pwc-archive/papers-with-abstracts huggingface dataset:
Concordance and the Smallest Covering Set of Preference Orderings
Authors: Zhiwei Lin, Hui Wang, Cees H. Elzinga
Date: 2016-09-15 00:00:00
Tasks: Decision Making, Marketing

Preference orderings are orderings of a set of items according to the
preferences (of judges). Such orderings arise in a variety of domains,
including group decision making, consumer marketing, voting and machine
learning. Measuring the mutual information and extracting the common patterns
in a set of preference orderings are key to these areas. In this paper we deal
with the representation of sets of preference orderings, the quantification of
the degree to which judges agree on their ordering of the items (i.e. the
concordance), and the efficient, meaningful description of such sets.
  We propose to represent the orderings in a subsequence-based feature space
and present a new algorithm to calculate the size of the set of all common
subsequences - the basis of a quantification of concordance, not only for pairs
of orderings but also for sets of orderings. The new algorithm is fast and
storage efficient with a time complexity of only $O(Nn^2)$ for the orderings of
$n$ items by $N$ judges and a space complexity of only $O(\min\{Nn,n^2\})$.
  Also, we propose to represent the set of all $N$ orderings through a smallest
set of covering preferences and present an algorithm to construct this smallest
covering set.
  The source code for the algorithms is available at
https://github.com/zhiweiuu/secs
Semi-supervised Graph Embedding Approach to Dynamic Link Prediction
Authors: Ryohei Hisano
Date: 2016-10-14 00:00:00
Tasks: Dynamic Link Prediction, Graph Embedding, Link Prediction, Prediction

We propose a simple discrete time semi-supervised graph embedding approach to
link prediction in dynamic networks. The learned embedding reflects information
from both the temporal and cross-sectional network structures, which is
performed by defining the loss function as a weighted sum of the supervised
loss from past dynamics and the unsupervised loss of predicting the
neighborhood context in the current network. Our model is also capable of
learning different embeddings for both formation and dissolution dynamics.
These key aspects contributes to the predictive performance of our model and we
provide experiments with three real--world dynamic networks showing that our
method is comparable to state of the art methods in link formation prediction
and outperforms state of the art baseline methods in link dissolution
prediction.
A Language-independent and Compositional Model for Personality Trait Recognition from Short Texts
Authors: Fei Liu, Julien Perez, Scott Nowson
Date: 2016-10-14 00:00:00
Proceeding: EACL 2017 4
Tasks: Author Profiling, Feature Engineering, Personality Trait Recognition, regression, Sentence

Many methods have been used to recognize author personality traits from text,
typically combining linguistic feature engineering with shallow learning
models, e.g. linear regression or Support Vector Machines. This work uses
deep-learning-based models and atomic features of text, the characters, to
build hierarchical, vectorial word and sentence representations for trait
inference. This method, applied to a corpus of tweets, shows state-of-the-art
performance across five traits and three languages (English, Spanish and
Italian) compared with prior work in author profiling. The results, supported
by preliminary visualisation work, are encouraging for the ability to detect
complex human traits.
Higher-Order Factorization Machines
Authors: Mathieu Blondel, Akinori Fujino, Naonori Ueda, Masakazu Ishihata
Date: 2016-07-25 00:00:00
Proceeding: NeurIPS 2016 12
Tasks: Link Prediction, Prediction

Factorization machines (FMs) are a supervised learning approach that can use
second-order feature combinations even when the data is very high-dimensional.
Unfortunately, despite increasing interest in FMs, there exists to date no
efficient training algorithm for higher-order FMs (HOFMs). In this paper, we
present the first generic yet efficient algorithms for training arbitrary-order
HOFMs. We also present new variants of HOFMs with shared parameters, which
greatly reduce model size and prediction times while maintaining similar
accuracy. We demonstrate the proposed approaches on four different link
prediction tasks.
Spectral Inference Methods on Sparse Graphs: Theory and Applications
Authors: Alaa Saade
Date: 2016-10-14 00:00:00
Tasks: Clustering, Community Detection, Matrix Completion

