pwc-archive/papers-with-abstracts huggingface dataset:
Distinguishing short and long $Fermi$ gamma-ray bursts
Authors: Mariusz Tarnopolski
Date: 2015-07-17 00:00:00
Tasks: Descriptive, General Classification

Two classes of gamma-ray bursts (GRBs), short and long, have been determined
without any doubts, and are usually ascribed to different progenitors, yet
these classes overlap for a variety of descriptive parameters. A subsample of
46 long and 22 short $Fermi$ GRBs with estimated Hurst Exponents (HEs),
complemented by minimum variability time-scales (MVTS) and durations ($T_{90}$)
is used to perform a supervised Machine Learning (ML) and Monte Carlo (MC)
simulation using a Support Vector Machine (SVM) algorithm. It is found that
while $T_{90}$ itself performs very well in distinguishing short and long GRBs,
the overall success ratio is higher when the training set is complemented by
MVTS and HE. These results may allow to introduce a new (non-linear) parameter
that might provide less ambiguous classification of GRBs.
Human Action Recognition using Factorized Spatio-Temporal Convolutional Networks
Authors: Lin Sun, Kui Jia, Dit-yan Yeung, Bertram E. Shi
Date: 2015-10-02 00:00:00
Proceeding: ICCV 2015 12
Tasks: Action Recognition, image-classification, Image Classification, Temporal Action Localization

Human actions in video sequences are three-dimensional (3D) spatio-temporal
signals characterizing both the visual appearance and motion dynamics of the
involved humans and objects. Inspired by the success of convolutional neural
networks (CNN) for image classification, recent attempts have been made to
learn 3D CNNs for recognizing human actions in videos. However, partly due to
the high complexity of training 3D convolution kernels and the need for large
quantities of training videos, only limited success has been reported. This has
triggered us to investigate in this paper a new deep architecture which can
handle 3D signals more effectively. Specifically, we propose factorized
spatio-temporal convolutional networks (FstCN) that factorize the original 3D
convolution kernel learning as a sequential process of learning 2D spatial
kernels in the lower layers (called spatial convolutional layers), followed by
learning 1D temporal kernels in the upper layers (called temporal convolutional
layers). We introduce a novel transformation and permutation operator to make
factorization in FstCN possible. Moreover, to address the issue of sequence
alignment, we propose an effective training and inference strategy based on
sampling multiple video clips from a given action video sequence. We have
tested FstCN on two commonly used benchmark datasets (UCF-101 and HMDB-51).
Without using auxiliary training videos to boost the performance, FstCN
outperforms existing CNN based methods and achieves comparable performance with
a recent method that benefits from using auxiliary training videos.
Marginalizing Gaussian Process Hyperparameters using Sequential Monte Carlo
Authors: Andreas Svensson, Johan Dahlin, Thomas B. Schön
Date: 2015-02-06 00:00:00
Tasks: regression

Gaussian process regression is a popular method for non-parametric
probabilistic modeling of functions. The Gaussian process prior is
characterized by so-called hyperparameters, which often have a large influence
on the posterior model and can be difficult to tune. This work provides a
method for numerical marginalization of the hyperparameters, relying on the
rigorous framework of sequential Monte Carlo. Our method is well suited for
online problems, and we demonstrate its ability to handle real-world problems
with several dimensions and compare it to other marginalization methods. We
also conclude that our proposed method is a competitive alternative to the
commonly used point estimates maximizing the likelihood, both in terms of
computational load and its ability to handle multimodal posteriors.
See the Difference: Direct Pre-Image Reconstruction and Pose Estimation by Differentiating HOG
Authors: Wei-Chen Chiu, Mario Fritz
Date: 2015-05-04 00:00:00
Proceeding: ICCV 2015 12
Tasks: Image Reconstruction, Pose Estimation

