pwc-archive/papers-with-abstracts huggingface dataset:
Semi-supervised Inference: General Theory and Estimation of Means
Authors: Anru Zhang, Lawrence D. Brown, T. Tony Cai
Date: 2016-06-23 00:00:00
Tasks: Form

We propose a general semi-supervised inference framework focused on the
estimation of the population mean. As usual in semi-supervised settings, there
exists an unlabeled sample of covariate vectors and a labeled sample consisting
of covariate vectors along with real-valued responses ("labels"). Otherwise,
the formulation is "assumption-lean" in that no major conditions are imposed on
the statistical or functional form of the data. We consider both the ideal
semi-supervised setting where infinitely many unlabeled samples are available,
as well as the ordinary semi-supervised setting in which only a finite number
of unlabeled samples is available.
  Estimators are proposed along with corresponding confidence intervals for the
population mean. Theoretical analysis on both the asymptotic distribution and
$\ell_2$-risk for the proposed procedures are given. Surprisingly, the proposed
estimators, based on a simple form of the least squares method, outperform the
ordinary sample mean. The simple, transparent form of the estimator lends
confidence to the perception that its asymptotic improvement over the ordinary
sample mean also nearly holds even for moderate size samples. The method is
further extended to a nonparametric setting, in which the oracle rate can be
achieved asymptotically. The proposed estimators are further illustrated by
simulation studies and a real data example involving estimation of the homeless
population.
On the Theoretical Capacity of Evolution Strategies to Statistically Learn the Landscape Hessian
Authors: Ofer M. Shir, Jonathan Roslund, Amir Yehudayoff
Date: 2016-06-23 00:00:00

We study the theoretical capacity to statistically learn local landscape
information by Evolution Strategies (ESs). Specifically, we investigate the
covariance matrix when constructed by ESs operating with the selection operator
alone. We model continuous generation of candidate solutions about quadratic
basins of attraction, with deterministic selection of the decision vectors that
minimize the objective function values. Our goal is to rigorously show that
accumulation of winning individuals carries the potential to reveal valuable
information about the search landscape, e.g., as already practically utilized
by derandomized ES variants. We first show that the statistically-constructed
covariance matrix over such winning decision vectors shares the same
eigenvectors with the Hessian matrix about the optimum. We then provide an
analytic approximation of this covariance matrix for a non-elitist multi-child
$(1,\lambda)$-strategy, which holds for a large population size $\lambda$.
Finally, we also numerically corroborate our results.
Verbs Taking Clausal and Non-Finite Arguments as Signals of Modality - Revisiting the Issue of Meaning Grounded in Syntax
Authors: Judith Eckle-Kohler
Date: 2015-09-11 00:00:00
Tasks: Classification, General Classification, Translation

We revisit Levin's theory about the correspondence of verb meaning and syntax
and infer semantic classes from a large syntactic classification of more than
600 German verbs taking clausal and non-finite arguments. Grasping the meaning
components of Levin-classes is known to be hard. We address this challenge by
setting up a multi-perspective semantic characterization of the inferred
classes. To this end, we link the inferred classes and their English
translation to independently constructed semantic classes in three different
lexicons - the German wordnet GermaNet, VerbNet and FrameNet - and perform a
detailed analysis and evaluation of the resulting German-English classification
(available at www.ukp.tu-darmstadt.de/modality-verbclasses/).
Log-based Evaluation of Label Splits for Process Models
Authors: Niek Tax, Natalia Sidorova, Reinder Haakma, Wil M. P. van der Aalst
Date: 2016-06-23 00:00:00

