pwc-archive/papers-with-abstracts huggingface dataset:
A Synthetic Approach for Recommendation: Combining Ratings, Social Relations, and Reviews
Authors: Guang-Neng Hu, Xin-yu Dai, Yunya Song, Shu-Jian Huang, Jia-Jun Chen
Date: 2016-01-11 00:00:00
Tasks: Recommendation Systems

Recommender systems (RSs) provide an effective way of alleviating the
information overload problem by selecting personalized choices. Online social
networks and user-generated content provide diverse sources for recommendation
beyond ratings, which present opportunities as well as challenges for
traditional RSs. Although social matrix factorization (Social MF) can integrate
ratings with social relations and topic matrix factorization can integrate
ratings with item reviews, both of them ignore some useful information. In this
paper, we investigate the effective data fusion by combining the two
approaches, in two steps. First, we extend Social MF to exploit the graph
structure of neighbors. Second, we propose a novel framework MR3 to jointly
model these three types of information effectively for rating prediction by
aligning latent factors and hidden topics. We achieve more accurate rating
prediction on two real-life datasets. Furthermore, we measure the contribution
of each data source to the proposed framework.
Temporal Multinomial Mixture for Instance-Oriented Evolutionary Clustering
Authors: Young-Min Kim, Julien Velcin, Stéphane Bonnevay, Marian-Andrei Rizoiu
Date: 2016-01-11 00:00:00
Tasks: Clustering

Evolutionary clustering aims at capturing the temporal evolution of clusters.
This issue is particularly important in the context of social media data that
are naturally temporally driven. In this paper, we propose a new probabilistic
model-based evolutionary clustering technique. The Temporal Multinomial Mixture
(TMM) is an extension of classical mixture model that optimizes feature
co-occurrences in the trade-off with temporal smoothness. Our model is
evaluated for two recent case studies on opinion aggregation over time. We
compare four different probabilistic clustering models and we show the
superiority of our proposal in the task of instance-oriented clustering.
How to Use Temporal-Driven Constrained Clustering to Detect Typical Evolutions
Authors: Marian-Andrei Rizoiu, Julien Velcin, Stéphane Lallich
Date: 2016-01-11 00:00:00
Tasks: Clustering, Constrained Clustering

In this paper, we propose a new time-aware dissimilarity measure that takes
into account the temporal dimension. Observations that are close in the
description space, but distant in time are considered as dissimilar. We also
propose a method to enforce the segmentation contiguity, by introducing, in the
objective function, a penalty term inspired from the Normal Distribution
Function. We combine the two propositions into a novel time-driven constrained
clustering algorithm, called TDCK-Means, which creates a partition of coherent
clusters, both in the multidimensional space and in the temporal space. This
algorithm uses soft semi-supervised constraints, to encourage adjacent
observations belonging to the same entity to be assigned to the same cluster.
We apply our algorithm to a Political Studies dataset in order to detect
typical evolution phases. We adapt the Shannon entropy in order to measure the
entity contiguity, and we show that our proposition consistently improves
temporal cohesion of clusters, without any significant loss in the
multidimensional variance.
Scale Up Nonlinear Component Analysis with Doubly Stochastic Gradients
Authors: Bo Xie, YIngyu Liang, Le Song
Date: 2015-04-14 00:00:00
Proceeding: NeurIPS 2015 12

Nonlinear component analysis such as kernel Principle Component Analysis
(KPCA) and kernel Canonical Correlation Analysis (KCCA) are widely used in
machine learning, statistics and data analysis, but they can not scale up to
big datasets. Recent attempts have employed random feature approximations to
convert the problem to the primal form for linear computational complexity.
However, to obtain high quality solutions, the number of random features should
be the same order of magnitude as the number of data points, making such
approach not directly applicable to the regime with millions of data points.
  We propose a simple, computationally efficient, and memory friendly algorithm
based on the "doubly stochastic gradients" to scale up a range of kernel
nonlinear component analysis, such as kernel PCA, CCA and SVD. Despite the
\emph{non-convex} nature of these problems, our method enjoys theoretical
guarantees that it converges at the rate $\tilde{O}(1/t)$ to the global
optimum, even for the top $k$ eigen subspace. Unlike many alternatives, our
algorithm does not require explicit orthogonalization, which is infeasible on
big datasets. We demonstrate the effectiveness and scalability of our algorithm
on large scale synthetic and real world datasets.
Directional Decision Lists
Authors: Marc Goessling, Shan Kang
Date: 2015-08-30 00:00:00

