pwc-archive/papers-with-abstracts huggingface dataset:
Learning Concept Embeddings with Combined Human-Machine Expertise
Authors: Michael J. Wilber, Iljung S. Kwak, David Kriegman, Serge Belongie
Date: 2015-09-24 00:00:00
Proceeding: ICCV 2015 12

This paper presents our work on "SNaCK," a low-dimensional concept embedding
algorithm that combines human expertise with automatic machine similarity
kernels. Both parts are complimentary: human insight can capture relationships
that are not apparent from the object's visual similarity and the machine can
help relieve the human from having to exhaustively specify many constraints. We
show that our SNaCK embeddings are useful in several tasks: distinguishing
prime and nonprime numbers on MNIST, discovering labeling mistakes in the
Caltech UCSD Birds (CUB) dataset with the help of deep-learned features,
creating training datasets for bird classifiers, capturing subjective human
taste on a new dataset of 10,000 foods, and qualitatively exploring an
unstructured set of pictographic characters. Comparisons with the
state-of-the-art in these tasks show that SNaCK produces better concept
embeddings that require less human supervision than the leading methods.
An Innovative Approach for online Meta Search Engine Optimization
Authors: Jai Manral, Mohammed Alamgir Hossain
Date: 2015-09-28 00:00:00

This paper presents an approach to identify efficient techniques used in Web
Search Engine Optimization (SEO). Understanding SEO factors which can influence
page ranking in search engine is significant for webmasters who wish to attract
large number of users to their website. Different from previous relevant
research, in this study we developed an intelligent Meta search engine which
aggregates results from various search engines and ranks them based on several
important SEO parameters. The research tries to establish that using more SEO
parameters in ranking algorithms helps in retrieving better search results thus
increasing user satisfaction. Initial results generated from Meta search engine
outperformed existing search engines in terms of better retrieved search
results with high precision.
Efficient Discriminative Nonorthogonal Binary Subspace with its Application to Visual Tracking
Authors: Ang Li, Feng Tang, Yanwen Guo, Hai Tao
Date: 2015-09-28 00:00:00
Tasks: feature selection, Object, Object Tracking, Template Matching, Visual Tracking

One of the crucial problems in visual tracking is how the object is
represented. Conventional appearance-based trackers are using increasingly more
complex features in order to be robust. However, complex representations
typically not only require more computation for feature extraction, but also
make the state inference complicated. We show that with a careful feature
selection scheme, extremely simple yet discriminative features can be used for
robust object tracking. The central component of the proposed method is a
succinct and discriminative representation of the object using discriminative
non-orthogonal binary subspace (DNBS) which is spanned by Haar-like features.
The DNBS representation inherits the merits of the original NBS in that it
efficiently describes the object. It also incorporates the discriminative
information to distinguish foreground from background. However, the problem of
finding the DNBS bases from an over-complete dictionary is NP-hard. We propose
a greedy algorithm called discriminative optimized orthogonal matching pursuit
(D-OOMP) to solve this problem. An iterative formulation named iterative D-OOMP
is further developed to drastically reduce the redundant computation between
iterations and a hierarchical selection strategy is integrated for reducing the
search space of features. The proposed DNBS representation is applied to object
tracking through SSD-based template matching. We validate the effectiveness of
our method through extensive experiments on challenging videos with comparisons
against several state-of-the-art trackers and demonstrate its capability to
track objects in clutter and moving background.
Compressive spectral embedding: sidestepping the SVD
Authors: Dinesh Ramasamy, Upamanyu Madhow
Date: 2015-09-28 00:00:00
Proceeding: NeurIPS 2015 12
Tasks: Clustering, Dimensionality Reduction

