pwc-archive/papers-with-abstracts huggingface dataset:
Image Denoising Using Low Rank Minimization With Modified Noise Estimation
Authors: Zahid Hussain Shamsi, Hyun Sook Oh, Dai-Gyoung Kim
Date: 2015-04-14 00:00:00
Tasks: Denoising, Image Denoising, Noise Estimation

Recently, the application of low rank minimization to image denoising has
shown remarkable denoising results which are equivalent or better than those of
the existing state-of-the-art algorithms. However, due to iterative nature of
low rank optimization, estimation of residual noise is an essential requirement
after each iteration. Currently, this noise is estimated by using the filtered
noise in the previous iteration without considering the geometric structure of
the given image. This estimate may be affected in the presence of moderate and
severe levels of noise. To obtain a more reliable estimate of residual noise,
we propose a modified algorithm (GWNNM) which includes the contribution of the
geometric structure of an image to the existing noise estimation. Furthermore,
the proposed algorithm exploits the difference of large and small singular
values to enhance the edges and textures during the denoising process.
Consequently, the proposed modifications achieve significant improvements in
the denoising results of the existing low rank optimization algorithms.
Situational Object Boundary Detection
Authors: Jasper Uijlings, Vittorio Ferrari
Date: 2015-04-24 00:00:00
Proceeding: CVPR 2015 6
Tasks: Boundary Detection, Contour Detection, Object

Intuitively, the appearance of true object boundaries varies from image to
image. Hence the usual monolithic approach of training a single boundary
predictor and applying it to all images regardless of their content is bound to
be suboptimal. In this paper we therefore propose situational object boundary
detection: We first define a variety of situations and train a specialized
object boundary detector for each of them using [Dollar and Zitnick 2013]. Then
given a test image, we classify it into these situations using its context,
which we model by global image appearance. We apply the corresponding
situational object boundary detectors, and fuse them based on the
classification probabilities. In experiments on ImageNet, Microsoft COCO, and
Pascal VOC 2012 segmentation we show that our situational object boundary
detection gives significant improvements over a monolithic approach.
Additionally, our method substantially outperforms [Hariharan et al. 2011] on
semantic contour detection on their SBD dataset.
$gen$CNN: A Convolutional Architecture for Word Sequence Prediction
Authors: Mingxuan Wang, Zhengdong Lu, Hang Li, Wenbin Jiang, Qun Liu
Date: 2015-03-17 00:00:00
Tasks: Language Modeling, Language Modelling, Machine Translation, Prediction, Re-Ranking, Text Generation, Translation

We propose a novel convolutional architecture, named $gen$CNN, for word
sequence prediction. Different from previous work on neural network-based
language modeling and generation (e.g., RNN or LSTM), we choose not to greedily
summarize the history of words as a fixed length vector. Instead, we use a
convolutional neural network to predict the next word with the history of words
of variable length. Also different from the existing feedforward networks for
language modeling, our model can effectively fuse the local correlation and
global correlation in the word sequence, with a convolution-gating strategy
specifically designed for the task. We argue that our model can give adequate
representation of the history, and therefore can naturally exploit both the
short and long range dependencies. Our model is fast, easy to train, and
readily parallelized. Our extensive experiments on text generation and $n$-best
re-ranking in machine translation show that $gen$CNN outperforms the
state-of-the-arts with big margins.
From Image-level to Pixel-level Labeling with Convolutional Networks
Authors: Pedro O. Pinheiro, Ronan Collobert
Date: 2014-11-23 00:00:00
Proceeding: CVPR 2015 6
Tasks: Multiple Instance Learning, Object, Segmentation, Semantic Segmentation, Weakly-Supervised Object Segmentation, Weakly supervised segmentation, Weakly-Supervised Semantic Segmentation

