pwc-archive/papers-with-abstracts huggingface dataset:
From safe screening rules to working sets for faster Lasso-type solvers
Authors: Mathurin Massias, Alexandre Gramfort, Joseph Salmon
Date: 2017-03-21 00:00:00
Tasks: Sparse Learning

Convex sparsity-promoting regularizations are ubiquitous in modern
statistical learning. By construction, they yield solutions with few non-zero
coefficients, which correspond to saturated constraints in the dual
optimization formulation. Working set (WS) strategies are generic optimization
techniques that consist in solving simpler problems that only consider a subset
of constraints, whose indices form the WS. Working set methods therefore
involve two nested iterations: the outer loop corresponds to the definition of
the WS and the inner loop calls a solver for the subproblems. For the Lasso
estimator a WS is a set of features, while for a Group Lasso it refers to a set
of groups. In practice, WS are generally small in this context so the
associated feature Gram matrix can fit in memory. Here we show that the
Gauss-Southwell rule (a greedy strategy for block coordinate descent
techniques) leads to fast solvers in this case. Combined with a working set
strategy based on an aggressive use of so-called Gap Safe screening rules, we
propose a solver achieving state-of-the-art performance on sparse learning
problems. Results are presented on Lasso and multi-task Lasso estimators.
Optimal Projected Variance Group-Sparse Block PCA
Authors: Marie Chavent, Guy Chavent
Date: 2017-05-01 00:00:00
Tasks: Dictionary Learning, Retrieval

We address the problem of defining a group sparse formulation for Principal Components Analysis (PCA) - or its equivalent formulations as Low Rank approximation or Dictionary Learning problems - which achieves a compromise between maximizing the variance explained by the components and promoting sparsity of the loadings. So we propose first a new definition of the variance explained by non necessarily orthogonal components, which is optimal in some aspect and compatible with the principal components situation. Then we use a specific regularization of this variance by the group-$\ell_{1}$ norm to define a Group Sparse Maximum Variance (GSMV) formulation of PCA. The GSMV formulation achieves our objective by construction, and has the nice property that the inner non smooth optimization problem can be solved analytically, thus reducing GSMV to the maximization of a smooth and convex function under unit norm and orthogonality constraints, which generalizes Journee et al. (2010) to group sparsity. Numerical comparison with deflation on synthetic data shows that GSMV produces steadily slightly better and more robust results for the retrieval of hidden sparse structures, and is about three times faster on these examples. Application to real data shows the interest of group sparsity for variables selection in PCA of mixed data (categorical/numerical) .
A closed-form approach to Bayesian inference in tree-structured graphical models
Authors: Loïc Schwaller, Stéphane Robin, Michael Stumpf
Date: 2015-04-10 00:00:00
Tasks: Bayesian Inference, Form

We consider the inference of the structure of an undirected graphical model
in an exact Bayesian framework. More specifically we aim at achieving the
inference with close-form posteriors, avoiding any sampling step. This task
would be intractable without any restriction on the considered graphs, so we
limit our exploration to mixtures of spanning trees. We consider the inference
of the structure of an undirected graphical model in a Bayesian framework. To
avoid convergence issues and highly demanding Monte Carlo sampling, we focus on
exact inference. More specifically we aim at achieving the inference with
close-form posteriors, avoiding any sampling step. To this aim, we restrict the
set of considered graphs to mixtures of spanning trees. We investigate under
which conditions on the priors - on both tree structures and parameters - exact
Bayesian inference can be achieved. Under these conditions, we derive a fast an
exact algorithm to compute the posterior probability for an edge to belong to
{the tree model} using an algebraic result called the Matrix-Tree theorem. We
show that the assumption we have made does not prevent our approach to perform
well on synthetic and flow cytometry data.
Detecting Drivable Area for Self-driving Cars: An Unsupervised Approach
Authors: Ziyi Liu, Siyu Yu, Xiao Wang, Nanning Zheng
Date: 2017-05-01 00:00:00
Tasks: Self-Driving Cars