Spectral embedding based on the Singular Value Decomposition (SVD) is a
widely used "preprocessing" step in many learning tasks, typically leading to
dimensionality reduction by projecting onto a number of dominant singular
vectors and rescaling the coordinate axes (by a predefined function of the
singular value). However, the number of such vectors required to capture
problem structure grows with problem size, and even partial SVD computation
becomes a bottleneck. In this paper, we propose a low-complexity it compressive
spectral embedding algorithm, which employs random projections and finite order
polynomial expansions to compute approximations to SVD-based embedding. For an
m times n matrix with T non-zeros, its time complexity is O((T+m+n)log(m+n)),
and the embedding dimension is O(log(m+n)), both of which are independent of
the number of singular vectors whose effect we wish to capture. To the best of
our knowledge, this is the first work to circumvent this dependence on the
number of singular vectors for general SVD-based embeddings. The key to
sidestepping the SVD is the observation that, for downstream inference tasks
such as clustering and classification, we are only interested in using the
resulting embedding to evaluate pairwise similarity metrics derived from the
euclidean norm, rather than capturing the effect of the underlying matrix on
arbitrary vectors as a partial SVD tries to do. Our numerical results on
network datasets demonstrate the efficacy of the proposed method, and motivate
further exploration of its application to large-scale inference tasks.
A Preliminary Study on the Learning Informativeness of Data Subsets
Authors: Simon Kaltenbacher, Nicholas H. Kirk, Dongheui Lee
Date: 2015-09-28 00:00:00
Tasks: Informativeness

Estimating the internal state of a robotic system is complex: this is
performed from multiple heterogeneous sensor inputs and knowledge sources.
Discretization of such inputs is done to capture saliences, represented as
symbolic information, which often presents structure and recurrence. As these
sequences are used to reason over complex scenarios, a more compact
representation would aid exactness of technical cognitive reasoning
capabilities, which are today constrained by computational complexity issues
and fallback to representational heuristics or human intervention. Such
problems need to be addressed to ensure timely and meaningful human-robot
interaction. Our work is towards understanding the variability of learning
informativeness when training on subsets of a given input dataset. This is in
view of reducing the training size while retaining the majority of the symbolic
learning potential. We prove the concept on human-written texts, and conjecture
this work will reduce training data size of sequential instructions, while
preserving semantic relations, when gathering information from large remote
sources.
Vision System and Depth Processing for DRC-HUBO+
Authors: Inwook Shim, Seunghak Shin, Yunsu Bok, Kyungdon Joo, Dong-Geol Choi, Joon-Young Lee, Jaesik Park, Jun-Ho Oh, In So Kweon
Date: 2015-09-21 00:00:00
Tasks: object-detection, Object Detection, Pose Estimation

This paper presents a vision system and a depth processing algorithm for
DRC-HUBO+, the winner of the DRC finals 2015. Our system is designed to
reliably capture 3D information of a scene and objects robust to challenging
environment conditions. We also propose a depth-map upsampling method that
produces an outliers-free depth map by explicitly handling depth outliers. Our
system is suitable for an interactive robot with real-world that requires
accurate object detection and pose estimation. We evaluate our depth processing
algorithm over state-of-the-art algorithms on several synthetic and real-world
datasets.
Theoretical Analysis of the Optimal Free Responses of Graph-Based SFA for the Design of Training Graphs
Authors: Alberto N. Escalante-B., Laurenz Wiskott
Date: 2015-09-28 00:00:00
Tasks: regression, Time Series, Time Series Analysis

Slow feature analysis (SFA) is an unsupervised learning algorithm that
extracts slowly varying features from a time series. Graph-based SFA (GSFA) is
a supervised extension that can solve regression problems if followed by a
post-processing regression algorithm. A training graph specifies arbitrary
connections between the training samples. The connections in current graphs,
however, only depend on the rank of the involved labels. Exploiting the exact
label values makes further improvements in estimation accuracy possible.
  In this article, we propose the exact label learning (ELL) method to create a
graph that codes the desired label explicitly, so that GSFA is able to extract
a normalized version of it directly. The ELL method is used for three tasks:
(1) We estimate gender from artificial images of human faces (regression) and
show the advantage of coding additional labels, particularly skin color. (2) We
analyze two existing graphs for regression. (3) We extract compact
discriminative features to classify traffic sign images. When the number of
output features is limited, a higher classification rate is obtained compared
to a graph equivalent to nonlinear Fisher discriminant analysis. The method is
versatile, directly supports multiple labels, and provides higher accuracy
compared to current graphs for the problems considered.
What Properties are Desirable from an Electron Microscopy Segmentation Algorithm
Authors: Toufiq Parag
Date: 2015-03-18 00:00:00
Tasks: Active Learning, Segmentation, Superpixels