We are interested in inferring object segmentation by leveraging only object
class information, and by considering only minimal priors on the object
segmentation task. This problem could be viewed as a kind of weakly supervised
segmentation task, and naturally fits the Multiple Instance Learning (MIL)
framework: every training image is known to have (or not) at least one pixel
corresponding to the image class label, and the segmentation task can be
rewritten as inferring the pixels belonging to the class of the object (given
one image, and its object class). We propose a Convolutional Neural
Network-based model, which is constrained during training to put more weight on
pixels which are important for classifying the image. We show that at test
time, the model has learned to discriminate the right pixels well enough, such
that it performs very well on an existing segmentation benchmark, by adding
only few smoothing priors. Our system is trained using a subset of the Imagenet
dataset and the segmentation experiments are performed on the challenging
Pascal VOC dataset (with no fine-tuning of the model on Pascal VOC). Our model
beats the state of the art results in weakly supervised object segmentation
task by a large margin. We also compare the performance of our model with state
of the art fully-supervised segmentation approaches.
Social Trust Prediction via Max-norm Constrained 1-bit Matrix Completion
Authors: Jing Wang, Jie Shen, Huan Xu
Date: 2015-04-24 00:00:00
Tasks: Matrix Completion

Social trust prediction addresses the significant problem of exploring
interactions among users in social networks. Naturally, this problem can be
formulated in the matrix completion framework, with each entry indicating the
trustness or distrustness. However, there are two challenges for the social
trust problem: 1) the observed data are with sign (1-bit) measurements; 2) they
are typically sampled non-uniformly. Most of the previous matrix completion
methods do not well handle the two issues. Motivated by the recent progress of
max-norm, we propose to solve the problem with a 1-bit max-norm constrained
formulation. Since max-norm is not easy to optimize, we utilize a reformulation
of max-norm which facilitates an efficient projected gradient decent algorithm.
We demonstrate the superiority of our formulation on two benchmark datasets.
On the Stability of Online Language Features: How Much Text do you Need to know a Person?
Authors: Eben M. Haber
Date: 2015-04-24 00:00:00

In recent years, numerous studies have inferred personality and other traits
from people's online writing. While these studies are encouraging, more
information is needed in order to use these techniques with confidence. How do
linguistic features vary across different online media, and how much text is
required to have a representative sample for a person? In this paper, we
examine several large sets of online, user-generated text, drawn from Twitter,
email, blogs, and online discussion forums. We examine and compare
population-wide results for the linguistic measure LIWC, and the inferred
traits of Big5 Personality and Basic Human Values. We also empirically measure
the stability of these traits across different sized samples for each
individual. Our results highlight the importance of tuning models to each
online medium, and include guidelines for the minimum amount of text required
for a representative result.
Logical Conditional Preference Theories
Authors: Cristina Cornelio, Andrea Loreggia, Vijay Saraswat
Date: 2015-04-24 00:00:00

CP-nets represent the dominant existing framework for expressing qualitative
conditional preferences between alternatives, and are used in a variety of
areas including constraint solving. Over the last fifteen years, a significant
literature has developed exploring semantics, algorithms, implementation and
use of CP-nets. This paper introduces a comprehensive new framework for
conditional preferences: logical conditional preference theories (LCP
theories). To express preferences, the user specifies arbitrary (constraint)
Datalog programs over a binary ordering relation on outcomes. We show how LCP
theories unify and generalize existing conditional preference proposals, and
leverage the rich semantic, algorithmic and implementation frameworks of
Datalog.
Visual Recognition Using Directional Distribution Distance
Authors: Jianxin Wu, Bin-Bin Gao, Guoqing Liu
Date: 2015-04-19 00:00:00

