pwc-archive/papers-with-abstracts huggingface dataset:
The Risk of Machine Learning
Authors: Alberto Abadie, Maximilian Kasy
Date: 2017-03-31 00:00:00
Tasks: BIG-bench Machine Learning

Many applied settings in empirical economics involve simultaneous estimation
of a large number of parameters. In particular, applied economists are often
interested in estimating the effects of many-valued treatments (like teacher
effects or location effects), treatment effects for many groups, and prediction
models with many regressors. In these settings, machine learning methods that
combine regularized estimation and data-driven choices of regularization
parameters are useful to avoid over-fitting. In this article, we analyze the
performance of a class of machine learning estimators that includes ridge,
lasso and pretest in contexts that require simultaneous estimation of many
parameters. Our analysis aims to provide guidance to applied researchers on (i)
the choice between regularized estimators in practice and (ii) data-driven
selection of regularization parameters. To address (i), we characterize the
risk (mean squared error) of regularized estimators and derive their relative
performance as a function of simple features of the data generating process. To
address (ii), we show that data-driven choices of regularization parameters,
based on Stein's unbiased risk estimate or on cross-validation, yield
estimators with risk uniformly close to the risk attained under the optimal
(unfeasible) choice of regularization parameters. We use data from recent
examples in the empirical economics literature to illustrate the practical
applicability of our results.
Feature functional theory - binding predictor (FFT-BP) for the blind prediction of binding free energies
Authors: Bao Wang, Zhixiong Zhao, Duc D. Nguyen, Guo-Wei Wei
Date: 2017-03-31 00:00:00

We present a feature functional theory - binding predictor (FFT-BP) for the
protein-ligand binding affinity prediction. The underpinning assumptions of
FFT-BP are as follows: i) representability: there exists a microscopic feature
vector that can uniquely characterize and distinguish one protein-ligand
complex from another; ii) feature-function relationship: the macroscopic
features, including binding free energy, of a complex is a functional of
microscopic feature vectors; and iii) similarity: molecules with similar
microscopic features have similar macroscopic features, such as binding
affinity. Physical models, such as implicit solvent models and quantum theory,
are utilized to extract microscopic features, while machine learning algorithms
are employed to rank the similarity among protein-ligand complexes. A large
variety of numerical validations and tests confirms the accuracy and robustness
of the proposed FFT-BP model. The root mean square errors (RMSEs) of FFT-BP
blind predictions of a benchmark set of 100 complexes, the PDBBind v2007 core
set of 195 complexes and the PDBBind v2015 core set of 195 complexes are 1.99,
2.02 and 1.92 kcal/mol, respectively. Their corresponding Pearson correlation
coefficients are 0.75, 0.80, and 0.78, respectively.
EMULATOR vs REAL PHONE: Android Malware Detection Using Machine Learning
Authors: Mohammed K. Alzaylaee, Suleiman Y. Yerima, Sakir Sezer
Date: 2017-03-31 00:00:00
Tasks: Android Malware Detection, BIG-bench Machine Learning, Malware Detection

The Android operating system has become the most popular operating system for
smartphones and tablets leading to a rapid rise in malware. Sophisticated
Android malware employ detection avoidance techniques in order to hide their
malicious activities from analysis tools. These include a wide range of
anti-emulator techniques, where the malware programs attempt to hide their
malicious activities by detecting the emulator. For this reason,
countermeasures against antiemulation are becoming increasingly important in
Android malware detection. Analysis and detection based on real devices can
alleviate the problems of anti-emulation as well as improve the effectiveness
of dynamic analysis. Hence, in this paper we present an investigation of
machine learning based malware detection using dynamic analysis on real
devices. A tool is implemented to automatically extract dynamic features from
Android phones and through several experiments, a comparative analysis of
emulator based vs. device based detection by means of several machine learning
algorithms is undertaken. Our study shows that several features could be
extracted more effectively from the on-device dynamic analysis compared to
emulators. It was also found that approximately 24% more apps were successfully
analysed on the phone. Furthermore, all of the studied machine learning based
detection performed better when applied to features extracted from the
on-device dynamic analysis.
Fast Predictive Multimodal Image Registration
Authors: Xiao Yang, Roland Kwitt, Martin Styner, Marc Niethammer
Date: 2017-03-31 00:00:00
Tasks: Decoder, Image Registration

