pwc-archive/papers-with-abstracts huggingface dataset:
23-bit Metaknowledge Template Towards Big Data Knowledge Discovery and Management
Authors: Nima Bari, Roman Vichr, Kamran Kowsari, Simon Y. Berkovich
Date: 2015-03-01 00:00:00
Tasks: Clustering, Management

The global influence of Big Data is not only growing but seemingly endless.
The trend is leaning towards knowledge that is attained easily and quickly from
massive pools of Big Data. Today we are living in the technological world that
Dr. Usama Fayyad and his distinguished research fellows discussed in the
introductory explanations of Knowledge Discovery in Databases (KDD) predicted
nearly two decades ago. Indeed, they were precise in their outlook on Big Data
analytics. In fact, the continued improvement of the interoperability of
machine learning, statistics, database building and querying fused to create
this increasingly popular science- Data Mining and Knowledge Discovery. The
next generation computational theories are geared towards helping to extract
insightful knowledge from even larger volumes of data at higher rates of speed.
As the trend increases in popularity, the need for a highly adaptive solution
for knowledge discovery will be necessary. In this research paper, we are
introducing the investigation and development of 23 bit-questions for a
Metaknowledge template for Big Data Processing and clustering purposes. This
research aims to demonstrate the construction of this methodology and proves
the validity and the beneficial utilization that brings Knowledge Discovery
from Big Data.
Task Allocation in Robotic Swarms: Explicit Communication Based Approaches
Authors: Aryo Jamshidpey, Mohsen Afsharchi
Date: 2015-03-01 00:00:00

In this paper we study multi robot cooperative task allocation issue in a
situation where a swarm of robots is deployed in a confined unknown environment
where the number of colored spots which represent tasks and the ratios of them
are unknown. The robots should cover this spots as far as possible to do
cleaning and sampling actions desirably. It means that they should discover the
spots cooperatively and spread proportional to the spots area and avoid from
remaining idle. We proposed 4 self-organized distributed methods which are
called hybrid methods for coping with this scenario. In two different
experiments the performance of the methods is analyzed. We compared them with
each other and investigated their scalability and robustness in term of single
point of failure.
Extraction of Salient Sentences from Labelled Documents
Authors: Misha Denil, Alban Demiraj, Nando de Freitas
Date: 2014-12-21 00:00:00
Tasks: Sentence

We present a hierarchical convolutional document model with an architecture
designed to support introspection of the document structure. Using this model,
we show how to use visualisation techniques from the computer vision literature
to identify and extract topic-relevant sentences.
  We also introduce a new scalable evaluation technique for automatic sentence
extraction systems that avoids the need for time consuming human annotation of
validation data.
The NLP Engine: A Universal Turing Machine for NLP
Authors: Jiwei Li, Eduard Hovy
Date: 2015-02-28 00:00:00
Tasks: Machine Translation, Part-Of-Speech Tagging, Translation

It is commonly accepted that machine translation is a more complex task than
part of speech tagging. But how much more complex? In this paper we make an
attempt to develop a general framework and methodology for computing the
informational and/or processing complexity of NLP applications and tasks. We
define a universal framework akin to a Turning Machine that attempts to fit
(most) NLP tasks into one paradigm. We calculate the complexities of various
NLP tasks using measures of Shannon Entropy, and compare `simple' ones such as
part of speech tagging to `complex' ones such as machine translation. This
paper provides a first, though far from perfect, attempt to quantify NLP tasks
under a uniform paradigm. We point out current deficiencies and suggest some
avenues for fruitful research.
Supervised learning sets benchmark for robust spike detection from calcium imaging signals
Authors: Lucas Theis, Philipp Berens, Emmanouil Froudarakis, Jacob Reimer, Miroslav Román Rosón, Tom Baden, Thomas Euler, Andreas Tolias, Matthias Bethge
Date: 2015-02-28 00:00:00