The Histogram of Oriented Gradient (HOG) descriptor has led to many advances
in computer vision over the last decade and is still part of many state of the
art approaches. We realize that the associated feature computation is piecewise
differentiable and therefore many pipelines which build on HOG can be made
differentiable. This lends to advanced introspection as well as opportunities
for end-to-end optimization. We present our implementation of $\nabla$HOG based
on the auto-differentiation toolbox Chumpy and show applications to pre-image
visualization and pose estimation which extends the existing differentiable
renderer OpenDR pipeline. Both applications improve on the respective
state-of-the-art HOG approaches.
Local Higher-Order Statistics (LHS) describing images with statistics of local non-binarized pixel patterns
Authors: Gaurav Sharma, Frederic Jurie
Date: 2015-10-02 00:00:00
Tasks: Image Description, Quantization

We propose a new image representation for texture categorization and facial
analysis, relying on the use of higher-order local differential statistics as
features. It has been recently shown that small local pixel pattern
distributions can be highly discriminative while being extremely efficient to
compute, which is in contrast to the models based on the global structure of
images. Motivated by such works, we propose to use higher-order statistics of
local non-binarized pixel patterns for the image description. The proposed
model does not require either (i) user specified quantization of the space (of
pixel patterns) or (ii) any heuristics for discarding low occupancy volumes of
the space. We propose to use a data driven soft quantization of the space, with
parametric mixture models, combined with higher-order statistics, based on
Fisher scores. We demonstrate that this leads to a more expressive
representation which, when combined with discriminatively learned classifiers
and metrics, achieves state-of-the-art performance on challenging texture and
facial analysis datasets, in low complexity setup. Further, it is complementary
to higher complexity features and when combined with them improves performance.
Implementing Efficient All Solutions SAT Solvers
Authors: Takahisa Toda, Takehide Soh
Date: 2015-10-02 00:00:00
Tasks: All

All solutions SAT (AllSAT for short) is a variant of propositional
satisfiability problem. Despite its significance, AllSAT has been relatively
unexplored compared to other variants. We thus survey and discuss major
techniques of AllSAT solvers. We faithfully implement them and conduct
comprehensive experiments using a large number of instances and various types
of solvers including one of the few public softwares. The experiments reveal
solver's characteristics. Our implemented solvers are made publicly available
so that other researchers can easily develop their solver by modifying our
codes and compare it with existing methods.
Effective Object Tracking in Unstructured Crowd Scenes
Authors: Ishan Jindal, Shanmuganathan Raman
Date: 2015-10-02 00:00:00
Tasks: Object, Object Tracking

In this paper, we are presenting a rotation variant Oriented Texture Curve
(OTC) descriptor based mean shift algorithm for tracking an object in an
unstructured crowd scene. The proposed algorithm works by first obtaining the
OTC features for a manually selected object target, then a visual vocabulary is
created by using all the OTC features of the target. The target histogram is
obtained using codebook encoding method which is then used in mean shift
framework to perform similarity search. Results are obtained on different
videos of challenging scenes and the comparison of the proposed approach with
several state-of-the-art approaches are provided. The analysis shows the
advantages and limitations of the proposed approach for tracking an object in
unstructured crowd scenes.
Learning a Discriminative Model for the Perception of Realism in Composite Images
Authors: Jun-Yan Zhu, Philipp Krähenbühl, Eli Shechtman, Alexei A. Efros
Date: 2015-10-02 00:00:00
Proceeding: ICCV 2015 12

What makes an image appear realistic? In this work, we are answering this
question from a data-driven perspective by learning the perception of visual
realism directly from large amounts of data. In particular, we train a
Convolutional Neural Network (CNN) model that distinguishes natural photographs
from automatically generated composite images. The model learns to predict
visual realism of a scene in terms of color, lighting and texture
compatibility, without any human annotations pertaining to it. Our model
outperforms previous works that rely on hand-crafted heuristics, for the task
of classifying realistic vs. unrealistic photos. Furthermore, we apply our
learned model to compute optimal parameters of a compositing method, to
maximize the visual realism score predicted by our CNN model. We demonstrate
its advantage against existing methods via a human perception study.
Learning like a Child: Fast Novel Visual Concept Learning from Sentence Descriptions of Images
Authors: Junhua Mao, Wei Xu, Yi Yang, Jiang Wang, Zhiheng Huang, Alan Yuille
Date: 2015-04-25 00:00:00
Proceeding: ICCV 2015 12
Tasks: Image Captioning, Novel Concepts, Sentence

