pwc-archive/papers-with-abstracts huggingface dataset:
On the use of convolutional neural networks for robust classification of multiple fingerprint captures
Authors: Daniel Peralta, Isaac Triguero, Salvador García, Yvan Saeys, Jose M. Benitez, Francisco Herrera
Date: 2017-03-21 00:00:00
Tasks: Classification, General Classification, Robust classification

Fingerprint classification is one of the most common approaches to accelerate
the identification in large databases of fingerprints. Fingerprints are grouped
into disjoint classes, so that an input fingerprint is compared only with those
belonging to the predicted class, reducing the penetration rate of the search.
The classification procedure usually starts by the extraction of features from
the fingerprint image, frequently based on visual characteristics. In this
work, we propose an approach to fingerprint classification using convolutional
neural networks, which avoid the necessity of an explicit feature extraction
process by incorporating the image processing within the training of the
classifier. Furthermore, such an approach is able to predict a class even for
low-quality fingerprints that are rejected by commonly used algorithms, such as
FingerCode. The study gives special importance to the robustness of the
classification for different impressions of the same fingerprint, aiming to
minimize the penetration in the database. In our experiments, convolutional
neural networks yielded better accuracy and penetration rate than
state-of-the-art classifiers based on explicit feature extraction. The tested
networks also improved on the runtime, as a result of the joint optimization of
both feature extraction and classification.
Tuning Modular Networks with Weighted Losses for Hand-Eye Coordination
Authors: Fangyi Zhang, Jürgen Leitner, Michael Milford, Peter I. Corke
Date: 2017-05-15 00:00:00

This paper introduces an end-to-end fine-tuning method to improve hand-eye
coordination in modular deep visuo-motor policies (modular networks) where each
module is trained independently. Benefiting from weighted losses, the
fine-tuning method significantly improves the performance of the policies for a
robotic planar reaching task.
More is Less: A More Complicated Network with Less Inference Complexity
Authors: Xuanyi Dong, Junshi Huang, Yi Yang, Shuicheng Yan
Date: 2017-03-25 00:00:00
Proceeding: CVPR 2017 7

In this paper, we present a novel and general network structure towards
accelerating the inference process of convolutional neural networks, which is
more complicated in network structure yet with less inference complexity. The
core idea is to equip each original convolutional layer with another low-cost
collaborative layer (LCCL), and the element-wise multiplication of the ReLU
outputs of these two parallel layers produces the layer-wise output. The
combined layer is potentially more discriminative than the original
convolutional layer, and its inference is faster for two reasons: 1) the zero
cells of the LCCL feature maps will remain zero after element-wise
multiplication, and thus it is safe to skip the calculation of the
corresponding high-cost convolution in the original convolutional layer, 2)
LCCL is very fast if it is implemented as a 1*1 convolution or only a single
filter shared by all channels. Extensive experiments on the CIFAR-10, CIFAR-100
and ILSCRC-2012 benchmarks show that our proposed network structure can
accelerate the inference process by 32\% on average with negligible performance
drop.
Semi-Supervised Learning via Sparse Label Propagation
Authors: Alexander Jung, Alfred O. Hero III, Alexandru Mara, Saeed Jahromi
Date: 2016-12-05 00:00:00
Tasks: compressed sensing

This work proposes a novel method for semi-supervised learning from partially
labeled massive network-structured datasets, i.e., big data over networks. We
model the underlying hypothesis, which relates data points to labels, as a
graph signal, defined over some graph (network) structure intrinsic to the
dataset. Following the key principle of supervised learning, i.e., similar
inputs yield similar outputs, we require the graph signals induced by labels to
have small total variation. Accordingly, we formulate the problem of learning
the labels of data points as a non-smooth convex optimization problem which
amounts to balancing between the empirical loss, i.e., the discrepancy with
some partially available label information, and the smoothness quantified by
the total variation of the learned graph signal. We solve this optimization
problem by appealing to a recently proposed preconditioned variant of the
popular primal-dual method by Pock and Chambolle, which results in a sparse
label propagation algorithm. This learning algorithm allows for a highly
scalable implementation as message passing over the underlying data graph. By
applying concepts of compressed sensing to the learning problem, we are also
able to provide a transparent sufficient condition on the underlying network
structure such that accurate learning of the labels is possible. We also
present an implementation of the message passing formulation allows for a
highly scalable implementation in big data frameworks.
Learning Semantics for Image Annotation
Authors: Amara Tariq, Hassan Foroosh
Date: 2017-05-15 00:00:00
Tasks: Image Retrieval, Retrieval

