pwc-archive/papers-with-abstracts huggingface dataset:
Sparse principal component regression for generalized linear models
Authors: Shuichi. Kawano, Hironori Fujisawa, Toyoyuki Takada, Toshihiko Shiroishi
Date: 2016-09-28 00:00:00
Tasks: parameter estimation, regression

Principal component regression (PCR) is a widely used two-stage procedure:
principal component analysis (PCA), followed by regression in which the
selected principal components are regarded as new explanatory variables in the
model. Note that PCA is based only on the explanatory variables, so the
principal components are not selected using the information on the response
variable. In this paper, we propose a one-stage procedure for PCR in the
framework of generalized linear models. The basic loss function is based on a
combination of the regression loss and PCA loss. An estimate of the regression
parameter is obtained as the minimizer of the basic loss function with a sparse
penalty. We call the proposed method sparse principal component regression for
generalized linear models (SPCR-glm). Taking the two loss function into
consideration simultaneously, SPCR-glm enables us to obtain sparse principal
component loadings that are related to a response variable. However, a
combination of loss functions may cause a parameter identification problem, but
this potential problem is avoided by virtue of the sparse penalty. Thus, the
sparse penalty plays two roles in this method. The parameter estimation
procedure is proposed using various update algorithms with the coordinate
descent algorithm. We apply SPCR-glm to two real datasets, doctor visits data
and mouse consomic strain data. SPCR-glm provides more easily interpretable
principal component (PC) scores and clearer classification on PC plots than the
usual PCA.
Statistical Inference Using Mean Shift Denoising
Authors: Yunhua Xiang, Yen-Chi Chen
Date: 2016-10-13 00:00:00
Tasks: Anomaly Detection, Clustering, Denoising

In this paper, we study how the mean shift algorithm can be used to denoise a
dataset. We introduce a new framework to analyze the mean shift algorithm as a
denoising approach by viewing the algorithm as an operator on a distribution
function. We investigate how the mean shift algorithm changes the distribution
and show that data points shifted by the mean shift concentrate around high
density regions of the underlying density function. By using the mean shift as
a denoising method, we enhance the performance of several clustering
techniques, improve the power of two-sample tests, and obtain a new method for
anomaly detection.
Gated Word-Character Recurrent Language Model
Authors: Yasumasa Miyamoto, Kyunghyun Cho
Date: 2016-06-06 00:00:00
Proceeding: EMNLP 2016 11
Tasks: Language Modeling, Language Modelling, model

We introduce a recurrent neural network language model (RNN-LM) with long
short-term memory (LSTM) units that utilizes both character-level and
word-level inputs. Our model has a gate that adaptively finds the optimal
mixture of the character-level and word-level inputs. The gate creates the
final vector representation of a word by combining two distinct representations
of the word. The character-level inputs are converted into vector
representations of words using a bidirectional LSTM. The word-level inputs are
projected into another high-dimensional space by a word lookup table. The final
vector representations of words are used in the LSTM language model which
predicts the next word given all the preceding words. Our model with the gating
mechanism effectively utilizes the character-level inputs for rare and
out-of-vocabulary words and outperforms word-level language models on several
English corpora.
An Operator Theoretic Approach to Nonparametric Mixture Models
Authors: Robert A. Vandermeulen, Clayton D. Scott
Date: 2016-06-30 00:00:00

When estimating finite mixture models, it is common to make assumptions on
the mixture components, such as parametric assumptions. In this work, we make
no distributional assumptions on the mixture components and instead assume that
observations from the mixture model are grouped, such that observations in the
same group are known to be drawn from the same mixture component. We precisely
characterize the number of observations $n$ per group needed for the mixture
model to be identifiable, as a function of the number $m$ of mixture
components. In addition to our assumption-free analysis, we also study the
settings where the mixture components are either linearly independent or
jointly irreducible. Furthermore, our analysis considers two kinds of
identifiability -- where the mixture model is the simplest one explaining the
data, and where it is the only one. As an application of these results, we
precisely characterize identifiability of multinomial mixture models. Our
analysis relies on an operator-theoretic framework that associates mixture
models in the grouped-sample setting with certain infinite-dimensional tensors.
Based on this framework, we introduce general spectral algorithms for
recovering the mixture components and illustrate their use on a synthetic data
set.
Generalized Mirror Descents in Congestion Games
Authors: Po-An Chen, Chi-Jen Lu
Date: 2016-05-25 00:00:00