Process mining techniques aim to extract insights in processes from event
logs. One of the challenges in process mining is identifying interesting and
meaningful event labels that contribute to a better understanding of the
process. Our application area is mining data from smart homes for elderly,
where the ultimate goal is to signal deviations from usual behavior and provide
timely recommendations in order to extend the period of independent living.
Extracting individual process models showing user behavior is an important
instrument in achieving this goal. However, the interpretation of sensor data
at an appropriate abstraction level is not straightforward. For example, a
motion sensor in a bedroom can be triggered by tossing and turning in bed or by
getting up. We try to derive the actual activity depending on the context
(time, previous events, etc.). In this paper we introduce the notion of label
refinements, which links more abstract event descriptions with their more
refined counterparts. We present a statistical evaluation method to determine
the usefulness of a label refinement for a given event log from a process
perspective. Based on data from smart homes, we show how our statistical
evaluation method for label refinements can be used in practice. Our method was
able to select two label refinements out of a set of candidate label
refinements that both had a positive effect on model precision.
Saliency Driven Object recognition in egocentric videos with deep CNN
Authors: Philippe Pérez de San Roman, Jenny Benois-Pineau, Jean-Philippe Domenger, Florent Paclet, Daniel Cataert, Aymar de Rugy
Date: 2016-06-23 00:00:00
Tasks: Computational Efficiency, Object Recognition

The problem of object recognition in natural scenes has been recently
successfully addressed with Deep Convolutional Neuronal Networks giving a
significant break-through in recognition scores. The computational efficiency
of Deep CNNs as a function of their depth, allows for their use in real-time
applications. One of the key issues here is to reduce the number of windows
selected from images to be submitted to a Deep CNN. This is usually solved by
preliminary segmentation and selection of specific windows, having outstanding
"objectiveness" or other value of indicators of possible location of objects.
In this paper we propose a Deep CNN approach and the general framework for
recognition of objects in a real-time scenario and in an egocentric
perspective. Here the window of interest is built on the basis of visual
attention map computed over gaze fixations measured by a glass-worn
eye-tracker. The application of this set-up is an interactive user-friendly
environment for upper-limb amputees. Vision has to help the subject to control
his worn neuro-prosthesis in case of a small amount of remaining muscles when
the EMG control becomes unefficient. The recognition results on a specifically
recorded corpus of 151 videos with simple geometrical objects show the mAP of
64,6\% and the computational time at the generalization lower than a time of a
visual fixation on the object-of-interest.
Rotation-Invariant Restricted Boltzmann Machine Using Shared Gradient Filters
Authors: Mario Valerio Giuffrida, Sotirios A. Tsaftaris
Date: 2016-04-24 00:00:00

Finding suitable features has been an essential problem in computer vision.
We focus on Restricted Boltzmann Machines (RBMs), which, despite their
versatility, cannot accommodate transformations that may occur in the scene. As
a result, several approaches have been proposed that consider a set of
transformations, which are used to either augment the training set or transform
the actual learned filters. In this paper, we propose the Explicit
Rotation-Invariant Restricted Boltzmann Machine, which exploits prior
information coming from the dominant orientation of images. Our model extends
the standard RBM, by adding a suitable number of weight matrices, associated
with each dominant gradient. We show that our approach is able to learn
rotation-invariant features, comparing it with the classic formulation of RBM
on the MNIST benchmark dataset. Overall, requiring less hidden units, our
method learns compact features, which are robust to rotations.
Algorithmic Composition of Melodies with Deep Recurrent Neural Networks
Authors: Florian Colombo, Samuel P. Muscinelli, Alexander Seeholzer, Johanni Brea, Wulfram Gerstner
Date: 2016-06-23 00:00:00
Tasks: Rhythm

A big challenge in algorithmic composition is to devise a model that is both
easily trainable and able to reproduce the long-range temporal dependencies
typical of music. Here we investigate how artificial neural networks can be
trained on a large corpus of melodies and turned into automated music composers
able to generate new melodies coherent with the style they have been trained
on. We employ gated recurrent unit networks that have been shown to be
particularly efficient in learning complex sequential activations with
arbitrary long time lags. Our model processes rhythm and melody in parallel
while modeling the relation between these two features. Using such an approach,
we were able to generate interesting complete melodies or suggest possible
continuations of a melody fragment that is coherent with the characteristics of
the fragment itself.
Human Computer Interaction Using Marker Based Hand Gesture Recognition
Authors: Sayem Mohammad Siam, Jahidul Adnan Sakel, Md. Hasanul Kabir
Date: 2016-06-23 00:00:00
Tasks: Gesture Recognition, Hand Gesture Recognition, Hand-Gesture Recognition, Template Matching

