pwc-archive/papers-with-abstracts huggingface dataset:
Population size predicts lexical diversity, but so does the mean sea level - why it is important to correctly account for the structure of temporal data
Authors: Alexander Koplenig, Carolin Mueller-Spitzer
Date: 2015-11-06 00:00:00
Tasks: Diversity, Time Series Analysis

In order to demonstrate why it is important to correctly account for the
(serial dependent) structure of temporal data, we document an apparently
spectacular relationship between population size and lexical diversity: for
five out of seven investigated languages, there is a strong relationship
between population size and lexical diversity of the primary language in this
country. We show that this relationship is the result of a misspecified model
that does not consider the temporal aspect of the data by presenting a similar
but nonsensical relationship between the global annual mean sea level and
lexical diversity. Given the fact that in the recent past, several studies were
published that present surprising links between different economic, cultural,
political and (socio-)demographical variables on the one hand and cultural or
linguistic characteristics on the other hand, but seem to suffer from exactly
this problem, we explain the cause of the misspecification and show that it has
profound consequences. We demonstrate how simple transformation of the time
series can often solve problems of this type and argue that the evaluation of
the plausibility of a relationship is important in this context. We hope that
our paper will help both researchers and reviewers to understand why it is
important to use special models for the analysis of data with a natural
temporal ordering.
Fast and Accurate Deep Network Learning by Exponential Linear Units (ELUs)
Authors: Djork-Arné Clevert, Thomas Unterthiner, Sepp Hochreiter
Date: 2015-11-23 00:00:00
Tasks: General Classification, Image Classification

We introduce the "exponential linear unit" (ELU) which speeds up learning in
deep neural networks and leads to higher classification accuracies. Like
rectified linear units (ReLUs), leaky ReLUs (LReLUs) and parametrized ReLUs
(PReLUs), ELUs alleviate the vanishing gradient problem via the identity for
positive values. However, ELUs have improved learning characteristics compared
to the units with other activation functions. In contrast to ReLUs, ELUs have
negative values which allows them to push mean unit activations closer to zero
like batch normalization but with lower computational complexity. Mean shifts
toward zero speed up learning by bringing the normal gradient closer to the
unit natural gradient because of a reduced bias shift effect. While LReLUs and
PReLUs have negative values, too, they do not ensure a noise-robust
deactivation state. ELUs saturate to a negative value with smaller inputs and
thereby decrease the forward propagated variation and information. Therefore,
ELUs code the degree of presence of particular phenomena in the input, while
they do not quantitatively model the degree of their absence. In experiments,
ELUs lead not only to faster learning, but also to significantly better
generalization performance than ReLUs and LReLUs on networks with more than 5
layers. On CIFAR-100 ELUs networks significantly outperform ReLU networks with
batch normalization while batch normalization does not improve ELU networks.
ELU networks are among the top 10 reported CIFAR-10 results and yield the best
published result on CIFAR-100, without resorting to multi-view evaluation or
model averaging. On ImageNet, ELU networks considerably speed up learning
compared to a ReLU network with the same architecture, obtaining less than 10%
classification error for a single crop, single model network.
Eye-CU: Sleep Pose Classification for Healthcare using Multimodal Multiview Data
Authors: Carlos Torres, Victor Fragoso, Scott D. Hammond, Jeffrey C. Fried, B. S. Manjunath
Date: 2016-02-07 00:00:00
Tasks: General Classification