In computer vision, an entity such as an image or video is often represented
as a set of instance vectors, which can be SIFT, motion, or deep learning
feature vectors extracted from different parts of that entity. Thus, it is
essential to design efficient and effective methods to compare two sets of
instance vectors. Existing methods such as FV, VLAD or Super Vectors have
achieved excellent results. However, this paper shows that these methods are
designed based on a generative perspective, and a discriminative method can be
more effective in categorizing images or videos. The proposed D3
(discriminative distribution distance) method effectively compares two sets as
two distributions, and proposes a directional total variation distance (DTVD)
to measure how separated are they. Furthermore, a robust classifier-based
method is proposed to estimate DTVD robustly. The D3 method is evaluated in
action and image recognition tasks and has achieved excellent accuracy and
speed. D3 also has a synergy with FV. The combination of D3 and FV has
advantages over D3, FV, and VLAD.
Unsupervised learning of clutter-resistant visual representations from natural videos
Authors: Qianli Liao, Joel Z. Leibo, Tomaso Poggio
Date: 2014-09-12 00:00:00
Tasks: Face Recognition

Populations of neurons in inferotemporal cortex (IT) maintain an explicit
code for object identity that also tolerates transformations of object
appearance e.g., position, scale, viewing angle [1, 2, 3]. Though the learning
rules are not known, recent results [4, 5, 6] suggest the operation of an
unsupervised temporal-association-based method e.g., Foldiak's trace rule [7].
Such methods exploit the temporal continuity of the visual world by assuming
that visual experience over short timescales will tend to have invariant
identity content. Thus, by associating representations of frames from nearby
times, a representation that tolerates whatever transformations occurred in the
video may be achieved. Many previous studies verified that such rules can work
in simple situations without background clutter, but the presence of visual
clutter has remained problematic for this approach. Here we show that temporal
association based on large class-specific filters (templates) avoids the
problem of clutter. Our system learns in an unsupervised way from natural
videos gathered from the internet, and is able to perform a difficult
unconstrained face recognition task on natural images: Labeled Faces in the
Wild [8].
Classificatory Sorites, Probabilistic Supervenience, and Rule-Making
Authors: Damir D. Dzhafarov, Ehtibar N. Dzhafarov
Date: 2015-02-04 00:00:00

We view sorites in terms of stimuli acting upon a system and evoking this
system's responses. Supervenience of responses on stimuli implies that they
either lack tolerance (i.e., they change in every vicinity of some of the
stimuli), or stimuli are not always connectable by finite chains of stimuli in
which successive members are `very similar'. If supervenience does not hold,
the properties of tolerance and connectedness cannot be formulated and
therefore soritical sequences cannot be constructed. We hypothesize that
supervenience in empirical systems (such as people answering questions) is
fundamentally probabilistic. The supervenience of probabilities of responses on
stimuli is stable, in the sense that `higher-order' probability distributions
can always be reduced to `ordinary' ones. In making rules about which stimuli
ought to correspond to which responses, the main characterization of choices in
soritical situations is their arbitrariness. We argue that arbitrariness poses
no problems for classical logic.
Use of Ensembles of Fourier Spectra in Capturing Recurrent Concepts in Data Streams
Authors: Sripirakas Sakthithasan, Russel Pears, Albert Bifet, Bernhard Pfahringer
Date: 2015-04-23 00:00:00
Tasks: General Classification

In this research, we apply ensembles of Fourier encoded spectra to capture
and mine recurring concepts in a data stream environment. Previous research
showed that compact versions of Decision Trees can be obtained by applying the
Discrete Fourier Transform to accurately capture recurrent concepts in a data
stream. However, in highly volatile environments where new concepts emerge
often, the approach of encoding each concept in a separate spectrum is no
longer viable due to memory overload and thus in this research we present an
ensemble approach that addresses this problem. Our empirical results on real
world data and synthetic data exhibiting varying degrees of recurrence reveal
that the ensemble approach outperforms the single spectrum approach in terms of
classification accuracy, memory and execution time.
On the Runtime of Randomized Local Search and Simple Evolutionary Algorithms for Dynamic Makespan Scheduling
Authors: Frank Neumann, Carsten Witt
Date: 2015-04-23 00:00:00
Tasks: Combinatorial Optimization, Evolutionary Algorithms, Scheduling

