pwc-archive/papers-with-abstracts huggingface dataset:
Insights into Entity Name Evolution on Wikipedia
Authors: Helge Holzmann, Thomas Risse
Date: 2017-02-03 00:00:00
Tasks: Articles, Information Retrieval, Retrieval

Working with Web archives raises a number of issues caused by their temporal
characteristics. Depending on the age of the content, additional knowledge
might be needed to find and understand older texts. Especially facts about
entities are subject to change. Most severe in terms of information retrieval
are name changes. In order to find entities that have changed their name over
time, search engines need to be aware of this evolution. We tackle this problem
by analyzing Wikipedia in terms of entity evolutions mentioned in articles
regardless the structural elements. We gathered statistics and automatically
extracted minimum excerpts covering name changes by incorporating lists
dedicated to that subject. In future work, these excerpts are going to be used
to discover patterns and detect changes in other sources. In this work we
investigate whether or not Wikipedia is a suitable source for extracting the
required knowledge.
Edge-exchangeable graphs and sparsity (NIPS 2016)
Authors: Diana Cai, Trevor Campbell, Tamara Broderick
Date: 2016-12-16 00:00:00

Many popular network models rely on the assumption of (vertex)
exchangeability, in which the distribution of the graph is invariant to
relabelings of the vertices. However, the Aldous-Hoover theorem guarantees that
these graphs are dense or empty with probability one, whereas many real-world
graphs are sparse. We present an alternative notion of exchangeability for
random graphs, which we call edge exchangeability, in which the distribution of
a graph sequence is invariant to the order of the edges. We demonstrate that
edge-exchangeable models, unlike models that are traditionally vertex
exchangeable, can exhibit sparsity. To do so, we outline a general framework
for graph generative models; by contrast to the pioneering work of Caron and
Fox (2015), models within our framework are stationary across steps of the
graph sequence. In particular, our model grows the graph by instantiating more
latent atoms of a single random measure as the dataset size increases, rather
than adding new atoms to the measure.
An Analysis of 1-to-First Matching in Iris Recognition
Authors: Andrey Kuehlkamp, Kevin W. Bowyer
Date: 2017-02-03 00:00:00
Tasks: Iris Recognition

Iris recognition systems are a mature technology that is widely used
throughout the world. In identification (as opposed to verification) mode, an
iris to be recognized is typically matched against all N enrolled irises. This
is the classic "1-to-N search". In order to improve the speed of large-scale
identification, a modified "1-to-First" search has been used in some
operational systems. A 1-to-First search terminates with the first
below-threshold match that is found, whereas a 1-to-N search always finds the
best match across all enrollments. We know of no previous studies that evaluate
how the accuracy of 1-to-First search differs from that of 1-to-N search. Using
a dataset of over 50,000 iris images from 2,800 different irises, we perform
experiments to evaluate the relative accuracy of 1-to-First and 1-to-N search.
We evaluate how the accuracy difference changes with larger numbers of enrolled
irises, and with larger ranges of rotational difference allowed between iris
images. We find that False Match error rate for 1-to-First is higher than for
1-to-N, and the the difference grows with larger number of enrolled irises and
with larger range of rotation.
Learning to Decode for Future Success
Authors: Jiwei Li, Will Monroe, Dan Jurafsky
Date: 2017-01-23 00:00:00
Tasks: Abstractive Text Summarization, Decision Making, Decoder, Machine Translation, Translation

We introduce a simple, general strategy to manipulate the behavior of a
neural decoder that enables it to generate outputs that have specific
properties of interest (e.g., sequences of a pre-specified length). The model
can be thought of as a simple version of the actor-critic model that uses an
interpolation of the actor (the MLE-based token generation policy) and the
critic (a value function that estimates the future values of the desired
property) for decision making. We demonstrate that the approach is able to
incorporate a variety of properties that cannot be handled by standard neural
sequence decoders, such as sequence length and backward probability
(probability of sources given targets), in addition to yielding consistent
improvements in abstractive summarization and machine translation when the
property to be optimized is BLEU or ROUGE scores.
Query Efficient Posterior Estimation in Scientific Experiments via Bayesian Active Learning
Authors: Kirthevasan Kandasamy, Jeff Schneider, Barnabás Póczos
Date: 2017-02-03 00:00:00
Tasks: Active Learning, Gaussian Processes