Different types of dynamics have been studied in repeated game play, and one
of them which has received much attention recently consists of those based on
"no-regret" algorithms from the area of machine learning. It is known that
dynamics based on generic no-regret algorithms may not converge to Nash
equilibria in general, but to a larger set of outcomes, namely coarse
correlated equilibria. Moreover, convergence results based on generic no-regret
algorithms typically use a weaker notion of convergence: the convergence of the
average plays instead of the actual plays. Some work has been done showing that
when using a specific no-regret algorithm, the well-known multiplicative
updates algorithm, convergence of actual plays to equilibria can be shown and
better quality of outcomes in terms of the price of anarchy can be reached for
atomic congestion games and load balancing games. Are there more cases of
natural no-regret dynamics that perform well in suitable classes of games in
terms of convergence and quality of outcomes that the dynamics converge to?
  We answer this question positively in the bulletin-board model by showing
that when employing the mirror-descent algorithm, a well-known generic
no-regret algorithm, the actual plays converge quickly to equilibria in
nonatomic congestion games. Furthermore, the bandit model considers a probably
more realistic and prevalent setting with only partial information, in which at
each time step each player only knows the cost of her own currently played
strategy, but not any costs of unplayed strategies. For the class of atomic
congestion games, we propose a family of bandit algorithms based on the
mirror-descent algorithms previously presented, and show that when each player
individually adopts such a bandit algorithm, their joint (mixed) strategy
profile quickly converges with implications.
Localized Multiple Kernel Learning---A Convex Approach
Authors: Yunwen Lei, Alexander Binder, Ürün Dogan, Marius Kloft
Date: 2015-06-14 00:00:00

We propose a localized approach to multiple kernel learning that can be
formulated as a convex optimization problem over a given cluster structure. For
which we obtain generalization error guarantees and derive an optimization
algorithm based on the Fenchel dual representation. Experiments on real-world
datasets from the application domains of computational biology and computer
vision show that convex localized multiple kernel learning can achieve higher
prediction accuracies than its global and non-convex local counterparts.
Neural Paraphrase Generation with Stacked Residual LSTM Networks
Authors: Aaditya Prakash, Sadid A. Hasan, Kathy Lee, Vivek Datla, Ashequl Qadir, Joey Liu, Oladimeji Farri
Date: 2016-10-10 00:00:00
Proceeding: COLING 2016 12
Tasks: Deep Learning, Paraphrase Generation, Sentence, Sentence Similarity

In this paper, we propose a novel neural approach for paraphrase generation.
Conventional para- phrase generation methods either leverage hand-written rules
and thesauri-based alignments, or use statistical machine learning principles.
To the best of our knowledge, this work is the first to explore deep learning
models for paraphrase generation. Our primary contribution is a stacked
residual LSTM network, where we add residual connections between LSTM layers.
This allows for efficient training of deep LSTMs. We evaluate our model and
other state-of-the-art deep learning models on three different datasets: PPDB,
WikiAnswers and MSCOCO. Evaluation results demonstrate that our model
outperforms sequence to sequence, attention-based and bi- directional LSTM
models on BLEU, METEOR, TER and an embedding-based sentence similarity metric.
Semi-Coupled Two-Stream Fusion ConvNets for Action Recognition at Extremely Low Resolutions
Authors: Jiawei Chen, Jonathan Wu, Janusz Konrad, Prakash Ishwar
Date: 2016-10-12 00:00:00
Tasks: Action Recognition, Temporal Action Localization

Deep convolutional neural networks (ConvNets) have been recently shown to
attain state-of-the-art performance for action recognition on
standard-resolution videos. However, less attention has been paid to
recognition performance at extremely low resolutions (eLR) (e.g., 16 x 12
pixels). Reliable action recognition using eLR cameras would address privacy
concerns in various application environments such as private homes, hospitals,
nursing/rehabilitation facilities, etc. In this paper, we propose a
semi-coupled filter-sharing network that leverages high resolution (HR) videos
during training in order to assist an eLR ConvNet. We also study methods for
fusing spatial and temporal ConvNets customized for eLR videos in order to take
advantage of appearance and motion information. Our method outperforms
state-of-the-art methods at extremely low resolutions on IXMAS (93.7%) and HMDB
(29.2%) datasets.
ABA+: Assumption-Based Argumentation with Preferences
Authors: Kristijonas Čyras, Francesca Toni
Date: 2016-10-10 00:00:00

