pwc-archive/papers-with-abstracts huggingface dataset:
Fast Edge Detection Using Structured Forests
Authors: Piotr Dollár, C. Lawrence Zitnick
Date: 2014-06-20 00:00:00
Tasks: Edge Detection, Image Segmentation, Semantic Segmentation

Edge detection is a critical component of many vision systems, including
object detectors and image segmentation algorithms. Patches of edges exhibit
well-known forms of local structure, such as straight lines or T-junctions. In
this paper we take advantage of the structure present in local image patches to
learn both an accurate and computationally efficient edge detector. We
formulate the problem of predicting local edge masks in a structured learning
framework applied to random decision forests. Our novel approach to learning
decision trees robustly maps the structured labels to a discrete space on which
standard information gain measures may be evaluated. The result is an approach
that obtains realtime performance that is orders of magnitude faster than many
competing state-of-the-art approaches, while also achieving state-of-the-art
edge detection results on the BSDS500 Segmentation dataset and NYU Depth
dataset. Finally, we show the potential of our approach as a general purpose
edge detector by showing our learned edge models generalize well across
datasets.
One Vector is Not Enough: Entity-Augmented Distributional Semantics for Discourse Relations
Authors: Yangfeng Ji, Jacob Eisenstein
Date: 2014-11-25 00:00:00
Tasks: Relation

Discourse relations bind smaller linguistic units into coherent texts.
However, automatically identifying discourse relations is difficult, because it
requires understanding the semantics of the linked arguments. A more subtle
challenge is that it is not enough to represent the meaning of each argument of
a discourse relation, because the relation may depend on links between
lower-level components, such as entity mentions. Our solution computes
distributional meaning representations by composition up the syntactic parse
tree. A key difference from previous work on compositional distributional
semantics is that we also compute representations for entity mentions, using a
novel downward compositional pass. Discourse relations are predicted from the
distributional representations of the arguments, and also of their coreferent
entity mentions. The resulting system obtains substantial improvements over the
previous state-of-the-art in predicting implicit discourse relations in the
Penn Discourse Treebank.
A Greedy, Flexible Algorithm to Learn an Optimal Bayesian Network Structure
Authors: Amir Arsalan Soltani
Date: 2014-11-24 00:00:00

In this report paper we first present a report of the Advanced Machine
Learning Course Project on the provided data set and then present a novel
heuristic algorithm for exact Bayesian network (BN) structure discovery that
uses decomposable scoring functions. Our algorithm follows a different approach
to solve the problem of BN structure discovery than the previously used methods
such as Dynamic Programming (DP) and Branch and Bound to reduce the search
space and find the global optima space for the problem. The algorithm we
propose has some degree of flexibility that can make it more or less greedy.
The more the algorithm is set to be greedy, the more the speed of the algorithm
will be, and the less optimal the final structure. Our algorithm runs in a much
less time than the previously known methods and guarantees to have an
optimality of close to 99%. Therefore, it sacrifices less than one percent of
score of an optimal structure in order to gain a much lower running time and
make the algorithm feasible for large data sets (we may note that we never used
any toolbox except for result validation)
Application of the Ring Theory in the Segmentation of Digital Images
Authors: Yasel Garcés, Esley Torres, Osvaldo Pereira, Roberto Rodríguez
Date: 2014-02-17 00:00:00
Tasks: Abstract Algebra, Image Segmentation, Segmentation, Semantic Segmentation

Ring theory is one of the branches of the abstract algebra that has been
broadly used in images. However, ring theory has not been very related with
image segmentation. In this paper, we propose a new index of similarity among
images using Zn rings and the entropy function. This new index was applied as a
new stopping criterion to the Mean Shift Iterative Algorithm with the goal to
reach a better segmentation. An analysis on the performance of the algorithm
with this new stopping criterion is carried out. The obtained results proved
that the new index is a suitable tool to compare images.
Rational Deployment of Multiple Heuristics in IDA*
Authors: David Tolpin, Oded Betzalel, Ariel Felner, Solomon Eyal Shimony
Date: 2014-11-24 00:00:00

