pwc-archive/papers-with-abstracts huggingface dataset:
Beyond Short Snippets: Deep Networks for Video Classification
Authors: Joe Yue-Hei Ng, Matthew Hausknecht, Sudheendra Vijayanarasimhan, Oriol Vinyals, Rajat Monga, George Toderici
Date: 2015-03-31 00:00:00
Proceeding: CVPR 2015 6
Tasks: Action Recognition, Classification, General Classification, Optical Flow Estimation, Retrieval, Video Classification

Convolutional neural networks (CNNs) have been extensively applied for image
recognition problems giving state-of-the-art results on recognition, detection,
segmentation and retrieval. In this work we propose and evaluate several deep
neural network architectures to combine image information across a video over
longer time periods than previously attempted. We propose two methods capable
of handling full length videos. The first method explores various convolutional
temporal feature pooling architectures, examining the various design choices
which need to be made when adapting a CNN for this task. The second proposed
method explicitly models the video as an ordered sequence of frames. For this
purpose we employ a recurrent neural network that uses Long Short-Term Memory
(LSTM) cells which are connected to the output of the underlying CNN. Our best
networks exhibit significant performance improvements over previously published
results on the Sports 1 million dataset (73.1% vs. 60.9%) and the UCF-101
datasets with (88.6% vs. 88.0%) and without additional optical flow information
(82.6% vs. 72.8%).
Multiple Measurements and Joint Dimensionality Reduction for Large Scale Image Search with Short Vectors - Extended Version
Authors: Filip Radenovic, Herve Jegou, Ondrej Chum
Date: 2015-04-13 00:00:00
Tasks: Dimensionality Reduction, Image Retrieval, Retrieval

This paper addresses the construction of a short-vector (128D) image
representation for large-scale image and particular object retrieval. In
particular, the method of joint dimensionality reduction of multiple
vocabularies is considered. We study a variety of vocabulary generation
techniques: different k-means initializations, different descriptor
transformations, different measurement regions for descriptor extraction. Our
extensive evaluation shows that different combinations of vocabularies, each
partitioning the descriptor space in a different yet complementary manner,
results in a significant performance improvement, which exceeds the
state-of-the-art.
Egyptian Dialect Stopword List Generation from Social Network Data
Authors: Walaa Medhat, Ahmed H. Yousef, Hoda Korashy
Date: 2015-04-13 00:00:00
Tasks: feature selection, General Classification, Sentiment Analysis, text-classification, Text Classification

This paper proposes a methodology for generating a stopword list from online
social network (OSN) corpora in Egyptian Dialect(ED). The aim of the paper is
to investigate the effect of removingED stopwords on the Sentiment Analysis
(SA) task. The stopwords lists generated before were on Modern Standard Arabic
(MSA) which is not the common language used in OSN. We have generated a
stopword list of Egyptian dialect to be used with the OSN corpora. We compare
the efficiency of text classification when using the generated list along with
previously generated lists of MSA and combining the Egyptian dialect list with
the MSA list. The text classification was performed using Na\"ive Bayes and
Decision Tree classifiers and two feature selection approaches, unigram and
bigram. The experiments show that removing ED stopwords give better performance
than using lists of MSA stopwords only.
Optimal Parameter Choices Through Self-Adjustment: Applying the 1/5-th Rule in Discrete Settings
Authors: Benjamin Doerr, Carola Doerr
Date: 2015-04-13 00:00:00
Tasks: Evolutionary Algorithms

While evolutionary algorithms are known to be very successful for a broad
range of applications, the algorithm designer is often left with many
algorithmic choices, for example, the size of the population, the mutation
rates, and the crossover rates of the algorithm. These parameters are known to
have a crucial influence on the optimization time, and thus need to be chosen
carefully, a task that often requires substantial efforts. Moreover, the
optimal parameters can change during the optimization process. It is therefore
of great interest to design mechanisms that dynamically choose best-possible
parameters. An example for such an update mechanism is the one-fifth success
rule for step-size adaption in evolutionary strategies. While in continuous
domains this principle is well understood also from a mathematical point of
view, no comparable theory is available for problems in discrete domains.
  In this work we show that the one-fifth success rule can be effective also in
discrete settings. We regard the $(1+(\lambda,\lambda))$~GA proposed in
[Doerr/Doerr/Ebel: From black-box complexity to designing new genetic
algorithms, TCS 2015]. We prove that if its population size is chosen according
to the one-fifth success rule then the expected optimization time on
\textsc{OneMax} is linear. This is better than what \emph{any} static
population size $\lambda$ can achieve and is asymptotically optimal also among
all adaptive parameter choices.
Sensitivity Analysis for additive STDP rule
Authors: Subhajit Sengupta, Karthik S. Gurumoorthy, Arunava Banerjee
Date: 2015-02-28 00:00:00
Tasks: Sensitivity

