pwc-archive/papers-with-abstracts huggingface dataset:
Unitary-Group Invariant Kernels and Features from Transformed Unlabeled Data
Authors: Dipan K. Pal, Marios Savvides
Date: 2015-11-18 00:00:00

The study of representations invariant to common transformations of the data
is important to learning. Most techniques have focused on local approximate
invariance implemented within expensive optimization frameworks lacking
explicit theoretical guarantees. In this paper, we study kernels that are
invariant to the unitary group while having theoretical guarantees in
addressing practical issues such as (1) unavailability of transformed versions
of labelled data and (2) not observing all transformations. We present a
theoretically motivated alternate approach to the invariant kernel SVM. Unlike
previous approaches to the invariant SVM, the proposed formulation solves both
issues mentioned. We also present a kernel extension of a recent technique to
extract linear unitary-group invariant features addressing both issues and
extend some guarantees regarding invariance and stability. We present
experiments on the UCI ML datasets to illustrate and validate our methods.
On the Global Linear Convergence of Frank-Wolfe Optimization Variants
Authors: Simon Lacoste-Julien, Martin Jaggi
Date: 2015-11-18 00:00:00
Proceeding: NeurIPS 2015 12

The Frank-Wolfe (FW) optimization algorithm has lately re-gained popularity
thanks in particular to its ability to nicely handle the structured constraints
appearing in machine learning applications. However, its convergence rate is
known to be slow (sublinear) when the solution lies at the boundary. A simple
less-known fix is to add the possibility to take 'away steps' during
optimization, an operation that importantly does not require a feasibility
oracle. In this paper, we highlight and clarify several variants of the
Frank-Wolfe optimization algorithm that have been successfully applied in
practice: away-steps FW, pairwise FW, fully-corrective FW and Wolfe's minimum
norm point algorithm, and prove for the first time that they all enjoy global
linear convergence, under a weaker condition than strong convexity of the
objective. The constant in the convergence rate has an elegant interpretation
as the product of the (classical) condition number of the function with a novel
geometric quantity that plays the role of a 'condition number' of the
constraint set. We provide pointers to where these algorithms have made a
difference in practice, in particular with the flow polytope, the marginal
polytope and the base polytope for submodular optimization.
Combining Neural Networks and Log-linear Models to Improve Relation Extraction
Authors: Thien Huu Nguyen, Ralph Grishman
Date: 2015-11-18 00:00:00
Tasks: Relation, Relation Extraction, Sentence

The last decade has witnessed the success of the traditional feature-based
method on exploiting the discrete structures such as words or lexical patterns
to extract relations from text. Recently, convolutional and recurrent neural
networks has provided very effective mechanisms to capture the hidden
structures within sentences via continuous representations, thereby
significantly advancing the performance of relation extraction. The advantage
of convolutional neural networks is their capacity to generalize the
consecutive k-grams in the sentences while recurrent neural networks are
effective to encode long ranges of sentence context. This paper proposes to
combine the traditional feature-based method, the convolutional and recurrent
neural networks to simultaneously benefit from their advantages. Our systematic
evaluation of different network architectures and combination methods
demonstrates the effectiveness of this approach and results in the
state-of-the-art performance on the ACE 2005 and SemEval dataset.
Collecting and Annotating the Large Continuous Action Dataset
Authors: Daniel Paul Barrett, ran Xu, Haonan Yu, Jeffrey Mark Siskind
Date: 2015-11-18 00:00:00
Tasks: Action Recognition, Temporal Action Localization

We make available to the community a new dataset to support
action-recognition research. This dataset is different from prior datasets in
several key ways. It is significantly larger. It contains streaming video with
long segments containing multiple action occurrences that often overlap in
space and/or time. All actions were filmed in the same collection of
backgrounds so that background gives little clue as to action class. We had
five humans replicate the annotation of temporal extent of action occurrences
labeled with their class and measured a surprisingly low level of intercoder
agreement. A baseline experiment shows that recent state-of-the-art methods
perform poorly on this dataset. This suggests that this will be a challenging
dataset to foster advances in action-recognition research. This manuscript
serves to describe the novel content and characteristics of the LCA dataset,
present the design decisions made when filming the dataset, and document the
novel methods employed to annotate the dataset.
Multilingual Image Description with Neural Sequence Models
Authors: Desmond Elliott, Stella Frank, Eva Hasler
Date: 2015-10-15 00:00:00
Tasks: Image Captioning, Image Description, Translation

