pwc-archive/papers-with-abstracts huggingface dataset:
Elicitation for Preferences Single Peaked on Trees
Authors: Palash Dey, Neeldhara Misra
Date: 2016-04-15 00:00:00

In multiagent systems, we often have a set of agents each of which have a
preference ordering over a set of items and one would like to know these
preference orderings for various tasks, for example, data analysis, preference
aggregation, voting etc. However, we often have a large number of items which
makes it impractical to ask the agents for their complete preference ordering.
In such scenarios, we usually elicit these agents' preferences by asking (a
hopefully small number of) comparison queries --- asking an agent to compare
two items. Prior works on preference elicitation focus on unrestricted domain
and the domain of single peaked preferences and show that the preferences in
single peaked domain can be elicited by much less number of queries compared to
unrestricted domain. We extend this line of research and study preference
elicitation for single peaked preferences on trees which is a strict superset
of the domain of single peaked preferences. We show that the query complexity
crucially depends on the number of leaves, the path cover number, and the
distance from path of the underlying single peaked tree, whereas the other
natural parameters like maximum degree, diameter, pathwidth do not play any
direct role in determining query complexity. We then investigate the query
complexity for finding a weak Condorcet winner for preferences single peaked on
a tree and show that this task has much less query complexity than preference
elicitation. Here again we observe that the number of leaves in the underlying
single peaked tree and the path cover number of the tree influence the query
complexity of the problem.
Computationally Efficient Bayesian Learning of Gaussian Process State Space Models
Authors: Andreas Svensson, Arno Solin, Simo Särkkä, Thomas B. Schön
Date: 2015-06-07 00:00:00
Tasks: Gaussian Processes, State Space Models

Gaussian processes allow for flexible specification of prior assumptions of
unknown dynamics in state space models. We present a procedure for efficient
Bayesian learning in Gaussian process state space models, where the
representation is formed by projecting the problem onto a set of approximate
eigenfunctions derived from the prior covariance structure. Learning under this
family of models can be conducted using a carefully crafted particle MCMC
algorithm. This scheme is computationally efficient and yet allows for a fully
Bayesian treatment of the problem. Compared to conventional system
identification tools or existing learning methods, we show competitive
performance and reliable quantification of uncertainties in the model.
Precomputed Real-Time Texture Synthesis with Markovian Generative Adversarial Networks
Authors: Chuan Li, Michael Wand
Date: 2016-04-15 00:00:00
Tasks: Style Transfer, Texture Synthesis

This paper proposes Markovian Generative Adversarial Networks (MGANs), a
method for training generative neural networks for efficient texture synthesis.
While deep neural network approaches have recently demonstrated remarkable
results in terms of synthesis quality, they still come at considerable
computational costs (minutes of run-time for low-res images). Our paper
addresses this efficiency issue. Instead of a numerical deconvolution in
previous work, we precompute a feed-forward, strided convolutional network that
captures the feature statistics of Markovian patches and is able to directly
generate outputs of arbitrary dimensions. Such network can directly decode
brown noise to realistic texture, or photos to artistic paintings. With
adversarial training, we obtain quality comparable to recent neural texture
synthesis methods. As no optimization is required any longer at generation
time, our run-time performance (0.25M pixel images at 25Hz) surpasses previous
neural texture synthesizers by a significant margin (at least 500 times
faster). We apply this idea to texture synthesis, style transfer, and video
stylization.
Match-SRNN: Modeling the Recursive Matching Structure with Spatial RNN
Authors: Shengxian Wan, Yanyan Lan, Jun Xu, Jiafeng Guo, Liang Pang, Xue-Qi Cheng
Date: 2016-04-15 00:00:00
Tasks: Position