Image search and retrieval engines rely heavily on textual annotation in
order to match word queries to a set of candidate images. A system that can
automatically annotate images with meaningful text can be highly beneficial for
such engines. Currently, the approaches to develop such systems try to
establish relationships between keywords and visual features of images. In this
paper, We make three main contributions to this area: (i) We transform this
problem from the low-level keyword space to the high-level semantics space that
we refer to as the "{\em image theme}", (ii) Instead of treating each possible
keyword independently, we use latent Dirichlet allocation to learn image themes
from the associated texts in a training phase. Images are then annotated with
image themes rather than keywords, using a modified continuous relevance model,
which takes into account the spatial coherence and the visual continuity among
images of common theme. (iii) To achieve more coherent annotations among images
of common theme, we have integrated ConceptNet in learning the semantics of
images, and hence augment image descriptions beyond annotations provided by
humans. Images are thus further annotated by a few most significant words of
the prominent image theme. Our extensive experiments show that a coherent
theme-based image annotation using high-level semantics results in improved
precision and recall as compared with equivalent classical keyword annotation
systems.
Towards Automated Network Mitigation Analysis (extended)
Authors: Patrick Speicher, Marcel Steinmetz, Jörg Hoffmann, Michael Backes, Robert Künnemann
Date: 2017-05-15 00:00:00

Penetration testing is a well-established practical concept for the
identification of potentially exploitable security weaknesses and an important
component of a security audit. Providing a holistic security assessment for
networks consisting of several hundreds hosts is hardly feasible though without
some sort of mechanization. Mitigation, prioritizing counter-measures subject
to a given budget, currently lacks a solid theoretical understanding and is
hence more art than science. In this work, we propose the first approach for
conducting comprehensive what-if analyses in order to reason about mitigation
in a conceptually well-founded manner. To evaluate and compare mitigation
strategies, we use simulated penetration testing, i.e., automated
attack-finding, based on a network model to which a subset of a given set of
mitigation actions, e.g., changes to the network topology, system updates,
configuration changes etc. is applied. Using Stackelberg planning, we determine
optimal combinations that minimize the maximal attacker success (similar to a
Stackelberg game), and thus provide a well-founded basis for a holistic
mitigation strategy. We show that these Stackelberg planning models can largely
be derived from network scan, public vulnerability databases and manual
inspection with various degrees of automation and detail, and we simulate
mitigation analysis on networks of different size and vulnerability.
Active Learning for Graph Embedding
Authors: Hongyun Cai, Vincent W. Zheng, Kevin Chen-Chuan Chang
Date: 2017-05-15 00:00:00
Tasks: Active Learning, Graph Embedding, Node Classification