Manual analysis of body poses of bed-ridden patients requires staff to
continuously track and record patient poses. Two limitations in the
dissemination of pose-related therapies are scarce human resources and
unreliable automated systems. This work addresses these issues by introducing a
new method and a new system for robust automated classification of sleep poses
in an Intensive Care Unit (ICU) environment. The new method,
coupled-constrained Least-Squares (cc-LS), uses multimodal and multiview (MM)
data and finds the set of modality trust values that minimizes the difference
between expected and estimated labels. The new system, Eye-CU, is an affordable
multi-sensor modular system for unobtrusive data collection and analysis in
healthcare. Experimental results indicate that the performance of cc-LS matches
the performance of existing methods in ideal scenarios. This method outperforms
the latest techniques in challenging scenarios by 13% for those with poor
illumination and by 70% for those with both poor illumination and occlusions.
Results also show that a reduced Eye-CU configuration can classify poses
without pressure information with only a slight drop in its performance.
Structured Learning of Binary Codes with Column Generation
Authors: Guosheng Lin, Fayao Liu, Chunhua Shen, Jianxin Wu, Heng Tao Shen
Date: 2016-02-22 00:00:00
Tasks: Image Retrieval, Information Retrieval, Retrieval, Triplet

Hashing methods aim to learn a set of hash functions which map the original
features to compact binary codes with similarity preserving in the Hamming
space. Hashing has proven a valuable tool for large-scale information
retrieval. We propose a column generation based binary code learning framework
for data-dependent hash function learning. Given a set of triplets that encode
the pairwise similarity comparison information, our column generation based
method learns hash functions that preserve the relative comparison relations
within the large-margin learning framework. Our method iteratively learns the
best hash functions during the column generation procedure. Existing hashing
methods optimize over simple objectives such as the reconstruction error or
graph Laplacian related loss functions, instead of the performance evaluation
criteria of interest---multivariate performance measures such as the AUC and
NDCG. Our column generation based method can be further generalized from the
triplet loss to a general structured learning based framework that allows one
to directly optimize multivariate performance measures. For optimizing general
ranking measures, the resulting optimization problem can involve exponentially
or infinitely many variables and constraints, which is more challenging than
standard structured output learning. We use a combination of column generation
and cutting-plane techniques to solve the optimization problem. To speed-up the
training we further explore stage-wise training and propose to use a simplified
NDCG loss for efficient inference. We demonstrate the generality of our method
by applying it to ranking prediction and image retrieval, and show that it
outperforms a few state-of-the-art hashing methods.
Planogram Compliance Checking Based on Detection of Recurring Patterns
Authors: Song Liu, Wanqing Li, Stephen Davis, Christian Ritz, Hongda Tian
Date: 2016-02-22 00:00:00
Tasks: Graph Matching

In this paper, a novel method for automatic planogram compliance checking in
retail chains is proposed without requiring product template images for
training. Product layout is extracted from an input image by means of
unsupervised recurring pattern detection and matched via graph matching with
the expected product layout specified by a planogram to measure the level of
compliance. A divide and conquer strategy is employed to improve the speed.
Specifically, the input image is divided into several regions based on the
planogram. Recurring patterns are detected in each region respectively and then
merged together to estimate the product layout. Experimental results on real
data have verified the efficacy of the proposed method. Compared with a
template-based method, higher accuracies are achieved by the proposed method
over a wide range of products.
Creating Simplified 3D Models with High Quality Textures
Authors: Song Liu, Wanqing Li, Philip Ogunbona, Yang-Wai Chow
Date: 2016-02-22 00:00:00
Tasks: GPU, Vocal Bursts Intensity Prediction

This paper presents an extension to the KinectFusion algorithm which allows
creating simplified 3D models with high quality RGB textures. This is achieved
through (i) creating model textures using images from an HD RGB camera that is
calibrated with Kinect depth camera, (ii) using a modified scheme to update
model textures in an asymmetrical colour volume that contains a higher number
of voxels than that of the geometry volume, (iii) simplifying dense polygon
mesh model using quadric-based mesh decimation algorithm, and (iv) creating and
mapping 2D textures to every polygon in the output 3D model. The proposed
method is implemented in real-time by means of GPU parallel processing.
Visualization via ray casting of both geometry and colour volumes provides
users with a real-time feedback of the currently scanned 3D model. Experimental
results show that the proposed method is capable of keeping the model texture
quality even for a heavily decimated model and that, when reconstructing small
objects, photorealistic RGB textures can still be reconstructed.
Statistical Mechanics of High-Dimensional Inference
Authors: Madhu Advani, Surya Ganguli
Date: 2016-01-18 00:00:00
Tasks: Bayesian Inference, compressed sensing, Vocal Bursts Intensity Prediction