In this paper we present an approach to multi-language image description
bringing together insights from neural machine translation and neural image
description. To create a description of an image for a given target language,
our sequence generation models condition on feature vectors from the image, the
description from the source language, and/or a multimodal vector computed over
the image and a description in the source language. In image description
experiments on the IAPR-TC12 dataset of images aligned with English and German
sentences, we find significant and substantial improvements in BLEU4 and Meteor
scores for models trained over multiple languages, compared to a monolingual
baseline.
Eigenspectra optoacoustic tomography achieves quantitative blood oxygenation imaging deep in tissues
Authors: Stratis Tzoumas, Antonio Nunes, Ivan Olefir, Stefan Stangl, Panagiotis Symvoulidis, Sarah Glasl, Christine Bayer, Gabriele Multhoff, Vasilis Ntziachristos
Date: 2015-11-18 00:00:00

Light propagating in tissue attains a spectrum that varies with location due
to wavelength-dependent fluence attenuation by tissue optical properties, an
effect that causes spectral corruption. Predictions of the spectral variations
of light fluence in tissue are challenging since the spatial distribution of
optical properties in tissue cannot be resolved in high resolution or with high
accuracy by current methods. Spectral corruption has fundamentally limited the
quantification accuracy of optical and optoacoustic methods and impeded the
long sought-after goal of imaging blood oxygen saturation (sO2) deep in
tissues; a critical but still unattainable target for the assessment of
oxygenation in physiological processes and disease. We discover a new principle
underlying light fluence in tissues, which describes the wavelength dependence
of light fluence as an affine function of a few reference base spectra,
independently of the specific distribution of tissue optical properties. This
finding enables the introduction of a previously undocumented concept termed
eigenspectra Multispectral Optoacoustic Tomography (eMSOT) that can effectively
account for wavelength dependent light attenuation without explicit knowledge
of the tissue optical properties. We validate eMSOT in more than 2000
simulations and with phantom and animal measurements. We find that eMSOT can
quantitatively image tissue sO2 reaching in many occasions a better than
10-fold improved accuracy over conventional spectral optoacoustic methods.
Then, we show that eMSOT can spatially resolve sO2 in muscle and tumor;
revealing so far unattainable tissue physiology patterns. Last, we related
eMSOT readings to cancer hypoxia and found congruence between eMSOT tumor sO2
images and tissue perfusion and hypoxia maps obtained by correlative
histological analysis.
Using Machine Learning to Predict the Outcome of English County twenty over Cricket Matches
Authors: Stylianos Kampakis, William Thomas
Date: 2015-11-18 00:00:00
Tasks: BIG-bench Machine Learning, Game of Cricket

Cricket betting is a multi-billion dollar market. Therefore, there is a
strong incentive for models that can predict the outcomes of games and beat the
odds provided by bookers. The aim of this study was to investigate to what
degree it is possible to predict the outcome of cricket matches. The target
competition was the English twenty over county cricket cup. The original
features alongside engineered features gave rise to more than 500 team and
player statistics. The models were optimized firstly with team features only
and then both team and player features. The performance of the models was
tested over individual seasons from 2009 to 2014 having been trained over
previous season data in each case. The optimal model was a simple prediction
method combined with complex hierarchical features and was shown to
significantly outperform a gambling industry benchmark.
Enhancements in statistical spoken language translation by de-normalization of ASR results
Authors: Agnieszka Wołk, Krzysztof Wołk, Krzysztof Marasek
Date: 2015-11-18 00:00:00
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), Machine Translation, Segmentation, Sentence, Sentence segmentation, speech-recognition, Speech Recognition, Translation