Human Computer Interaction (HCI) has been redefined in this era. People want
to interact with their devices in such a way that has physical significance in
the real world, in other words, they want ergonomic input devices. In this
paper, we propose a new method of interaction with computing devices having a
consumer grade camera, that uses two colored markers (red and green) worn on
tips of the fingers to generate desired hand gestures, and for marker detection
and tracking we used template matching with kalman filter. We have implemented
all the usual system commands, i.e., cursor movement, right click, left click,
double click, going forward and backward, zoom in and out through different
hand gestures. Our system can easily recognize these gestures and give
corresponding system commands. Our system is suitable for both desktop devices
and devices where touch screen is not feasible like large screens or projected
screens.
Non Local Spatial and Angular Matching : Enabling higher spatial resolution diffusion MRI datasets through adaptive denoising
Authors: Samuel St-Jean, Pierrick Coupé, Maxime Descoteaux
Date: 2016-06-23 00:00:00
Tasks: Denoising, Diffusion  MRI

Diffusion magnetic resonance imaging datasets suffer from low Signal-to-Noise
Ratio, especially at high b-values. Acquiring data at high b-values contains
relevant information and is now of great interest for microstructural and
connectomics studies. High noise levels bias the measurements due to the
non-Gaussian nature of the noise, which in turn can lead to a false and biased
estimation of the diffusion parameters. Additionally, the usage of in-plane
acceleration techniques during the acquisition leads to a spatially varying
noise distribution, which depends on the parallel acceleration method
implemented on the scanner. This paper proposes a novel diffusion MRI denoising
technique that can be used on all existing data, without adding to the scanning
time. We first apply a statistical framework to convert the noise to Gaussian
distributed noise, effectively removing the bias. We then introduce a spatially
and angular adaptive denoising technique, the Non Local Spatial and Angular
Matching (NLSAM) algorithm. Each volume is first decomposed in small 4D
overlapping patches to capture the structure of the diffusion data and a
dictionary of atoms is learned on those patches. A local sparse decomposition
is then found by bounding the reconstruction error with the local noise
variance. We compare against three other state-of-the-art denoising methods and
show quantitative local and connectivity results on a synthetic phantom and on
an in-vivo high resolution dataset. Overall, our method restores perceptual
information, removes the noise bias in common diffusion metrics, restores the
extracted peaks coherence and improves reproducibility of tractography. Our
work paves the way for higher spatial resolution acquisition of diffusion MRI
datasets, which could in turn reveal new anatomical details that are not
discernible at the spatial resolution currently used by the diffusion MRI
community.
Adaptive Task Assignment in Online Learning Environments
Authors: Per-Arne Andersen, Christian Kråkevik, Morten Goodwin, Anis Yazidi
Date: 2016-06-23 00:00:00

With the increasing popularity of online learning, intelligent tutoring
systems are regaining increased attention. In this paper, we introduce adaptive
algorithms for personalized assignment of learning tasks to student so that to
improve his performance in online learning environments. As main contribution
of this paper, we propose a a novel Skill-Based Task Selector (SBTS) algorithm
which is able to approximate a student's skill level based on his performance
and consequently suggest adequate assignments. The SBTS is inspired by the
class of multi-armed bandit algorithms. However, in contrast to standard
multi-armed bandit approaches, the SBTS aims at acquiring two criteria related
to student learning, namely: which topics should the student work on, and what
level of difficulty should the task be. The SBTS centers on innovative reward
and punishment schemes in a task and skill matrix based on the student
behaviour.
  To verify the algorithm, the complex student behaviour is modelled using a
neighbour node selection approach based on empirical estimations of a students
learning curve. The algorithm is evaluated with a practical scenario from a
basic java programming course. The SBTS is able to quickly and accurately adapt
to the composite student competency --- even with a multitude of student
models.
The combinatorial structure of beta negative binomial processes
Authors: Creighton Heaukulani, Daniel M. Roy
Date: 2013-12-31 00:00:00