Recent advances in metareasoning for search has shown its usefulness in
improving numerous search algorithms. This paper applies rational metareasoning
to IDA* when several admissible heuristics are available. The obvious basic
approach of taking the maximum of the heuristics is improved upon by lazy
evaluation of the heuristics, resulting in a variant known as Lazy IDA*. We
introduce a rational version of lazy IDA* that decides whether to compute the
more expensive heuristics or to bypass it, based on a myopic expected regret
estimate. Empirical evaluation in several domains supports the theoretical
results, and shows that rational lazy IDA* is a state-of-the-art heuristic
combination method.
Consistency of Cheeger and Ratio Graph Cuts
Authors: Nicolas Garcia Trillos, Dejan Slepcev, James Von Brecht, Thomas Laurent, Xavier Bresson
Date: 2014-11-24 00:00:00
Tasks: Clustering

This paper establishes the consistency of a family of graph-cut-based
algorithms for clustering of data clouds. We consider point clouds obtained as
samples of a ground-truth measure. We investigate approaches to clustering
based on minimizing objective functionals defined on proximity graphs of the
given sample. Our focus is on functionals based on graph cuts like the Cheeger
and ratio cuts. We show that minimizers of the these cuts converge as the
sample size increases to a minimizer of a corresponding continuum cut (which
partitions the ground truth measure). Moreover, we obtain sharp conditions on
how the connectivity radius can be scaled with respect to the number of sample
points for the consistency to hold. We provide results for two-way and for
multiway cuts. Furthermore we provide numerical experiments that illustrate the
results and explore the optimality of scaling in dimension two.
bartMachine: Machine Learning with Bayesian Additive Regression Trees
Authors: Adam Kapelner, Justin Bleich
Date: 2013-12-08 00:00:00
Tasks: BIG-bench Machine Learning, Diagnostic, Future prediction, regression, Variable Selection

We present a new package in R implementing Bayesian additive regression trees
(BART). The package introduces many new features for data analysis using BART
such as variable selection, interaction detection, model diagnostic plots,
incorporation of missing data and the ability to save trees for future
prediction. It is significantly faster than the current R implementation,
parallelized, and capable of handling both large sample sizes and
high-dimensional data.
Noise Benefits in Expectation-Maximization Algorithms
Authors: Osonde Adekorede Osoba
Date: 2014-11-24 00:00:00
Tasks: Bayesian Inference, Clustering, parameter estimation

This dissertation shows that careful injection of noise into sample data can
substantially speed up Expectation-Maximization algorithms.
Expectation-Maximization algorithms are a class of iterative algorithms for
extracting maximum likelihood estimates from corrupted or incomplete data. The
convergence speed-up is an example of a noise benefit or "stochastic resonance"
in statistical signal processing. The dissertation presents derivations of
sufficient conditions for such noise-benefits and demonstrates the speed-up in
some ubiquitous signal-processing algorithms. These algorithms include
parameter estimation for mixture models, the $k$-means clustering algorithm,
the Baum-Welch algorithm for training hidden Markov models, and backpropagation
for training feedforward artificial neural networks. This dissertation also
analyses the effects of data and model corruption on the more general Bayesian
inference estimation framework. The main finding is a theorem guaranteeing that
uniform approximators for Bayesian model functions produce uniform
approximators for the posterior pdf via Bayes theorem. This result also applies
to hierarchical and multidimensional Bayesian models.
Distributed Coordinate Descent for L1-regularized Logistic Regression
Authors: Ilya Trofimov, Alexander Genkin
Date: 2014-11-24 00:00:00
Tasks: regression

Solving logistic regression with L1-regularization in distributed settings is
an important problem. This problem arises when training dataset is very large
and cannot fit the memory of a single machine. We present d-GLMNET, a new
algorithm solving logistic regression with L1-regularization in the distributed
settings. We empirically show that it is superior over distributed online
learning via truncated gradient.
Persistent Evidence of Local Image Properties in Generic ConvNets
Authors: Ali Sharif Razavian, Hossein Azizpour, Atsuto Maki, Josephine Sullivan, Carl Henrik Ek, Stefan Carlsson
Date: 2014-11-24 00:00:00
Tasks: General Classification, Object