Semantic matching, which aims to determine the matching degree between two
texts, is a fundamental problem for many NLP applications. Recently, deep
learning approach has been applied to this problem and significant improvements
have been achieved. In this paper, we propose to view the generation of the
global interaction between two texts as a recursive process: i.e. the
interaction of two texts at each position is a composition of the interactions
between their prefixes as well as the word level interaction at the current
position. Based on this idea, we propose a novel deep architecture, namely
Match-SRNN, to model the recursive matching structure. Firstly, a tensor is
constructed to capture the word level interactions. Then a spatial RNN is
applied to integrate the local interactions recursively, with importance
determined by four types of gates. Finally, the matching score is calculated
based on the global interaction. We show that, after degenerated to the exact
matching scenario, Match-SRNN can approximate the dynamic programming process
of longest common subsequence. Thus, there exists a clear interpretation for
Match-SRNN. Our experiments on two semantic matching tasks showed the
effectiveness of Match-SRNN, and its ability of visualizing the learned
matching structure.
DARI: Distance metric And Representation Integration for Person Verification
Authors: Guangrun Wang, Liang Lin, Shengyong Ding, Ya Li, Qing Wang
Date: 2016-04-15 00:00:00
Tasks: Metric Learning, Person Re-Identification, Representation Learning, Triplet

The past decade has witnessed the rapid development of feature representation
learning and distance metric learning, whereas the two steps are often
discussed separately. To explore their interaction, this work proposes an
end-to-end learning framework called DARI, i.e. Distance metric And
Representation Integration, and validates the effectiveness of DARI in the
challenging task of person verification. Given the training images annotated
with the labels, we first produce a large number of triplet units, and each one
contains three images, i.e. one person and the matched/mismatch references. For
each triplet unit, the distance disparity between the matched pair and the
mismatched pair tends to be maximized. We solve this objective by building a
deep architecture of convolutional neural networks. In particular, the
Mahalanobis distance matrix is naturally factorized as one top fully-connected
layer that is seamlessly integrated with other bottom layers representing the
image feature. The image feature and the distance metric can be thus
simultaneously optimized via the one-shot backward propagation. On several
public datasets, DARI shows very promising performance on re-identifying
individuals cross cameras against various challenges, and outperforms other
state-of-the-art approaches.
StalemateBreaker: A Proactive Content-Introducing Approach to Automatic Human-Computer Conversation
Authors: Xiang Li, Lili Mou, Rui Yan, Ming Zhang
Date: 2016-04-15 00:00:00
Tasks: Reranking

Existing open-domain human-computer conversation systems are typically
passive: they either synthesize or retrieve a reply provided a human-issued
utterance. It is generally presumed that humans should take the role to lead
the conversation and introduce new content when a stalemate occurs, and that
the computer only needs to "respond." In this paper, we propose
StalemateBreaker, a conversation system that can proactively introduce new
content when appropriate. We design a pipeline to determine when, what, and how
to introduce new content during human-computer conversation. We further propose
a novel reranking algorithm Bi-PageRank-HITS to enable rich interaction between
conversation context and candidate replies. Experiments show that both the
content-introducing approach and the reranking algorithm are effective. Our
full StalemateBreaker model outperforms a state-of-the-practice conversation
system by +14.4% p@1 when a stalemate occurs.
High-performance Semantic Segmentation Using Very Deep Fully Convolutional Networks
Authors: Zifeng Wu, Chunhua Shen, Anton Van Den Hengel
Date: 2016-04-15 00:00:00
Tasks: GPU, Image Segmentation, Semantic Segmentation, Vocal Bursts Intensity Prediction