We introduce a deep encoder-decoder architecture for image deformation
prediction from multimodal images. Specifically, we design an image-patch-based
deep network that jointly (i) learns an image similarity measure and (ii) the
relationship between image patches and deformation parameters. While our method
can be applied to general image registration formulations, we focus on the
Large Deformation Diffeomorphic Metric Mapping (LDDMM) registration model. By
predicting the initial momentum of the shooting formulation of LDDMM, we
preserve its mathematical properties and drastically reduce the computation
time, compared to optimization-based approaches. Furthermore, we create a
Bayesian probabilistic version of the network that allows evaluation of
registration uncertainty via sampling of the network at test time. We evaluate
our method on a 3D brain MRI dataset using both T1- and T2-weighted images. Our
experiments show that our method generates accurate predictions and that
learning the similarity measure leads to more consistent registrations than
relying on generic multimodal image similarity measures, such as mutual
information. Our approach is an order of magnitude faster than
optimization-based LDDMM.
Unsupervised learning from video to detect foreground objects in single images
Authors: Ioana Croitoru, Simion-Vlad Bogolin, Marius Leordeanu
Date: 2017-03-31 00:00:00
Proceeding: ICCV 2017 10
Tasks: Object Discovery

Unsupervised learning from visual data is one of the most difficult
challenges in computer vision, being a fundamental task for understanding how
visual recognition works. From a practical point of view, learning from
unsupervised visual input has an immense practical value, as very large
quantities of unlabeled videos can be collected at low cost. In this paper, we
address the task of unsupervised learning to detect and segment foreground
objects in single images. We achieve our goal by training a student pathway,
consisting of a deep neural network. It learns to predict from a single input
image (a video frame) the output for that particular frame, of a teacher
pathway that performs unsupervised object discovery in video. Our approach is
different from the published literature that performs unsupervised discovery in
videos or in collections of images at test time. We move the unsupervised
discovery phase during the training stage, while at test time we apply the
standard feed-forward processing along the student pathway. This has a dual
benefit: firstly, it allows in principle unlimited possibilities of learning
and generalization during training, while remaining very fast at testing.
Secondly, the student not only becomes able to detect in single images
significantly better than its unsupervised video discovery teacher, but it also
achieves state of the art results on two important current benchmarks, YouTube
Objects and Object Discovery datasets. Moreover, at test time, our system is at
least two orders of magnitude faster than other previous methods.
Thin-Slicing Network: A Deep Structured Model for Pose Estimation in Videos
Authors: Jie Song, Li-Min Wang, Luc van Gool, Otmar Hilliges
Date: 2017-03-31 00:00:00
Proceeding: CVPR 2017 7
Tasks: Pose Estimation

Deep ConvNets have been shown to be effective for the task of human pose
estimation from single images. However, several challenging issues arise in the
video-based case such as self-occlusion, motion blur, and uncommon poses with
few or no examples in training data sets. Temporal information can provide
additional cues about the location of body joints and help to alleviate these
issues. In this paper, we propose a deep structured model to estimate a
sequence of human poses in unconstrained videos. This model can be efficiently
trained in an end-to-end manner and is capable of representing appearance of
body joints and their spatio-temporal relationships simultaneously. Domain
knowledge about the human body is explicitly incorporated into the network
providing effective priors to regularize the skeletal structure and to enforce
temporal consistency. The proposed end-to-end architecture is evaluated on two
widely used benchmarks (Penn Action dataset and JHMDB dataset) for video-based
pose estimation. Our approach significantly outperforms the existing
state-of-the-art methods.
Single Image Super Resolution - When Model Adaptation Matters
Authors: Yudong Liang, Radu Timofte, Jinjun Wang, Yihong Gong, Nanning Zheng
Date: 2017-03-31 00:00:00
Tasks: Image Super-Resolution, Super-Resolution

