pwc-archive/papers-with-abstracts huggingface dataset:
From Depth Data to Head Pose Estimation: a Siamese approach
Authors: Marco Venturelli, Guido Borghi, Roberto Vezzani, Rita Cucchiara
Date: 2017-03-10 00:00:00
Tasks: Driver Attention Monitoring, Head Pose Estimation, Pose Estimation, Position, regression

The correct estimation of the head pose is a problem of the great importance
for many applications. For instance, it is an enabling technology in automotive
for driver attention monitoring. In this paper, we tackle the pose estimation
problem through a deep learning network working in regression manner.
Traditional methods usually rely on visual facial features, such as facial
landmarks or nose tip position. In contrast, we exploit a Convolutional Neural
Network (CNN) to perform head pose estimation directly from depth data. We
exploit a Siamese architecture and we propose a novel loss function to improve
the learning of the regression network layer. The system has been tested on two
public datasets, Biwi Kinect Head Pose and ICT-3DHP database. The reported
results demonstrate the improvement in accuracy with respect to current
state-of-the-art approaches and the real time capabilities of the overall
framework.
Automatic Classification of Cancerous Tissue in Laserendomicroscopy Images of the Oral Cavity using Deep Learning
Authors: Marc Aubreville, Christian Knipfer, Nicolai Oetter, Christian Jaremenko, Erik Rodner, Joachim Denzler, Christopher Bohr, Helmut Neumann, Florian Stelzle, Andreas Maier
Date: 2017-03-05 00:00:00
Tasks: General Classification, Specificity

Oral Squamous Cell Carcinoma (OSCC) is a common type of cancer of the oral
epithelium. Despite their high impact on mortality, sufficient screening
methods for early diagnosis of OSCC often lack accuracy and thus OSCCs are
mostly diagnosed at a late stage. Early detection and accurate outline
estimation of OSCCs would lead to a better curative outcome and an reduction in
recurrence rates after surgical treatment.
  Confocal Laser Endomicroscopy (CLE) records sub-surface micro-anatomical
images for in vivo cell structure analysis. Recent CLE studies showed great
prospects for a reliable, real-time ultrastructural imaging of OSCC in situ.
  We present and evaluate a novel automatic approach for a highly accurate OSCC
diagnosis using deep learning technologies on CLE images. The method is
compared against textural feature-based machine learning approaches that
represent the current state of the art.
  For this work, CLE image sequences (7894 images) from patients diagnosed with
OSCC were obtained from 4 specific locations in the oral cavity, including the
OSCC lesion. The present approach is found to outperform the state of the art
in CLE image recognition with an area under the curve (AUC) of 0.96 and a mean
accuracy of 88.3% (sensitivity 86.6%, specificity 90%).
A Unifying View of Explicit and Implicit Feature Maps of Graph Kernels
Authors: Nils M. Kriege, Marion Neumann, Christopher Morris, Kristian Kersting, Petra Mutzel
Date: 2017-03-02 00:00:00
Tasks: Diversity

Non-linear kernel methods can be approximated by fast linear ones using suitable explicit feature maps allowing their application to large scale problems. We investigate how convolution kernels for structured data are composed from base kernels and construct corresponding feature maps. On this basis we propose exact and approximative feature maps for widely used graph kernels based on the kernel trick. We analyze for which kernels and graph properties computation by explicit feature maps is feasible and actually more efficient. In particular, we derive approximative, explicit feature maps for state-of-the-art kernels supporting real-valued attributes including the GraphHopper and graph invariant kernels. In extensive experiments we show that our approaches often achieve a classification accuracy close to the exact methods based on the kernel trick, but require only a fraction of their running time. Moreover, we propose and analyze algorithms for computing random walk, shortest-path and subgraph matching kernels by explicit and implicit feature maps. Our theoretical results are confirmed experimentally by observing a phase transition when comparing running time with respect to label diversity, walk lengths and subgraph size, respectively.
NetSpam: a Network-based Spam Detection Framework for Reviews in Online Social Media
Authors: Saeedreza Shehnepoor, Mostafa Salehi, Reza Farahbakhsh, Noel Crespi
Date: 2017-03-10 00:00:00
Tasks: Spam detection