Evolutionary algorithms have been frequently used for dynamic optimization
problems. With this paper, we contribute to the theoretical understanding of
this research area. We present the first computational complexity analysis of
evolutionary algorithms for a dynamic variant of a classical combinatorial
optimization problem, namely makespan scheduling. We study the model of a
strong adversary which is allowed to change one job at regular intervals.
Furthermore, we investigate the setting of random changes. Our results show
that randomized local search and a simple evolutionary algorithm are very
effective in dynamically tracking changes made to the problem instance.
Strategic Teaching and Learning in Games
Authors: Burkhard C. Schipper
Date: 2015-04-23 00:00:00

It is known that there are uncoupled learning heuristics leading to Nash
equilibrium in all finite games. Why should players use such learning
heuristics and where could they come from? We show that there is no uncoupled
learning heuristic leading to Nash equilibrium in all finite games that a
player has an incentive to adopt, that would be evolutionary stable or that
could "learn itself". Rather, a player has an incentive to strategically teach
such a learning opponent in order secure at least the Stackelberg leader
payoff. The impossibility result remains intact when restricted to the classes
of generic games, two-player games, potential games, games with strategic
complements or 2x2 games, in which learning is known to be "nice". More
generally, it also applies to uncoupled learning heuristics leading to
correlated equilibria, rationalizable outcomes, iterated admissible outcomes,
or minimal curb sets. A possibility result restricted to "strategically
trivial" games fails if some generic games outside this class are considered as
well.
Analysis of Stopping Active Learning based on Stabilizing Predictions
Authors: Michael Bloodgood, John Grothendieck
Date: 2015-04-23 00:00:00
Proceeding: WS 2013 8
Tasks: Active Learning

Within the natural language processing (NLP) community, active learning has
been widely investigated and applied in order to alleviate the annotation
bottleneck faced by developers of new NLP systems and technologies. This paper
presents the first theoretical analysis of stopping active learning based on
stabilizing predictions (SP). The analysis has revealed three elements that are
central to the success of the SP method: (1) bounds on Cohen's Kappa agreement
between successively trained models impose bounds on differences in F-measure
performance of the models; (2) since the stop set does not have to be labeled,
it can be made large in practice, helping to guarantee that the results
transfer to previously unseen streams of examples at test/application time; and
(3) good (low variance) sample estimates of Kappa between successive models can
be obtained. Proofs of relationships between the level of Kappa agreement and
the difference in performance between consecutive models are presented.
Specifically, if the Kappa agreement between two models exceeds a threshold T
(where $T>0$), then the difference in F-measure performance between those
models is bounded above by $\frac{4(1-T)}{T}$ in all cases. If precision of the
positive conjunction of the models is assumed to be $p$, then the bound can be
tightened to $\frac{4(1-T)}{(p+1)T}$.
Regularization-free estimation in trace regression with symmetric positive semidefinite matrices
Authors: Martin Slawski, Ping Li, Matthias Hein
Date: 2015-04-23 00:00:00
Proceeding: NeurIPS 2015 12
Tasks: compressed sensing, Matrix Completion, Quantum State Tomography, regression

Over the past few years, trace regression models have received considerable
attention in the context of matrix completion, quantum state tomography, and
compressed sensing. Estimation of the underlying matrix from
regularization-based approaches promoting low-rankedness, notably nuclear norm
regularization, have enjoyed great popularity. In the present paper, we argue
that such regularization may no longer be necessary if the underlying matrix is
symmetric positive semidefinite (\textsf{spd}) and the design satisfies certain
conditions. In this situation, simple least squares estimation subject to an
\textsf{spd} constraint may perform as well as regularization-based approaches
with a proper choice of the regularization parameter, which entails knowledge
of the noise level and/or tuning. By contrast, constrained least squares
estimation comes without any tuning parameter and may hence be preferred due to
its simplicity.
A new approach for physiological time series
Authors: Dong Mao, Yang Wang, Qiang Wu
Date: 2015-04-23 00:00:00
Tasks: Time Series, Time Series Analysis