Spike Timing Dependent Plasticity (STDP) is a Hebbian like synaptic learning
rule. The basis of STDP has strong experimental evidences and it depends on
precise input and output spike timings. In this paper we show that under
biologically plausible spiking regime, slight variability in the spike timing
leads to drastically different evolution of synaptic weights when its dynamics
are governed by the additive STDP rule.
Learning Descriptors for Object Recognition and 3D Pose Estimation
Authors: Paul Wohlhart, Vincent Lepetit
Date: 2015-02-20 00:00:00
Proceeding: CVPR 2015 6
Tasks: 3D Pose Estimation, Object, Object Recognition, Pose Estimation

Detecting poorly textured objects and estimating their 3D pose reliably is
still a very challenging problem. We introduce a simple but powerful approach
to computing descriptors for object views that efficiently capture both the
object identity and 3D pose. By contrast with previous manifold-based
approaches, we can rely on the Euclidean distance to evaluate the similarity
between descriptors, and therefore use scalable Nearest Neighbor search methods
to efficiently handle a large number of objects under a large range of poses.
To achieve this, we train a Convolutional Neural Network to compute these
descriptors by enforcing simple similarity and dissimilarity constraints
between the descriptors. We show that our constraints nicely untangle the
images from different objects and different views into clusters that are not
only well-separated but also structured as the corresponding sets of poses: The
Euclidean distance between descriptors is large when the descriptors are from
different objects, and directly related to the distance between the poses when
the descriptors are from the same object. These important properties allow us
to outperform state-of-the-art object views representations on challenging RGB
and RGB-D data.
Adaptive Randomized Dimension Reduction on Massive Data
Authors: Gregory Darnell, Stoyan Georgiev, Sayan Mukherjee, Barbara E. Engelhardt
Date: 2015-04-13 00:00:00
Tasks: Dimensionality Reduction, parameter estimation

The scalability of statistical estimators is of increasing importance in
modern applications. One approach to implementing scalable algorithms is to
compress data into a low dimensional latent space using dimension reduction
methods. In this paper we develop an approach for dimension reduction that
exploits the assumption of low rank structure in high dimensional data to gain
both computational and statistical advantages. We adapt recent randomized
low-rank approximation algorithms to provide an efficient solution to principal
component analysis (PCA), and we use this efficient solver to improve parameter
estimation in large-scale linear mixed models (LMM) for association mapping in
statistical and quantitative genomics. A key observation in this paper is that
randomization serves a dual role, improving both computational and statistical
performance by implicitly regularizing the covariance matrix estimate of the
random effect in a LMM. These statistical and computational advantages are
highlighted in our experiments on simulated data and large-scale genomic
studies.
Streaming, Memory Limited Matrix Completion with Noise
Authors: Se-Young Yun, Marc Lelarge, Alexandre Proutiere
Date: 2015-04-13 00:00:00
Tasks: Matrix Completion

In this paper, we consider the streaming memory-limited matrix completion
problem when the observed entries are noisy versions of a small random fraction
of the original entries. We are interested in scenarios where the matrix size
is very large so the matrix is very hard to store and manipulate. Here, columns
of the observed matrix are presented sequentially and the goal is to complete
the missing entries after one pass on the data with limited memory space and
limited computational complexity. We propose a streaming algorithm which
produces an estimate of the original matrix with a vanishing mean square error,
uses memory space scaling linearly with the ambient dimension of the matrix,
i.e. the memory required to store the output alone, and spends computations as
much as the number of non-zero entries of the input matrix.
Fuzzy approaches to context variable in fuzzy geographically weighted clustering
Authors: Nguyen Van Minh, Le Hoang Son
Date: 2015-04-13 00:00:00
Tasks: Clustering