It has been well recognized that detecting drivable area is central to
self-driving cars. Most of existing methods attempt to locate road surface by
using lane line, thereby restricting to drivable area on which have a clear
lane mark. This paper proposes an unsupervised approach for detecting drivable
area utilizing both image data from a monocular camera and point cloud data
from a 3D-LIDAR scanner. Our approach locates initial drivable areas based on a
"direction ray map" obtained by image-LIDAR data fusion. Besides, a fusion of
the feature level is also applied for more robust performance. Once the initial
drivable areas are described by different features, the feature fusion problem
is formulated as a Markov network and a belief propagation algorithm is
developed to perform the model inference. Our approach is unsupervised and
avoids common hypothesis, yet gets state-of-the-art results on ROAD-KITTI
benchmark. Experiments show that our unsupervised approach is efficient and
robust for detecting drivable area for self-driving cars.
Learning Topic-Sensitive Word Representations
Authors: Marzieh Fadaee, Arianna Bisazza, Christof Monz
Date: 2017-05-01 00:00:00
Proceeding: ACL 2017 7

Distributed word representations are widely used for modeling words in NLP
tasks. Most of the existing models generate one representation per word and do
not consider different meanings of a word. We present two approaches to learn
multiple topic-sensitive representations per word by using Hierarchical
Dirichlet Process. We observe that by modeling topics and integrating topic
distributions for each document we obtain representations that are able to
distinguish between different meanings of a given word. Our models yield
statistically significant improvements for the lexical substitution task
indicating that commonly used single word representations, even when combined
with contextual information, are insufficient for this task.
Data Augmentation for Low-Resource Neural Machine Translation
Authors: Marzieh Fadaee, Arianna Bisazza, Christof Monz
Date: 2017-05-01 00:00:00
Proceeding: ACL 2017 7
Tasks: Data Augmentation, Low Resource Neural Machine Translation, Low-Resource Neural Machine Translation, Machine Translation, Sentence, Translation

The quality of a Neural Machine Translation system depends substantially on
the availability of sizable parallel corpora. For low-resource language pairs
this is not the case, resulting in poor translation quality. Inspired by work
in computer vision, we propose a novel data augmentation approach that targets
low-frequency words by generating new sentence pairs containing rare words in
new, synthetically created contexts. Experimental results on simulated
low-resource settings show that our method improves translation quality by up
to 2.9 BLEU points over the baseline and up to 3.2 BLEU over back-translation.
Multi-View Product Image Search Using Deep ConvNets Representations
Authors: Muhammet Bastan, Ozgur Yilmaz
Date: 2016-08-11 00:00:00
Tasks: Image Retrieval, Retrieval

Multi-view product image queries can improve retrieval performance over
single view queries significantly. In this paper, we investigated the
performance of deep convolutional neural networks (ConvNets) on multi-view
product image search. First, we trained a VGG-like network to learn deep
ConvNets representations of product images. Then, we computed the deep ConvNets
representations of database and query images and performed single view queries,
and multi-view queries using several early and late fusion approaches.
  We performed extensive experiments on the publicly available Multi-View
Object Image Dataset (MVOD 5K) with both clean background queries from the
Internet and cluttered background queries from a mobile phone. We compared the
performance of ConvNets to the classical bag-of-visual-words (BoWs). We
concluded that (1) multi-view queries with deep ConvNets representations
perform significantly better than single view queries, (2) ConvNets perform
much better than BoWs and have room for further improvement, (3) pre-training
of ConvNets on a different image dataset with background clutter is needed to
obtain good performance on cluttered product image queries obtained with a
mobile phone.
A Statistical Model for Simultaneous Template Estimation, Bias Correction, and Registration of 3D Brain Images
Authors: Akshay Pai, Stefan Sommer, Lars Lau Raket, Line Kühnel, Sune Darkner, Lauge Sørensen, Mads Nielsen
Date: 2017-05-01 00:00:00
Tasks: Anatomy, Image Registration