To model modern large-scale datasets, we need efficient algorithms to infer a
set of $P$ unknown model parameters from $N$ noisy measurements. What are
fundamental limits on the accuracy of parameter inference, given finite
signal-to-noise ratios, limited measurements, prior information, and
computational tractability requirements? How can we combine prior information
with measurements to achieve these limits? Classical statistics gives incisive
answers to these questions as the measurement density $\alpha =
\frac{N}{P}\rightarrow \infty$. However, these classical results are not
relevant to modern high-dimensional inference problems, which instead occur at
finite $\alpha$. We formulate and analyze high-dimensional inference as a
problem in the statistical physics of quenched disorder. Our analysis uncovers
fundamental limits on the accuracy of inference in high dimensions, and reveals
that widely cherished inference algorithms like maximum likelihood (ML) and
maximum-a posteriori (MAP) inference cannot achieve these limits. We further
find optimal, computationally tractable algorithms that can achieve these
limits. Intriguingly, in high dimensions, these optimal algorithms become
computationally simpler than MAP and ML, while still outperforming them. For
example, such optimal algorithms can lead to as much as a 20% reduction in the
amount of data to achieve the same performance relative to MAP. Moreover, our
analysis reveals simple relations between optimal high dimensional inference
and low dimensional scalar Bayesian inference, insights into the nature of
generalization and predictive power in high dimensions, information theoretic
limits on compressed sensing, phase transitions in quadratic inference, and
connections to central mathematical objects in convex optimization theory and
random matrix theory.
Handling Class Imbalance in Link Prediction using Learning to Rank Techniques
Authors: Bopeng Li, Sougata Chaudhuri, Ambuj Tewari
Date: 2015-11-13 00:00:00
Tasks: Binary Classification, Learning-To-Rank, Link Prediction, Prediction

We consider the link prediction problem in a partially observed network,
where the objective is to make predictions in the unobserved portion of the
network. Many existing methods reduce link prediction to binary classification
problem. However, the dominance of absent links in real world networks makes
misclassification error a poor performance metric. Instead, researchers have
argued for using ranking performance measures, like AUC, AP and NDCG, for
evaluation. Our main contribution is to recast the link prediction problem as a
learning to rank problem and use effective learning to rank techniques directly
during training. This is in contrast to existing work that uses ranking
measures only during evaluation. Our approach is able to deal with the class
imbalance problem by using effective, scalable learning to rank techniques
during training. Furthermore, our approach allows us to combine network
topology and node features. As a demonstration of our general approach, we
develop a link prediction method by optimizing the cross-entropy surrogate,
originally used in the popular ListNet ranking algorithm. We conduct extensive
experiments on publicly available co-authorship, citation and metabolic
networks to demonstrate the merits of our method.
Document Context Language Models
Authors: Yangfeng Ji, Trevor Cohn, Lingpeng Kong, Chris Dyer, Jacob Eisenstein
Date: 2015-11-12 00:00:00
Tasks: Sentence

Text documents are structured on multiple levels of detail: individual words
are related by syntax, but larger units of text are related by discourse
structure. Existing language models generally fail to account for discourse
structure, but it is crucial if we are to have language models that reward
coherence and generate coherent texts. We present and empirically evaluate a
set of multi-level recurrent neural network language models, called
Document-Context Language Models (DCLM), which incorporate contextual
information both within and beyond the sentence. In comparison with word-level
recurrent neural network language models, the DCLM models obtain slightly
better predictive likelihoods, and considerably better assessments of document
coherence.
2-Bit Random Projections, NonLinear Estimators, and Approximate Near Neighbor Search
Authors: Ping Li, Michael Mitzenmacher, Anshumali Shrivastava
Date: 2016-02-21 00:00:00
Tasks: Quantization, Re-Ranking