Nowadays, a big part of people rely on available content in social media in
their decisions (e.g. reviews and feedback on a topic or product). The
possibility that anybody can leave a review provide a golden opportunity for
spammers to write spam reviews about products and services for different
interests. Identifying these spammers and the spam content is a hot topic of
research and although a considerable number of studies have been done recently
toward this end, but so far the methodologies put forth still barely detect
spam reviews, and none of them show the importance of each extracted feature
type. In this study, we propose a novel framework, named NetSpam, which
utilizes spam features for modeling review datasets as heterogeneous
information networks to map spam detection procedure into a classification
problem in such networks. Using the importance of spam features help us to
obtain better results in terms of different metrics experimented on real-world
review datasets from Yelp and Amazon websites. The results show that NetSpam
outperforms the existing methods and among four categories of features;
including review-behavioral, user-behavioral, reviewlinguistic,
user-linguistic, the first type of features performs better than the other
categories.
Multi-frequency image reconstruction for radio-interferometry with self-tuned regularization parameters
Authors: Rita Ammanouil, André Ferrari, Rémi Flamary, Chiara Ferrari, David Mary
Date: 2017-03-10 00:00:00
Tasks: Image Reconstruction, Radio Interferometry

As the world's largest radio telescope, the Square Kilometer Array (SKA) will
provide radio interferometric data with unprecedented detail. Image
reconstruction algorithms for radio interferometry are challenged to scale well
with TeraByte image sizes never seen before. In this work, we investigate one
such 3D image reconstruction algorithm known as MUFFIN (MUlti-Frequency image
reconstruction For radio INterferometry). In particular, we focus on the
challenging task of automatically finding the optimal regularization parameter
values. In practice, finding the regularization parameters using classical grid
search is computationally intensive and nontrivial due to the lack of ground-
truth. We adopt a greedy strategy where, at each iteration, the optimal
parameters are found by minimizing the predicted Stein unbiased risk estimate
(PSURE). The proposed self-tuned version of MUFFIN involves parallel and
computationally efficient steps, and scales well with large- scale data.
Finally, numerical results on a 3D image are presented to showcase the
performance of the proposed approach.
Averaged-DQN: Variance Reduction and Stabilization for Deep Reinforcement Learning
Authors: Oron Anschel, Nir Baram, Nahum Shimkin
Date: 2016-11-07 00:00:00
Proceeding: ICML 2017 8
Tasks: Atari Games, Deep Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Instability and variability of Deep Reinforcement Learning (DRL) algorithms
tend to adversely affect their performance. Averaged-DQN is a simple extension
to the DQN algorithm, based on averaging previously learned Q-values estimates,
which leads to a more stable training procedure and improved performance by
reducing approximation error variance in the target values. To understand the
effect of the algorithm, we examine the source of value function estimation
errors and provide an analytical comparison within a simplified model. We
further present experiments on the Arcade Learning Environment benchmark that
demonstrate significantly improved stability and performance due to the
proposed extension.
High SNR Consistent Compressive Sensing
Authors: Sreejith Kallummil, Sheetal Kalyani
Date: 2017-03-10 00:00:00
Tasks: Compressive Sensing, Model Selection, regression, Vocal Bursts Intensity Prediction

High signal to noise ratio (SNR) consistency of model selection criteria in
linear regression models has attracted a lot of attention recently. However,
most of the existing literature on high SNR consistency deals with model order
selection. Further, the limited literature available on the high SNR
consistency of subset selection procedures (SSPs) is applicable to linear
regression with full rank measurement matrices only. Hence, the performance of
SSPs used in underdetermined linear models (a.k.a compressive sensing (CS)
algorithms) at high SNR is largely unknown. This paper fills this gap by
deriving necessary and sufficient conditions for the high SNR consistency of
popular CS algorithms like $l_0$-minimization, basis pursuit de-noising or
LASSO, orthogonal matching pursuit and Dantzig selector. Necessary conditions
analytically establish the high SNR inconsistency of CS algorithms when used
with the tuning parameters discussed in literature. Novel tuning parameters
with SNR adaptations are developed using the sufficient conditions and the
choice of SNR adaptations are discussed analytically using convergence rate
analysis. CS algorithms with the proposed tuning parameters are numerically
shown to be high SNR consistent and outperform existing tuning parameters in
the moderate to high SNR regime.
Using Deep Learning Method for Classification: A Proposed Algorithm for the ISIC 2017 Skin Lesion Classification Challenge
Authors: Wenhao Zhang, Liangcai Gao, Runtao Liu
Date: 2017-03-07 00:00:00
Tasks: Classification, Diagnostic, General Classification, Lesion Classification, Lesion Detection, Lesion Segmentation, Skin Lesion Classification