Graph embedding provides an efficient solution for graph analysis by
converting the graph into a low-dimensional space which preserves the structure
information. In contrast to the graph structure data, the i.i.d. node embedding
can be processed efficiently in terms of both time and space. Current
semi-supervised graph embedding algorithms assume the labelled nodes are given,
which may not be always true in the real world. While manually label all
training data is inapplicable, how to select the subset of training data to
label so as to maximize the graph analysis task performance is of great
importance. This motivates our proposed active graph embedding (AGE) framework,
in which we design a general active learning query strategy for any
semi-supervised graph embedding algorithm. AGE selects the most informative
nodes as the training labelled nodes based on the graphical information (i.e.,
node centrality) as well as the learnt node embedding (i.e., node
classification uncertainty and node embedding representativeness). Different
query criteria are combined with the time-sensitive parameters which shift the
focus from graph based query criteria to embedding based criteria as the
learning progresses. Experiments have been conducted on three public data sets
and the results verified the effectiveness of each component of our query
strategy and the power of combining them using time-sensitive parameters. Our
code is available online at: https://github.com/vwz/AGE.
Single Image Super-Resolution Using Multi-Scale Convolutional Neural Network
Authors: Xiaoyi Jia, Xiangmin Xu, Bolun Cai, Kailing Guo
Date: 2017-05-15 00:00:00
Tasks: Image Super-Resolution, Super-Resolution

Methods based on convolutional neural network (CNN) have demonstrated
tremendous improvements on single image super-resolution. However, the previous
methods mainly restore images from one single area in the low resolution (LR)
input, which limits the flexibility of models to infer various scales of
details for high resolution (HR) output. Moreover, most of them train a
specific model for each up-scale factor. In this paper, we propose a
multi-scale super resolution (MSSR) network. Our network consists of
multi-scale paths to make the HR inference, which can learn to synthesize
features from different scales. This property helps reconstruct various kinds
of regions in HR images. In addition, only one single model is needed for
multiple up-scale factors, which is more efficient without loss of restoration
quality. Experiments on four public datasets demonstrate that the proposed
method achieved state-of-the-art performance with fast speed.
DeepGO: Predicting protein functions from sequence and interactions using a deep ontology-aware classifier
Authors: Maxat Kulmanov, Mohammed Asif Khan, Robert Hoehndorf
Date: 2017-05-15 00:00:00

A large number of protein sequences are becoming available through the
application of novel high-throughput sequencing technologies. Experimental
functional characterization of these proteins is time-consuming and expensive,
and is often only done rigorously for few selected model organisms.
Computational function prediction approaches have been suggested to fill this
gap. The functions of proteins are classified using the Gene Ontology (GO),
which contains over 40,000 classes. Additionally, proteins have multiple
functions, making function prediction a large-scale, multi-class, multi-label
problem.
  We have developed a novel method to predict protein function from sequence.
We use deep learning to learn features from protein sequences as well as a
cross-species protein-protein interaction network. Our approach specifically
outputs information in the structure of the GO and utilizes the dependencies
between GO classes as background information to construct a deep learning
model. We evaluate our method using the standards established by the
Computational Assessment of Function Annotation (CAFA) and demonstrate a
significant improvement over baseline methods such as BLAST, with significant
improvement for predicting cellular locations.
Learning to Discover Cross-Domain Relations with Generative Adversarial Networks
Authors: Taeksoo Kim, Moonsu Cha, Hyunsoo Kim, Jung Kwon Lee, Jiwon Kim
Date: 2017-03-15 00:00:00
Proceeding: ICML 2017 8

While humans easily recognize relations between data from different domains
without any supervision, learning to automatically discover them is in general
very challenging and needs many ground-truth pairs that illustrate the
relations. To avoid costly pairing, we address the task of discovering
cross-domain relations given unpaired data. We propose a method based on
generative adversarial networks that learns to discover relations between
different domains (DiscoGAN). Using the discovered relations, our proposed
network successfully transfers style from one domain to another while
preserving key attributes such as orientation and face identity. Source code
for official implementation is publicly available
https://github.com/SKTBrain/DiscoGAN
Speeding up Convolutional Neural Networks By Exploiting the Sparsity of Rectifier Units
Authors: Shaohuai Shi, Xiaowen Chu
Date: 2017-04-25 00:00:00