We propose a method for high-performance semantic image segmentation (or
semantic pixel labelling) based on very deep residual networks, which achieves
the state-of-the-art performance. A few design factors are carefully considered
to this end.
  We make the following contributions. (i) First, we evaluate different
variations of a fully convolutional residual network so as to find the best
configuration, including the number of layers, the resolution of feature maps,
and the size of field-of-view. Our experiments show that further enlarging the
field-of-view and increasing the resolution of feature maps are typically
beneficial, which however inevitably leads to a higher demand for GPU memories.
To walk around the limitation, we propose a new method to simulate a high
resolution network with a low resolution network, which can be applied during
training and/or testing. (ii) Second, we propose an online bootstrapping method
for training. We demonstrate that online bootstrapping is critically important
for achieving good accuracy. (iii) Third we apply the traditional dropout to
some of the residual blocks, which further improves the performance. (iv)
Finally, our method achieves the currently best mean intersection-over-union
78.3\% on the PASCAL VOC 2012 dataset, as well as on the recent dataset
Cityscapes.
Facial expression recognition based on local region specific features and support vector machines
Authors: Deepak Ghimire, Sunghwan Jeong, Joonwhoan Lee, Sang Hyun Park
Date: 2016-04-15 00:00:00
Tasks: Emotion Recognition, Facial Expression Recognition, Facial Expression Recognition (FER)

Facial expressions are one of the most powerful, natural and immediate means
for human being to communicate their emotions and intensions. Recognition of
facial expression has many applications including human-computer interaction,
cognitive science, human emotion analysis, personality development etc. In this
paper, we propose a new method for the recognition of facial expressions from
single image frame that uses combination of appearance and geometric features
with support vector machines classification. In general, appearance features
for the recognition of facial expressions are computed by dividing face region
into regular grid (holistic representation). But, in this paper we extracted
region specific appearance features by dividing the whole face region into
domain specific local regions. Geometric features are also extracted from
corresponding domain specific regions. In addition, important local regions are
determined by using incremental search approach which results in the reduction
of feature dimension and improvement in recognition accuracy. The results of
facial expressions recognition using features from domain specific regions are
also compared with the results obtained using holistic representation. The
performance of the proposed facial expression recognition system has been
validated on publicly available extended Cohn-Kanade (CK+) facial expression
data sets.
Recognition of facial expressions based on salient geometric features and support vector machines
Authors: Deepak Ghimire, Joonwhoan Lee, Ze-Nian Li, Sunghwan Jeong
Date: 2016-04-15 00:00:00
Tasks: Facial Expression Recognition, Facial Expression Recognition (FER), Graph Matching

Facial expressions convey nonverbal cues which play an important role in
interpersonal relations, and are widely used in behavior interpretation of
emotions, cognitive science, and social interactions. In this paper we analyze
different ways of representing geometric feature and present a fully automatic
facial expression recognition (FER) system using salient geometric features. In
geometric feature-based FER approach, the first important step is to initialize
and track dense set of facial points as the expression evolves over time in
consecutive frames. In the proposed system, facial points are initialized using
elastic bunch graph matching (EBGM) algorithm and tracking is performed using
Kanade-Lucas-Tomaci (KLT) tracker. We extract geometric features from point,
line and triangle composed of tracking results of facial points. The most
discriminative line and triangle features are extracted using feature selective
multi-class AdaBoost with the help of extreme learning machine (ELM)
classification. Finally the geometric features for FER are extracted from the
boosted line, and triangles composed of facial points. The recognition accuracy
using features from point, line and triangle are analyzed independently. The
performance of the proposed FER system is evaluated on three different data
sets: namely CK+, MMI and MUG facial expression data sets.
Improving the Robustness of Deep Neural Networks via Stability Training
Authors: Stephan Zheng, Yang song, Thomas Leung, Ian Goodfellow
Date: 2016-04-15 00:00:00
Proceeding: CVPR 2016 6
Tasks: General Classification

In this paper we address the issue of output instability of deep neural
networks: small perturbations in the visual input can significantly distort the
feature embeddings and output of a neural network. Such instability affects
many deep architectures with state-of-the-art performance on a wide range of
computer vision tasks. We present a general stability training method to
stabilize deep networks against small input distortions that result from
various types of common image processing, such as compression, rescaling, and
cropping. We validate our method by stabilizing the state-of-the-art Inception
architecture against these types of distortions. In addition, we demonstrate
that our stabilized model gives robust state-of-the-art performance on
large-scale near-duplicate detection, similar-image ranking, and classification
on noisy datasets.
LLNet: A Deep Autoencoder Approach to Natural Low-light Image Enhancement
Authors: Kin Gwn Lore, Adedotun Akintayo, Soumik Sarkar
Date: 2015-11-12 00:00:00
Tasks: Denoising, Image Enhancement, Low-Light Image Enhancement