A common problem in disciplines of applied Statistics research such as
Astrostatistics is of estimating the posterior distribution of relevant
parameters. Typically, the likelihoods for such models are computed via
expensive experiments such as cosmological simulations of the universe. An
urgent challenge in these research domains is to develop methods that can
estimate the posterior with few likelihood evaluations.
  In this paper, we study active posterior estimation in a Bayesian setting
when the likelihood is expensive to evaluate. Existing techniques for posterior
estimation are based on generating samples representative of the posterior.
Such methods do not consider efficiency in terms of likelihood evaluations. In
order to be query efficient we treat posterior estimation in an active
regression framework. We propose two myopic query strategies to choose where to
evaluate the likelihood and implement them using Gaussian processes. Via
experiments on a series of synthetic and real examples we demonstrate that our
approach is significantly more query efficient than existing techniques and
other heuristics for posterior estimation.
Energy Prediction using Spatiotemporal Pattern Networks
Authors: Zhanhong Jiang, Chao Liu, Adedotun Akintayo, Gregor Henze, Soumik Sarkar
Date: 2017-02-03 00:00:00
Tasks: Prediction

This paper presents a novel data-driven technique based on the spatiotemporal
pattern network (STPN) for energy/power prediction for complex dynamical
systems. Built on symbolic dynamic filtering, the STPN framework is used to
capture not only the individual system characteristics but also the pair-wise
causal dependencies among different sub-systems. For quantifying the causal
dependency, a mutual information based metric is presented. An energy
prediction approach is subsequently proposed based on the STPN framework. For
validating the proposed scheme, two case studies are presented, one involving
wind turbine power prediction (supply side energy) using the Western Wind
Integration data set generated by the National Renewable Energy Laboratory
(NREL) for identifying the spatiotemporal characteristics, and the other,
residential electric energy disaggregation (demand side energy) using the
Building America 2010 data set from NREL for exploring the temporal features.
In the energy disaggregation context, convex programming techniques beyond the
STPN framework are developed and applied to achieve improved disaggregation
performance.
Multilingual Multi-modal Embeddings for Natural Language Processing
Authors: Iacer Calixto, Qun Liu, Nick Campbell
Date: 2017-02-03 00:00:00
Tasks: Machine Translation, NMT, Re-Ranking, Semantic Textual Similarity, Sentence, STS, Translation

We propose a novel discriminative model that learns embeddings from
multilingual and multi-modal data, meaning that our model can take advantage of
images and descriptions in multiple languages to improve embedding quality. To
that end, we introduce a modification of a pairwise contrastive estimation
optimisation function as our training objective. We evaluate our embeddings on
an image-sentence ranking (ISR), a semantic textual similarity (STS), and a
neural machine translation (NMT) task. We find that the additional multilingual
signals lead to improvements on both the ISR and STS tasks, and the
discriminative cost can also be used in re-ranking $n$-best lists produced by
NMT models, yielding strong improvements.
Aggregating Binary Local Descriptors for Image Retrieval
Authors: Giuseppe Amato, Fabrizio Falchi, Lucia Vadicamo
Date: 2016-08-02 00:00:00
Tasks: Content-Based Image Retrieval, Image Retrieval, Retrieval

Content-Based Image Retrieval based on local features is computationally
expensive because of the complexity of both extraction and matching of local
feature. On one hand, the cost for extracting, representing, and comparing
local visual descriptors has been dramatically reduced by recently proposed
binary local features. On the other hand, aggregation techniques provide a
meaningful summarization of all the extracted feature of an image into a single
descriptor, allowing us to speed up and scale up the image search. Only a few
works have recently mixed together these two research directions, defining
aggregation methods for binary local features, in order to leverage on the
advantage of both approaches. In this paper, we report an extensive comparison
among state-of-the-art aggregation methods applied to binary features. Then, we
mathematically formalize the application of Fisher Kernels to Bernoulli Mixture
Models. Finally, we investigate the combination of the aggregated binary
features with the emerging Convolutional Neural Network (CNN) features. Our
results show that aggregation methods on binary features are effective and
represent a worthwhile alternative to the direct matching. Moreover, the
combination of the CNN with the Fisher Vector (FV) built upon binary features
allowed us to obtain a relative improvement over the CNN results that is in
line with that recently obtained using the combination of the CNN with the FV
built upon SIFTs. The advantage of using the FV built upon binary features is
that the extraction process of binary features is about two order of magnitude
faster than SIFTs.
Intrinsic Grassmann Averages for Online Linear, Robust and Nonlinear Subspace Learning
Authors: Rudrasis Chakraborty, Søren Hauberg, Baba C. Vemuri
Date: 2017-02-03 00:00:00
Tasks: Dimensionality Reduction