In an era of unprecedented deluge of (mostly unstructured) data, graphs are
proving more and more useful, across the sciences, as a flexible abstraction to
capture complex relationships between complex objects. One of the main
challenges arising in the study of such networks is the inference of
macroscopic, large-scale properties affecting a large number of objects, based
solely on the microscopic interactions between their elementary constituents.
Statistical physics, precisely created to recover the macroscopic laws of
thermodynamics from an idealized model of interacting particles, provides
significant insight to tackle such complex networks.
  In this dissertation, we use methods derived from the statistical physics of
disordered systems to design and study new algorithms for inference on graphs.
Our focus is on spectral methods, based on certain eigenvectors of carefully
chosen matrices, and sparse graphs, containing only a small amount of
information. We develop an original theory of spectral inference based on a
relaxation of various mean-field free energy optimizations. Our approach is
therefore fully probabilistic, and contrasts with more traditional motivations
based on the optimization of a cost function. We illustrate the efficiency of
our approach on various problems, including community detection, randomized
similarity-based clustering, and matrix completion.
Learning and Fusing Multimodal Features from and for Multi-task Facial Computing
Authors: Wei Li, Zhigang Zhu
Date: 2016-10-14 00:00:00
Tasks: Face Recognition

We propose a deep learning-based feature fusion approach for facial computing
including face recognition as well as gender, race and age detection. Instead
of training a single classifier on face images to classify them based on the
features of the person whose face appears in the image, we first train four
different classifiers for classifying face images based on race, age, gender
and identification (ID). Multi-task features are then extracted from the
trained models and cross-task-feature training is conducted which shows the
value of fusing multimodal features extracted from multi-tasks. We have found
that features trained for one task can be used for other related tasks. More
interestingly, the features trained for a task with more classes (e.g. ID) and
then used in another task with fewer classes (e.g. race) outperforms the
features trained for the other task itself. The final feature fusion is
performed by combining the four types of features extracted from the images by
the four classifiers. The feature fusion approach improves the classifications
accuracy by a 7.2%, 20.1%, 22.2%, 21.8% margin, respectively, for ID, age, race
and gender recognition, over the results of single classifiers trained only on
their individual features. The proposed method can be applied to applications
in which different types of data or features can be extracted.
Subspace clustering based on low rank representation and weighted nuclear norm minimization
Authors: Yu Song, Yiquan Wu
Date: 2016-10-12 00:00:00
Tasks: Clustering

Subspace clustering refers to the problem of segmenting a set of data points
approximately drawn from a union of multiple linear subspaces. Aiming at the
subspace clustering problem, various subspace clustering algorithms have been
proposed and low rank representation based subspace clustering is a very
promising and efficient subspace clustering algorithm. Low rank representation
method seeks the lowest rank representation among all the candidates that can
represent the data points as linear combinations of the bases in a given
dictionary. Nuclear norm minimization is adopted to minimize the rank of the
representation matrix. However, nuclear norm is not a very good approximation
of the rank of a matrix and the representation matrix thus obtained can be of
high rank which will affect the final clustering accuracy. Weighted nuclear
norm (WNN) is a better approximation of the rank of a matrix and WNN is adopted
in this paper to describe the rank of the representation matrix. The convex
program is solved via conventional alternation direction method of multipliers
(ADMM) and linearized alternating direction method of multipliers (LADMM) and
they are respectively refer to as WNNM-LRR and WNNM-LRR(L). Experimental
results show that, compared with low rank representation method and several
other state-of-the-art subspace clustering methods, WNNM-LRR and WNNM-LRR(L)
can get higher clustering accuracy.
Localized Lasso for High-Dimensional Regression
Authors: Makoto Yamada, Koh Takeuchi, Tomoharu Iwata, John Shawe-Taylor, Samuel Kaski
Date: 2016-03-22 00:00:00
Tasks: Diversity, regression, Vocal Bursts Intensity Prediction