We present ABA+, a new approach to handling preferences in a well known
structured argumentation formalism, Assumption-Based Argumentation (ABA). In
ABA+, preference information given over assumptions is incorporated directly
into the attack relation, thus resulting in attack reversal. ABA+
conservatively extends ABA and exhibits various desirable features regarding
relationship among argumentation semantics as well as preference handling. We
also introduce Weak Contraposition, a principle concerning reasoning with rules
and preferences that relaxes the standard principle of contraposition, while
guaranteeing additional desirable features for ABA+.
Video Depth-From-Defocus
Authors: Hyeongwoo Kim, Christian Richardt, Christian Theobalt
Date: 2016-10-12 00:00:00

Many compelling video post-processing effects, in particular aesthetic focus
editing and refocusing effects, are feasible if per-frame depth information is
available. Existing computational methods to capture RGB and depth either
purposefully modify the optics (coded aperture, light-field imaging), or employ
active RGB-D cameras. Since these methods are less practical for users with
normal cameras, we present an algorithm to capture all-in-focus RGB-D video of
dynamic scenes with an unmodified commodity video camera. Our algorithm turns
the often unwanted defocus blur into a valuable signal. The input to our method
is a video in which the focus plane is continuously moving back and forth
during capture, and thus defocus blur is provoked and strongly visible. This
can be achieved by manually turning the focus ring of the lens during
recording. The core algorithmic ingredient is a new video-based
depth-from-defocus algorithm that computes space-time-coherent depth maps,
deblurred all-in-focus video, and the focus distance for each frame. We
extensively evaluate our approach, and show that it enables compelling video
post-processing effects, such as different types of refocusing.
Deep disentangled representations for volumetric reconstruction
Authors: Edward Grant, Pushmeet Kohli, Marcel van Gerven
Date: 2016-10-12 00:00:00
Tasks: Decoder

We introduce a convolutional neural network for inferring a compact
disentangled graphical description of objects from 2D images that can be used
for volumetric reconstruction. The network comprises an encoder and a
twin-tailed decoder. The encoder generates a disentangled graphics code. The
first decoder generates a volume, and the second decoder reconstructs the input
image using a novel training regime that allows the graphics code to learn a
separate representation of the 3D object and a description of its lighting and
pose conditions. We demonstrate this method by generating volumes and
disentangled graphical descriptions from images and videos of faces and chairs.
SentiHood: Targeted Aspect Based Sentiment Analysis Dataset for Urban Neighbourhoods
Authors: Marzieh Saeidi, Guillaume Bouchard, Maria Liakata, Sebastian Riedel
Date: 2016-10-12 00:00:00
Proceeding: COLING 2016 12
Tasks: Aspect-Based Sentiment Analysis, Aspect-Based Sentiment Analysis (ABSA), Opinion Mining, Question Answering, Sentiment Analysis

In this paper, we introduce the task of targeted aspect-based sentiment
analysis. The goal is to extract fine-grained information with respect to
entities mentioned in user comments. This work extends both aspect-based
sentiment analysis that assumes a single entity per document and targeted
sentiment analysis that assumes a single sentiment towards a target entity. In
particular, we identify the sentiment towards each aspect of one or more
entities. As a testbed for this task, we introduce the SentiHood dataset,
extracted from a question answering (QA) platform where urban neighbourhoods
are discussed by users. In this context units of text often mention several
aspects of one or more neighbourhoods. This is the first time that a generic
social media platform in this case a QA platform, is used for fine-grained
opinion mining. Text coming from QA platforms is far less constrained compared
to text from review specific platforms which current datasets are based on. We
develop several strong baselines, relying on logistic regression and
state-of-the-art recurrent neural networks.
Semi-supervised Discovery of Informative Tweets During the Emerging Disasters
Authors: Shanshan Zhang, Slobodan Vucetic
Date: 2016-10-12 00:00:00
Tasks: Classification, Clustering, General Classification