In this paper we introduce a novel family of decision lists consisting of
highly interpretable models which can be learned efficiently in a greedy
manner. The defining property is that all rules are oriented in the same
direction. Particular examples of this family are decision lists with
monotonically decreasing (or increasing) probabilities. On simulated data we
empirically confirm that the proposed model family is easier to train than
general decision lists. We exemplify the practical usability of our approach by
identifying problem symptoms in a manufacturing process.
A Sufficient Statistics Construction of Bayesian Nonparametric Exponential Family Conjugate Models
Authors: Robert Finn, Brian Kulis
Date: 2016-01-10 00:00:00
Tasks: Learning Theory

Conjugate pairs of distributions over infinite dimensional spaces are
prominent in statistical learning theory, particularly due to the widespread
adoption of Bayesian nonparametric methodologies for a host of models and
applications. Much of the existing literature in the learning community focuses
on processes possessing some form of computationally tractable conjugacy as is
the case for the beta and gamma processes (and, via normalization, the
Dirichlet process). For these processes, proofs of conjugacy and requisite
derivation of explicit computational formulae for posterior density parameters
are idiosyncratic to the stochastic process in question. As such, Bayesian
Nonparametric models are currently available for a limited number of conjugate
pairs, e.g. the Dirichlet-multinomial and beta-Bernoulli process pairs. In each
of these above cases the likelihood process belongs to the class of discrete
exponential family distributions. The exclusion of continuous likelihood
distributions from the known cases of Bayesian Nonparametric Conjugate models
stands as a disparity in the researcher's toolbox.
  In this paper we first address the problem of obtaining a general
construction of prior distributions over infinite dimensional spaces possessing
distributional properties amenable to conjugacy. Second, we bridge the divide
between the discrete and continuous likelihoods by illustrating a canonical
construction for stochastic processes whose Levy measure densities are from
positive exponential families, and then demonstrate that these processes in
fact form the prior, likelihood, and posterior in a conjugate family. Our
canonical construction subsumes known computational formulae for posterior
density parameters in the cases where the likelihood is from a discrete
distribution belonging to an exponential family.
Bayesian Optimization in a Billion Dimensions via Random Embeddings
Authors: Ziyu Wang, Frank Hutter, Masrour Zoghi, David Matheson, Nando de Freitas
Date: 2013-01-09 00:00:00
Tasks: Bayesian Optimization

Bayesian optimization techniques have been successfully applied to robotics,
planning, sensor placement, recommendation, advertising, intelligent user
interfaces and automatic algorithm configuration. Despite these successes, the
approach is restricted to problems of moderate dimension, and several workshops
on Bayesian optimization have identified its scaling to high-dimensions as one
of the holy grails of the field. In this paper, we introduce a novel random
embedding idea to attack this problem. The resulting Random EMbedding Bayesian
Optimization (REMBO) algorithm is very simple, has important invariance
properties, and applies to domains with both categorical and continuous
variables. We present a thorough theoretical analysis of REMBO. Empirical
results confirm that REMBO can effectively solve problems with billions of
dimensions, provided the intrinsic dimensionality is low. They also show that
REMBO achieves state-of-the-art performance in optimizing the 47 discrete
parameters of a popular mixed integer linear programming solver.
Parallel Stroked Multi Line: a model-based method for compressing large fingerprint databases
Authors: Hamid Mansouri, Hamid-Reza Pourreza
Date: 2016-01-10 00:00:00

