pwc-archive/papers-with-abstracts huggingface dataset:
Real-Time Action Detection in Video Surveillance using Sub-Action Descriptor with Multi-CNN
Authors: Cheng-Bin Jin, Shengzhe Li, Hakil Kim
Date: 2017-10-10 00:00:00
Tasks: Action Detection, Action Recognition, Temporal Action Localization

When we say a person is texting, can you tell the person is walking or
sitting? Emphatically, no. In order to solve this incomplete representation
problem, this paper presents a sub-action descriptor for detailed action
detection. The sub-action descriptor consists of three levels: the posture, the
locomotion, and the gesture level. The three levels give three sub-action
categories for one action to address the representation problem. The proposed
action detection model simultaneously localizes and recognizes the actions of
multiple individuals in video surveillance using appearance-based temporal
features with multi-CNN. The proposed approach achieved a mean average
precision (mAP) of 76.6% at the frame-based and 83.5% at the video-based
measurement on the new large-scale ICVL video surveillance dataset that the
authors introduce and make available to the community with this paper.
Extensive experiments on the benchmark KTH dataset demonstrate that the
proposed approach achieved better performance, which in turn boosts the action
recognition performance over the state-of-the-art. The action detection model
can run at around 25 fps on the ICVL and more than 80 fps on the KTH dataset,
which is suitable for real-time surveillance applications.
DAGGER: A sequential algorithm for FDR control on DAGs
Authors: Aaditya Ramdas, Jianbo Chen, Martin J. Wainwright, Michael. I. Jordan
Date: 2017-09-29 00:00:00
Tasks: Model Selection

We propose a linear-time, single-pass, top-down algorithm for multiple
testing on directed acyclic graphs (DAGs), where nodes represent hypotheses and
edges specify a partial ordering in which hypotheses must be tested. The
procedure is guaranteed to reject a sub-DAG with bounded false discovery rate
(FDR) while satisfying the logical constraint that a rejected node's parents
must also be rejected. It is designed for sequential testing settings, when the
DAG structure is known a priori, but the $p$-values are obtained selectively
(such as in a sequence of experiments), but the algorithm is also applicable in
non-sequential settings when all $p$-values can be calculated in advance (such
as variable/model selection). Our DAGGER algorithm, shorthand for Greedily
Evolving Rejections on DAGs, provably controls the false discovery rate under
independence, positive dependence or arbitrary dependence of the $p$-values.
The DAGGER procedure specializes to known algorithms in the special cases of
trees and line graphs, and simplifies to the classical Benjamini-Hochberg
procedure when the DAG has no edges. We explore the empirical performance of
DAGGER using simulations, as well as a real dataset corresponding to a gene
ontology, showing favorable performance in terms of time and power.
Energy-efficient Amortized Inference with Cascaded Deep Classifiers
Authors: Jiaqi Guan, Yang Liu, Qiang Liu, Jian Peng
Date: 2017-10-10 00:00:00
Tasks: image-classification, Image Classification

Deep neural networks have been remarkable successful in various AI tasks but
often cast high computation and energy cost for energy-constrained applications
such as mobile sensing. We address this problem by proposing a novel framework
that optimizes the prediction accuracy and energy cost simultaneously, thus
enabling effective cost-accuracy trade-off at test time. In our framework, each
data instance is pushed into a cascade of deep neural networks with increasing
sizes, and a selection module is used to sequentially determine when a
sufficiently accurate classifier can be used for this data instance. The
cascade of neural networks and the selection module are jointly trained in an
end-to-end fashion by the REINFORCE algorithm to optimize a trade-off between
the computational cost and the predictive accuracy. Our method is able to
simultaneously improve the accuracy and efficiency by learning to assign easy
instances to fast yet sufficiently accurate classifiers to save computation and
energy cost, while assigning harder instances to deeper and more powerful
classifiers to ensure satisfiable accuracy. With extensive experiments on
several image classification datasets using cascaded ResNet classifiers, we
demonstrate that our method outperforms the standard well-trained ResNets in
accuracy but only requires less than 20% and 50% FLOPs cost on the CIFAR-10/100
datasets and 66% on the ImageNet dataset, respectively.
Deep Learning Paradigm with Transformed Monolingual Word Embeddings for Multilingual Sentiment Analysis
Authors: Yujie Lu, Tatsunori Mori
Date: 2017-10-09 00:00:00
Proceeding: PACLIC 2018 12
Tasks: General Classification, Machine Translation, Sentiment Analysis, Translation, Word Embeddings