Skin cancer, the most common human malignancy, is primarily diagnosed
visually by physicians [1]. Classification with an automated method like CNN
[2, 3] shows potential for challenging tasks [1]. By now, the deep
convolutional neural networks are on par with human dermatologist [1]. This
abstract is dedicated on developing a Deep Learning method for ISIC [5] 2017
Skin Lesion Detection Competition hosted at [6] to classify the dermatology
pictures, which is aimed at improving the diagnostic accuracy rate and general
level of the human health. The challenge falls into three sub-challenges,
including Lesion Segmentation, Lesion Dermoscopic Feature Extraction and Lesion
Classification. This project only participates in the Lesion Classification
part. This algorithm is comprised of three steps: (1) original images
preprocessing, (2) modelling the processed images using CNN [2, 3] in Caffe [4]
framework, (3) predicting the test images and calculating the scores that
represent the likelihood of corresponding classification. The models are built
on the source images are using the Caffe [4] framework. The scores in
prediction step are obtained by two different models from the source images.
Turkish PoS Tagging by Reducing Sparsity with Morpheme Tags in Small Datasets
Authors: Burcu Can, Ahmet Üstün, Murathan Kurfali
Date: 2017-03-09 00:00:00
Tasks: Part-Of-Speech Tagging, POS, POS Tagging

Sparsity is one of the major problems in natural language processing. The
problem becomes even more severe in agglutinating languages that are highly
prone to be inflected. We deal with sparsity in Turkish by adopting
morphological features for part-of-speech tagging. We learn inflectional and
derivational morpheme tags in Turkish by using conditional random fields (CRF)
and we employ the morpheme tags in part-of-speech (PoS) tagging by using hidden
Markov models (HMMs) to mitigate sparsity. Results show that using morpheme
tags in PoS tagging helps alleviate the sparsity in emission probabilities. Our
model outperforms other hidden Markov model based PoS tagging models for small
training datasets in Turkish. We obtain an accuracy of 94.1% in morpheme
tagging and 89.2% in PoS tagging on a 5K training dataset.
A New Evaluation Protocol and Benchmarking Results for Extendable Cross-media Retrieval
Authors: Ruoyu Liu, Yao Zhao, Liang Zheng, Shikui Wei, Yi Yang
Date: 2017-03-10 00:00:00
Tasks: Benchmarking, Image Retrieval, Retrieval

This paper proposes a new evaluation protocol for cross-media retrieval which
better fits the real-word applications. Both image-text and text-image
retrieval modes are considered. Traditionally, class labels in the training and
testing sets are identical. That is, it is usually assumed that the query falls
into some pre-defined classes. However, in practice, the content of a query
image/text may vary extensively, and the retrieval system does not necessarily
know in advance the class label of a query. Considering the inconsistency
between the real-world applications and laboratory assumptions, we think that
the existing protocol that works under identical train/test classes can be
modified and improved.
  This work is dedicated to addressing this problem by considering the protocol
under an extendable scenario, \ie, the training and testing classes do not
overlap. We provide extensive benchmarking results obtained by the existing
protocol and the proposed new protocol on several commonly used datasets. We
demonstrate a noticeable performance drop when the testing classes are unseen
during training. Additionally, a trivial solution, \ie, directly using the
predicted class label for cross-media retrieval, is tested. We show that the
trivial solution is very competitive in traditional non-extendable retrieval,
but becomes less so under the new settings. The train/test split, evaluation
code, and benchmarking results are publicly available on our website.
Deep Biaffine Attention for Neural Dependency Parsing
Authors: Timothy Dozat, Christopher D. Manning
Date: 2016-11-06 00:00:00
Tasks: Dependency Parsing