In this paper, we address the task of learning novel visual concepts, and
their interactions with other concepts, from a few images with sentence
descriptions. Using linguistic context and visual features, our method is able
to efficiently hypothesize the semantic meaning of new words and add them to
its word dictionary so that they can be used to describe images which contain
these novel concepts. Our method has an image captioning module based on m-RNN
with several improvements. In particular, we propose a transposed weight
sharing scheme, which not only improves performance on image captioning, but
also makes the model more suitable for the novel concept learning task. We
propose methods to prevent overfitting the new concepts. In addition, three
novel concept datasets are constructed for this new task. In the experiments,
we show that our method effectively learns novel visual concepts from a few
examples without disturbing the previously learned concepts. The project page
is http://www.stat.ucla.edu/~junhua.mao/projects/child_learning.html
Towards Machine Wald
Authors: Houman Owhadi, Clint Scovel
Date: 2015-08-10 00:00:00
Tasks: Bayesian Inference, Stochastic Optimization, Uncertainty Quantification

The past century has seen a steady increase in the need of estimating and
predicting complex systems and making (possibly critical) decisions with
limited information. Although computers have made possible the numerical
evaluation of sophisticated statistical models, these models are still designed
\emph{by humans} because there is currently no known recipe or algorithm for
dividing the design of a statistical model into a sequence of arithmetic
operations. Indeed enabling computers to \emph{think} as \emph{humans} have the
ability to do when faced with uncertainty is challenging in several major ways:
(1) Finding optimal statistical models remains to be formulated as a well posed
problem when information on the system of interest is incomplete and comes in
the form of a complex combination of sample data, partial knowledge of
constitutive relations and a limited description of the distribution of input
random variables. (2) The space of admissible scenarios along with the space of
relevant information, assumptions, and/or beliefs, tend to be infinite
dimensional, whereas calculus on a computer is necessarily discrete and finite.
With this purpose, this paper explores the foundations of a rigorous framework
for the scientific computation of optimal statistical estimators/models and
reviews their connections with Decision Theory, Machine Learning, Bayesian
Inference, Stochastic Optimization, Robust Optimization, Optimal Uncertainty
Quantification and Information Based Complexity.
Response to Liu, Xu, and Liang (2015) and Ferrer-i-Cancho and Gómez-Rodríguez (2015) on Dependency Length Minimization
Authors: Richard Futrell, Kyle Mahowald, Edward Gibson
Date: 2015-10-01 00:00:00

We address recent criticisms (Liu et al., 2015; Ferrer-i-Cancho and
G\'omez-Rodr\'iguez, 2015) of our work on empirical evidence of dependency
length minimization across languages (Futrell et al., 2015). First, we
acknowledge error in failing to acknowledge Liu (2008)'s previous work on
corpora of 20 languages with similar aims. A correction will appear in PNAS.
Nevertheless, we argue that our work provides novel, strong evidence for
dependency length minimization as a universal quantitative property of
languages, beyond this previous work, because it provides baselines which focus
on word order preferences. Second, we argue that our choices of baselines were
appropriate because they control for alternative theories.
An Asynchronous Implementation of the Limited Memory CMA-ES
Authors: Viktor Arkhipov, Maxim Buzdalov, Anatoly Shalyto
Date: 2015-10-01 00:00:00

We present our asynchronous implementation of the LM-CMA-ES algorithm, which
is a modern evolution strategy for solving complex large-scale continuous
optimization problems. Our implementation brings the best results when the
number of cores is relatively high and the computational complexity of the
fitness function is also high. The experiments with benchmark functions show
that it is able to overcome its origin on the Sphere function, reaches certain
thresholds faster on the Rosenbrock and Ellipsoid function, and surprisingly
performs much better than the original version on the Rastrigin function.
A Pseudo-Euclidean Iteration for Optimal Recovery in Noisy ICA
Authors: James Voss, Mikhail Belkin, Luis Rademacher
Date: 2015-02-13 00:00:00
Proceeding: NeurIPS 2015 12