Fuzzy Geographically Weighted Clustering (FGWC) is considered as a suitable
tool for the analysis of geo-demographic data that assists the provision and
planning of products and services to local people. Context variables were
attached to FGWC in order to accelerate the computing speed of the algorithm
and to focus the results on the domain of interests. Nonetheless, the
determination of exact, crisp values of the context variable is a hard task. In
this paper, we propose two novel methods using fuzzy approaches for that
determination. A numerical example is given to illustrate the uses of the
proposed methods.
Learning Multiple Visual Tasks while Discovering their Structure
Authors: Carlo Ciliberto, Lorenzo Rosasco, Silvia Villa
Date: 2015-04-13 00:00:00
Proceeding: CVPR 2015 6
Tasks: Denoising, General Classification, Multi-Task Learning, object-detection, Object Detection

Multi-task learning is a natural approach for computer vision applications
that require the simultaneous solution of several distinct but related
problems, e.g. object detection, classification, tracking of multiple agents,
or denoising, to name a few. The key idea is that exploring task relatedness
(structure) can lead to improved performances.
  In this paper, we propose and study a novel sparse, non-parametric approach
exploiting the theory of Reproducing Kernel Hilbert Spaces for vector-valued
functions. We develop a suitable regularization framework which can be
formulated as a convex optimization problem, and is provably solvable using an
alternating minimization approach. Empirical tests show that the proposed
method compares favorably to state of the art techniques and further allows to
recover interpretable structures, a problem of interest in its own right.
Striving for Simplicity: The All Convolutional Net
Authors: Jost Tobias Springenberg, Alexey Dosovitskiy, Thomas Brox, Martin Riedmiller
Date: 2014-12-21 00:00:00
Tasks: All, Image Classification, Object, Object Recognition

Most modern convolutional neural networks (CNNs) used for object recognition
are built using the same principles: Alternating convolution and max-pooling
layers followed by a small number of fully connected layers. We re-evaluate the
state of the art for object recognition from small images with convolutional
networks, questioning the necessity of different components in the pipeline. We
find that max-pooling can simply be replaced by a convolutional layer with
increased stride without loss in accuracy on several image recognition
benchmarks. Following this finding -- and building on other recent work for
finding simple network structures -- we propose a new architecture that
consists solely of convolutional layers and yields competitive or state of the
art performance on several object recognition datasets (CIFAR-10, CIFAR-100,
ImageNet). To analyze the network we introduce a new variant of the
"deconvolution approach" for visualizing features learned by CNNs, which can be
applied to a broader range of network structures than existing approaches.
Topic Modeling of Hierarchical Corpora
Authors: Do-kyum Kim, Geoffrey M. Voelker, Lawrence K. Saul
Date: 2014-09-11 00:00:00
Tasks: Computer Security

We study the problem of topic modeling in corpora whose documents are
organized in a multi-level hierarchy. We explore a parametric approach to this
problem, assuming that the number of topics is known or can be estimated by
cross-validation. The models we consider can be viewed as special
(finite-dimensional) instances of hierarchical Dirichlet processes (HDPs). For
these models we show that there exists a simple variational approximation for
probabilistic inference. The approximation relies on a previously unexploited
inequality that handles the conditional dependence between Dirichlet latent
variables in adjacent levels of the model's hierarchy. We compare our approach
to existing implementations of nonparametric HDPs. On several benchmarks we
find that our approach is faster than Gibbs sampling and able to learn more
predictive models than existing variational methods. Finally, we demonstrate
the large-scale viability of our approach on two newly available corpora from
researchers in computer security---one with 350,000 documents and over 6,000
internal subcategories, the other with a five-level deep hierarchy.
Factorization of View-Object Manifolds for Joint Object Recognition and Pose Estimation
Authors: Haopeng Zhang, Tarek El-Gaaly, Ahmed Elgammal, Zhiguo Jiang
Date: 2015-03-23 00:00:00
Tasks: Object, Object Recognition, Pose Estimation, Visual Reasoning