We introduce the localized Lasso, which is suited for learning models that
are both interpretable and have a high predictive power in problems with high
dimensionality $d$ and small sample size $n$. More specifically, we consider a
function defined by local sparse models, one at each data point. We introduce
sample-wise network regularization to borrow strength across the models, and
sample-wise exclusive group sparsity (a.k.a., $\ell_{1,2}$ norm) to introduce
diversity into the choice of feature sets in the local models. The local models
are interpretable in terms of similarity of their sparsity patterns. The cost
function is convex, and thus has a globally optimal solution. Moreover, we
propose a simple yet efficient iterative least-squares based optimization
procedure for the localized Lasso, which does not need a tuning parameter, and
is guaranteed to converge to a globally optimal solution. The solution is
empirically shown to outperform alternatives for both simulated and genomic
personalized medicine data.
Recurrent 3D Attentional Networks for End-to-End Active Object Recognition
Authors: Min Liu, Yifei Shi, Lintao Zheng, Kai Xu, Hui Huang, Dinesh Manocha
Date: 2016-10-14 00:00:00
Tasks: Object Recognition, Reinforcement Learning

Active vision is inherently attention-driven: The agent actively selects views to attend in order to fast achieve the vision task while improving its internal representation of the scene being observed. Inspired by the recent success of attention-based models in 2D vision tasks based on single RGB images, we propose to address the multi-view depth-based active object recognition using attention mechanism, through developing an end-to-end recurrent 3D attentional network. The architecture takes advantage of a recurrent neural network (RNN) to store and update an internal representation. Our model, trained with 3D shape datasets, is able to iteratively attend to the best views targeting an object of interest for recognizing it. To realize 3D view selection, we derive a 3D spatial transformer network which is differentiable for training with backpropagation, achieving much faster convergence than the reinforcement learning employed by most existing attention-based models. Experiments show that our method, with only depth input, achieves state-of-the-art next-best-view performance in time efficiency and recognition accuracy.
Post Selection Inference with Kernels
Authors: Makoto Yamada, Yuta Umezu, Kenji Fukumizu, Ichiro Takeuchi
Date: 2016-10-12 00:00:00
Tasks: General Classification, Multi-class Classification

We propose a novel kernel based post selection inference (PSI) algorithm,
which can not only handle non-linearity in data but also structured output such
as multi-dimensional and multi-label outputs. Specifically, we develop a PSI
algorithm for independence measures, and propose the Hilbert-Schmidt
Independence Criterion (HSIC) based PSI algorithm (hsicInf). The novelty of the
proposed algorithm is that it can handle non-linearity and/or structured data
through kernels. Namely, the proposed algorithm can be used for wider range of
applications including nonlinear multi-class classification and multi-variate
regressions, while existing PSI algorithms cannot handle them. Through
synthetic experiments, we show that the proposed approach can find a set of
statistically significant features for both regression and classification
problems. Moreover, we apply the hsicInf algorithm to a real-world data, and
show that hsicInf can successfully identify important features.
Language as a Latent Variable: Discrete Generative Models for Sentence Compression
Authors: Yishu Miao, Phil Blunsom
Date: 2016-09-23 00:00:00
Proceeding: EMNLP 2016 11
Tasks: Language Modeling, Language Modelling, Sentence, Sentence Compression

In this work we explore deep generative models of text in which the latent
representation of a document is itself drawn from a discrete language model
distribution. We formulate a variational auto-encoder for inference in this
model and apply it to the task of compressing sentences. In this application
the generative model first draws a latent summary sentence from a background
language model, and then subsequently draws the observed sentence conditioned
on this latent summary. In our empirical evaluation we show that generative
formulations of both abstractive and extractive compression yield
state-of-the-art results when trained on a large amount of supervised data.
Further, we explore semi-supervised compression scenarios where we show that it
is possible to achieve performance competitive with previously proposed
supervised models while training on a fraction of the supervised data.
Analyzing the Affect of a Group of People Using Multi-modal Framework
Authors: Xiaohua Huang, Abhinav Dhall, Xin Liu, Guoying Zhao, Jingang Shi, Roland Goecke, Matti Pietikainen
Date: 2016-10-12 00:00:00
Tasks: Emotion Recognition