In the recent years impressive advances were made for single image
super-resolution. Deep learning is behind a big part of this success. Deep(er)
architecture design and external priors modeling are the key ingredients. The
internal contents of the low resolution input image is neglected with deep
modeling despite the earlier works showing the power of using such internal
priors. In this paper we propose a novel deep convolutional neural network
carefully designed for robustness and efficiency at both learning and testing.
Moreover, we propose a couple of model adaptation strategies to the internal
contents of the low resolution input image and analyze their strong points and
weaknesses. By trading runtime and using internal priors we achieve 0.1 up to
0.3dB PSNR improvements over best reported results on standard datasets. Our
adaptation especially favors images with repetitive structures or under large
resolutions. Moreover, it can be combined with other simple techniques, such as
back-projection or enhanced prediction, for further improvements.
Bi-class classification of humpback whale sound units against complex background noise with Deep Convolution Neural Network
Authors: Cazau Dorian, Riwal Lefort, Julien Bonnel, Jean-Luc Zarader, Olivier Adam
Date: 2017-03-31 00:00:00
Tasks: General Classification

Automatically detecting sound units of humpback whales in complex
time-varying background noises is a current challenge for scientists. In this
paper, we explore the applicability of Convolution Neural Network (CNN) method
for this task. In the evaluation stage, we present 6 bi-class classification
experimentations of whale sound detection against different background noise
types (e.g., rain, wind). In comparison to classical FFT-based representation
like spectrograms, we showed that the use of image-based pretrained CNN
features brought higher performance to classify whale sounds and background
noise.
Hankel Matrix Nuclear Norm Regularized Tensor Completion for $N$-dimensional Exponential Signals
Authors: Jiaxi Ying, Hengfa Lu, Qingtao Wei, Jian-Feng Cai, Di Guo, Jihui Wu, Zhong Chen, Xiaobo Qu
Date: 2016-04-06 00:00:00

Signals are generally modeled as a superposition of exponential functions in
spectroscopy of chemistry, biology and medical imaging. For fast data
acquisition or other inevitable reasons, however, only a small amount of
samples may be acquired and thus how to recover the full signal becomes an
active research topic. But existing approaches can not efficiently recover
$N$-dimensional exponential signals with $N\geq 3$. In this paper, we study the
problem of recovering N-dimensional (particularly $N\geq 3$) exponential
signals from partial observations, and formulate this problem as a low-rank
tensor completion problem with exponential factor vectors. The full signal is
reconstructed by simultaneously exploiting the CANDECOMP/PARAFAC structure and
the exponential structure of the associated factor vectors. The latter is
promoted by minimizing an objective function involving the nuclear norm of
Hankel matrices. Experimental results on simulated and real magnetic resonance
spectroscopy data show that the proposed approach can successfully recover full
signals from very limited samples and is robust to the estimated tensor rank.
Noisy Softplus: an activation function that enables SNNs to be trained as ANNs
Authors: Qian Liu, Yunhua Chen, Steve Furber
Date: 2017-03-31 00:00:00
Tasks: General Classification

We extended the work of proposed activation function, Noisy Softplus, to fit
into training of layered up spiking neural networks (SNNs). Thus, any ANN
employing Noisy Softplus neurons, even of deep architecture, can be trained
simply by the traditional algorithm, for example Back Propagation (BP), and the
trained weights can be directly used in the spiking version of the same network
without any conversion. Furthermore, the training method can be generalised to
other activation units, for instance Rectified Linear Units (ReLU), to train
deep SNNs off-line. This research is crucial to provide an effective approach
for SNN training, and to increase the classification accuracy of SNNs with
biological characteristics and to close the gap between the performance of SNNs
and ANNs.
On the Futility of Learning Complex Frame-Level Language Models for Chord Recognition
Authors: Filip Korzeniowski, Gerhard Widmer
Date: 2017-02-01 00:00:00
Tasks: Chord Recognition