Rectifier neuron units (ReLUs) have been widely used in deep convolutional
networks. An ReLU converts negative values to zeros, and does not change
positive values, which leads to a high sparsity of neurons. In this work, we
first examine the sparsity of the outputs of ReLUs in some popular deep
convolutional architectures. And then we use the sparsity property of ReLUs to
accelerate the calculation of convolution by skipping calculations of
zero-valued neurons. The proposed sparse convolution algorithm achieves some
speedup improvements on CPUs compared to the traditional matrix-matrix
multiplication algorithm for convolution when the sparsity is not less than
0.9.
Sequential Matching Network: A New Architecture for Multi-turn Response Selection in Retrieval-based Chatbots
Authors: Yu Wu, Wei Wu, Chen Xing, Ming Zhou, Zhoujun Li
Date: 2016-12-06 00:00:00
Proceeding: ACL 2017 7
Tasks: Conversational Response Selection, Retrieval

We study response selection for multi-turn conversation in retrieval-based
chatbots. Existing work either concatenates utterances in context or matches a
response with a highly abstract context vector finally, which may lose
relationships among utterances or important contextual information. We propose
a sequential matching network (SMN) to address both problems. SMN first matches
a response with each utterance in the context on multiple levels of
granularity, and distills important matching information from each pair as a
vector with convolution and pooling operations. The vectors are then
accumulated in a chronological order through a recurrent neural network (RNN)
which models relationships among utterances. The final matching score is
calculated with the hidden states of the RNN. An empirical study on two public
data sets shows that SMN can significantly outperform state-of-the-art methods
for response selection in multi-turn conversation.
Winning on the Merits: The Joint Effects of Content and Style on Debate Outcomes
Authors: Lu Wang, Nick Beauchamp, Sarah Shugars, Kechen Qin
Date: 2017-05-15 00:00:00
Proceeding: TACL 2017 1

Debate and deliberation play essential roles in politics and government, but
most models presume that debates are won mainly via superior style or agenda
control. Ideally, however, debates would be won on the merits, as a function of
which side has the stronger arguments. We propose a predictive model of debate
that estimates the effects of linguistic features and the latent persuasive
strengths of different topics, as well as the interactions between the two.
Using a dataset of 118 Oxford-style debates, our model's combination of content
(as latent topics) and style (as linguistic features) allows us to predict
audience-adjudicated winners with 74% accuracy, significantly outperforming
linguistic features alone (66%). Our model finds that winning sides employ
stronger arguments, and allows us to identify the linguistic features
associated with strong or weak arguments.
Joint Modeling of Content and Discourse Relations in Dialogues
Authors: Kechen Qin, Lu Wang, Joseph Kim
Date: 2017-05-14 00:00:00
Proceeding: ACL 2017 7
Tasks: Relation Prediction

We present a joint modeling approach to identify salient discussion points in
spoken meetings as well as to label the discourse relations between speaker
turns. A variation of our model is also discussed when discourse relations are
treated as latent variables. Experimental results on two popular meeting
corpora show that our joint model can outperform state-of-the-art approaches
for both phrase-based content selection and discourse relation prediction
tasks. We also evaluate our model on predicting the consistency among team
members' understanding of their group decisions. Classifiers trained with
features constructed from our model achieve significant better predictive
performance than the state-of-the-art.
A Correspondence Relaxation Approach for 3D Shape Reconstruction
Authors: Yong Khoo
Date: 2017-05-14 00:00:00
Tasks: 3D Reconstruction, 3D Shape Reconstruction, Object

This paper presents a new method for 3D shape reconstruction based on two
existing methods. A 3D reconstruction from a single photograph is introduced by
both papers: the first one uses a photograph and a set of existing 3D model to
generate the 3D object in the photograph, while the second one uses a
photograph and a selected similar model to create the 3D object in the
photograph. According to their difference, we propose a relaxation based method
for more accurate correspondence establishment and shape recovery. The
experiment demonstrates promising results compared to the state-of-the-art work
on 3D shape estimation.
Adaptive Accelerated Gradient Converging Methods under Holderian Error Bound Condition
Authors: Mingrui Liu, Tianbao Yang
Date: 2016-11-23 00:00:00