We characterize the combinatorial structure of conditionally-i.i.d. sequences
of negative binomial processes with a common beta process base measure. In
Bayesian nonparametric applications, such processes have served as models for
latent multisets of features underlying data. Analogously, random subsets arise
from conditionally-i.i.d. sequences of Bernoulli processes with a common beta
process base measure, in which case the combinatorial structure is described by
the Indian buffet process. Our results give a count analogue of the Indian
buffet process, which we call a negative binomial Indian buffet process. As an
intermediate step toward this goal, we provide a construction for the beta
negative binomial process that avoids a representation of the underlying beta
process base measure. We describe the key Markov kernels needed to use a NB-IBP
representation in a Markov Chain Monte Carlo algorithm targeting a posterior
distribution.
Joint M-Best-Diverse Labelings as a Parametric Submodular Minimization
Authors: Alexander Kirillov, Alexander Shekhovtsov, Carsten Rother, Bogdan Savchynskyy
Date: 2016-06-22 00:00:00
Proceeding: NeurIPS 2016 12
Tasks: Diversity

We consider the problem of jointly inferring the M-best diverse labelings for
a binary (high-order) submodular energy of a graphical model. Recently, it was
shown that this problem can be solved to a global optimum, for many practically
interesting diversity measures. It was noted that the labelings are, so-called,
nested. This nestedness property also holds for labelings of a class of
parametric submodular minimization problems, where different values of the
global parameter $\gamma$ give rise to different solutions. The popular example
of the parametric submodular minimization is the monotonic parametric max-flow
problem, which is also widely used for computing multiple labelings. As the
main contribution of this work we establish a close relationship between
diversity with submodular energies and the parametric submodular minimization.
In particular, the joint M-best diverse labelings can be obtained by running a
non-parametric submodular minimization (in the special case - max-flow) solver
for M different values of $\gamma$ in parallel, for certain diversity measures.
Importantly, the values for $\gamma$ can be computed in a closed form in
advance, prior to any optimization. These theoretical results suggest two
simple yet efficient algorithms for the joint M-best diverse problem, which
outperform competitors in terms of runtime and quality of results. In
particular, as we show in the paper, the new methods compute the exact M-best
diverse labelings faster than a popular method of Batra et al., which in some
sense only obtains approximate solutions.
Unanimous Prediction for 100% Precision with Application to Learning Semantic Mappings
Authors: Fereshte Khani, Martin Rinard, Percy Liang
Date: 2016-06-20 00:00:00
Tasks: Semantic Parsing

Can we train a system that, on any new input, either says "don't know" or
makes a prediction that is guaranteed to be correct? We answer the question in
the affirmative provided our model family is well-specified. Specifically, we
introduce the unanimity principle: only predict when all models consistent with
the training data predict the same output. We operationalize this principle for
semantic parsing, the task of mapping utterances to logical forms. We develop a
simple, efficient method that reasons over the infinite set of all consistent
models by only checking two of the models. We prove that our method obtains
100% precision even with a modest amount of training data from a possibly
adversarial distribution. Empirically, we demonstrate the effectiveness of our
approach on the standard GeoQuery dataset.
Toward a Deep Neural Approach for Knowledge-Based IR
Authors: Gia-Hung Nguyen, Lynda Tamine, Laure Soulier, Nathalie Bricon-Souf
Date: 2016-06-23 00:00:00
Tasks: Ad-Hoc Information Retrieval, Document Ranking, Implicit Relations, Information Retrieval, Retrieval