Supervised training of a convolutional network for object classification
should make explicit any information related to the class of objects and
disregard any auxiliary information associated with the capture of the image or
the variation within the object class. Does this happen in practice? Although
this seems to pertain to the very final layers in the network, if we look at
earlier layers we find that this is not the case. Surprisingly, strong spatial
information is implicit. This paper addresses this, in particular, exploiting
the image representation at the first fully connected layer, i.e. the global
image descriptor which has been recently shown to be most effective in a range
of visual recognition tasks. We empirically demonstrate evidences for the
finding in the contexts of four different tasks: 2d landmark detection, 2d
object keypoints prediction, estimation of the RGB values of input image, and
recovery of semantic label of each pixel. We base our investigation on a simple
framework with ridge rigression commonly across these tasks, and show results
which all support our insight. Such spatial information can be used for
computing correspondence of landmarks to a good accuracy, but should
potentially be useful for improving the training of the convolutional nets for
classification purposes.
Solving the Periodic Timetabling Problem using a Genetic Algorithm
Authors: Diego Arenas, Remy Chevirer, Said Hanafi, Joaquin Rodriguez
Date: 2014-11-24 00:00:00

In railway operations, a timetable is established to determine the departure
and arrival times for the trains or other rolling stock at the different
stations or relevant points inside the rail network or a subset of this
network. The elaboration of this timetable is done to respond to the commercial
requirements for both passenger and freight traffic, but also it must respect a
set of security and capacity constraints associated with the railway network,
rolling stock and legislation. Combining these requirements and constraints, as
well as the important number of trains and schedules to plan, makes the
preparation of a feasible timetable a complex and time-consuming process, that
normally takes several months to be completed. This article addresses the
problem of generating periodic timetables, which means that the involved trains
operate in a recurrent pattern. For instance, the trains belonging to the same
train line, depart from some station every 15 minutes or one hour. To tackle
the problem, we present a constraint-based model suitable for this kind of
problem. Then, we propose a genetic algorithm, allowing a rapid generation of
feasible periodic timetables. Finally, two case studies are presented, the
first, describing a sub-set of the Netherlands rail network, and the second a
large portion of the Nord-pas-de-Calais regional rail network, both of them are
then solved using our algorithm and the results are presented and discussed.
Towards a Consistent, Sound and Complete Conceptual Knowledge
Authors: Gowri Shankar Ramaswamy, F Sagayaraj Francis
Date: 2014-11-24 00:00:00

Knowledge is only good if it is sound, consistent and complete. The same
holds true for conceptual knowledge, which holds knowledge about concepts and
its association. Conceptual knowledge no matter what format they are
represented in, must be consistent, sound and complete in order to realise its
practical use. This paper discusses consistency, soundness and completeness in
the ambit of conceptual knowledge and the need to consider these factors as
fundamental to the development of conceptual knowledge.
The Application of Two-level Attention Models in Deep Convolutional Neural Network for Fine-grained Image Classification
Authors: Tianjun Xiao, Yichong Xu, Kuiyuan Yang, Jiaxing Zhang, Yuxin Peng, Zheng Zhang
Date: 2014-11-24 00:00:00
Proceeding: CVPR 2015 6
Tasks: Classification, Fine-Grained Image Classification, General Classification, image-classification, Image Classification, Object