Millions of images on the web enable us to explore images from social events
such as a family party, thus it is of interest to understand and model the
affect exhibited by a group of people in images. But analysis of the affect
expressed by multiple people is challenging due to varied indoor and outdoor
settings, and interactions taking place between various numbers of people. A
few existing works on Group-level Emotion Recognition (GER) have investigated
on face-level information. Due to the challenging environments, face may not
provide enough information to GER. Relatively few studies have investigated
multi-modal GER. Therefore, we propose a novel multi-modal approach based on a
new feature description for understanding emotional state of a group of people
in an image. In this paper, we firstly exploit three kinds of rich information
containing face, upperbody and scene in a group-level image. Furthermore, in
order to integrate multiple person's information in a group-level image, we
propose an information aggregation method to generate three features for face,
upperbody and scene, respectively. We fuse face, upperbody and scene
information for robustness of GER against the challenging environments.
Intensive experiments are performed on two challenging group-level emotion
databases to investigate the role of face, upperbody and scene as well as
multi-modal framework. Experimental results demonstrate that our framework
achieves very promising performance for GER.
Assessing Threat of Adversarial Examples on Deep Neural Networks
Authors: Abigail Graese, Andras Rozsa, Terrance E. Boult
Date: 2016-10-13 00:00:00
Tasks: Binarization, General Classification

Deep neural networks are facing a potential security threat from adversarial
examples, inputs that look normal but cause an incorrect classification by the
deep neural network. For example, the proposed threat could result in
hand-written digits on a scanned check being incorrectly classified but looking
normal when humans see them. This research assesses the extent to which
adversarial examples pose a security threat, when one considers the normal
image acquisition process. This process is mimicked by simulating the
transformations that normally occur in acquiring the image in a real world
application, such as using a scanner to acquire digits for a check amount or
using a camera in an autonomous car. These small transformations negate the
effect of the carefully crafted perturbations of adversarial examples,
resulting in a correct classification by the deep neural network. Thus just
acquiring the image decreases the potential impact of the proposed security
threat. We also show that the already widely used process of averaging over
multiple crops neutralizes most adversarial examples. Normal preprocessing,
such as text binarization, almost completely neutralizes adversarial examples.
This is the first paper to show that for text driven classification,
adversarial examples are an academic curiosity, not a security threat.
Local Perturb-and-MAP for Structured Prediction
Authors: Gedas Bertasius, Qiang Liu, Lorenzo Torresani, Jianbo Shi
Date: 2016-05-24 00:00:00
Tasks: Combinatorial Optimization, global-optimization, Prediction, Structured Prediction

Conditional random fields (CRFs) provide a powerful tool for structured
prediction, but cast significant challenges in both the learning and inference
steps. Approximation techniques are widely used in both steps, which should be
considered jointly to guarantee good performance (a.k.a. "inferning").
Perturb-and-MAP models provide a promising alternative to CRFs, but require
global combinatorial optimization and hence they are usable only on specific
models. In this work, we present a new Local Perturb-and-MAP (locPMAP)
framework that replaces the global optimization with a local optimization by
exploiting our observed connection between locPMAP and the pseudolikelihood of
the original CRF model. We test our approach on three different vision tasks
and show that our method achieves consistently improved performance over other
approximate inference techniques optimized to a pseudolikelihood objective.
Additionally, we demonstrate that we can integrate our method in the fully
convolutional network framework to increase our model's complexity. Finally,
our observed connection between locPMAP and the pseudolikelihood leads to a
novel perspective for understanding and using pseudolikelihood.
Neurally-Guided Procedural Models: Amortized Inference for Procedural Graphics Programs using Neural Networks
Authors: Daniel Ritchie, Anna Thomas, Pat Hanrahan, Noah D. Goodman
Date: 2016-03-19 00:00:00
Proceeding: NeurIPS 2016 12

Probabilistic inference algorithms such as Sequential Monte Carlo (SMC)
provide powerful tools for constraining procedural models in computer graphics,
but they require many samples to produce desirable results. In this paper, we
show how to create procedural models which learn how to satisfy constraints. We
augment procedural models with neural networks which control how the model
makes random choices based on the output it has generated thus far. We call
such models neurally-guided procedural models. As a pre-computation, we train
these models to maximize the likelihood of example outputs generated via SMC.
They are then used as efficient SMC importance samplers, generating
high-quality results with very few samples. We evaluate our method on
L-system-like models with image-based constraints. Given a desired quality
threshold, neurally-guided models can generate satisfactory results up to 10x
faster than unguided models.
A Parallel Memory-efficient Epistemic Logic Program Solver: Harder, Better, Faster
Authors: Patrick Thor Kahl, Anthony P. Leclerc, Tran Cao Son
Date: 2016-08-24 00:00:00