A fundamental challenge in calcium imaging has been to infer the timing of
action potentials from the measured noisy calcium fluorescence traces. We
systematically evaluate a range of spike inference algorithms on a large
benchmark dataset recorded from varying neural tissue (V1 and retina) using
different calcium indicators (OGB-1 and GCamp6). We show that a new algorithm
based on supervised learning in flexible probabilistic models outperforms all
previously published techniques, setting a new standard for spike inference
from calcium signals. Importantly, it performs better than other algorithms
even on datasets not seen during training. Future data acquired in new
experimental conditions can easily be used to further improve its spike
prediction accuracy and generalization performance. Finally, we show that
comparing algorithms on artificial data is not informative about performance on
real population imaging data, suggesting that a benchmark dataset may greatly
facilitate future algorithmic developments.
Non-linear Learning for Statistical Machine Translation
Authors: Shujian Huang, Huadong Chen, Xin-yu Dai, Jia-Jun Chen
Date: 2015-02-28 00:00:00
Proceeding: IJCNLP 2015 7
Tasks: Machine Translation, Translation

Modern statistical machine translation (SMT) systems usually use a linear
combination of features to model the quality of each translation hypothesis.
The linear combination assumes that all the features are in a linear
relationship and constrains that each feature interacts with the rest features
in an linear manner, which might limit the expressive power of the model and
lead to a under-fit model on the current data. In this paper, we propose a
non-linear modeling for the quality of translation hypotheses based on neural
networks, which allows more complex interaction between features. A learning
framework is presented for training the non-linear models. We also discuss
possible heuristics in designing the network structure which may improve the
non-linear learning performance. Experimental results show that with the basic
features of a hierarchical phrase-based machine translation system, our method
produce translations that are better than a linear model.
Online Fair Division: analysing a Food Bank problem
Authors: Martin Aleksandrov, Haris Aziz, Serge Gaspers, Toby Walsh
Date: 2015-02-26 00:00:00

We study an online model of fair division designed to capture features of a
real world charity problem. We consider two simple mechanisms for this model in
which agents simply declare what items they like. We analyse several axiomatic
properties of these mechanisms like strategy-proofness and envy-freeness.
Finally, we perform a competitive analysis and compute the price of anarchy.
Improved Image Deblurring based on Salient-region Segmentation
Authors: Chongyang Zhang, Weiyao Lin, Wei Li, Bing Zhou, Jun Xie, Jijia Li
Date: 2015-02-28 00:00:00
Tasks: Deblurring, Image Deblurring, Saliency Detection

Image deblurring techniques play important roles in many image processing
applications. As the blur varies spatially across the image plane, it calls for
robust and effective methods to deal with the spatially-variant blur problem.
In this paper, a Saliency-based Deblurring (SD) approach is proposed based on
the saliency detection for salient-region segmentation and a corresponding
compensate method for image deblurring. We also propose a PDE-based deblurring
method which introduces an anisotropic Partial Differential Equation (PDE)
model for latent image prediction and employs an adaptive optimization model in
the kernel estimation and deconvolution steps. Experimental results demonstrate
the effectiveness of the proposed algorithm.
Why does Deep Learning work? - A perspective from Group Theory
Authors: Arnab Paul, Suresh Venkatasubramanian
Date: 2014-12-20 00:00:00
Tasks: Deep Learning

Why does Deep Learning work? What representations does it capture? How do
higher-order representations emerge? We study these questions from the
perspective of group theory, thereby opening a new approach towards a theory of
Deep learning.
  One factor behind the recent resurgence of the subject is a key algorithmic
step called pre-training: first search for a good generative model for the
input samples, and repeat the process one layer at a time. We show deeper
implications of this simple principle, by establishing a connection with the
interplay of orbits and stabilizers of group actions. Although the neural
networks themselves may not form groups, we show the existence of {\em shadow}
groups whose elements serve as close approximations.
  Over the shadow groups, the pre-training step, originally introduced as a
mechanism to better initialize a network, becomes equivalent to a search for
features with minimal orbits. Intuitively, these features are in a way the {\em
simplest}. Which explains why a deep learning network learns simple features
first. Next, we show how the same principle, when repeated in the deeper
layers, can capture higher order representations, and why representation
complexity increases as the layers get deeper.
Macroblock Classification Method for Video Applications Involving Motions
Authors: Weiyao Lin, Ming-Ting Sun, Hongxiang Li, Zhenzhong Chen, Wei Li, Bing Zhou
Date: 2015-02-28 00:00:00
Tasks: Change Detection, Classification, General Classification, Motion Estimation