In surveillance, monitoring and tactical reconnaissance, gathering the right
visual information from a dynamic environment and accurately processing such
data are essential ingredients to making informed decisions which determines
the success of an operation. Camera sensors are often cost-limited in ability
to clearly capture objects without defects from images or videos taken in a
poorly-lit environment. The goal in many applications is to enhance the
brightness, contrast and reduce noise content of such images in an on-board
real-time manner. We propose a deep autoencoder-based approach to identify
signal features from low-light images handcrafting and adaptively brighten
images without over-amplifying the lighter parts in images (i.e., without
saturation of image pixels) in high dynamic range. We show that a variant of
the recently proposed stacked-sparse denoising autoencoder can learn to
adaptively enhance and denoise from synthetically darkened and noisy training
examples. The network can then be successfully applied to naturally low-light
environment and/or hardware degraded images. Results show significant
credibility of deep learning based approaches both visually and by quantitative
comparison with various popular enhancing, state-of-the-art denoising and
hybrid enhancing-denoising techniques.
Unsupervised Nonlinear Spectral Unmixing based on a Multilinear Mixing Model
Authors: Qi Wei, Marcus Chen, Jean-Yves Tourneret, Simon Godsill
Date: 2016-04-14 00:00:00

In the community of remote sensing, nonlinear mixing models have recently
received particular attention in hyperspectral image processing. In this paper,
we present a novel nonlinear spectral unmixing method following the recent
multilinear mixing model of [1], which includes an infinite number of terms
related to interactions between different endmembers. The proposed unmixing
method is unsupervised in the sense that the endmembers are estimated jointly
with the abundances and other parameters of interest, i.e., the transition
probability of undergoing further interactions. Non-negativity and sum-to one
constraints are imposed on abundances while only nonnegativity is considered
for endmembers. The resulting unmixing problem is formulated as a constrained
nonlinear optimization problem, which is solved by a block coordinate descent
strategy, consisting of updating the endmembers, abundances and transition
probability iteratively. The proposed method is evaluated and compared with
linear unmixing methods for synthetic and real hyperspectral datasets acquired
by the AVIRIS sensor. The advantage of using non-linear unmixing as opposed to
linear unmixing is clearly shown in these examples.
Clustering Financial Time Series: How Long is Enough?
Authors: Gautier Marti, Sébastien Andler, Frank Nielsen, Philippe Donnat
Date: 2016-03-13 00:00:00
Tasks: Clustering, Time Series, Time Series Analysis

Researchers have used from 30 days to several years of daily returns as
source data for clustering financial time series based on their correlations.
This paper sets up a statistical framework to study the validity of such
practices. We first show that clustering correlated random variables from their
observed values is statistically consistent. Then, we also give a first
empirical answer to the much debated question: How long should the time series
be? If too short, the clusters found can be spurious; if too long, dynamics can
be smoothed out.
Slow and steady feature analysis: higher order temporal coherence in video
Authors: Dinesh Jayaraman, Kristen Grauman
Date: 2015-06-15 00:00:00
Proceeding: CVPR 2016 6
Tasks: Action Recognition, Temporal Action Localization

How can unlabeled video augment visual learning? Existing methods perform
"slow" feature analysis, encouraging the representations of temporally close
frames to exhibit only small differences. While this standard approach captures
the fact that high-level visual signals change slowly over time, it fails to
capture *how* the visual content changes. We propose to generalize slow feature
analysis to "steady" feature analysis. The key idea is to impose a prior that
higher order derivatives in the learned feature space must be small. To this
end, we train a convolutional neural network with a regularizer on tuples of
sequential frames from unlabeled video. It encourages feature changes over time
to be smooth, i.e., similar to the most recent changes. Using five diverse
datasets, including unlabeled YouTube and KITTI videos, we demonstrate our
method's impact on object, scene, and action recognition tasks. We further show
that our features learned from unlabeled video can even surpass a standard
heavily supervised pretraining approach.
Modeling Electrical Daily Demand in Presence of PHEVs in Smart Grids with Supervised Learning
Authors: Marco Pellegrini, Farshad Rassaei
Date: 2016-04-14 00:00:00