We developed a new approach for the analysis of physiological time series. An
iterative convolution filter is used to decompose the time series into various
components. Statistics of these components are extracted as features to
characterize the mechanisms underlying the time series. Motivated by the
studies that show many normal physiological systems involve irregularity while
the decrease of irregularity usually implies the abnormality, the statistics
for "outliers" in the components are used as features measuring irregularity.
Support vector machines are used to select the most relevant features that are
able to differentiate the time series from normal and abnormal systems. This
new approach is successfully used in the study of congestive heart failure by
heart beat interval time series.
Evolving Fuzzy Image Segmentation with Self-Configuration
Authors: Ahmed Othman, Hamid. R. Tizhoosh, Farzad Khalvati
Date: 2015-04-23 00:00:00
Tasks: feature selection, Image Segmentation, Segmentation, Semantic Segmentation

Current image segmentation techniques usually require that the user tune
several parameters in order to obtain maximum segmentation accuracy, a
computationally inefficient approach, especially when a large number of images
must be processed sequentially in daily practice. The use of evolving fuzzy
systems for designing a method that automatically adjusts parameters to segment
medical images according to the quality expectation of expert users has been
proposed recently (Evolving fuzzy image segmentation EFIS). However, EFIS
suffers from a few limitations when used in practice mainly due to some fixed
parameters. For instance, EFIS depends on auto-detection of the object of
interest for feature calculation, a task that is highly application-dependent.
This shortcoming limits the applicability of EFIS, which was proposed with the
ultimate goal of offering a generic but adjustable segmentation scheme. In this
paper, a new version of EFIS is proposed to overcome these limitations. The new
EFIS, called self-configuring EFIS (SC-EFIS), uses available training data to
self-estimate the parameters that are fixed in EFIS. As well, the proposed
SC-EFIS relies on a feature selection process that does not require
auto-detection of an ROI. The proposed SC-EFIS was evaluated using the same
segmentation algorithms and the same dataset as for EFIS. The results show that
SC-EFIS can provide the same results as EFIS but with a higher level of
automation.
Multiple Object Recognition with Visual Attention
Authors: Jimmy Ba, Volodymyr Mnih, Koray Kavukcuoglu
Date: 2014-12-24 00:00:00
Tasks: Object, Object Recognition, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

We present an attention-based model for recognizing multiple objects in
images. The proposed model is a deep recurrent neural network trained with
reinforcement learning to attend to the most relevant regions of the input
image. We show that the model learns to both localize and recognize multiple
objects despite being given only class labels during training. We evaluate the
model on the challenging task of transcribing house number sequences from
Google Street View images and show that it is both more accurate than the
state-of-the-art convolutional networks and uses fewer parameters and less
computation.
Person Re-identification with Correspondence Structure Learning
Authors: Yang Shen, Weiyao Lin, Junchi Yan, Mingliang Xu, Jianxin Wu, Jingdong Wang
Date: 2015-04-23 00:00:00
Proceeding: ICCV 2015 12
Tasks: Patch Matching, Person Re-Identification

This paper addresses the problem of handling spatial misalignments due to
camera-view changes or human-pose variations in person re-identification. We
first introduce a boosting-based approach to learn a correspondence structure
which indicates the patch-wise matching probabilities between images from a
target camera pair. The learned correspondence structure can not only capture
the spatial correspondence pattern between cameras but also handle the
viewpoint or human-pose variation in individual images. We further introduce a
global-based matching process. It integrates a global matching constraint over
the learned correspondence structure to exclude cross-view misalignments during
the image patch matching process, hence achieving a more reliable matching
score between images. Experimental results on various datasets demonstrate the
effectiveness of our approach.