The method of random projections has become a standard tool for machine
learning, data mining, and search with massive data at Web scale. The effective
use of random projections requires efficient coding schemes for quantizing
(real-valued) projected data into integers. In this paper, we focus on a simple
2-bit coding scheme. In particular, we develop accurate nonlinear estimators of
data similarity based on the 2-bit strategy. This work will have important
practical applications. For example, in the task of near neighbor search, a
crucial step (often called re-ranking) is to compute or estimate data
similarities once a set of candidate data points have been identified by hash
table techniques. This re-ranking step can take advantage of the proposed
coding scheme and estimator.
  As a related task, in this paper, we also study a simple uniform quantization
scheme for the purpose of building hash tables with projected data. Our
analysis shows that typically only a small number of bits are needed. For
example, when the target similarity level is high, 2 or 3 bits might be
sufficient. When the target similarity level is not so high, it is preferable
to use only 1 or 2 bits. Therefore, a 2-bit scheme appears to be overall a good
choice for the task of sublinear time approximate near neighbor search via hash
tables.
  Combining these results, we conclude that 2-bit random projections should be
recommended for approximate near neighbor search and similarity estimation.
Extensive experimental results are provided.
Interactive Storytelling over Document Collections
Authors: Dipayan Maiti, Mohammad Raihanul Islam, Scotland Leman, Naren Ramakrishnan
Date: 2016-02-21 00:00:00

Storytelling algorithms aim to 'connect the dots' between disparate documents
by linking starting and ending documents through a series of intermediate
documents. Existing storytelling algorithms are based on notions of coherence
and connectivity, and thus the primary way by which users can steer the story
construction is via design of suitable similarity functions. We present an
alternative approach to storytelling wherein the user can interactively and
iteratively provide 'must use' constraints to preferentially support the
construction of some stories over others. The three innovations in our approach
are distance measures based on (inferred) topic distributions, the use of
constraints to define sets of linear inequalities over paths, and the
introduction of slack and surplus variables to condition the topic distribution
to preferentially emphasize desired terms over others. We describe experimental
results to illustrate the effectiveness of our interactive storytelling
approach over multiple text datasets.
Automatic Building Extraction in Aerial Scenes Using Convolutional Networks
Authors: Jiangye Yuan
Date: 2016-02-21 00:00:00

Automatic building extraction from aerial and satellite imagery is highly
challenging due to extremely large variations of building appearances. To
attack this problem, we design a convolutional network with a final stage that
integrates activations from multiple preceding stages for pixel-wise
prediction, and introduce the signed distance function of building boundaries
as the output representation, which has an enhanced representation power. We
leverage abundant building footprint data available from geographic information
systems (GIS) to compile training data. The trained network achieves superior
performance on datasets that are significantly larger and more complex than
those used in prior work, demonstrating that the proposed method provides a
promising and scalable solution for automating this labor-intensive task.
Non-linear Causal Inference using Gaussianity Measures
Authors: Daniel Hernández-Lobato, Pablo Morales-Mombiela, David Lopez-Paz, Alberto Suárez
Date: 2014-09-16 00:00:00
Tasks: Causal Inference

We provide theoretical and empirical evidence for a type of asymmetry between
causes and effects that is present when these are related via linear models
contaminated with additive non-Gaussian noise. Assuming that the causes and the
effects have the same distribution, we show that the distribution of the
residuals of a linear fit in the anti-causal direction is closer to a Gaussian
than the distribution of the residuals in the causal direction. This
Gaussianization effect is characterized by reduction of the magnitude of the
high-order cumulants and by an increment of the differential entropy of the
residuals. The problem of non-linear causal inference is addressed by
performing an embedding in an expanded feature space, in which the relation
between causes and effects can be assumed to be linear. The effectiveness of a
method to discriminate between causes and effects based on this type of
asymmetry is illustrated in a variety of experiments using different measures
of Gaussianity. The proposed method is shown to be competitive with
state-of-the-art techniques for causal inference.
Predictive Entropy Search for Multi-objective Bayesian Optimization
Authors: Daniel Hernández-Lobato, José Miguel Hernández-Lobato, Amar Shah, Ryan P. Adams
Date: 2015-11-17 00:00:00
Tasks: Bayesian Optimization