Fine-grained classification is challenging because categories can only be
discriminated by subtle and local differences. Variances in the pose, scale or
rotation usually make the problem more difficult. Most fine-grained
classification systems follow the pipeline of finding foreground object or
object parts (where) to extract discriminative features (what).
  In this paper, we propose to apply visual attention to fine-grained
classification task using deep neural network. Our pipeline integrates three
types of attention: the bottom-up attention that propose candidate patches, the
object-level top-down attention that selects relevant patches to a certain
object, and the part-level top-down attention that localizes discriminative
parts. We combine these attentions to train domain-specific deep nets, then use
it to improve both the what and where aspects. Importantly, we avoid using
expensive annotations like bounding box or part information from end-to-end.
The weak supervision constraint makes our work easier to generalize.
  We have verified the effectiveness of the method on the subsets of ILSVRC2012
dataset and CUB200_2011 dataset. Our pipeline delivered significant
improvements and achieved the best accuracy under the weakest supervision
condition. The performance is competitive against other methods that rely on
additional annotations.
Encoding High Dimensional Local Features by Sparse Coding Based Fisher Vectors
Authors: Lingqiao Liu, Chunhua Shen, Lei Wang, Anton Van Den Hengel, Chao Wang
Date: 2014-11-24 00:00:00
Proceeding: NeurIPS 2014 12
Tasks: Fine-Grained Image Classification, General Classification, image-classification, Image Classification, Object Recognition, Vocal Bursts Intensity Prediction

Deriving from the gradient vector of a generative model of local features,
Fisher vector coding (FVC) has been identified as an effective coding method
for image classification. Most, if not all, % FVC implementations employ the
Gaussian mixture model (GMM) to characterize the generation process of local
features. This choice has shown to be sufficient for traditional low
dimensional local features, e.g., SIFT; and typically, good performance can be
achieved with only a few hundred Gaussian distributions. However, the same
number of Gaussians is insufficient to model the feature space spanned by
higher dimensional local features, which have become popular recently. In order
to improve the modeling capacity for high dimensional features, it turns out to
be inefficient and computationally impractical to simply increase the number of
Gaussians. In this paper, we propose a model in which each local feature is
drawn from a Gaussian distribution whose mean vector is sampled from a
subspace. With certain approximation, this model can be converted to a sparse
coding procedure and the learning/inference problems can be readily solved by
standard sparse coding methods. By calculating the gradient vector of the
proposed model, we derive a new fisher vector encoding strategy, termed Sparse
Coding based Fisher Vector Coding (SCFVC). Moreover, we adopt the recently
developed Deep Convolutional Neural Network (CNN) descriptor as a high
dimensional local feature and implement image classification with the proposed
SCFVC. Our experimental evaluations demonstrate that our method not only
significantly outperforms the traditional GMM based Fisher vector encoding but
also achieves the state-of-the-art performance in generic object recognition,
indoor scene, and fine-grained image classification problems.
Multiclass learnability and the ERM principle
Authors: Amit Daniely, Sivan Sabato, Shai Ben-David, Shai Shalev-Shwartz
Date: 2013-08-13 00:00:00
Tasks: Binary Classification, General Classification

We study the sample complexity of multiclass prediction in several learning
settings. For the PAC setting our analysis reveals a surprising phenomenon: In
sharp contrast to binary classification, we show that there exist multiclass
hypothesis classes for which some Empirical Risk Minimizers (ERM learners) have
lower sample complexity than others. Furthermore, there are classes that are
learnable by some ERM learners, while other ERM learners will fail to learn
them. We propose a principle for designing good ERM learners, and use this
principle to prove tight bounds on the sample complexity of learning {\em
symmetric} multiclass hypothesis classes---classes that are invariant under
permutations of label names. We further provide a characterization of mistake
and regret bounds for multiclass learning in the online setting and the bandit
setting, using new generalizations of Littlestone's dimension.
Scale-Invariant Convolutional Neural Networks
Authors: Yichong Xu, Tianjun Xiao, Jiaxing Zhang, Kuiyuan Yang, Zheng Zhang
Date: 2014-11-24 00:00:00
Tasks: Data Augmentation, General Classification