With increasing usage of fingerprints as an important biometric data, the
need to compress the large fingerprint databases has become essential. The most
recommended compression algorithm, even by standards, is JPEG2K. But at high
compression rates, this algorithm is ineffective. In this paper, a model is
proposed which is based on parallel lines with same orientations, arbitrary
widths and same gray level values located on rectangle with constant gray level
value as background. We refer to this algorithm as Parallel Stroked Multi Line
(PSML). By using Adaptive Geometrical Wavelet and employing PSML, a compression
algorithm is developed. This compression algorithm can preserve fingerprint
structure and minutiae. The exact algorithm of computing the PSML model take
exponential time. However, we have proposed an alternative approximation
algorithm, which reduces the time complexity to $O(n^3)$. The proposed PSML
alg. has significant advantage over Wedgelets Transform in PSNR value and
visual quality in compressed images. The proposed method, despite the lower
PSNR values than JPEG2K algorithm in common range of compression rates, in all
compression rates have nearly equal or greater advantage over JPEG2K when used
by Automatic Fingerprint Identification Systems (AFIS). At high compression
rates, according to PSNR values, mean EER rate and visual quality, the encoded
images with JPEG2K can not be identified from each other after compression.
But, images encoded by the PSML alg. retained the sufficient information to
maintain fingerprint identification performances similar to the ones obtained
by raw images without compression. One the U.are.U 400 database, the mean EER
rate for uncompressed images is 4.54%, while at 267:1 compression ratio, this
value becomes 49.41% and 6.22% for JPEG2K and PSML, respectively. This result
shows a significant improvement over the standard JPEG2K algorithm.
Joint Object-Material Category Segmentation from Audio-Visual Cues
Authors: Anurag Arnab, Michael Sapienza, Stuart Golodetz, Julien Valentin, Ondrej Miksik, Shahram Izadi, Philip Torr
Date: 2016-01-10 00:00:00
Tasks: Object

It is not always possible to recognise objects and infer material properties
for a scene from visual cues alone, since objects can look visually similar
whilst being made of very different materials. In this paper, we therefore
present an approach that augments the available dense visual cues with sparse
auditory cues in order to estimate dense object and material labels. Since
estimates of object class and material properties are mutually informative, we
optimise our multi-output labelling jointly using a random-field framework. We
evaluate our system on a new dataset with paired visual and auditory data that
we make publicly available. We demonstrate that this joint estimation of object
and material labels significantly outperforms the estimation of either category
in isolation.
Where To Look: Focus Regions for Visual Question Answering
Authors: Kevin J. Shih, Saurabh Singh, Derek Hoiem
Date: 2015-11-23 00:00:00
Proceeding: CVPR 2016 6
Tasks: Question Answering, Visual Question Answering, Visual Question Answering (VQA)

We present a method that learns to answer visual questions by selecting image
regions relevant to the text-based query. Our method exhibits significant
improvements in answering questions such as "what color," where it is necessary
to evaluate a specific location, and "what room," where it selectively
identifies informative image regions. Our model is tested on the VQA dataset
which is the largest human-annotated visual question answering dataset to our
knowledge.
On Clustering Time Series Using Euclidean Distance and Pearson Correlation
Authors: Michael R. Berthold, Frank Höppner
Date: 2016-01-10 00:00:00
Tasks: Clustering, General Classification, Time Series, Time Series Analysis, valid

For time series comparisons, it has often been observed that z-score
normalized Euclidean distances far outperform the unnormalized variant. In this
paper we show that a z-score normalized, squared Euclidean Distance is, in
fact, equal to a distance based on Pearson Correlation. This has profound
impact on many distance-based classification or clustering methods. In addition
to this theoretically sound result we also show that the often used k-Means
algorithm formally needs a mod ification to keep the interpretation as Pearson
correlation strictly valid. Experimental results demonstrate that in many cases
the standard k-Means algorithm generally produces the same results.
Identifying Stable Patterns over Time for Emotion Recognition from EEG
Authors: Wei-Long Zheng, Jia-Yi Zhu, Bao-liang Lu
Date: 2016-01-10 00:00:00
Tasks: BIG-bench Machine Learning, EEG, Electroencephalogram (EEG), Emotion Recognition, feature selection