The first objective towards the effective use of microblogging services such
as Twitter for situational awareness during the emerging disasters is discovery
of the disaster-related postings. Given the wide range of possible disasters,
using a pre-selected set of disaster-related keywords for the discovery is
suboptimal. An alternative that we focus on in this work is to train a
classifier using a small set of labeled postings that are becoming available as
a disaster is emerging. Our hypothesis is that utilizing large quantities of
historical microblogs could improve the quality of classification, as compared
to training a classifier only on the labeled data. We propose to use unlabeled
microblogs to cluster words into a limited number of clusters and use the word
clusters as features for classification. To evaluate the proposed
semi-supervised approach, we used Twitter data from 6 different disasters. Our
results indicate that when the number of labeled tweets is 100 or less, the
proposed approach is superior to the standard classification based on the bag
or words feature representation. Our results also reveal that the choice of the
unlabeled corpus, the choice of word clustering algorithm, and the choice of
hyperparameters can have a significant impact on the classification accuracy.
Exploring the Entire Regularization Path for the Asymmetric Cost Linear Support Vector Machine
Authors: Daniel Wesierski
Date: 2016-10-12 00:00:00

We propose an algorithm for exploring the entire regularization path of
asymmetric-cost linear support vector machines. Empirical evidence suggests the
predictive power of support vector machines depends on the regularization
parameters of the training algorithms. The algorithms exploring the entire
regularization paths have been proposed for single-cost support vector machines
thereby providing the complete knowledge on the behavior of the trained model
over the hyperparameter space. Considering the problem in two-dimensional
hyperparameter space though enables our algorithm to maintain greater
flexibility in dealing with special cases and sheds light on problems
encountered by algorithms building the paths in one-dimensional spaces. We
demonstrate two-dimensional regularization paths for linear support vector
machines that we train on synthetic and real data.
Parallel Wavelet Schemes for Images
Authors: David Barina, Michal Kula, Pavel Zemcik
Date: 2016-05-02 00:00:00
Tasks: Image Compression

In this paper, we introduce several new schemes for calculation of discrete wavelet transforms of images. These schemes reduce the number of steps and, as a consequence, allow to reduce the number of synchronizations on parallel architectures. As an additional useful property, the proposed schemes can reduce also the number of arithmetic operations. The schemes are primarily demonstrated on CDF 5/3 and CDF 9/7 wavelets employed in JPEG 2000 image compression standard. However, the presented method is general, and it can be applied on any wavelet transform. As a result, our scheme requires only two memory barriers for 2-D CDF 5/3 transform compared to four barriers in the original separable form or three barriers in the non-separable scheme recently published. Our reasoning is supported by exhaustive experiments on high-end graphics cards.
Optimistic Semi-supervised Least Squares Classification
Authors: Jesse H. Krijthe, Marco Loog
Date: 2016-10-12 00:00:00
Tasks: Classification, General Classification, Self-Learning

The goal of semi-supervised learning is to improve supervised classifiers by
using additional unlabeled training examples. In this work we study a simple
self-learning approach to semi-supervised learning applied to the least squares
classifier. We show that a soft-label and a hard-label variant of self-learning
can be derived by applying block coordinate descent to two related but slightly
different objective functions. The resulting soft-label approach is related to
an idea about dealing with missing data that dates back to the 1930s. We show
that the soft-label variant typically outperforms the hard-label variant on
benchmark datasets and partially explain this behaviour by studying the
relative difficulty of finding good local minima for the corresponding
objective functions.
Multistage SFM: A Coarse-to-Fine Approach for 3D Reconstruction
Authors: Rajvi Shah, Aditya Deshpande, P. J. Narayanan
Date: 2015-12-19 00:00:00
Tasks: 3D Reconstruction