In this paper, a macroblock classification method is proposed for various
video processing applications involving motions. Based on the analysis of the
Motion Vector field in the compressed video, we propose to classify Macroblocks
of each video frame into different classes and use this class information to
describe the frame content. We demonstrate that this low-computation-complexity
method can efficiently catch the characteristics of the frame. Based on the
proposed macroblock classification, we further propose algorithms for different
video processing applications, including shot change detection, motion
discontinuity detection, and outlier rejection for global motion estimation.
Experimental results demonstrate that the methods based on the proposed
approach can work effectively on these applications.
Group Event Detection with a Varying Number of Group Members for Video Surveillance
Authors: Weiyao Lin, Ming-Ting Sun, Radha Poovendran, Zhengyou Zhang
Date: 2015-02-28 00:00:00
Tasks: Action Detection, Activity Detection, Event Detection

This paper presents a novel approach for automatic recognition of group
activities for video surveillance applications. We propose to use a group
representative to handle the recognition with a varying number of group
members, and use an Asynchronous Hidden Markov Model (AHMM) to model the
relationship between people. Furthermore, we propose a group activity detection
algorithm which can handle both symmetric and asymmetric group activities, and
demonstrate that this approach enables the detection of hierarchical
interactions between people. Experimental results show the effectiveness of our
approach.
Activity Recognition Using A Combination of Category Components And Local Models for Video Surveillance
Authors: Weiyao Lin, Ming-Ting Sun, Radha Poovendran, Zhengyou Zhang
Date: 2015-02-28 00:00:00
Tasks: Activity Recognition

This paper presents a novel approach for automatic recognition of human
activities for video surveillance applications. We propose to represent an
activity by a combination of category components, and demonstrate that this
approach offers flexibility to add new activities to the system and an ability
to deal with the problem of building models for activities lacking training
data. For improving the recognition accuracy, a Confident-Frame- based
Recognition algorithm is also proposed, where the video frames with high
confidence for recognizing an activity are used as a specialized local model to
help classify the remainder of the video frames. Experimental results show the
effectiveness of the proposed approach.
Real-Time Grasp Detection Using Convolutional Neural Networks
Authors: Joseph Redmon, Anelia Angelova
Date: 2014-12-09 00:00:00
Tasks: General Classification, GPU, Object, Region Proposal, regression, Robotic Grasping

We present an accurate, real-time approach to robotic grasp detection based
on convolutional neural networks. Our network performs single-stage regression
to graspable bounding boxes without using standard sliding window or region
proposal techniques. The model outperforms state-of-the-art approaches by 14
percentage points and runs at 13 frames per second on a GPU. Our network can
simultaneously perform classification so that in a single step it recognizes
the object and finds a good grasp rectangle. A modification to this model
predicts multiple grasps per object by using a locally constrained prediction
mechanism. The locally constrained model performs significantly better,
especially on objects that can be grasped in a variety of ways.
DeepTrack: Learning Discriminative Feature Representations Online for Robust Visual Tracking
Authors: Hanxi Li, Yi Li, Fatih Porikli
Date: 2015-02-28 00:00:00
Tasks: Visual Tracking