Independent Component Analysis (ICA) is a popular model for blind signal
separation. The ICA model assumes that a number of independent source signals
are linearly mixed to form the observed signals. We propose a new algorithm,
PEGI (for pseudo-Euclidean Gradient Iteration), for provable model recovery for
ICA with Gaussian noise. The main technical innovation of the algorithm is to
use a fixed point iteration in a pseudo-Euclidean (indefinite "inner product")
space. The use of this indefinite "inner product" resolves technical issues
common to several existing algorithms for noisy ICA. This leads to an algorithm
which is conceptually simple, efficient and accurate in testing.
  Our second contribution is combining PEGI with the analysis of objectives for
optimal recovery in the noisy ICA model. It has been observed that the direct
approach of demixing with the inverse of the mixing matrix is suboptimal for
signal recovery in terms of the natural Signal to Interference plus Noise Ratio
(SINR) criterion. There have been several partial solutions proposed in the ICA
literature. It turns out that any solution to the mixing matrix reconstruction
problem can be used to construct an SINR-optimal ICA demixing, despite the fact
that SINR itself cannot be computed from data. That allows us to obtain a
practical and provably SINR-optimal recovery method for ICA with arbitrary
Gaussian noise.
RDF Knowledge Graph Visualization From a Knowledge Extraction System
Authors: Fadhela Kerdjoudj, Olivier Curé
Date: 2015-10-01 00:00:00
Tasks: Knowledge Graphs

In this paper, we present a system to visualize RDF knowledge graphs. These
graphs are obtained from a knowledge extraction system designed by
GEOLSemantics. This extraction is performed using natural language processing
and trigger detection. The user can visualize subgraphs by selecting some
ontology features like concepts or individuals. The system is also
multilingual, with the use of the annotated ontology in English, French, Arabic
and Chinese.
Determination of the Internet Anonymity Influence on the Level of Aggression and Usage of Obscene Lexis
Authors: Rodmonga Potapova, Denis Gordeev
Date: 2015-10-01 00:00:00

This article deals with the analysis of the semantic content of the anonymous
Russian-speaking forum 2ch.hk, different verbal means of expressing of the
emotional state of aggression are revealed for this site, and aggression is
classified by its directions. The lexis of different Russian-and English-
speaking anonymous forums (2ch.hk and iichan.hk, 4chan.org) and public
community "MDK" of the Russian-speaking social network VK is analyzed and
compared with the Open Corpus of the Russian language (Opencorpora.org and
Brown corpus). The analysis shows that anonymity has no influence on the amount
of invective items usage. The effectiveness of moderation was shown for
anonymous forums. It was established that Russian obscene lexis was used to
express the emotional state of aggression only in 60.4% of cases for 2ch.hk.
These preliminary results show that the Russian obscene lexis on the Internet
does not have direct dependence on the emotional state of aggression.
Data Association for an Adaptive Multi-target Particle Filter Tracking System
Authors: R. Alampay, K. Teknomo
Date: 2015-10-01 00:00:00
Tasks: object-detection, Object Detection

This paper presents a novel approach to improve the accuracy of tracking
multiple objects in a static scene using a particle filter system by
introducing a data association step, a state queue for the collection of
tracked objects and adaptive parameters to the system. The data association
step makes use of the object detection phase and appearance model to determine
if the approximated targets given by the particle filter step match the given
set of detected objects. The remaining detected objects are used as information
to instantiate new objects for tracking. State queues are also used for each
tracked object to deal with occlusion events and occlusion recovery. Finally we
present how the parameters adjust to occlusion events. The adaptive property of
the system is also used for possible occlusion recovery. Results of the system
are then compared to a ground truth data set for performance evaluation. Our
system produced accurate results and was able to handle partially occluded
objects as well as proper occlusion recovery from tracking multiple objects
First Steps Towards a Runtime Comparison of Natural and Artificial Evolution
Authors: Tiago Paixão, Jorge Pérez Heredia, Dirk Sudholt, Barbora Trubenová
Date: 2015-04-23 00:00:00
Tasks: Evolutionary Algorithms