Spoken language translation (SLT) has become very important in an
increasingly globalized world. Machine translation (MT) for automatic speech
recognition (ASR) systems is a major challenge of great interest. This research
investigates that automatic sentence segmentation of speech that is important
for enriching speech recognition output and for aiding downstream language
processing. This article focuses on the automatic sentence segmentation of
speech and improving MT results. We explore the problem of identifying sentence
boundaries in the transcriptions produced by automatic speech recognition
systems in the Polish language. We also experiment with reverse normalization
of the recognized speech samples.
Harvesting comparable corpora and mining them for equivalent bilingual sentences using statistical classification and analogy- based heuristics
Authors: Krzysztof Wołk, Emilia Rejmund, Krzysztof Marasek
Date: 2015-11-18 00:00:00
Tasks: General Classification, Machine Translation, Retrieval, Translation

Parallel sentences are a relatively scarce but extremely useful resource for
many applications including cross-lingual retrieval and statistical machine
translation. This research explores our new methodologies for mining such data
from previously obtained comparable corpora. The task is highly practical since
non-parallel multilingual data exist in far greater quantities than parallel
corpora, but parallel sentences are a much more useful resource. Here we
propose a web crawling method for building subject-aligned comparable corpora
from e.g. Wikipedia dumps and Euronews web page. The improvements in machine
translation are shown on Polish-English language pair for various text domains.
We also tested another method of building parallel corpora based on comparable
corpora data. It lets automatically broad existing corpus of sentences from
subject of corpora based on analogies between them.
Factorization, Inference and Parameter Learning in Discrete AMP Chain Graphs
Authors: Jose M. Peña
Date: 2015-01-27 00:00:00

We address some computational issues that may hinder the use of AMP chain
graphs in practice. Specifically, we show how a discrete probability
distribution that satisfies all the independencies represented by an AMP chain
graph factorizes according to it. We show how this factorization makes it
possible to perform inference and parameter learning efficiently, by adapting
existing algorithms for Markov and Bayesian networks. Finally, we turn our
attention to another issue that may hinder the use of AMP CGs, namely the lack
of an intuitive interpretation of their edges. We provide one such
interpretation.
Labeled pupils in the wild: A dataset for studying pupil detection in unconstrained environments
Authors: Marc Tonsen, Xucong Zhang, Yusuke Sugano, Andreas Bulling
Date: 2015-11-18 00:00:00
Tasks: Pupil Detection

We present labelled pupils in the wild (LPW), a novel dataset of 66
high-quality, high-speed eye region videos for the development and evaluation
of pupil detection algorithms. The videos in our dataset were recorded from 22
participants in everyday locations at about 95 FPS using a state-of-the-art
dark-pupil head-mounted eye tracker. They cover people with different
ethnicities, a diverse set of everyday indoor and outdoor illumination
environments, as well as natural gaze direction distributions. The dataset also
includes participants wearing glasses, contact lenses, as well as make-up. We
benchmark five state-of-the-art pupil detection algorithms on our dataset with
respect to robustness and accuracy. We further study the influence of image
resolution, vision aids, as well as recording location (indoor, outdoor) on
pupil detection performance. Our evaluations provide valuable insights into the
general pupil detection problem and allow us to identify key challenges for
robust pupil detection on head-mounted eye trackers.
Image Question Answering using Convolutional Neural Network with Dynamic Parameter Prediction
Authors: Hyeonwoo Noh, Paul Hongsuck Seo, Bohyung Han
Date: 2015-11-18 00:00:00
Proceeding: CVPR 2016 6
Tasks: Image Retrieval with Multi-Modal Query, Parameter Prediction, Prediction, Question Answering, Visual Question Answering (VQA)

