to speech features, we show that speech
features are not affected by prompt variation, whereas ngrams are. Since the
approach followed can be easily adapted to any language provided we have enough
training data, we believe these results will provide useful insights for the
development of accent recognition systems and for the study of accents in the
context of language learning.
Ranking Sentences for Extractive Summarization with Reinforcement Learning
Authors: Shashi Narayan, Shay B. Cohen, Mirella Lapata
Date: 2018-02-23 00:00:00
Proceeding: NAACL 2018 6
Tasks: Document Summarization, Extractive Summarization, Extractive Text Summarization, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL), Sentence

Single document summarization is the task of producing a shorter version of a
document while preserving its principal information content. In this paper we
conceptualize extractive summarization as a sentence ranking task and propose a
novel training algorithm which globally optimizes the ROUGE evaluation metric
through a reinforcement learning objective. We use our algorithm to train a
neural summarization model on the CNN and DailyMail datasets and demonstrate
experimentally that it outperforms state-of-the-art extractive and abstractive
systems when evaluated automatically and by humans.
Organization and Independence or Interdependence? Study of the Neurophysiological Dynamics of Syntactic and Semantic Processing
Authors: Sabine Ploux, Viviane Déprez
Date: 2018-04-16 00:00:00
Tasks: EEG, Electroencephalogram (EEG), Form

In this article we present a multivariate model for determining the different
syntactic, semantic, and form (surface-structure) processes underlying the
comprehension of simple phrases. This model is applied to EEG signals recorded
during a reading task. The results show a hierarchical precedence of the
neurolinguistic processes : form, then syntactic and lastly semantic processes.
We also found (a) that verbs are at the heart of phrase syntax processing, (b)
an interaction between syntactic movement within the phrase, and semantic
processes derived from a person-centered reference frame. Eigenvectors of the
multivariate model provide electrode-times profiles that separate the
distinctive linguistic processes and/or highlight their interaction. The
accordance of these findings with different linguistic theories are discussed.
Neural Kinematic Networks for Unsupervised Motion Retargetting
Authors: Ruben Villegas, Jimei Yang, Duygu Ceylan, Honglak Lee
Date: 2018-04-16 00:00:00
Proceeding: CVPR 2018 6

We propose a recurrent neural network architecture with a Forward Kinematics
layer and cycle consistency based adversarial training objective for
unsupervised motion retargetting. Our network captures the high-level
properties of an input motion by the forward kinematics layer, and adapts them
to a target character with different skeleton bone lengths (e.g., shorter,
longer arms etc.). Collecting paired motion training sequences from different
characters is expensive. Instead, our network utilizes cycle consistency to
learn to solve the Inverse Kinematics problem in an unsupervised manner. Our
method works online, i.e., it adapts the motion sequence on-the-fly as new
frames are received. In our experiments, we use the Mixamo animation data to
test our method for a variety of motions and characters and achieve
state-of-the-art results. We also demonstrate motion retargetting from
monocular human videos to 3D characters using an off-the-shelf 3D pose
estimator.
IterGANs: Iterative GANs to Learn and Control 3D Object Transformation
Authors: Ysbrand Galama, Thomas Mensink
Date: 2018-04-16 00:00:00
Tasks: Object

We are interested in learning visual representations which allow for 3D manipulations of visual objects based on a single 2D image. We cast this into an image-to-image transformation task, and propose Iterative Generative Adversarial Networks (IterGANs) which iteratively transform an input image into an output image. Our models learn a visual representation that can be used for objects seen in training, but also for never seen objects. Since object manipulation requires a full understanding of the geometry and appearance of the object, our IterGANs learn an implicit 3D model and a full appearance model of the object, which are both inferred from a single (test) image. Two advantages of IterGANs are that the intermediate generated images can be used for an additional supervision signal, even in an unsupervised fashion, and that the number of iterations can be used as a control signal to steer the transformation. Experiments on rotated objects and scenes show how IterGANs help with the generation process.
Arabic Named Entity Recognition using Word Representations
Authors: Ismail El Bazi, Nabil Laachfoubi
Date: 2018-04-16 00:00:00
Tasks: Clustering, named-entity-recognition, Named Entity Recognition, Named Entity Recognition (NER), NER

Recent work has shown the effectiveness of the word representations features
in significantly improving supervised NER for the English language. In this
study we investigate whether word representations can also boost supervised NER
in Arabic. We use word representations as additional features in a Conditional
Random Field (CRF) model and we systematically compare three popular neural
word embedding algorithms (SKIP-gram, CBOW and GloVe) and six different
approaches for integrating word representations into NER system. Experimental
results show that Brown Clustering achieves the best performance among the six
approaches. Concerning the word embedding features, the clustering embedding
features outperform other embedding features and the distributional prototypes
produce the second best result. Moreover, the combination of Brown clusters and
word embedding features provides additional improvement of nearly 10% in
F1-score over the baseline.
Direct Sparse Visual-Inertial Odometry using Dynamic Marginalization
Authors: Lukas von Stumberg, Vladyslav Usenko, Daniel Cremers
Date: 2018-04-16 00:00:00

We present VI-DSO, a novel approach for visual-inertial odometry, which
jointly estimates camera poses and sparse scene geometry by minimizing
photometric and IMU measurement errors in a combined energy functional. The
visual part of the system performs a bundle-adjustment like optimization on a
sparse set of points, but unlike key-point based systems it directly minimizes
a photometric error. This makes it possible for the system to track not only
corners, but any pixels with large enough intensity gradients. IMU information
is accumulated between several frames using measurement preintegration, and is
inserted into the optimization as an additional constraint between keyframes.
We explicitly include scale and gravity direction into our model and jointly
optimize them together with other variables such as poses. As the scale is
often not immediately observable using IMU data this allows us to initialize
our visual-inertial system with an arbitrary scale instead of having to delay
the initialization until everything is observable. We perform partial
marginalization of old variables so that updates can be computed in a
reasonable time. In order to keep the system consistent we propose a novel
strategy which we call "dynamic marginalization". This technique allows us to
use partial marginalization even in cases where the initial scale estimate is
far from the optimum. We evaluate our method on the challenging EuRoC dataset,
showing that VI-DSO outperforms the state of the art.
Forex trading and Twitter: Spam, bots, and reputation manipulation
Authors: Igor Mozetič, Peter Gabrovšek, Petra Kralj Novak
Date: 2018-04-06 00:00:00
Tasks: Stance Classification

Currency trading (Forex) is the largest world market in terms of volume. We
analyze trading and tweeting about the EUR-USD currency pair over a period of
three years. First, a large number of tweets were manually labeled, and a
Twitter stance classification model is constructed. The model then classifies
all the tweets by the trading stance signal: buy, hold, or sell (EUR vs. USD).
The Twitter stance is compared to the actual currency rates by applying the
event study methodology, well-known in financial economics. It turns out that
there are large differences in Twitter stance distribution and potential
trading returns between the four groups of Twitter users: trading robots,
spammers, trading companies, and individual traders. Additionally, we observe
attempts of reputation manipulation by post festum removal of tweets with poor
predictions, and deleting/reposting of identical tweets to increase the
visibility without tainting one's Twitter timeline.