Template estimation plays a crucial role in computational anatomy since it
provides reference frames for performing statistical analysis of the underlying
anatomical population variability. While building models for template
estimation, variability in sites and image acquisition protocols need to be
accounted for. To account for such variability, we propose a generative
template estimation model that makes simultaneous inference of both bias fields
in individual images, deformations for image registration, and variance
hyperparameters. In contrast, existing maximum a posterori based methods need
to rely on either bias-invariant similarity measures or robust image
normalization. Results on synthetic and real brain MRI images demonstrate the
capability of the model to capture heterogeneity in intensities and provide a
reliable template estimation from registration.
Sub-Pixel Registration of Wavelet-Encoded Images
Authors: Vildan Atalay Aydin, Hassan Foroosh
Date: 2017-05-01 00:00:00
Tasks: Compressive Sensing, Motion Compensation, Super-Resolution

Sub-pixel registration is a crucial step for applications such as super-resolution in remote sensing, motion compensation in magnetic resonance imaging, and non-destructive testing in manufacturing, to name a few. Recently, these technologies have been trending towards wavelet encoded imaging and sparse/compressive sensing. The former plays a crucial role in reducing imaging artifacts, while the latter significantly increases the acquisition speed. In view of these new emerging needs for applications of wavelet encoded imaging, we propose a sub-pixel registration method that can achieve direct wavelet domain registration from a sparse set of coefficients. We make the following contributions: (i) We devise a method of decoupling scale, rotation, and translation parameters in the Haar wavelet domain, (ii) We derive explicit mathematical expressions that define in-band sub-pixel registration in terms of wavelet coefficients, (iii) Using the derived expressions, we propose an approach to achieve in-band subpixel registration, avoiding back and forth transformations. (iv) Our solution remains highly accurate even when a sparse set of coefficients are used, which is due to localization of signals in a sparse set of wavelet coefficients. We demonstrate the accuracy of our method, and show that it outperforms the state-of-the-art on simulated and real data, even when the data is sparse.
Model Transfer for Tagging Low-resource Languages using a Bilingual Dictionary
Authors: Meng Fang, Trevor Cohn
Date: 2017-05-01 00:00:00
Proceeding: ACL 2017 7
Tasks: Active Learning, Cross-Lingual Word Embeddings, Word Embeddings

Cross-lingual model transfer is a compelling and popular method for
predicting annotations in a low-resource language, whereby parallel corpora
provide a bridge to a high-resource language and its associated annotated
corpora. However, parallel data is not readily available for many languages,
limiting the applicability of these approaches. We address these drawbacks in
our framework which takes advantage of cross-lingual word embeddings trained
solely on a high coverage bilingual dictionary. We propose a novel neural
network model for joint training from both sources of data based on
cross-lingual word embeddings, and show substantial empirical improvements over
baseline techniques. We also propose several active learning heuristics, which
result in improvements over competitive benchmark methods.
Matrix completion with queries
Authors: Natali Ruchansky, Mark Crovella, Evimaria Terzi
Date: 2017-05-01 00:00:00
Tasks: Matrix Completion, Recommendation Systems

In many applications, e.g., recommender systems and traffic monitoring, the
data comes in the form of a matrix that is only partially observed and low
rank. A fundamental data-analysis task for these datasets is matrix completion,
where the goal is to accurately infer the entries missing from the matrix. Even
when the data satisfies the low-rank assumption, classical matrix-completion
methods may output completions with significant error -- in that the
reconstructed matrix differs significantly from the true underlying matrix.
Often, this is due to the fact that the information contained in the observed
entries is insufficient. In this work, we address this problem by proposing an
active version of matrix completion, where queries can be made to the true
underlying matrix. Subsequently, we design Order&Extend, which is the first
algorithm to unify a matrix-completion approach and a querying strategy into a
single algorithm. Order&Extend is able identify and alleviate insufficient
information by judiciously querying a small number of additional entries. In an
extensive experimental evaluation on real-world datasets, we demonstrate that
our algorithm is efficient and is able to accurately reconstruct the true
matrix while asking only a small number of queries.
Inverse Moment Methods for Sufficient Forecasting using High-Dimensional Predictors
Authors: Wei Luo, Lingzhou Xue, Jiawei Yao, Xiufan Yu
Date: 2017-05-01 00:00:00
Tasks: Dimensionality Reduction, Model Selection, Time Series, Time Series Analysis, Vocal Bursts Intensity Prediction