We tackle image question answering (ImageQA) problem by learning a
convolutional neural network (CNN) with a dynamic parameter layer whose weights
are determined adaptively based on questions. For the adaptive parameter
prediction, we employ a separate parameter prediction network, which consists
of gated recurrent unit (GRU) taking a question as its input and a
fully-connected layer generating a set of candidate weights as its output.
However, it is challenging to construct a parameter prediction network for a
large number of parameters in the fully-connected dynamic parameter layer of
the CNN. We reduce the complexity of this problem by incorporating a hashing
technique, where the candidate weights given by the parameter prediction
network are selected using a predefined hash function to determine individual
weights in the dynamic parameter layer. The proposed network---joint network
with the CNN for ImageQA and the parameter prediction network---is trained
end-to-end through back-propagation, where its weights are initialized using a
pre-trained CNN and GRU. The proposed algorithm illustrates the
state-of-the-art performance on all available public ImageQA benchmarks.
backShift: Learning causal cyclic graphs from unknown shift interventions
Authors: Dominik Rothenhäusler, Christina Heinze, Jonas Peters, Nicolai Meinshausen
Date: 2015-06-08 00:00:00
Proceeding: NeurIPS 2015 12
Tasks: Time Series, Time Series Analysis

We propose a simple method to learn linear causal cyclic models in the
presence of latent variables. The method relies on equilibrium data of the
model recorded under a specific kind of interventions ("shift interventions").
The location and strength of these interventions do not have to be known and
can be estimated from the data. Our method, called backShift, only uses second
moments of the data and performs simple joint matrix diagonalization, applied
to differences between covariance matrices. We give a sufficient and necessary
condition for identifiability of the system, which is fulfilled almost surely
under some quite general assumptions if and only if there are at least three
distinct experimental settings, one of which can be pure observational data. We
demonstrate the performance on some simulated data and applications in flow
cytometry and financial time series. The code is made available as R-package
backShift.
Solution Repair/Recovery in Uncertain Optimization Environment
Authors: Oumaima Khaled
Date: 2015-11-18 00:00:00
Tasks: Management, Scheduling

Operation management problems (such as Production Planning and Scheduling)
are represented and formulated as optimization models. The resolution of such
optimization models leads to solutions which have to be operated in an
organization. However, the conditions under which the optimal solution is
obtained rarely correspond exactly to the conditions under which the solution
will be operated in the organization.Therefore, in most practical contexts, the
computed optimal solution is not anymore optimal under the conditions in which
it is operated. Indeed, it can be "far from optimal" or even not feasible. For
different reasons, we hadn't the possibility to completely re-optimize the
existing solution or plan. As a consequence, it is necessary to look for
"repair solutions", i.e., solutions that have a good behavior with respect to
possible scenarios, or with respect to uncertainty of the parameters of the
model. To tackle the problem, the computed solution should be such that it is
possible to "repair" it through a local re-optimization guided by the user or
through a limited change aiming at minimizing the impact of taking into
consideration the scenarios.
Sparse learning of maximum likelihood model for optimization of complex loss function
Authors: Ning Zhang, Prathamesh Chandrasekar
Date: 2015-11-18 00:00:00
Tasks: image-classification, Image Classification, Sparse Learning

Traditional machine learning methods usually minimize a simple loss function
to learn a predictive model, and then use a complex performance measure to
measure the prediction performance. However, minimizing a simple loss function
cannot guarantee that an optimal performance. In this paper, we study the
problem of optimizing the complex performance measure directly to obtain a
predictive model. We proposed to construct a maximum likelihood model for this
problem, and to learn the model parameter, we minimize a com- plex loss
function corresponding to the desired complex performance measure. To optimize
the loss function, we approximate the upper bound of the complex loss. We also
propose impose the sparsity to the model parameter to obtain a sparse model. An
objective is constructed by combining the upper bound of the loss function and
the sparsity of the model parameter, and we develop an iterative algorithm to
minimize it by using the fast iterative shrinkage- thresholding algorithm
framework. The experiments on optimization on three different complex
performance measures, including F-score, receiver operating characteristic
curve, and recall precision curve break even point, over three real-world
applications, aircraft event recognition of civil aviation safety, in- trusion
detection in wireless mesh networks, and image classification, show the
advantages of the proposed method over state-of-the-art methods.
A Random Forest Guided Tour
Authors: Gérard Biau, Erwan Scornet
Date: 2015-11-18 00:00:00
Tasks: General Classification