In this paper, we investigate stable patterns of electroencephalogram (EEG)
over time for emotion recognition using a machine learning approach. Up to now,
various findings of activated patterns associated with different emotions have
been reported. However, their stability over time has not been fully
investigated yet. In this paper, we focus on identifying EEG stability in
emotion recognition. To validate the efficiency of the machine learning
algorithms used in this study, we systematically evaluate the performance of
various popular feature extraction, feature selection, feature smoothing and
pattern classification methods with the DEAP dataset and a newly developed
dataset for this study. The experimental results indicate that stable patterns
exhibit consistency across sessions; the lateral temporal areas activate more
for positive emotion than negative one in beta and gamma bands; the neural
patterns of neutral emotion have higher alpha responses at parietal and
occipital sites; and for negative emotion, the neural patterns have significant
higher delta responses at parietal and occipital sites and higher gamma
responses at prefrontal sites. The performance of our emotion recognition
system shows that the neural patterns are relatively stable within and between
sessions.
Empirical Gaussian priors for cross-lingual transfer learning
Authors: Anders Søgaard
Date: 2016-01-09 00:00:00
Tasks: Cross-Lingual Transfer, Part-Of-Speech Tagging, POS, POS Tagging, Transfer Learning

Sequence model learning algorithms typically maximize log-likelihood minus
the norm of the model (or minimize Hamming loss + norm). In cross-lingual
part-of-speech (POS) tagging, our target language training data consists of
sequences of sentences with word-by-word labels projected from translations in
$k$ languages for which we have labeled data, via word alignments. Our training
data is therefore very noisy, and if Rademacher complexity is high, learning
algorithms are prone to overfit. Norm-based regularization assumes a constant
width and zero mean prior. We instead propose to use the $k$ source language
models to estimate the parameters of a Gaussian prior for learning new POS
taggers. This leads to significantly better performance in multi-source
transfer set-ups. We also present a drop-out version that injects (empirical)
Gaussian noise during online learning. Finally, we note that using empirical
Gaussian priors leads to much lower Rademacher complexity, and is superior to
optimally weighted model interpolation.
Kernelized LRR on Grassmann Manifolds for Subspace Clustering
Authors: Boyue Wang, Yongli Hu, Junbin Gao, Yanfeng Sun, Bao-Cai Yin
Date: 2016-01-09 00:00:00
Tasks: Clustering

Low rank representation (LRR) has recently attracted great interest due to
its pleasing efficacy in exploring low-dimensional sub- space structures
embedded in data. One of its successful applications is subspace clustering, by
which data are clustered according to the subspaces they belong to. In this
paper, at a higher level, we intend to cluster subspaces into classes of
subspaces. This is naturally described as a clustering problem on Grassmann
manifold. The novelty of this paper is to generalize LRR on Euclidean space
onto an LRR model on Grassmann manifold in a uniform kernelized LRR framework.
The new method has many applications in data analysis in computer vision tasks.
The proposed models have been evaluated on a number of practical data analysis
applications. The experimental results show that the proposed models outperform
a number of state-of-the-art subspace clustering methods.
Supervised multiview learning based on simultaneous learning of multiview intact and single view classifier
Authors: Qingjun Wang, Haiyan Lv, Jun Yue, Eugene Mitchell
Date: 2016-01-09 00:00:00
Tasks: Multiview Learning

Multiview learning problem refers to the problem of learning a classifier
from multiple view data. In this data set, each data points is presented by
multiple different views. In this paper, we propose a novel method for this
problem. This method is based on two assumptions. The first assumption is that
each data point has an intact feature vector, and each view is obtained by a
linear transformation from the intact vector. The second assumption is that the
intact vectors are discriminative, and in the intact space, we have a linear
classifier to separate the positive class from the negative class. We define an
intact vector for each data point, and a view-conditional transformation matrix
for each view, and propose to reconstruct the multiple view feature vectors by
the product of the corresponding intact vectors and transformation matrices.
Moreover, we also propose a linear classifier in the intact space, and learn it
jointly with the intact vectors. The learning problem is modeled by a
minimization problem, and the objective function is composed of a Cauchy error
estimator-based view-conditional reconstruction term over all data points and
views, and a classification error term measured by hinge loss over all the
intact vectors of all the data points. Some regularization terms are also
imposed to different variables in the objective function. The minimization
problem is solve by an iterative algorithm using alternate optimization
strategy and gradient descent algorithm. The proposed algorithm shows it
advantage in the compression to other multiview learning algorithms on
benchmark data sets.
Multicuts and Perturb & MAP for Probabilistic Graph Clustering
Authors: Jörg Hendrik Kappes, Paul Swoboda, Bogdan Savchynskyy, Tamir Hazan, Christoph Schnörr
Date: 2016-01-09 00:00:00
Tasks: Clustering, Graph Clustering, Image Segmentation, Semantic Segmentation, valid