Principal Component Analysis (PCA) and Kernel Principal Component Analysis
(KPCA) are fundamental methods in machine learning for dimensionality
reduction. The former is a technique for finding this approximation in finite
dimensions and the latter is often in an infinite dimensional Reproducing
Kernel Hilbert-space (RKHS). In this paper, we present a geometric framework
for computing the principal linear subspaces in both situations as well as for
the robust PCA case, that amounts to computing the intrinsic average on the
space of all subspaces: the Grassmann manifold. Points on this manifold are
defined as the subspaces spanned by $K$-tuples of observations. The intrinsic
Grassmann average of these subspaces are shown to coincide with the principal
components of the observations when they are drawn from a Gaussian
distribution. We show similar results in the RKHS case and provide an efficient
algorithm for computing the projection onto the this average subspace. The
result is a method akin to KPCA which is substantially faster. Further, we
present a novel online version of the KPCA using our geometric framework.
Competitive performance of all our algorithms are demonstrated on a variety of
real and synthetic data sets.
Maximum Persistency via Iterative Relaxed Inference with Graphical Models
Authors: Alexander Shekhovtsov, Paul Swoboda, Bogdan Savchynskyy
Date: 2015-08-31 00:00:00
Proceeding: CVPR 2015 6

We consider the NP-hard problem of MAP-inference for undirected discrete
graphical models. We propose a polynomial time and practically efficient
algorithm for finding a part of its optimal solution. Specifically, our
algorithm marks some labels of the considered graphical model either as (i)
optimal, meaning that they belong to all optimal solutions of the inference
problem; (ii) non-optimal if they provably do not belong to any solution. With
access to an exact solver of a linear programming relaxation to the
MAP-inference problem, our algorithm marks the maximal possible (in a specified
sense) number of labels. We also present a version of the algorithm, which has
access to a suboptimal dual solver only and still can ensure the
(non-)optimality for the marked labels, although the overall number of the
marked labels may decrease. We propose an efficient implementation, which runs
in time comparable to a single run of a suboptimal dual solver. Our method is
well-scalable and shows state-of-the-art results on computational benchmarks
from machine learning and computer vision.
Appraisal of data-driven and mechanistic emulators of nonlinear hydrodynamic urban drainage simulators
Authors: Juan Pablo Carbajal, João Paulo Leitão, Carlo Albert, Jörg Rieckermann
Date: 2016-09-25 00:00:00
Tasks: Gaussian Processes

Many model based scientific and engineering methodologies, such as system
identification, sensitivity analysis, optimization and control, require a large
number of model evaluations. In particular, model based real-time control of
urban water infrastructures and online flood alarm systems require fast
prediction of the network response at different actuation and/or parameter
values. General purpose urban drainage simulators are too slow for this
application. Fast surrogate models, so-called emulators, provide a solution to
this efficiency demand. Emulators are attractive, because they sacrifice
unneeded accuracy in favor of speed. However, they have to be fine-tuned to
predict the system behavior satisfactorily. Also, some emulators fail to
extrapolate the system behavior beyond the training set. Although, there are
many strategies for developing emulators, up until now the selection of the
emulation strategy remains subjective. In this paper, we therefore compare the
performance of two families of emulators for open channel flows in the context
of urban drainage simulators. We compare emulators that explicitly use
knowledge of the simulator's equations, i.e. mechanistic emulators based on
Gaussian Processes, with purely data-driven emulators using matrix
factorization. Our results suggest that in many urban applications, naive
data-driven emulation outperforms mechanistic emulation. Nevertheless, we
discuss scenarios in which we think that mechanistic emulation might be
favorable for i) extrapolation in time and ii) dealing with sparse and unevenly
sampled data. We also provide many references to advances in the field of
Machine Learning that have not yet permeated into the Bayesian environmental
science community.
An information theoretic formulation of the Dictionary Learning and Sparse Coding Problems on Statistical Manifolds
Authors: Rudrasis Chakraborty, Monami Banerjee, Victoria Crawford, Baba C. Vemuri
Date: 2016-04-23 00:00:00
Tasks: Dictionary Learning, General Classification