This paper builds off recent work from Kiperwasser & Goldberg (2016) using
neural attention in a simple graph-based dependency parser. We use a larger but
more thoroughly regularized parser than other recent BiLSTM-based approaches,
with biaffine classifiers to predict arcs and labels. Our parser gets state of
the art or near state of the art performance on standard treebanks for six
different languages, achieving 95.7% UAS and 94.1% LAS on the most popular
English PTB dataset. This makes it the highest-performing graph-based parser on
this benchmark---outperforming Kiperwasser Goldberg (2016) by 1.8% and
2.2%---and comparable to the highest performing transition-based parser
(Kuncoro et al., 2016), which achieves 95.8% UAS and 94.6% LAS. We also show
which hyperparameter choices had a significant effect on parsing accuracy,
allowing us to achieve large gains over other graph-based approaches.
Robustness to Adversarial Examples through an Ensemble of Specialists
Authors: Mahdieh Abbasi, Christian Gagné
Date: 2017-02-22 00:00:00

We are proposing to use an ensemble of diverse specialists, where speciality
is defined according to the confusion matrix. Indeed, we observed that for
adversarial instances originating from a given class, labeling tend to be done
into a small subset of (incorrect) classes. Therefore, we argue that an
ensemble of specialists should be better able to identify and reject fooling
instances, with a high entropy (i.e., disagreement) over the decisions in the
presence of adversaries. Experimental results obtained confirm that
interpretation, opening a way to make the system more robust to adversarial
examples through a rejection mechanism, rather than trying to classify them
properly at any cost.
Decorrelated Jet Substructure Tagging using Adversarial Neural Networks
Authors: Chase Shimmin, Peter Sadowski, Pierre Baldi, Edison Weik, Daniel Whiteson, Edward Goul, Andreas Søgaard
Date: 2017-03-10 00:00:00
Tasks: Jet Tagging

We describe a strategy for constructing a neural network jet substructure
tagger which powerfully discriminates boosted decay signals while remaining
largely uncorrelated with the jet mass. This reduces the impact of systematic
uncertainties in background modeling while enhancing signal purity, resulting
in improved discovery significance relative to existing taggers. The network is
trained using an adversarial strategy, resulting in a tagger that learns to
balance classification accuracy with decorrelation. As a benchmark scenario, we
consider the case where large-radius jets originating from a boosted resonance
decay are discriminated from a background of nonresonant quark and gluon jets.
We show that in the presence of systematic uncertainties on the background
rate, our adversarially-trained, decorrelated tagger considerably outperforms a
conventionally trained neural network, despite having a slightly worse
signal-background separation power. We generalize the adversarial training
technique to include a parametric dependence on the signal hypothesis, training
a single network that provides optimized, interpolatable decorrelated jet
tagging across a continuous range of hypothetical resonance masses, after
training on discrete choices of the signal mass.
Attention-Based Multimodal Fusion for Video Description
Authors: Chiori Hori, Takaaki Hori, Teng-Yok Lee, Kazuhiro Sumi, John R. Hershey, Tim K. Marks
Date: 2017-01-11 00:00:00
Proceeding: ICCV 2017 10
Tasks: Decoder, Sentence, Video Description

Currently successful methods for video description are based on
encoder-decoder sentence generation using recur-rent neural networks (RNNs).
Recent work has shown the advantage of integrating temporal and/or spatial
attention mechanisms into these models, in which the decoder net-work predicts
each word in the description by selectively giving more weight to encoded
features from specific time frames (temporal attention) or to features from
specific spatial regions (spatial attention). In this paper, we propose to
expand the attention model to selectively attend not just to specific times or
spatial regions, but to specific modalities of input such as image features,
motion features, and audio features. Our new modality-dependent attention
mechanism, which we call multimodal attention, provides a natural way to fuse
multimodal information for video description. We evaluate our method on the
Youtube2Text dataset, achieving results that are competitive with current state
of the art. More importantly, we demonstrate that our model incorporating
multimodal attention as well as temporal attention significantly outperforms
the model that uses temporal attention alone.
Identifying Best Interventions through Online Importance Sampling
Authors: Rajat Sen, Karthikeyan Shanmugam, Alexandros G. Dimakis, Sanjay Shakkottai
Date: 2017-01-10 00:00:00
Proceeding: ICML 2017 8