The random forest algorithm, proposed by L. Breiman in 2001, has been
extremely successful as a general-purpose classification and regression method.
The approach, which combines several randomized decision trees and aggregates
their predictions by averaging, has shown excellent performance in settings
where the number of variables is much larger than the number of observations.
Moreover, it is versatile enough to be applied to large-scale problems, is
easily adapted to various ad-hoc learning tasks, and returns measures of
variable importance. The present article reviews the most recent theoretical
and methodological developments for random forests. Emphasis is placed on the
mathematical forces driving the algorithm, with special attention given to the
selection of parameters, the resampling mechanism, and variable importance
measures. This review is intended to provide non-experts easy access to the
main ideas.
Stochastic Expectation Propagation
Authors: Yingzhen Li, Jose Miguel Hernandez-Lobato, Richard E. Turner
Date: 2015-06-12 00:00:00
Proceeding: NeurIPS 2015 12
Tasks: Variational Inference

Expectation propagation (EP) is a deterministic approximation algorithm that
is often used to perform approximate Bayesian parameter learning. EP
approximates the full intractable posterior distribution through a set of local
approximations that are iteratively refined for each datapoint. EP can offer
analytic and computational advantages over other approximations, such as
Variational Inference (VI), and is the method of choice for a number of models.
The local nature of EP appears to make it an ideal candidate for performing
Bayesian learning on large models in large-scale dataset settings. However, EP
has a crucial limitation in this context: the number of approximating factors
needs to increase with the number of data-points, N, which often entails a
prohibitively large memory overhead. This paper presents an extension to EP,
called stochastic expectation propagation (SEP), that maintains a global
posterior approximation (like VI) but updates it in a local way (like EP).
Experiments on a number of canonical learning problems using synthetic and
real-world datasets indicate that SEP performs almost as well as full EP, but
reduces the memory consumption by a factor of $N$. SEP is therefore ideally
suited to performing approximate Bayesian learning in the large model, large
dataset setting.
Online learning in repeated auctions
Authors: Jonathan Weed, Vianney Perchet, Philippe Rigollet
Date: 2015-11-18 00:00:00

Motivated by online advertising auctions, we consider repeated Vickrey
auctions where goods of unknown value are sold sequentially and bidders only
learn (potentially noisy) information about a good's value once it is
purchased. We adopt an online learning approach with bandit feedback to model
this problem and derive bidding strategies for two models: stochastic and
adversarial. In the stochastic model, the observed values of the goods are
random variables centered around the true value of the good. In this case,
logarithmic regret is achievable when competing against well behaved
adversaries. In the adversarial model, the goods need not be identical and we
simply compare our performance against that of the best fixed bid in hindsight.
We show that sublinear regret is also achievable in this case and prove
matching minimax lower bounds. To our knowledge, this is the first complete set
of strategies for bidders participating in auctions of this type.
Using Abduction in Markov Logic Networks for Root Cause Analysis
Authors: Joerg Schoenfisch, Janno von Stulpnagel, Jens Ortmann, Christian Meilicke, Heiner Stuckenschmidt
Date: 2015-11-18 00:00:00
Tasks: Management

IT infrastructure is a crucial part in most of today's business operations.
High availability and reliability, and short response times to outages are
essential. Thus a high amount of tool support and automation in risk management
is desirable to decrease outages. We propose a new approach for calculating the
root cause for an observed failure in an IT infrastructure. Our approach is
based on Abduction in Markov Logic Networks. Abduction aims to find an
explanation for a given observation in the light of some background knowledge.
In failure diagnosis, the explanation corresponds to the root cause, the
observation to the failure of a component, and the background knowledge to the
dependency graph extended by potential risks. We apply a method to extend a
Markov Logic Network in order to conduct abductive reasoning, which is not
naturally supported in this formalism. Our approach exhibits a high amount of
reusability and enables users without specific knowledge of a concrete
infrastructure to gain viable insights in the case of an incident. We
implemented the method in a tool and illustrate its suitability for root cause
analysis by applying it to a sample scenario.