In this work, we propose a novel information theoretic framework for
dictionary learning (DL) and sparse coding (SC) on a statistical manifold (the
manifold of probability distributions). Unlike the traditional DL and SC
framework, our new formulation {\it does not explicitly incorporate any
sparsity inducing norm in the cost function but yet yields SCs}. Moreover, we
extend this framework to the manifold of symmetric positive definite matrices,
$\mathcal{P}_n$. Our algorithm approximates the data points, which are
probability distributions, by the weighted Kullback-Leibeler center (KL-center)
of the dictionary atoms. The KL-center is the minimizer of the maximum
KL-divergence between the unknown center and members of the set whose center is
being sought. Further, {\it we proved that this KL-center is a sparse
combination of the dictionary atoms}. Since, the data reside on a statistical
manifold, the data fidelity term can not be as simple as in the case of the
vector-space data. We therefore employ the geodesic distance between the data
and a sparse approximation of the data element. This cost function is minimized
using an acceleterated gradient descent algorithm. An extensive set of
experimental results show the effectiveness of our proposed framework. We
present several experiments involving a variety of classification problems in
Computer Vision applications. Further, we demonstrate the performance of our
algorithm by comparing it to several state-of-the-art methods both in terms of
classification accuracy and sparsity.
Detecting changes in slope with an $L_0$ penalty
Authors: Robert Maidstone, Paul Fearnhead, Adam Letchford
Date: 2017-01-06 00:00:00
Tasks: Time Series, Time Series Analysis

Whilst there are many approaches to detecting changes in mean for a
univariate time-series, the problem of detecting multiple changes in slope has
comparatively been ignored. Part of the reason for this is that detecting
changes in slope is much more challenging. For example, simple binary
segmentation procedures do not work for this problem, whilst efficient dynamic
programming methods that work well for the change in mean problem cannot be
directly used for detecting changes in slope. We present a novel dynamic
programming approach, CPOP, for finding the "best" continuous piecewise-linear
fit to data. We define best based on a criterion that measures fit to data
using the residual sum of squares, but penalises complexity based on an $L_0$
penalty on changes in slope. We show that using such a criterion is more
reliable at estimating changepoint locations than approaches that penalise
complexity using an $L_1$ penalty. Empirically CPOP has good computational
properties, and can analyse a time-series with over 10,000 observations and
over 100 changes in a few minutes. Our method is used to analyse data on the
motion of bacteria, and provides fits to the data that both have substantially
smaller residual sum of squares and are more parsimonious than two competing
approaches.
Deep Learning with Low Precision by Half-wave Gaussian Quantization
Authors: Zhaowei Cai, Xiaodong He, Jian Sun, Nuno Vasconcelos
Date: 2017-02-03 00:00:00
Proceeding: CVPR 2017 7
Tasks: Deep Learning, Quantization

The problem of quantizing the activations of a deep neural network is
considered. An examination of the popular binary quantization approach shows
that this consists of approximating a classical non-linearity, the hyperbolic
tangent, by two functions: a piecewise constant sign function, which is used in
feedforward network computations, and a piecewise linear hard tanh function,
used in the backpropagation step during network learning. The problem of
approximating the ReLU non-linearity, widely used in the recent deep learning
literature, is then considered. An half-wave Gaussian quantizer (HWGQ) is
proposed for forward approximation and shown to have efficient implementation,
by exploiting the statistics of of network activations and batch normalization
operations commonly used in the literature. To overcome the problem of gradient
mismatch, due to the use of different forward and backward approximations,
several piece-wise backward approximators are then investigated. The
implementation of the resulting quantized network, denoted as HWGQ-Net, is
shown to achieve much closer performance to full precision networks, such as
AlexNet, ResNet, GoogLeNet and VGG-Net, than previously available low-precision
networks, with 1-bit binary weights and 2-bit quantized activations.
Scalable Nearest Neighbor Search based on kNN Graph
Authors: Wan-Lei Zhao, Jie Yang, Cheng-Hao Deng
Date: 2017-01-30 00:00:00
Tasks: graph construction, Quantization