We present PESMO, a Bayesian method for identifying the Pareto set of
multi-objective optimization problems, when the functions are expensive to
evaluate. The central idea of PESMO is to choose evaluation points so as to
maximally reduce the entropy of the posterior distribution over the Pareto set.
Critically, the PESMO multi-objective acquisition function can be decomposed as
a sum of objective-specific acquisition functions, which enables the algorithm
to be used in \emph{decoupled} scenarios in which the objectives can be
evaluated separately and perhaps with different costs. This decoupling
capability also makes it possible to identify difficult objectives that require
more evaluations. PESMO also offers gains in efficiency, as its cost scales
linearly with the number of objectives, in comparison to the exponential cost
of other methods. We compare PESMO with other related methods for
multi-objective Bayesian optimization on synthetic and real-world problems. The
results show that PESMO produces better recommendations with a smaller number
of evaluations of the objectives, and that a decoupled evaluation can lead to
improvements in performance, particularly when the number of objectives is
large.
Determining the best attributes for surveillance video keywords generation
Authors: Liangchen Liu, Arnold Wiliem, Shaokang Chen, Kun Zhao, Brian C. Lovell
Date: 2016-02-21 00:00:00
Tasks: Attribute

Automatic video keyword generation is one of the key ingredients in reducing
the burden of security officers in analyzing surveillance videos. Keywords or
attributes are generally chosen manually based on expert knowledge of
surveillance. Most existing works primarily aim at either supervised learning
approaches relying on extensive manual labelling or hierarchical probabilistic
models that assume the features are extracted using the bag-of-words approach;
thus limiting the utilization of the other features. To address this, we turn
our attention to automatic attribute discovery approaches. However, it is not
clear which automatic discovery approach can discover the most meaningful
attributes. Furthermore, little research has been done on how to compare and
choose the best automatic attribute discovery methods. In this paper, we
propose a novel approach, based on the shared structure exhibited amongst
meaningful attributes, that enables us to compare between different automatic
attribute discovery approaches.We then validate our approach by comparing
various attribute discovery methods such as PiCoDeS on two attribute datasets.
The evaluation shows that our approach is able to select the automatic
discovery approach that discovers the most meaningful attributes. We then
employ the best discovery approach to generate keywords for videos recorded
from a surveillance system. This work shows it is possible to massively reduce
the amount of manual work in generating video keywords without limiting
ourselves to a particular video feature descriptor.
Machine learning meets network science: dimensionality reduction for fast and efficient embedding of networks in the hyperbolic space
Authors: Josephine Maria Thomas, Alessandro Muscoloni, Sara Ciucci, Ginestra Bianconi, Carlo Vittorio Cannistraci
Date: 2016-02-21 00:00:00
Tasks: Community Detection, Dimensionality Reduction, Link Prediction

Complex network topologies and hyperbolic geometry seem specularly connected,
and one of the most fascinating and challenging problems of recent complex
network theory is to map a given network to its hyperbolic space. The
Popularity Similarity Optimization (PSO) model represents - at the moment - the
climax of this theory. It suggests that the trade-off between node popularity
and similarity is a mechanism to explain how complex network topologies emerge
- as discrete samples - from the continuous world of hyperbolic geometry. The
hyperbolic space seems appropriate to represent real complex networks. In fact,
it preserves many of their fundamental topological properties, and can be
exploited for real applications such as, among others, link prediction and
community detection. Here, we observe for the first time that a
topological-based machine learning class of algorithms - for nonlinear
unsupervised dimensionality reduction - can directly approximate the network's
node angular coordinates of the hyperbolic model into a two-dimensional space,
according to a similar topological organization that we named angular
coalescence. On the basis of this phenomenon, we propose a new class of
algorithms that offers fast and accurate coalescent embedding of networks in
the hyperbolic space even for graphs with thousands of nodes.