Evolutionary algorithms (EAs) form a popular optimisation paradigm inspired
by natural evolution. In recent years the field of evolutionary computation has
developed a rigorous analytical theory to analyse their runtime on many
illustrative problems. Here we apply this theory to a simple model of natural
evolution. In the Strong Selection Weak Mutation (SSWM) evolutionary regime the
time between occurrence of new mutations is much longer than the time it takes
for a new beneficial mutation to take over the population. In this situation,
the population only contains copies of one genotype and evolution can be
modelled as a (1+1)-type process where the probability of accepting a new
genotype (improvements or worsenings) depends on the change in fitness.
  We present an initial runtime analysis of SSWM, quantifying its performance
for various parameters and investigating differences to the (1+1)EA. We show
that SSWM can have a moderate advantage over the (1+1)EA at crossing fitness
valleys and study an example where SSWM outperforms the (1+1)EA by taking
advantage of information on the fitness gradient.
Ask Your Neurons: A Neural-based Approach to Answering Questions about Images
Authors: Mateusz Malinowski, Marcus Rohrbach, Mario Fritz
Date: 2015-05-05 00:00:00
Proceeding: ICCV 2015 12
Tasks: Question Answering

We address a question answering task on real-world images that is set up as a
Visual Turing Test. By combining latest advances in image representation and
natural language processing, we propose Neural-Image-QA, an end-to-end
formulation to this problem for which all parts are trained jointly. In
contrast to previous efforts, we are facing a multi-modal problem where the
language output (answer) is conditioned on visual and natural language input
(image and question). Our approach Neural-Image-QA doubles the performance of
the previous best approach on this problem. We provide additional insights into
the problem by analyzing how much information is contained only in the language
part for which we provide a new human baseline. To study human consensus, which
is related to the ambiguities inherent in this challenging task, we propose two
novel metrics and collect additional answers which extends the original DAQUAR
dataset to DAQUAR-Consensus.
Label-Embedding for Image Classification
Authors: Zeynep Akata, Florent Perronnin, Zaid Harchaoui, Cordelia Schmid
Date: 2015-03-30 00:00:00
Tasks: Attribute, Classification, General Classification, image-classification, Image Classification, Multi-label zero-shot learning, Zero-Shot Action Recognition, Zero-Shot Learning

Attributes act as intermediate representations that enable parameter sharing
between classes, a must when training data is scarce. We propose to view
attribute-based image classification as a label-embedding problem: each class
is embedded in the space of attribute vectors. We introduce a function that
measures the compatibility between an image and a label embedding. The
parameters of this function are learned on a training set of labeled samples to
ensure that, given an image, the correct classes rank higher than the incorrect
ones. Results on the Animals With Attributes and Caltech-UCSD-Birds datasets
show that the proposed framework outperforms the standard Direct Attribute
Prediction baseline in a zero-shot learning scenario. Label embedding enjoys a
built-in ability to leverage alternative sources of information instead of or
in addition to attributes, such as e.g. class hierarchies or textual
descriptions. Moreover, label embedding encompasses the whole range of learning
settings from zero-shot learning to regular learning with a large number of
labeled examples.
Using consumer behavior data to reduce energy consumption in smart homes
Authors: Daniel Schweizer, Michael Zehnder, Holger Wache, Hans-Friedrich Witschel, Danilo Zanatta, Miguel Rodriguez
Date: 2015-10-01 00:00:00
Tasks: Recommendation Systems, Sequential Pattern Mining

This paper discusses how usage patterns and preferences of inhabitants can be
learned efficiently to allow smart homes to autonomously achieve energy
savings. We propose a frequent sequential pattern mining algorithm suitable for
real-life smart home event data. The performance of the proposed algorithm is
compared to existing algorithms regarding completeness/correctness of the
results, run times as well as memory consumption and elaborates on the
shortcomings of the different solutions. We also present a recommender system
based on the developed algorithm that provides recommendations to the users to
reduce their energy consumption. The recommender system was deployed to a set
of test homes. The test participants rated the impact of the recommendations on
their comfort. We used this feedback to adjust the system parameters and make
it more accurate during a second test phase.