We consider forecasting a single time series using a large number of predictors in the presence of a possible nonlinear forecast function. Assuming that the predictors affect the response through the latent factors, we propose to first conduct factor analysis and then apply sufficient dimension reduction on the estimated factors, to derive the reduced data for subsequent forecasting. Using directional regression and the inverse third-moment method in the stage of sufficient dimension reduction, the proposed methods can capture the non-monotone effect of factors on the response. We also allow a diverging number of factors and only impose general regularity conditions on the distribution of factors, avoiding the undesired time reversibility of the factors by the latter. These make the proposed methods fundamentally more applicable than the sufficient forecasting method in Fan et al. (2017). The proposed methods are demonstrated in both simulation studies and an empirical study of forecasting monthly macroeconomic data from 1959 to 2016. Also, our theory contributes to the literature of sufficient dimension reduction, as it includes an invariance result, a path to perform sufficient dimension reduction under the high-dimensional setting without assuming sparsity, and the corresponding order-determination procedure.
Stochastic Divergence Minimization for Biterm Topic Model
Authors: Zhenghang Cui, Issei Sato, Masashi Sugiyama
Date: 2017-05-01 00:00:00
Tasks: model, Topic Models, Variational Inference

As the emergence and the thriving development of social networks, a huge
number of short texts are accumulated and need to be processed. Inferring
latent topics of collected short texts is useful for understanding its hidden
structure and predicting new contents. Unlike conventional topic models such as
latent Dirichlet allocation (LDA), a biterm topic model (BTM) was recently
proposed for short texts to overcome the sparseness of document-level word
co-occurrences by directly modeling the generation process of word pairs.
Stochastic inference algorithms based on collapsed Gibbs sampling (CGS) and
collapsed variational inference have been proposed for BTM. However, they
either require large computational complexity, or rely on very crude
estimation. In this work, we develop a stochastic divergence minimization
inference algorithm for BTM to estimate latent topics more accurately in a
scalable way. Experiments demonstrate the superiority of our proposed algorithm
compared with existing inference algorithms.
Level Playing Field for Million Scale Face Recognition
Authors: Aaron Nech, Ira Kemelmacher-Shlizerman
Date: 2017-05-01 00:00:00
Proceeding: CVPR 2017 7
Tasks: Face Recognition

Face recognition has the perception of a solved problem, however when tested
at the million-scale exhibits dramatic variation in accuracies across the
different algorithms. Are the algorithms very different? Is access to good/big
training data their secret weapon? Where should face recognition improve? To
address those questions, we created a benchmark, MF2, that requires all
algorithms to be trained on same data, and tested at the million scale. MF2 is
a public large-scale set with 672K identities and 4.7M photos created with the
goal to level playing field for large scale face recognition. We contrast our
results with findings from the other two large-scale benchmarks MegaFace
Challenge and MS-Celebs-1M where groups were allowed to train on any
private/public/big/small set. Some key discoveries: 1) algorithms, trained on
MF2, were able to achieve state of the art and comparable results to algorithms
trained on massive private sets, 2) some outperformed themselves once trained
on MF2, 3) invariance to aging suffers from low accuracies as in MegaFace,
identifying the need for larger age variations possibly within identities or
adjustment of algorithms in future testings.
Duluth at SemEval--2016 Task 14 : Extending Gloss Overlaps to Enrich Semantic Taxonomies
Authors: Ted Pedersen
Date: 2017-05-01 00:00:00
Tasks: All

This paper describes the Duluth systems that participated in Task 14 of
SemEval 2016, Semantic Taxonomy Enrichment. There were three related systems in
the formal evaluation which are discussed here, along with numerous
post--evaluation runs. All of these systems identified synonyms between WordNet
and other dictionaries by measuring the gloss overlaps between them. These
systems perform better than the random baseline and one post--evaluation
variation was within a respectable margin of the median result attained by all
participating systems.
Towards Context-aware Interaction Recognition
Authors: Bohan Zhuang, Lingqiao Liu, Chunhua Shen, Ian Reid
Date: 2017-03-18 00:00:00