Deep neural networks, albeit their great success on feature learning in
various computer vision tasks, are usually considered as impractical for online
visual tracking because they require very long training time and a large number
of training samples. In this work, we present an efficient and very robust
tracking algorithm using a single Convolutional Neural Network (CNN) for
learning effective feature representations of the target object, in a purely
online manner. Our contributions are multifold: First, we introduce a novel
truncated structural loss function that maintains as many training samples as
possible and reduces the risk of tracking error accumulation. Second, we
enhance the ordinary Stochastic Gradient Descent approach in CNN training with
a robust sample selection mechanism. The sampling mechanism randomly generates
positive and negative samples from different temporal distributions, which are
generated by taking the temporal relations and label noise into account.
Finally, a lazy yet effective updating scheme is designed for CNN training.
Equipped with this novel updating algorithm, the CNN model is robust to some
long-existing difficulties in visual tracking such as occlusion or incorrect
detections, without loss of the effective adaption for significant appearance
changes. In the experiment, our CNN tracker outperforms all compared
state-of-the-art methods on two recently proposed benchmarks which in total
involve over 60 video sequences. The remarkable performance improvement over
the existing trackers illustrates the superiority of the feature
representations which are learned
Convolutional Neural Networks for joint object detection and pose estimation: A comparative study
Authors: Francisco Massa, Mathieu Aubry, Renaud Marlet
Date: 2014-12-22 00:00:00
Tasks: General Classification, Object, object-detection, Object Detection, Pose Estimation

In this paper we study the application of convolutional neural networks for
jointly detecting objects depicted in still images and estimating their 3D
pose. We identify different feature representations of oriented objects, and
energies that lead a network to learn this representations. The choice of the
representation is crucial since the pose of an object has a natural, continuous
structure while its category is a discrete variable. We evaluate the different
approaches on the joint object detection and pose estimation task of the
Pascal3D+ benchmark using Average Viewpoint Precision. We show that a
classification approach on discretized viewpoints achieves state-of-the-art
performance for joint object detection and pose estimation, and significantly
outperforms existing baselines on this benchmark.
Generating Multi-Sentence Lingual Descriptions of Indoor Scenes
Authors: Dahua Lin, Chen Kong, Sanja Fidler, Raquel Urtasun
Date: 2015-02-28 00:00:00
Tasks: Sentence, Text Generation

This paper proposes a novel framework for generating lingual descriptions of
indoor scenes. Whereas substantial efforts have been made to tackle this
problem, previous approaches focusing primarily on generating a single sentence
for each image, which is not sufficient for describing complex scenes. We
attempt to go beyond this, by generating coherent descriptions with multiple
sentences. Our approach is distinguished from conventional ones in several
aspects: (1) a 3D visual parsing system that jointly infers objects,
attributes, and relations; (2) a generative grammar learned automatically from
training text; and (3) a text generation algorithm that takes into account the
coherence among sentences. Experiments on the augmented NYU-v2 dataset show
that our framework can generate natural descriptions with substantially higher
ROGUE scores compared to those produced by the baseline.
Efficient Upsampling of Natural Images
Authors: Chinmay Hegde, Oncel Tuzel, Fatih Porikli
Date: 2015-02-28 00:00:00

We propose a novel method of efficient upsampling of a single natural image.
Current methods for image upsampling tend to produce high-resolution images
with either blurry salient edges, or loss of fine textural detail, or spurious
noise artifacts.
  In our method, we mitigate these effects by modeling the input image as a sum
of edge and detail layers, operating upon these layers separately, and merging
the upscaled results in an automatic fashion. We formulate the upsampled output
image as the solution to a non-convex energy minimization problem, and propose
an algorithm to obtain a tractable approximate solution. Our algorithm
comprises two main stages. 1) For the edge layer, we use a nonparametric
approach by constructing a dictionary of patches from a given image, and
synthesize edge regions in a higher-resolution version of the image. 2) For the
detail layer, we use a global parametric texture enhancement approach to
synthesize detail regions across the image.
  We demonstrate that our method is able to accurately reproduce sharp edges as
well as synthesize photorealistic textures, while avoiding common artifacts
such as ringing and haloing. In addition, our method involves no training phase
or estimation of model parameters, and is easily parallelizable. We demonstrate
the utility of our method on a number of challenging standard test photos.
Sequential Feature Explanations for Anomaly Detection
Authors: Md Amran Siddiqui, Alan Fern, Thomas G. Dietterich, Weng-Keen Wong
Date: 2015-02-28 00:00:00
Tasks: Anomaly Detection