Chord recognition systems use temporal models to post-process frame-wise
chord preditions from acoustic models. Traditionally, first-order models such
as Hidden Markov Models were used for this task, with recent works suggesting
to apply Recurrent Neural Networks instead. Due to their ability to learn
longer-term dependencies, these models are supposed to learn and to apply
musical knowledge, instead of just smoothing the output of the acoustic model.
In this paper, we argue that learning complex temporal models at the level of
audio frames is futile on principle, and that non-Markovian models do not
perform better than their first-order counterparts. We support our argument
through three experiments on the McGill Billboard dataset. The first two show
1) that when learning complex temporal models at the frame level, improvements
in chord sequence modelling are marginal; and 2) that these improvements do not
translate when applied within a full chord recognition system. The third, still
rather preliminary experiment gives first indications that the use of complex
sequential models for chord prediction at higher temporal levels might be more
promising.
Intraoperative margin assessment of human breast tissue in optical coherence tomography images using deep neural networks
Authors: Amal Rannen Triki, Matthew B. Blaschko, Yoon Mo Jung, Seungri Song, Hyun Ju Han, Seung Il Kim, Chulmin Joo
Date: 2017-03-31 00:00:00
Tasks: Specificity

Objective: In this work, we perform margin assessment of human breast tissue
from optical coherence tomography (OCT) images using deep neural networks
(DNNs). This work simulates an intraoperative setting for breast cancer
lumpectomy. Methods: To train the DNNs, we use both the state-of-the-art
methods (Weight Decay and DropOut) and a newly introduced regularization method
based on function norms. Commonly used methods can fail when only a small
database is available. The use of a function norm introduces a direct control
over the complexity of the function with the aim of diminishing the risk of
overfitting. Results: As neither the code nor the data of previous results are
publicly available, the obtained results are compared with reported results in
the literature for a conservative comparison. Moreover, our method is applied
to locally collected data on several data configurations. The reported results
are the average over the different trials. Conclusion: The experimental results
show that the use of DNNs yields significantly better results than other
techniques when evaluated in terms of sensitivity, specificity, F1 score,
G-mean and Matthews correlation coefficient. Function norm regularization
yielded higher and more robust results than competing methods. Significance: We
have demonstrated a system that shows high promise for (partially) automated
margin assessment of human breast tissue, Equal error rate (EER) is reduced
from approximately 12\% (the lowest reported in the literature) to 5\%\,--\,a
58\% reduction. The method is computationally feasible for intraoperative
application (less than 2 seconds per image).
End-To-End Face Detection and Recognition
Authors: Liying Chi, Hong-Xin Zhang, Mingxiu Chen
Date: 2017-03-31 00:00:00
Tasks: Face Alignment, Face Detection, Face Recognition

Plenty of face detection and recognition methods have been proposed and got
delightful results in decades. Common face recognition pipeline consists of: 1)
face detection, 2) face alignment, 3) feature extraction, 4) similarity
calculation, which are separated and independent from each other. The separated
face analyzing stages lead the model redundant calculation and are hard for
end-to-end training. In this paper, we proposed a novel end-to-end trainable
convolutional network framework for face detection and recognition, in which a
geometric transformation matrix was directly learned to align the faces,
instead of predicting the facial landmarks. In training stage, our single CNN
model is supervised only by face bounding boxes and personal identities, which
are publicly available from WIDER FACE \cite{Yang2016} dataset and
CASIA-WebFace \cite{Yi2014} dataset. Tested on Face Detection Dataset and
Benchmark (FDDB) \cite{Jain2010} dataset and Labeled Face in the Wild (LFW)
\cite{Huang2007} dataset, we have achieved 89.24\% recall for face detection
task and 98.63\% verification accuracy for face recognition task
simultaneously, which are comparable to state-of-the-art results.
Learning Spatial Regularization with Image-level Supervisions for Multi-label Image Classification
Authors: Feng Zhu, Hongsheng Li, Wanli Ouyang, Nenghai Yu, Xiaogang Wang
Date: 2017-02-20 00:00:00
Proceeding: CVPR 2017 7
Tasks: Classification, General Classification, image-classification, Image Classification, Multi-Label Classification, Multi-Label Image Classification