Nearest neighbor search is known as a challenging issue that has been studied
for several decades. Recently, this issue becomes more and more imminent in
viewing that the big data problem arises from various fields. In this paper, a
scalable solution based on hill-climbing strategy with the support of k-nearest
neighbor graph (kNN) is presented. Two major issues have been considered in the
paper. Firstly, an efficient kNN graph construction method based on two means
tree is presented. For the nearest neighbor search, an enhanced hill-climbing
procedure is proposed, which sees considerable performance boost over original
procedure. Furthermore, with the support of inverted indexing derived from
residue vector quantization, our method achieves close to 100% recall with high
speed efficiency in two state-of-the-art evaluation benchmarks. In addition, a
comparative study on both the compressional and traditional nearest neighbor
search methods is presented. We show that our method achieves the best
trade-off between search quality, efficiency and memory complexity.
A method of limiting performance loss of CNNs in noisy environments
Authors: James R. Geraci, Parichay Kapoor
Date: 2017-02-03 00:00:00
Tasks: Denoising

Convolutional Neural Network (CNN) recognition rates drop in the presence of
noise. We demonstrate a novel method of counteracting this drop in recognition
rate by adjusting the biases of the neurons in the convolutional layers
according to the noise conditions encountered at runtime. We compare our
technique to training one network for all possible noise levels, dehazing via
preprocessing a signal with a denoising autoencoder, and training a network
specifically for each noise level. Our system compares favorably in terms of
robustness, computational complexity and recognition rate.
FCSS: Fully Convolutional Self-Similarity for Dense Semantic Correspondence
Authors: Seungryong Kim, Dongbo Min, Bumsub Ham, Sangryul Jeon, Stephen Lin, Kwanghoon Sohn
Date: 2017-02-03 00:00:00
Proceeding: CVPR 2017 7
Tasks: Object, Semantic correspondence, Weakly-supervised Learning

We present a descriptor, called fully convolutional self-similarity (FCSS),
for dense semantic correspondence. To robustly match points among different
instances within the same object class, we formulate FCSS using local
self-similarity (LSS) within a fully convolutional network. In contrast to
existing CNN-based descriptors, FCSS is inherently insensitive to intra-class
appearance variations because of its LSS-based structure, while maintaining the
precise localization ability of deep neural networks. The sampling patterns of
local structure and the self-similarity measure are jointly learned within the
proposed network in an end-to-end and multi-scale manner. As training data for
semantic correspondence is rather limited, we propose to leverage object
candidate priors provided in existing image datasets and also correspondence
consistency between object pairs to enable weakly-supervised learning.
Experiments demonstrate that FCSS outperforms conventional handcrafted
descriptors and CNN-based descriptors on various benchmarks.
A Discriminatively Learned CNN Embedding for Person Re-identification
Authors: Zhedong Zheng, Liang Zheng, Yi Yang
Date: 2016-11-17 00:00:00
Tasks: General Classification, Image Retrieval, Person Re-Identification, Retrieval

We revisit two popular convolutional neural networks (CNN) in person
re-identification (re-ID), i.e, verification and classification models. The two
models have their respective advantages and limitations due to different loss
functions. In this paper, we shed light on how to combine the two models to
learn more discriminative pedestrian descriptors. Specifically, we propose a
new siamese network that simultaneously computes identification loss and
verification loss. Given a pair of training images, the network predicts the
identities of the two images and whether they belong to the same identity. Our
network learns a discriminative embedding and a similarity measurement at the
same time, thus making full usage of the annotations. Albeit simple, the
learned embedding improves the state-of-the-art performance on two public
person re-ID benchmarks. Further, we show our architecture can also be applied
in image retrieval.