Replacing a portion of current light duty vehicles (LDV) with plug-in hybrid
electric vehicles (PHEVs) offers the possibility to reduce the dependence on
petroleum fuels together with environmental and economic benefits. The charging
activity of PHEVs will certainly introduce new load to the power grid. In the
framework of the development of a smarter grid, the primary focus of the
present study is to propose a model for the electrical daily demand in presence
of PHEVs charging. Expected PHEV demand is modeled by the PHEV charging time
and the starting time of charge according to real world data. A normal
distribution for starting time of charge is assumed. Several distributions for
charging time are considered: uniform distribution, Gaussian with positive
support, Rician distribution and a non-uniform distribution coming from driving
patterns in real-world data. We generate daily demand profiles by using
real-world residential profiles throughout 2014 in the presence of different
expected PHEV demand models. Support vector machines (SVMs), a set of
supervised machine learning models, are employed in order to find the best
model to fit the data. SVMs with radial basis function (RBF) and polynomial
kernels were tested. Model performances are evaluated by means of mean squared
error (MSE) and mean absolute percentage error (MAPE). Best results are
obtained with RBF kernel: maximum (worst) values for MSE and MAPE were about
2.89 10-8 and 0.023, respectively.
Neural Implementation of Probabilistic Models of Cognition
Authors: Milad Kharratzadeh, Thomas R. Shultz
Date: 2015-01-13 00:00:00

Bayesian models of cognition hypothesize that human brains make sense of data
by representing probability distributions and applying Bayes' rule to find the
best explanation for available data. Understanding the neural mechanisms
underlying probabilistic models remains important because Bayesian models
provide a computational framework, rather than specifying mechanistic
processes. Here, we propose a deterministic neural-network model which
estimates and represents probability distributions from observable events --- a
phenomenon related to the concept of probability matching. Our model learns to
represent probabilities without receiving any representation of them from the
external world, but rather by experiencing the occurrence patterns of
individual events. Our neural implementation of probability matching is paired
with a neural module applying Bayes' rule, forming a comprehensive neural
scheme to simulate human Bayesian learning and inference. Our model also
provides novel explanations of base-rate neglect, a notable deviation from
Bayes.
1-bit Matrix Completion: PAC-Bayesian Analysis of a Variational Approximation
Authors: Vincent Cottet, Pierre Alquier
Date: 2016-04-14 00:00:00
Tasks: Collaborative Filtering, Learning Theory, Matrix Completion

Due to challenging applications such as collaborative filtering, the matrix
completion problem has been widely studied in the past few years. Different
approaches rely on different structure assumptions on the matrix in hand. Here,
we focus on the completion of a (possibly) low-rank matrix with binary entries,
the so-called 1-bit matrix completion problem. Our approach relies on tools
from machine learning theory: empirical risk minimization and its convex
relaxations. We propose an algorithm to compute a variational approximation of
the pseudo-posterior. Thanks to the convex relaxation, the corresponding
minimization problem is bi-convex, and thus the method behaves well in
practice. We also study the performance of this variational approximation
through PAC-Bayesian learning bounds. On the contrary to previous works that
focused on upper bounds on the estimation error of M with various matrix norms,
we are able to derive from this analysis a PAC bound on the prediction error of
our algorithm.
  We focus essentially on convex relaxation through the hinge loss, for which
we present the complete analysis, a complete simulation study and a test on the
MovieLens data set. However, we also discuss a variational approximation to
deal with the logistic loss.