Motivated by applications in computational advertising and systems biology,
we consider the problem of identifying the best out of several possible soft
interventions at a source node $V$ in an acyclic causal directed graph, to
maximize the expected value of a target node $Y$ (located downstream of $V$).
Our setting imposes a fixed total budget for sampling under various
interventions, along with cost constraints on different types of interventions.
We pose this as a best arm identification bandit problem with $K$ arms where
each arm is a soft intervention at $V,$ and leverage the information leakage
among the arms to provide the first gap dependent error and simple regret
bounds for this problem. Our results are a significant improvement over the
traditional best arm identification results. We empirically show that our
algorithms outperform the state of the art in the Flow Cytometry data-set, and
also apply our algorithm for model interpretation of the Inception-v3 deep net
that classifies images.
Accelerated Gradient Temporal Difference Learning
Authors: Yangchen Pan, Adam White, Martha White
Date: 2016-11-28 00:00:00

The family of temporal difference (TD) methods span a spectrum from
computationally frugal linear methods like TD({\lambda}) to data efficient
least squares methods. Least square methods make the best use of available data
directly computing the TD solution and thus do not require tuning a typically
highly sensitive learning rate parameter, but require quadratic computation and
storage. Recent algorithmic developments have yielded several sub-quadratic
methods that use an approximation to the least squares TD solution, but incur
bias. In this paper, we propose a new family of accelerated gradient TD (ATD)
methods that (1) provide similar data efficiency benefits to least-squares
methods, at a fraction of the computation and storage (2) significantly reduce
parameter sensitivity compared to linear TD methods, and (3) are asymptotically
unbiased. We illustrate these claims with a proof of convergence in expectation
and experiments on several benchmark domains and a large-scale industrial
energy allocation domain.
Deep Radial Kernel Networks: Approximating Radially Symmetric Functions with Deep Networks
Authors: Brendan McCane, Lech Szymanski
Date: 2017-03-09 00:00:00

We prove that a particular deep network architecture is more efficient at
approximating radially symmetric functions than the best known 2 or 3 layer
networks. We use this architecture to approximate Gaussian kernel SVMs, and
subsequently improve upon them with further training. The architecture and
initial weights of the Deep Radial Kernel Network are completely specified by
the SVM and therefore sidesteps the problem of empirically choosing an
appropriate deep network architecture.
Parallel Markov Chain Monte Carlo for the Indian Buffet Process
Authors: Michael M. Zhang, Avinava Dubey, Sinead A. Williamson
Date: 2017-03-09 00:00:00

Indian Buffet Process based models are an elegant way for discovering
underlying features within a data set, but inference in such models can be
slow. Inferring underlying features using Markov chain Monte Carlo either
relies on an uncollapsed representation, which leads to poor mixing, or on a
collapsed representation, which leads to a quadratic increase in computational
complexity. Existing attempts at distributing inference have introduced
additional approximation within the inference procedure. In this paper we
present a novel algorithm to perform asymptotically exact parallel Markov chain
Monte Carlo inference for Indian Buffet Process models. We take advantage of
the fact that the features are conditionally independent under the
beta-Bernoulli process. Because of this conditional independence, we can
partition the features into two parts: one part containing only the finitely
many instantiated features and the other part containing the infinite tail of
uninstantiated features. For the finite partition, parallel inference is simple
given the instantiation of features. But for the infinite tail, performing
uncollapsed MCMC leads to poor mixing and hence we collapse out the features.
The resulting hybrid sampler, while being parallel, produces samples
asymptotically from the true posterior.
Sample Efficient Feature Selection for Factored MDPs
Authors: Zhaohan Daniel Guo, Emma Brunskill
Date: 2017-03-09 00:00:00
Tasks: All, feature selection, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

In reinforcement learning, the state of the real world is often represented
by feature vectors. However, not all of the features may be pertinent for
solving the current task. We propose Feature Selection Explore and Exploit
(FS-EE), an algorithm that automatically selects the necessary features while
learning a Factored Markov Decision Process, and prove that under mild
assumptions, its sample complexity scales with the in-degree of the dynamics of
just the necessary features, rather than the in-degree of all features. This
can result in a much better sample complexity when the in-degree of the
necessary features is smaller than the in-degree of all features.