We present a probabilistic graphical model formulation for the graph
clustering problem. This enables to locally represent uncertainty of image
partitions by approximate marginal distributions in a mathematically
substantiated way, and to rectify local data term cues so as to close contours
and to obtain valid partitions.
  We exploit recent progress on globally optimal MAP inference by integer
programming and on perturbation-based approximations of the log-partition
function, in order to sample clusterings and to estimate marginal distributions
of node-pairs both more accurately and more efficiently than state-of-the-art
methods. Our approach works for any graphically represented problem instance.
This is demonstrated for image segmentation and social network cluster
analysis. Our mathematical ansatz should be relevant also for other
combinatorial problems.
What to talk about and how? Selective Generation using LSTMs with Coarse-to-Fine Alignment
Authors: Hongyuan Mei, Mohit Bansal, Matthew R. Walter
Date: 2015-09-02 00:00:00
Proceeding: NAACL 2016 6
Tasks: Data-to-Text Generation, Decoder

We propose an end-to-end, domain-independent neural encoder-aligner-decoder
model for selective generation, i.e., the joint task of content selection and
surface realization. Our model first encodes a full set of over-determined
database event records via an LSTM-based recurrent neural network, then
utilizes a novel coarse-to-fine aligner to identify the small subset of salient
records to talk about, and finally employs a decoder to generate free-form
descriptions of the aligned, selected records. Our model achieves the best
selection and generation results reported to-date (with 59% relative
improvement in generation) on the benchmark WeatherGov dataset, despite using
no specialized features or linguistic resources. Using an improved k-nearest
neighbor beam filter helps further. We also perform a series of ablations and
visualizations to elucidate the contributions of our key model components.
Lastly, we evaluate the generalizability of our model on the RoboCup dataset,
and get results that are competitive with or better than the state-of-the-art,
despite being severely data-starved.
Lifting GIS Maps into Strong Geometric Context for Scene Understanding
Authors: Raúl Díaz, Minhaeng Lee, Jochen Schubert, Charless C. Fowlkes
Date: 2015-07-14 00:00:00
Tasks: Depth Estimation, object-detection, Object Detection, Scene Understanding, Segmentation, Semantic Segmentation

Contextual information can have a substantial impact on the performance of
visual tasks such as semantic segmentation, object detection, and geometric
estimation. Data stored in Geographic Information Systems (GIS) offers a rich
source of contextual information that has been largely untapped by computer
vision. We propose to leverage such information for scene understanding by
combining GIS resources with large sets of unorganized photographs using
Structure from Motion (SfM) techniques. We present a pipeline to quickly
generate strong 3D geometric priors from 2D GIS data using SfM models aligned
with minimal user input. Given an image resectioned against this model, we
generate robust predictions of depth, surface normals, and semantic labels. We
show that the precision of the predicted geometry is substantially more
accurate other single-image depth estimation methods. We then demonstrate the
utility of these contextual constraints for re-scoring pedestrian detections,
and use these GIS contextual features alongside object detection score maps to
improve a CRF-based semantic segmentation framework, boosting accuracy over
baseline models.
Finding a Collective Set of Items: From Proportional Multirepresentation to Group Recommendation
Authors: Piotr Skowron, Piotr Faliszewski, Jerome Lang
Date: 2014-02-13 00:00:00

We consider the following problem: There is a set of items (e.g., movies) and
a group of agents (e.g., passengers on a plane); each agent has some intrinsic
utility for each of the items. Our goal is to pick a set of $K$ items that
maximize the total derived utility of all the agents (i.e., in our example we
are to pick $K$ movies that we put on the plane's entertainment system).
However, the actual utility that an agent derives from a given item is only a
fraction of its intrinsic one, and this fraction depends on how the agent ranks
the item among the chosen, available, ones. We provide a formal specification
of the model and provide concrete examples and settings where it is applicable.
We show that the problem is hard in general, but we show a number of
tractability results for its natural special cases.