Multi-label image classification is a fundamental but challenging task in
computer vision. Great progress has been achieved by exploiting semantic
relations between labels in recent years. However, conventional approaches are
unable to model the underlying spatial relations between labels in multi-label
images, because spatial annotations of the labels are generally not provided.
In this paper, we propose a unified deep neural network that exploits both
semantic and spatial relations between labels with only image-level
supervisions. Given a multi-label image, our proposed Spatial Regularization
Network (SRN) generates attention maps for all labels and captures the
underlying relations between them via learnable convolutions. By aggregating
the regularized classification results with original results by a ResNet-101
network, the classification performance can be consistently improved. The whole
deep neural network is trained end-to-end with only image-level annotations,
thus requires no additional efforts on image annotations. Extensive evaluations
on 3 public datasets with different types of labels show that our approach
significantly outperforms state-of-the-arts and has strong generalization
capability. Analysis of the learned SRN model demonstrates that it can
effectively capture both semantic and spatial relations of labels for improving
classification performance.
GIFT: A Real-time and Scalable 3D Shape Search Engine
Authors: Song Bai, Xiang Bai, Zhichao Zhou, Zhaoxiang Zhang, Longin Jan Latecki
Date: 2016-04-07 00:00:00
Proceeding: CVPR 2016 6
Tasks: 3D Shape Classification, 3D Shape Retrieval, GPU, Re-Ranking, Retrieval

Projective analysis is an important solution for 3D shape retrieval, since
human visual perceptions of 3D shapes rely on various 2D observations from
different view points. Although multiple informative and discriminative views
are utilized, most projection-based retrieval systems suffer from heavy
computational cost, thus cannot satisfy the basic requirement of scalability
for search engines. In this paper, we present a real-time 3D shape search
engine based on the projective images of 3D shapes. The real-time property of
our search engine results from the following aspects: (1) efficient projection
and view feature extraction using GPU acceleration; (2) the first inverted
file, referred as F-IF, is utilized to speed up the procedure of multi-view
matching; (3) the second inverted file (S-IF), which captures a local
distribution of 3D shapes in the feature manifold, is adopted for efficient
context-based re-ranking. As a result, for each query the retrieval task can be
finished within one second despite the necessary cost of IO overhead. We name
the proposed 3D shape search engine, which combines GPU acceleration and
Inverted File Twice, as GIFT. Besides its high efficiency, GIFT also
outperforms the state-of-the-art methods significantly in retrieval accuracy on
various shape benchmarks and competitions.
INTEL-TUT Dataset for Camera Invariant Color Constancy Research
Authors: Caglar Aytekin, Jarno Nikkanen, Moncef Gabbouj
Date: 2017-03-21 00:00:00
Tasks: Color Constancy

In this paper, we provide a novel dataset designed for camera invariant color
constancy research. Camera invariance corresponds to the robustness of an
algorithm's performance when run on images of the same scene taken by different
cameras. Accordingly, images in the database correspond to several lab and
field scenes each of which are captured by three different cameras with minimal
registration errors. The lab scenes are also captured under five different
illuminations. The spectral responses of cameras and the spectral power
distributions of the lab light sources are also provided, as they may prove
beneficial for training future algorithms to achieve color constancy. For a
fair evaluation of future methods, we provide guidelines for supervised methods
with indicated training, validation and testing partitions. Accordingly, we
evaluate a recently proposed convolutional neural network based color constancy
algorithm as a baseline for future research. As a side contribution, this
dataset also includes images taken by a mobile camera with color shading
corrected and uncorrected results. This allows research on the effect of color
shading as well.
Joining Hands: Exploiting Monolingual Treebanks for Parsing of Code-mixing Data
Authors: Irshad Ahmad Bhat, Riyaz Ahmad Bhat, Manish Shrivastava, Dipti Misra Sharma
Date: 2017-03-31 00:00:00
Proceeding: EACL 2017 4

In this paper, we propose efficient and less resource-intensive strategies
for parsing of code-mixed data. These strategies are not constrained by
in-domain annotations, rather they leverage pre-existing monolingual annotated
resources for training. We show that these methods can produce significantly
better results as compared to an informed baseline. Besides, we also present a
data set of 450 Hindi and English code-mixed tweets of Hindi multilingual
speakers for evaluation. The data set is manually annotated with Universal
Dependencies.