Recognizing how objects interact with each other is a crucial task in visual
recognition. If we define the context of the interaction to be the objects
involved, then most current methods can be categorized as either: (i) training
a single classifier on the combination of the interaction and its context; or
(ii) aiming to recognize the interaction independently of its explicit context.
Both methods suffer limitations: the former scales poorly with the number of
combinations and fails to generalize to unseen combinations, while the latter
often leads to poor interaction recognition performance due to the difficulty
of designing a context-independent interaction classifier. To mitigate those
drawbacks, this paper proposes an alternative, context-aware interaction
recognition framework. The key to our method is to explicitly construct an
interaction classifier which combines the context, and the interaction. The
context is encoded via word2vec into a semantic space, and is used to derive a
classification result for the interaction.
  The proposed method still builds one classifier for one interaction (as per
type (ii) above), but the classifier built is adaptive to context via weights
which are context dependent. The benefit of using the semantic space is that it
naturally leads to zero-shot generalizations in which semantically similar
contexts (subjectobject pairs) can be recognized as suitable contexts for an
interaction, even if they were not observed in the training set.
Targeted matrix completion
Authors: Natali Ruchansky, Mark Crovella, Evimaria Terzi
Date: 2017-04-30 00:00:00
Tasks: Matrix Completion, Recommendation Systems

Matrix completion is a problem that arises in many data-analysis settings
where the input consists of a partially-observed matrix (e.g., recommender
systems, traffic matrix analysis etc.). Classical approaches to matrix
completion assume that the input partially-observed matrix is low rank. The
success of these methods depends on the number of observed entries and the rank
of the matrix; the larger the rank, the more entries need to be observed in
order to accurately complete the matrix. In this paper, we deal with matrices
that are not necessarily low rank themselves, but rather they contain low-rank
submatrices. We propose Targeted, which is a general framework for completing
such matrices. In this framework, we first extract the low-rank submatrices and
then apply a matrix-completion algorithm to these low-rank submatrices as well
as the remainder matrix separately. Although for the completion itself we use
state-of-the-art completion methods, our results demonstrate that Targeted
achieves significantly smaller reconstruction errors than other classical
matrix-completion methods. One of the key technical contributions of the paper
lies in the identification of the low-rank submatrices from the input
partially-observed matrices.
Conversion Rate Optimization through Evolutionary Computation
Authors: Risto Miikkulainen, Neil Iscoe, Aaron Shagrin, Ron Cordell, Sam Nazari, Cory Schoolland, Myles Brundage, Jonathan Epstein, Randy Dean, Gurmeet Lamba
Date: 2017-03-01 00:00:00

Conversion optimization means designing a web interface so that as many users
as possible take a desired action on it, such as register or purchase. Such
design is usually done by hand, testing one change at a time through A/B
testing, or a limited number of combinations through multivariate testing,
making it possible to evaluate only a small fraction of designs in a vast
design space. This paper describes Sentient Ascend, an automatic conversion
optimization system that uses evolutionary optimization to create effective web
interface designs. Ascend makes it possible to discover and utilize
interactions between the design elements that are difficult to identify
otherwise. Moreover, evaluation of design candidates is done in parallel
online, i.e. with a large number of real users interacting with the system. A
case study on an existing media site shows that significant improvements (i.e.
over 43%) are possible beyond human design. Ascend can therefore be seen as an
approach to massively multivariate conversion optimization, based on a
massively parallel interactive evolution.
Deep-Anomaly: Fully Convolutional Neural Network for Fast Anomaly Detection in Crowded Scenes
Authors: Mohammad Sabokrou, Mohsen Fayyaz, Mahmood Fathy, Zahra Moayedd, Reinhard klette
Date: 2016-09-03 00:00:00
Tasks: Anomaly Detection, Outlier Detection

The detection of abnormal behaviours in crowded scenes has to deal with many
challenges. This paper presents an efficient method for detection and
localization of anomalies in videos. Using fully convolutional neural networks
(FCNs) and temporal data, a pre-trained supervised FCN is transferred into an
unsupervised FCN ensuring the detection of (global) anomalies in scenes. High
performance in terms of speed and accuracy is achieved by investigating the
cascaded detection as a result of reducing computation complexities. This
FCN-based architecture addresses two main tasks, feature representation and
cascaded outlier detection. Experimental results on two benchmarks suggest that
detection and localization of the proposed method outperforms existing methods
in terms of accuracy.