Even though convolutional neural networks (CNN) has achieved near-human
performance in various computer vision tasks, its ability to tolerate scale
variations is limited. The popular practise is making the model bigger first,
and then train it with data augmentation using extensive scale-jittering. In
this paper, we propose a scaleinvariant convolutional neural network (SiCNN), a
modeldesigned to incorporate multi-scale feature exaction and classification
into the network structure. SiCNN uses a multi-column architecture, with each
column focusing on a particular scale. Unlike previous multi-column strategies,
these columns share the same set of filter parameters by a scale transformation
among them. This design deals with scale variation without blowing up the model
size. Experimental results show that SiCNN detects features at various scales,
and the classification result exhibits strong robustness against object scale
variations.
On the mathematic modeling of non-parametric curves based on cubic Bézier curves
Authors: Ha Jong Won, Choe Chun Hwa, Li Kum Song
Date: 2014-11-24 00:00:00

B\'ezier splines are widely available in various systems with the curves and
surface designs. In general, the B\'ezier spline can be specified with the
B\'ezier curve segments and a B\'ezier curve segment can be fitted to any
number of control points. The number of control points determines the degree of
the B\'ezier polynomial. This paper presents a method which determines control
points for B\'ezier curves approximating segments of obtained image
outline(non-parametric curve) by using the properties of cubic B\'ezier curves.
Proposed method is a technique to determine the control points that has
generality and reduces the error of the B\'ezier curve approximation. Main
advantage of proposed method is that it has higher accuracy and compression
rate than previous methods. The cubic B\'ezier spline is obtained from cubic
B\'ezier curve segments. To demonstrate the various performances of the
proposed algorithm, experimental results are compared.
A Hybrid Solution to improve Iteration Efficiency in the Distributed Learning
Authors: Junxiong Wang, Hongzhi Wang, Chenxu Zhao
Date: 2014-11-24 00:00:00
Tasks: BIG-bench Machine Learning

Currently, many machine learning algorithms contain lots of iterations. When it comes to existing large-scale distributed systems, some slave nodes may break down or have lower efficiency. Therefore traditional machine learning algorithm may fail because of the instability of distributed system.We presents a hybrid approach which not only own a high fault-tolerant but also achieve a balance of performance and efficiency.For each iteration, the result of slow machines will be abandoned. Then, we discuss the relationship between accuracy and abandon rate. Next we debate the convergence speed of this process. Finally, our experiments demonstrate our idea can dramatically reduce calculation time and be used in many platforms.
Iteratively Reweighted Graph Cut for Multi-label MRFs with Non-convex Priors
Authors: Thalaiyasingam Ajanthan, Richard Hartley, Mathieu Salzmann, Hongdong Li
Date: 2014-11-24 00:00:00
Proceeding: CVPR 2015 6

While widely acknowledged as highly effective in computer vision, multi-label
MRFs with non-convex priors are difficult to optimize. To tackle this, we
introduce an algorithm that iteratively approximates the original energy with
an appropriately weighted surrogate energy that is easier to minimize. Our
algorithm guarantees that the original energy decreases at each iteration. In
particular, we consider the scenario where the global minimizer of the weighted
surrogate energy can be obtained by a multi-label graph cut algorithm, and show
that our algorithm then lets us handle of large variety of non-convex priors.
We demonstrate the benefits of our method over state-of-the-art MRF energy
minimization techniques on stereo and inpainting problems.
Diffusion of Lexical Change in Social Media
Authors: Jacob Eisenstein, Brendan O'Connor, Noah A. Smith, Eric P. Xing
Date: 2012-10-18 00:00:00

Computer-mediated communication is driving fundamental changes in the nature
of written language. We investigate these changes by statistical analysis of a
dataset comprising 107 million Twitter messages (authored by 2.7 million unique
user accounts). Using a latent vector autoregressive model to aggregate across
thousands of words, we identify high-level patterns in diffusion of linguistic
change over the United States. Our model is robust to unpredictable changes in
Twitter's sampling rate, and provides a probabilistic characterization of the
relationship of macro-scale linguistic influence to a set of demographic and
geographic predictors. The results of this analysis offer support for prior
arguments that focus on geographical proximity and population size. However,
demographic similarity -- especially with regard to race -- plays an even more
central role, as cities with similar racial demographics are far more likely to
share linguistic influence. Rather than moving towards a single unified
"netspeak" dialect, language evolution in computer-mediated communication
reproduces existing fault lines in spoken American English.