As the practical use of answer set programming (ASP) has grown with the
development of efficient solvers, we expect a growing interest in extensions of
ASP as their semantics stabilize and solvers supporting them mature. Epistemic
Specifications, which adds modal operators K and M to the language of ASP, is
one such extension. We call a program in this language an epistemic logic
program (ELP). Solvers have thus far been practical for only the simplest ELPs
due to exponential growth of the search space. We describe a solver that is
able to solve harder problems better (e.g., without exponentially-growing
memory needs w.r.t. K and M occurrences) and faster than any other known ELP
solver.
An efficient high-probability algorithm for Linear Bandits
Authors: Gábor Braun, Sebastian Pokutta
Date: 2016-10-06 00:00:00
Tasks: Learning Theory, Vocal Bursts Intensity Prediction

For the linear bandit problem, we extend the analysis of algorithm CombEXP
from [R. Combes, M. S. Talebi Mazraeh Shahi, A. Proutiere, and M. Lelarge.
Combinatorial bandits revisited. In C. Cortes, N. D. Lawrence, D. D. Lee, M.
Sugiyama, and R. Garnett, editors, Advances in Neural Information Processing
Systems 28, pages 2116--2124. Curran Associates, Inc., 2015. URL
http://papers.nips.cc/paper/5831-combinatorial-bandits-revisited.pdf] to the
high-probability case against adaptive adversaries, allowing actions to come
from an arbitrary polytope. We prove a high-probability regret of
\(O(T^{2/3})\) for time horizon \(T\). While this bound is weaker than the
optimal \(O(\sqrt{T})\) bound achieved by GeometricHedge in [P. L. Bartlett, V.
Dani, T. Hayes, S. Kakade, A. Rakhlin, and A. Tewari. High-probability regret
bounds for bandit online linear optimization. In 21th Annual Conference on
Learning Theory (COLT 2008), July 2008.
http://eprints.qut.edu.au/45706/1/30-Bartlett.pdf], CombEXP is computationally
efficient, requiring only an efficient linear optimization oracle over the
convex hull of the actions.
GPU-accelerated real-time stixel computation
Authors: Daniel Hernandez-Juarez, Antonio Espinosa, David Vázquez, Antonio Manuel López, Juan Carlos Moure
Date: 2016-10-13 00:00:00
Tasks: GPU

The Stixel World is a medium-level, compact representation of road scenes
that abstracts millions of disparity pixels into hundreds or thousands of
stixels. The goal of this work is to implement and evaluate a complete
multi-stixel estimation pipeline on an embedded, energy-efficient,
GPU-accelerated device. This work presents a full GPU-accelerated
implementation of stixel estimation that produces reliable results at 26 frames
per second (real-time) on the Tegra X1 for disparity images of 1024x440 pixels
and stixel widths of 5 pixels, and achieves more than 400 frames per second on
a high-end Titan X GPU card.
Embedded real-time stereo estimation via Semi-Global Matching on the GPU
Authors: Daniel Hernandez-Juarez, Alejandro Chacón, Antonio Espinosa, David Vázquez, Juan Carlos Moure, Antonio Manuel López
Date: 2016-10-13 00:00:00
Tasks: Autonomous Vehicles, Disparity Estimation, GPU

Dense, robust and real-time computation of depth information from
stereo-camera systems is a computationally demanding requirement for robotics,
advanced driver assistance systems (ADAS) and autonomous vehicles. Semi-Global
Matching (SGM) is a widely used algorithm that propagates consistency
constraints along several paths across the image. This work presents a
real-time system producing reliable disparity estimation results on the new
embedded energy-efficient GPU devices. Our design runs on a Tegra X1 at 42
frames per second (fps) for an image size of 640x480, 128 disparity levels, and
using 4 path directions for the SGM method.