Recent studies have shown that proximal gradient (PG) method and accelerated
gradient method (APG) with restarting can enjoy a linear convergence under a
weaker condition than strong convexity, namely a quadratic growth condition
(QGC). However, the faster convergence of restarting APG method relies on the
potentially unknown constant in QGC to appropriately restart APG, which
restricts its applicability. We address this issue by developing a novel
adaptive gradient converging methods, i.e., leveraging the magnitude of
proximal gradient as a criterion for restart and termination. Our analysis
extends to a much more general condition beyond the QGC, namely the
H\"{o}lderian error bound (HEB) condition. {\it The key technique} for our
development is a novel synthesis of {\it adaptive regularization and a
conditional restarting scheme}, which extends previous work focusing on
strongly convex problems to a much broader family of problems. Furthermore, we
demonstrate that our results have important implication and applications in
machine learning: (i) if the objective function is coercive and semi-algebraic,
PG's convergence speed is essentially $o(\frac{1}{t})$, where $t$ is the total
number of iterations; (ii) if the objective function consists of an $\ell_1$,
$\ell_\infty$, $\ell_{1,\infty}$, or huber norm regularization and a convex
smooth piecewise quadratic loss (e.g., squares loss, squared hinge loss and
huber loss), the proposed algorithm is parameter-free and enjoys a {\it faster
linear convergence} than PG without any other assumptions (e.g., restricted
eigen-value condition). It is notable that our linear convergence results for
the aforementioned problems are global instead of local. To the best of our
knowledge, these improved results are the first shown in this work.
Musical Instrument Recognition Using Their Distinctive Characteristics in Artificial Neural Networks
Authors: Babak Toghiani-Rizi, Marcus Windmark
Date: 2017-05-14 00:00:00
Tasks: Instrument Recognition

In this study an Artificial Neural Network was trained to classify musical
instruments, using audio samples transformed to the frequency domain. Different
features of the sound, in both time and frequency domain, were analyzed and
compared in relation to how much information that could be derived from that
limited data. The study concluded that in comparison with the base experiment,
that had an accuracy of 93.5%, using the attack only resulted in 80.2% and the
initial 100 Hz in 64.2%.
Machine learning methods for multimedia information retrieval
Authors: Bálint Zoltán Daróczy
Date: 2017-05-14 00:00:00
Tasks: BIG-bench Machine Learning, Classification, General Classification, Image Retrieval, Information Retrieval, Retrieval, Session-Based Recommendations, Time Series Analysis

In this thesis we examined several multimodal feature extraction and learning
methods for retrieval and classification purposes. We reread briefly some
theoretical results of learning in Section 2 and reviewed several generative
and discriminative models in Section 3 while we described the similarity kernel
in Section 4. We examined different aspects of the multimodal image retrieval
and classification in Section 5 and suggested methods for identifying quality
assessments of Web documents in Section 6. In our last problem we proposed
similarity kernel for time-series based classification. The experiments were
carried over publicly available datasets and source codes for the most
essential parts are either open source or released. Since the used similarity
graphs (Section 4.2) are greatly constrained for computational purposes, we
would like to continue work with more complex, evolving and capable graphs and
apply for different problems such as capturing the rapid change in the
distribution (e.g. session based recommendation) or complex graphs of the
literature work. The similarity kernel with the proper metrics reaches and in
many cases improves over the state-of-the-art. Hence we may conclude generative
models based on instance similarities with multiple modes is a generally
applicable model for classification and regression tasks ranging over various
domains, including but not limited to the ones presented in this thesis. More
generally, the Fisher kernel is not only unique in many ways but one of the
most powerful kernel functions. Therefore we may exploit the Fisher kernel in
the future over widely used generative models, such as Boltzmann Machines
[Hinton et al., 1984], a particular subset, the Restricted Boltzmann Machines
and Deep Belief Networks [Hinton et al., 2006]), Latent Dirichlet Allocation
[Blei et al., 2003] or Hidden Markov Models [Baum and Petrie, 1966] to name a
few.