The prospect of neural reconstruction from Electron Microscopy (EM) images
has been elucidated by the automatic segmentation algorithms. Although
segmentation algorithms eliminate the necessity of tracing the neurons by hand,
significant manual effort is still essential for correcting the mistakes they
make. A considerable amount of human labor is also required for annotating
groundtruth volumes for training the classifiers of a segmentation framework.
It is critically important to diminish the dependence on human interaction in
the overall reconstruction system. This study proposes a novel classifier
training algorithm for EM segmentation aimed to reduce the amount of manual
effort demanded by the groundtruth annotation and error refinement tasks.
Instead of using an exhaustive pixel level groundtruth, an active learning
algorithm is proposed for sparse labeling of pixel and boundaries of
superpixels. Because over-segmentation errors are in general more tolerable and
easier to correct than the under-segmentation errors, our algorithm is designed
to prioritize minimization of false-merges over false-split mistakes. Our
experiments on both 2D and 3D data suggest that the proposed method yields
segmentation outputs that are more amenable to neural reconstruction than those
of existing methods.
Neural NILM: Deep Neural Networks Applied to Energy Disaggregation
Authors: Jack Kelly, William Knottenbelt
Date: 2015-07-23 00:00:00
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), Denoising, General Classification, image-classification, Image Classification, speech-recognition, Speech Recognition

Energy disaggregation estimates appliance-by-appliance electricity
consumption from a single meter that measures the whole home's electricity
demand. Recently, deep neural networks have driven remarkable improvements in
classification performance in neighbouring machine learning fields such as
image classification and automatic speech recognition. In this paper, we adapt
three deep neural network architectures to energy disaggregation: 1) a form of
recurrent neural network called `long short-term memory' (LSTM); 2) denoising
autoencoders; and 3) a network which regresses the start time, end time and
average power demand of each appliance activation. We use seven metrics to test
the performance of these algorithms on real aggregate power data from five
appliances. Tests are performed against a house not seen during training and
against houses seen during training. We find that all three neural nets achieve
better F1 scores (averaged over all five appliances) than either combinatorial
optimisation or factorial hidden Markov models and that our neural net
algorithms generalise well to an unseen house.
Analysis of Intelligent Classifiers and Enhancing the Detection Accuracy for Intrusion Detection System
Authors: Mohanad Albayati, Biju Issac
Date: 2015-09-28 00:00:00
Tasks: General Classification, Intrusion Detection

In this paper we discuss and analyze some of the intelligent classifiers
which allows for automatic detection and classification of networks attacks for
any intrusion detection system. We will proceed initially with their analysis
using the WEKA software to work with the classifiers on a well-known IDS
(Intrusion Detection Systems) dataset like NSL-KDD dataset. The NSL-KDD dataset
of network attacks was created in a military network by MIT Lincoln Labs. Then
we will discuss and experiment some of the hybrid AI (Artificial Intelligence)
classifiers that can be used for IDS, and finally we developed a Java software
with three most efficient classifiers and compared it with other options. The
outputs would show the detection accuracy and efficiency of the single and
combined classifiers used.
A Comprehensive Study On The Applications Of Machine Learning For Diagnosis Of Cancer
Authors: Mohnish Chakravarti, Tanay Kothari
Date: 2015-05-06 00:00:00
Tasks: BIG-bench Machine Learning, Diagnostic