In many applications, an anomaly detection system presents the most anomalous
data instance to a human analyst, who then must determine whether the instance
is truly of interest (e.g. a threat in a security setting). Unfortunately, most
anomaly detectors provide no explanation about why an instance was considered
anomalous, leaving the analyst with no guidance about where to begin the
investigation. To address this issue, we study the problems of computing and
evaluating sequential feature explanations (SFEs) for anomaly detectors. An SFE
of an anomaly is a sequence of features, which are presented to the analyst one
at a time (in order) until the information contained in the highlighted
features is enough for the analyst to make a confident judgement about the
anomaly. Since analyst effort is related to the amount of information that they
consider in an investigation, an explanation's quality is related to the number
of features that must be revealed to attain confidence. One of our main
contributions is to present a novel framework for large scale quantitative
evaluations of SFEs, where the quality measure is based on analyst effort. To
do this we construct anomaly detection benchmarks from real data sets along
with artificial experts that can be simulated for evaluation. Our second
contribution is to evaluate several novel explanation approaches within the
framework and on traditional anomaly detection benchmarks, offering several
insights into the approaches.
Parsing as Reduction
Authors: Daniel Fernández-González, André F. T. Martins
Date: 2015-02-27 00:00:00
Proceeding: IJCNLP 2015 7
Tasks: Dependency Parsing

We reduce phrase-representation parsing to dependency parsing. Our reduction
is grounded on a new intermediate representation, "head-ordered dependency
trees", shown to be isomorphic to constituent trees. By encoding order
information in the dependency labels, we show that any off-the-shelf, trainable
dependency parser can be used to produce constituents. When this parser is
non-projective, we can perform discontinuous parsing in a very natural manner.
Despite the simplicity of our approach, experiments show that the resulting
parsers are on par with strong baselines, such as the Berkeley parser for
English and the best single system in the SPMRL-2014 shared task. Results are
particularly striking for discontinuous parsing of German, where we surpass the
current state of the art by a wide margin.
Random Walk Initialization for Training Very Deep Feedforward Networks
Authors: David Sussillo, L. F. Abbott
Date: 2014-12-19 00:00:00

Training very deep networks is an important open problem in machine learning.
One of many difficulties is that the norm of the back-propagated error gradient
can grow or decay exponentially. Here we show that training very deep
feed-forward networks (FFNs) is not as difficult as previously thought. Unlike
when back-propagation is applied to a recurrent network, application to an FFN
amounts to multiplying the error gradient by a different random matrix at each
layer. We show that the successive application of correctly scaled random
matrices to an initial vector results in a random walk of the log of the norm
of the resulting vectors, and we compute the scaling that makes this walk
unbiased. The variance of the random walk grows only linearly with network
depth and is inversely proportional to the size of each layer. Practically,
this implies a gradient whose log-norm scales with the square root of the
network depth and shows that the vanishing gradient problem can be mitigated by
increasing the width of the layers. Mathematical analyses and experimental
results using stochastic gradient descent to optimize tasks related to the
MNIST and TIMIT datasets are provided to support these claims. Equations for
the optimal matrix scaling are provided for the linear and ReLU cases.
Plagiarism Detection in Polyphonic Music using Monaural Signal Separation
Authors: Soham De, Indradyumna Roy, Tarunima Prabhakar, Kriti Suneja, Sourish Chaudhuri, Rita Singh, Bhiksha Raj
Date: 2015-02-27 00:00:00
Tasks: General Classification

Given the large number of new musical tracks released each year, automated
approaches to plagiarism detection are essential to help us track potential
violations of copyright. Most current approaches to plagiarism detection are
based on musical similarity measures, which typically ignore the issue of
polyphony in music. We present a novel feature space for audio derived from
compositional modelling techniques, commonly used in signal separation, that
provides a mechanism to account for polyphony without incurring an inordinate
amount of computational overhead. We employ this feature representation in
conjunction with traditional audio feature representations in a classification
framework which uses an ensemble of distance features to characterize pairs of
songs as being plagiarized or not. Our experiments on a database of about 3000
musical track pairs show that the new feature space characterization produces
significant improvements over standard baselines.