This paper tackles the problem of the semantic gap between a document and a
query within an ad-hoc information retrieval task. In this context, knowledge
bases (KBs) have already been acknowledged as valuable means since they allow
the representation of explicit relations between entities. However, they do not
necessarily represent implicit relations that could be hidden in a corpora.
This latter issue is tackled by recent works dealing with deep representation
learn ing of texts. With this in mind, we argue that embedding KBs within deep
neural architectures supporting documentquery matching would give rise to
fine-grained latent representations of both words and their semantic relations.
In this paper, we review the main approaches of neural-based document ranking
as well as those approaches for latent representation of entities and relations
via KBs. We then propose some avenues to incorporate KBs in deep neural
approaches for document ranking. More particularly, this paper advocates that
KBs can be used either to support enhanced latent representations of queries
and documents based on both distributional and relational semantics or to serve
as a semantic translator between their latent distributional representations.
Mapping Unseen Words to Task-Trained Embedding Spaces
Authors: Pranava Swaroop Madhyastha, Mohit Bansal, Kevin Gimpel, Karen Livescu
Date: 2015-10-08 00:00:00
Proceeding: WS 2016 8
Tasks: Dependency Parsing, Sentiment Analysis, Word Embeddings

We consider the supervised training setting in which we learn task-specific
word embeddings. We assume that we start with initial embeddings learned from
unlabelled data and update them to learn task-specific embeddings for words in
the supervised training data. However, for new words in the test set, we must
use either their initial embeddings or a single unknown embedding, which often
leads to errors. We address this by learning a neural network to map from
initial embeddings to the task-specific embedding space, via a multi-loss
objective function. The technique is general, but here we demonstrate its use
for improved dependency parsing (especially for sentences with
out-of-vocabulary words), as well as for downstream improvements on sentiment
analysis.
Gender and Interest Targeting for Sponsored Post Advertising at Tumblr
Authors: Mihajlo Grbovic, Vladan Radosavljevic, Nemanja Djuric, Narayan Bhamidipati, Ananth Nagarajan
Date: 2016-06-23 00:00:00
Tasks: Gender Prediction, Language Modeling, Language Modelling

As one of the leading platforms for creative content, Tumblr offers
advertisers a unique way of creating brand identity. Advertisers can tell their
story through images, animation, text, music, video, and more, and promote that
content by sponsoring it to appear as an advertisement in the streams of Tumblr
users. In this paper we present a framework that enabled one of the key
targeted advertising components for Tumblr, specifically gender and interest
targeting. We describe the main challenges involved in development of the
framework, which include creating the ground truth for training gender
prediction models, as well as mapping Tumblr content to an interest taxonomy.
For purposes of inferring user interests we propose a novel semi-supervised
neural language model for categorization of Tumblr content (i.e., post tags and
post keywords). The model was trained on a large-scale data set consisting of
6.8 billion user posts, with very limited amount of categorized keywords, and
was shown to have superior performance over the bag-of-words model. We
successfully deployed gender and interest targeting capability in Yahoo
production systems, delivering inference for users that cover more than 90% of
daily activities at Tumblr. Online performance results indicate advantages of
the proposed approach, where we observed 20% lift in user engagement with
sponsored posts as compared to untargeted campaigns.
Exploring the Space of Adversarial Images
Authors: Pedro Tabacof, Eduardo Valle
Date: 2015-10-19 00:00:00

Adversarial examples have raised questions regarding the robustness and
security of deep neural networks. In this work we formalize the problem of
adversarial images given a pretrained classifier, showing that even in the
linear case the resulting optimization problem is nonconvex. We generate
adversarial images using shallow and deep classifiers on the MNIST and ImageNet
datasets. We probe the pixel space of adversarial images using noise of varying
intensity and distribution. We bring novel visualizations that showcase the
phenomenon and its high variability. We show that adversarial images appear in
large regions in the pixel space, but that, for the same task, a shallow
classifier seems more robust to adversarial images than a deep convolutional
network.