Collectively, lung cancer, breast cancer and melanoma was diagnosed in over
535,340 people out of which, 209,400 deaths were reported [13]. It is estimated
that over 600,000 people will be diagnosed with these forms of cancer in 2015.
Most of the deaths from lung cancer, breast cancer and melanoma result due to
late detection. All of these cancers, if detected early, are 100% curable. In
this study, we develop and evaluate algorithms to diagnose Breast cancer,
Melanoma, and Lung cancer. In the first part of the study, we employed a
normalised Gradient Descent and an Artificial Neural Network to diagnose breast
cancer with an overall accuracy of 91% and 95% respectively. In the second part
of the study, an artificial neural network coupled with image processing and
analysis algorithms was employed to achieve an overall accuracy of 93% A naive
mobile based application that allowed people to take diagnostic tests on their
phones was developed. Finally, a Support Vector Machine algorithm incorporating
image processing and image analysis algorithms was developed to diagnose lung
cancer with an accuracy of 94%. All of the aforementioned systems had very low
false positive and false negative rates. We are developing an online network
that incorporates all of these systems and allows people to collaborate
globally.
Fast Non-local Stereo Matching based on Hierarchical Disparity Prediction
Authors: Xuan Luo, Xuejiao Bai, Shuo Li, Hongtao Lu, Sei-ichiro Kamata
Date: 2015-09-28 00:00:00
Tasks: Stereo Matching, Stereo Matching Hand

Stereo matching is the key step in estimating depth from two or more images.
Recently, some tree-based non-local stereo matching methods have been proposed,
which achieved state-of-the-art performance. The algorithms employed some tree
structures to aggregate cost and thus improved the performance and reduced the
coputation load of the stereo matching. However, the computational complexity
of these tree-based algorithms is still high because they search over the
entire disparity range. In addition, the extreme greediness of the minimum
spanning tree (MST) causes the poor performance in large areas with similar
colors but varying disparities. In this paper, we propose an efficient stereo
matching method using a hierarchical disparity prediction (HDP) framework to
dramatically reduce the disparity search range so as to speed up the tree-based
non-local stereo methods. Our disparity prediction scheme works on a graph
pyramid derived from an image whose disparity to be estimated. We utilize the
disparity of a upper graph to predict a small disparity range for the lower
graph. Some independent disparity trees (DT) are generated to form a disparity
prediction forest (HDPF) over which the cost aggregation is made. When combined
with the state-of-the-art tree-based methods, our scheme not only dramatically
speeds up the original methods but also improves their performance by
alleviating the second drawback of the tree-based methods. This is partially
because our DTs overcome the extreme greediness of the MST. Extensive
experimental results on some benchmark datasets demonstrate the effectiveness
and efficiency of our framework. For example, the segment-tree based stereo
matching becomes about 25.57 times faster and 2.2% more accurate over the
Middlebury 2006 full-size dataset.
Robust video object tracking using particle filter with likelihood based feature fusion and adaptive template updating
Authors: Yi Dai, Bin Liu
Date: 2015-09-28 00:00:00
Tasks: Object, Object Tracking, Video Object Tracking

A robust algorithm solution is proposed for tracking an object in complex
video scenes. In this solution, the bootstrap particle filter (PF) is
initialized by an object detector, which models the time-evolving background of
the video signal by an adaptive Gaussian mixture. The motion of the object is
expressed by a Markov model, which defines the state transition prior. The
color and texture features are used to represent the object, and a marginal
likelihood based feature fusion approach is proposed. A corresponding object
template model updating procedure is developed to account for possible scale
changes of the object in the tracking process. Experimental results show that
our algorithm beats several existing alternatives in tackling challenging
scenarios in video tracking tasks.
TransA: An Adaptive Approach for Knowledge Graph Embedding
Authors: Han Xiao, Minlie Huang, Yu Hao, Xiaoyan Zhu
Date: 2015-09-18 00:00:00
Tasks: Graph Embedding, Knowledge Graph Embedding, Metric Learning, Translation

Knowledge representation is a major topic in AI, and many studies attempt to
represent entities and relations of knowledge base in a continuous vector
space. Among these attempts, translation-based methods build entity and
relation vectors by minimizing the translation loss from a head entity to a
tail one. In spite of the success of these methods, translation-based methods
also suffer from the oversimplified loss metric, and are not competitive enough
to model various and complex entities/relations in knowledge bases. To address
this issue, we propose \textbf{TransA}, an adaptive metric approach for
embedding, utilizing the metric learning ideas to provide a more flexible
embedding method. Experiments are conducted on the benchmark datasets and our
proposed method makes significant and consistent improvements over the
state-of-the-art baselines.
An intelligent extension of Variable Neighbourhood Search for labelling graph problems
Authors: Sergio Consoli, Josè Andrès Moreno Pèrez
Date: 2015-09-27 00:00:00