Several methods have been proposed for large-scale 3D reconstruction from
large, unorganized image collections. A large reconstruction problem is
typically divided into multiple components which are reconstructed
independently using structure from motion (SFM) and later merged together.
Incremental SFM methods are most popular for the basic structure recovery of a
single component. They are robust and effective but are strictly sequential in
nature. We present a multistage approach for SFM reconstruction of a single
component that breaks the sequential nature of the incremental SFM methods. Our
approach begins with quickly building a coarse 3D model using only a fraction
of features from given images. The coarse model is then enriched by localizing
remaining images and matching and triangulating remaining features in
subsequent stages. These stages are made efficient and highly parallel by
leveraging the geometry of the coarse model. Our method produces similar
quality models as compared to incremental SFM methods while being notably fast
and parallel.
SaberLDA: Sparsity-Aware Learning of Topic Models on GPUs
Authors: Kaiwei Li, Jianfei Chen, WenGuang Chen, Jun Zhu
Date: 2016-10-08 00:00:00
Tasks: CPU, GPU, Topic Models

Latent Dirichlet Allocation (LDA) is a popular tool for analyzing discrete
count data such as text and images. Applications require LDA to handle both
large datasets and a large number of topics. Though distributed CPU systems
have been used, GPU-based systems have emerged as a promising alternative
because of the high computational power and memory bandwidth of GPUs. However,
existing GPU-based LDA systems cannot support a large number of topics because
they use algorithms on dense data structures whose time and space complexity is
linear to the number of topics. In this paper, we propose SaberLDA, a GPU-based
LDA system that implements a sparsity-aware algorithm to achieve sublinear time
complexity and scales well to learn a large number of topics. To address the
challenges introduced by sparsity, we propose a novel data layout, a new
warp-based sampling kernel, and an efficient sparse count matrix updating
algorithm that improves locality, makes efficient utilization of GPU warps, and
reduces memory consumption. Experiments show that SaberLDA can learn from
billions-token-scale data with up to 10,000 topics, which is almost two orders
of magnitude larger than that of the previous GPU-based systems. With a single
GPU card, SaberLDA is able to learn 10,000 topics from a dataset of billions of
tokens in a few hours, which is only achievable with clusters with tens of
machines before.
Bayesian multi-tensor factorization
Authors: Suleiman A. Khan, Eemeli Leppäaho, Samuel Kaski
Date: 2014-12-15 00:00:00
Tasks: MULTI-VIEW LEARNING

We introduce Bayesian multi-tensor factorization, a model that is the first
Bayesian formulation for joint factorization of multiple matrices and tensors.
The research problem generalizes the joint matrix-tensor factorization problem
to arbitrary sets of tensors of any depth, including matrices, can be
interpreted as unsupervised multi-view learning from multiple data tensors, and
can be generalized to relax the usual trilinear tensor factorization
assumptions. The result is a factorization of the set of tensors into factors
shared by any subsets of the tensors, and factors private to individual
tensors. We demonstrate the performance against existing baselines in multiple
tensor factorization tasks in structural toxicogenomics and functional
neuroimaging.
ENFT: Efficient Non-Consecutive Feature Tracking for Robust Structure-from-Motion
Authors: Guofeng Zhang, Hao-Min Liu, Zilong Dong, Jiaya Jia, Tien-Tsin Wong, Hujun Bao
Date: 2015-10-27 00:00:00

Structure-from-motion (SfM) largely relies on feature tracking. In image
sequences, if disjointed tracks caused by objects moving in and out of the
field of view, occasional occlusion, or image noise, are not handled well,
corresponding SfM could be affected. This problem becomes severer for
large-scale scenes, which typically requires to capture multiple sequences to
cover the whole scene. In this paper, we propose an efficient non-consecutive
feature tracking (ENFT) framework to match interrupted tracks distributed in
different subsequences or even in different videos. Our framework consists of
steps of solving the feature `dropout' problem when indistinctive structures,
noise or large image distortion exists, and of rapidly recognizing and joining
common features located in different subsequences. In addition, we contribute
an effective segment-based coarse-to-fine SfM algorithm for robustly handling
large datasets. Experimental results on challenging video data demonstrate the
effectiveness of the proposed system.
Theory and computer simulation of the moiré patterns in single-layer cylindrical particles
Authors: Vladimir Saveljev, Irina Palchikova
Date: 2016-10-12 00:00:00

Basing on the theory for arbitrary oriented surfaces, we developed the theory
of the moir\'e effect for cylindrical single-layer objects in the paraxial
approximation. With using the dual grids, the moir\'e effect in the plane
gratings is simulated, as well as the near-axis moir\'e effect in cylinders
including the chiral layouts. The results can be applied to the graphene
layers, to single-walled nanotubes, and to cylinders in general.