Due to large variations in shape, appearance, and viewing conditions, object
recognition is a key precursory challenge in the fields of object manipulation
and robotic/AI visual reasoning in general. Recognizing object categories,
particular instances of objects and viewpoints/poses of objects are three
critical subproblems robots must solve in order to accurately grasp/manipulate
objects and reason about their environments. Multi-view images of the same
object lie on intrinsic low-dimensional manifolds in descriptor spaces (e.g.
visual/depth descriptor spaces). These object manifolds share the same topology
despite being geometrically different. Each object manifold can be represented
as a deformed version of a unified manifold. The object manifolds can thus be
parameterized by its homeomorphic mapping/reconstruction from the unified
manifold. In this work, we develop a novel framework to jointly solve the three
challenging recognition sub-problems, by explicitly modeling the deformations
of object manifolds and factorizing it in a view-invariant space for
recognition. We perform extensive experiments on several challenging datasets
and achieve state-of-the-art results.
Robust, scalable and fast bootstrap method for analyzing large scale data
Authors: Shahab Basiri, Esa Ollila, Visa Koivunen
Date: 2015-04-09 00:00:00

In this paper we address the problem of performing statistical inference for
large scale data sets i.e., Big Data. The volume and dimensionality of the data
may be so high that it cannot be processed or stored in a single computing
node. We propose a scalable, statistically robust and computationally efficient
bootstrap method, compatible with distributed processing and storage systems.
Bootstrap resamples are constructed with smaller number of distinct data points
on multiple disjoint subsets of data, similarly to the bag of little bootstrap
method (BLB) [1]. Then significant savings in computation is achieved by
avoiding the re-computation of the estimator for each bootstrap sample.
Instead, a computationally efficient fixed-point estimation equation is
analytically solved via a smart approximation following the Fast and Robust
Bootstrap method (FRB) [2]. Our proposed bootstrap method facilitates the use
of highly robust statistical methods in analyzing large scale data sets. The
favorable statistical properties of the method are established analytically.
Numerical examples demonstrate scalability, low complexity and robust
statistical performance of the method in analyzing large data sets.
Geometric Representations of Random Hypergraphs
Authors: Simón Lunagómez, Sayan Mukherjee, Robert L. Wolpert, Edoardo M. Airoldi
Date: 2009-12-18 00:00:00

A parametrization of hypergraphs based on the geometry of points in
$\mathbf{R}^d$ is developed. Informative prior distributions on hypergraphs are
induced through this parametrization by priors on point configurations via
spatial processes. This prior specification is used to infer conditional
independence models or Markov structure of multivariate distributions.
Specifically, we can recover both the junction tree factorization as well as
the hyper Markov law. This approach offers greater control on the distribution
of graph features than Erd\"os-R\'enyi random graphs, supports inference of
factorizations that cannot be retrieved by a graph alone, and leads to new
Metropolis\slash Hastings Markov chain Monte Carlo algorithms with both local
and global moves in graph space. We illustrate the utility of this
parametrization and prior specification using simulations.
Classification with Extreme Learning Machine and Ensemble Algorithms Over Randomly Partitioned Data
Authors: Ferhat Özgür Çatak
Date: 2015-04-12 00:00:00
Tasks: Classification, General Classification

In this age of Big Data, machine learning based data mining methods are
extensively used to inspect large scale data sets. Deriving applicable
predictive modeling from these type of data sets is a challenging obstacle
because of their high complexity. Opportunity with high data availability
levels, automated classification of data sets has become a critical and
complicated function. In this paper, the power of applying MapReduce based
Distributed AdaBoosting of Extreme Learning Machine (ELM) are explored to build
reliable predictive bag of classification models. Thus, (i) dataset ensembles
are build; (ii) ELM algorithm is used to build weak classification models; and
(iii) build a strong classification model from a set of weak classification
models. This training model is applied to the publicly available knowledge
discovery and data mining datasets.
Computing trading strategies based on financial sentiment data using evolutionary optimization
Authors: Ronald Hochreiter
Date: 2015-04-12 00:00:00