In this paper we describe an extension of the Variable Neighbourhood Search
(VNS) which integrates the basic VNS with other complementary approaches from
machine learning, statistics and experimental algorithmic, in order to produce
high-quality performance and to completely automate the resulting optimization
strategy. The resulting intelligent VNS has been successfully applied to a
couple of optimization problems where the solution space consists of the
subsets of a finite reference set. These problems are the labelled spanning
tree and forest problems that are formulated on an undirected labelled graph; a
graph where each edge has a label in a finite set of labels L. The problems
consist on selecting the subset of labels such that the subgraph generated by
these labels has an optimal spanning tree or forest, respectively. These
problems have several applications in the real-world, where one aims to ensure
connectivity by means of homogeneous connections.
Multi-view Convolutional Neural Networks for 3D Shape Recognition
Authors: Hang Su, Subhransu Maji, Evangelos Kalogerakis, Erik Learned-Miller
Date: 2015-05-05 00:00:00
Proceeding: ICCV 2015 12
Tasks: 3D Point Cloud Classification, 3D Shape Recognition

A longstanding question in computer vision concerns the representation of 3D
shapes for recognition: should 3D shapes be represented with descriptors
operating on their native 3D formats, such as voxel grid or polygon mesh, or
can they be effectively represented with view-based descriptors? We address
this question in the context of learning to recognize 3D shapes from a
collection of their rendered views on 2D images. We first present a standard
CNN architecture trained to recognize the shapes' rendered views independently
of each other, and show that a 3D shape can be recognized even from a single
view at an accuracy far higher than using state-of-the-art 3D shape
descriptors. Recognition rates further increase when multiple views of the
shapes are provided. In addition, we present a novel CNN architecture that
combines information from multiple views of a 3D shape into a single and
compact shape descriptor offering even better recognition performance. The same
architecture can be applied to accurately recognize human hand-drawn sketches
of shapes. We conclude that a collection of 2D views can be highly informative
for 3D shape recognition and is amenable to emerging CNN architectures and
their derivatives.
Feature Selection for classification of hyperspectral data by minimizing a tight bound on the VC dimension
Authors: Phool Preet, Sanjit Singh Batra, Jayadeva
Date: 2015-09-27 00:00:00
Tasks: Classification, feature selection, General Classification

Hyperspectral data consists of large number of features which require
sophisticated analysis to be extracted. A popular approach to reduce
computational cost, facilitate information representation and accelerate
knowledge discovery is to eliminate bands that do not improve the
classification and analysis methods being applied. In particular, algorithms
that perform band elimination should be designed to take advantage of the
specifics of the classification method being used. This paper employs a
recently proposed filter-feature-selection algorithm based on minimizing a
tight bound on the VC dimension. We have successfully applied this algorithm to
determine a reasonable subset of bands without any user-defined stopping
criteria on widely used hyperspectral images and demonstrate that this method
outperforms state-of-the-art methods in terms of both sparsity of feature set
as well as accuracy of classification.\end{abstract}
Fast R-CNN
Authors: Ross Girshick
Date: 2015-04-30 00:00:00
Proceeding: ICCV 2015 12
Tasks: Object, Object Detection

This paper proposes a Fast Region-based Convolutional Network method (Fast
R-CNN) for object detection. Fast R-CNN builds on previous work to efficiently
classify object proposals using deep convolutional networks. Compared to
previous work, Fast R-CNN employs several innovations to improve training and
testing speed while also increasing detection accuracy. Fast R-CNN trains the
very deep VGG16 network 9x faster than R-CNN, is 213x faster at test-time, and
achieves a higher mAP on PASCAL VOC 2012. Compared to SPPnet, Fast R-CNN trains
VGG16 3x faster, tests 10x faster, and is more accurate. Fast R-CNN is
implemented in Python and C++ (using Caffe) and is available under the
open-source MIT License at https://github.com/rbgirshick/fast-rcnn.