In this paper we apply evolutionary optimization techniques to compute
optimal rule-based trading strategies based on financial sentiment data. The
sentiment data was extracted from the social media service StockTwits to
accommodate the level of bullishness or bearishness of the online trading
community towards certain stocks. Numerical results for all stocks from the Dow
Jones Industrial Average (DJIA) index are presented and a comparison to
classical risk-return portfolio selection is provided.
Learning a Convolutional Neural Network for Non-uniform Motion Blur Removal
Authors: Jian Sun, Wenfei Cao, Zongben Xu, Jean Ponce
Date: 2015-03-02 00:00:00
Proceeding: CVPR 2015 6
Tasks: Deblurring

In this paper, we address the problem of estimating and removing non-uniform
motion blur from a single blurry image. We propose a deep learning approach to
predicting the probabilistic distribution of motion blur at the patch level
using a convolutional neural network (CNN). We further extend the candidate set
of motion kernels predicted by the CNN using carefully designed image
rotations. A Markov random field model is then used to infer a dense
non-uniform motion blur field enforcing motion smoothness. Finally, motion blur
is removed by a non-uniform deblurring model using patch-level image prior.
Experimental evaluations show that our approach can effectively estimate and
remove complex non-uniform motion blur that is not handled well by previous
approaches.
Deep Transform: Cocktail Party Source Separation via Complex Convolution in a Deep Neural Network
Authors: Andrew J. R. Simpson
Date: 2015-04-12 00:00:00

Convolutional deep neural networks (DNN) are state of the art in many
engineering problems but have not yet addressed the issue of how to deal with
complex spectrograms. Here, we use circular statistics to provide a convenient
probabilistic estimate of spectrogram phase in a complex convolutional DNN. In
a typical cocktail party source separation scenario, we trained a convolutional
DNN to re-synthesize the complex spectrograms of two source speech signals
given a complex spectrogram of the monaural mixture - a discriminative deep
transform (DT). We then used this complex convolutional DT to obtain
probabilistic estimates of the magnitude and phase components of the source
spectrograms. Our separation results are on a par with equivalent binary-mask
based non-complex separation approaches.
RoboBrain: Large-Scale Knowledge Engine for Robots
Authors: Ashutosh Saxena, Ashesh Jain, Ozan Sener, Aditya Jami, Dipendra K. Misra, Hema S. Koppula
Date: 2014-12-01 00:00:00

In this paper we introduce a knowledge engine, which learns and shares
knowledge representations, for robots to carry out a variety of tasks. Building
such an engine brings with it the challenge of dealing with multiple data
modalities including symbols, natural language, haptic senses, robot
trajectories, visual features and many others. The \textit{knowledge} stored in
the engine comes from multiple sources including physical interactions that
robots have while performing tasks (perception, planning and control),
knowledge bases from the Internet and learned representations from several
robotics research groups.
  We discuss various technical aspects and associated challenges such as
modeling the correctness of knowledge, inferring latent information and
formulating different robotic tasks as queries to the knowledge engine. We
describe the system architecture and how it supports different mechanisms for
users and robots to interact with the engine. Finally, we demonstrate its use
in three important research areas: grounding natural language, perception, and
planning, which are the key building blocks for many robotic tasks. This
knowledge engine is a collaborative effort and we call it RoboBrain.
Generalized Correntropy for Robust Adaptive Filtering
Authors: Badong Chen, Lei Xing, Haiquan Zhao, Nanning Zheng, José C. Príncipe
Date: 2015-04-12 00:00:00

As a robust nonlinear similarity measure in kernel space, correntropy has
received increasing attention in domains of machine learning and signal
processing. In particular, the maximum correntropy criterion (MCC) has recently
been successfully applied in robust regression and filtering. The default
kernel function in correntropy is the Gaussian kernel, which is, of course, not
always the best choice. In this work, we propose a generalized correntropy that
adopts the generalized Gaussian density (GGD) function as the kernel (not
necessarily a Mercer kernel), and present some important properties. We further
propose the generalized maximum correntropy criterion (GMCC), and apply it to
adaptive filtering. An adaptive algorithm, called the GMCC algorithm, is
derived, and the mean square convergence performance is studied. We show that
the proposed algorithm is very stable and can achieve zero probability of
divergence (POD). Simulation results confirm the theoretical expectations and
demonstrate the desirable performance of the new algorithm.