The surge of social media use brings huge demand of multilingual sentiment
analysis (MSA) for unveiling cultural difference. So far, traditional methods
resorted to machine translation---translating texts in other languages to
English, and then adopt the methods once worked in English. However, this
paradigm is conditioned by the quality of machine translation. In this paper,
we propose a new deep learning paradigm to assimilate the differences between
languages for MSA. We first pre-train monolingual word embeddings separately,
then map word embeddings in different spaces into a shared embedding space, and
then finally train a parameter-sharing deep neural network for MSA. The
experimental results show that our paradigm is effective. Especially, our CNN
model outperforms a state-of-the-art baseline by around 2.1% in terms of
classification accuracy.
Semantic-driven Generation of Hyperlapse from $360^\circ$ Video
Authors: Wei-Sheng Lai, Yujia Huang, Neel Joshi, Chris Buehler, Ming-Hsuan Yang, Sing Bing Kang
Date: 2017-03-31 00:00:00
Tasks: Video Stabilization

We present a system for converting a fully panoramic ($360^\circ$) video into
a normal field-of-view (NFOV) hyperlapse for an optimal viewing experience. Our
system exploits visual saliency and semantics to non-uniformly sample in space
and time for generating hyperlapses. In addition, users can optionally choose
objects of interest for customizing the hyperlapses. We first stabilize an
input $360^\circ$ video by smoothing the rotation between adjacent frames and
then compute regions of interest and saliency scores. An initial hyperlapse is
generated by optimizing the saliency and motion smoothness followed by the
saliency-aware frame selection. We further smooth the result using an efficient
2D video stabilization approach that adaptively selects the motion model to
generate the final hyperlapse. We validate the design of our system by showing
results for a variety of scenes and comparing against the state-of-the-art
method through a user study.
Anomaly Detection in Multivariate Non-stationary Time Series for Automatic DBMS Diagnosis
Authors: Doyup Lee
Date: 2017-08-08 00:00:00
Tasks: Anomaly Detection, Management, Time Series, Time Series Analysis

Anomaly detection in database management systems (DBMSs) is difficult because
of increasing number of statistics (stat) and event metrics in big data system.
In this paper, I propose an automatic DBMS diagnosis system that detects
anomaly periods with abnormal DB stat metrics and finds causal events in the
periods. Reconstruction error from deep autoencoder and statistical process
control approach are applied to detect time period with anomalies. Related
events are found using time series similarity measures between events and
abnormal stat metrics. After training deep autoencoder with DBMS metric data,
efficacy of anomaly detection is investigated from other DBMSs containing
anomalies. Experiment results show effectiveness of proposed model, especially,
batch temporal normalization layer. Proposed model is used for publishing
automatic DBMS diagnosis reports in order to determine DBMS configuration and
SQL tuning.
What does Attention in Neural Machine Translation Pay Attention to?
Authors: Hamidreza Ghader, Christof Monz
Date: 2017-10-09 00:00:00
Proceeding: IJCNLP 2017 11
Tasks: Machine Translation, Sentence, Translation

Attention in neural machine translation provides the possibility to encode
relevant parts of the source sentence at each translation step. As a result,
attention is considered to be an alignment model as well. However, there is no
work that specifically studies attention and provides analysis of what is being
learned by attention models. Thus, the question still remains that how
attention is similar or different from the traditional alignment. In this
paper, we provide detailed analysis of attention and compare it to traditional
alignment. We answer the question of whether attention is only capable of
modelling translational equivalent or it captures more information. We show
that attention is different from alignment in some cases and is capturing
useful information other than alignments.
Geo-referencing Place from Everyday Natural Language Descriptions
Authors: Hao Chen, Maria Vasardani, Stephan Winter
Date: 2017-10-09 00:00:00
Tasks: Toponym Resolution

Natural language place descriptions in everyday communication provide a rich
source of spatial knowledge about places. An important step to utilize such
knowledge in information systems is geo-referencing all the places referred to
in these descriptions. Current techniques for geo-referencing places from text
documents are using place name recognition and disambiguation; however, place
descriptions often contain place references that are not known by gazetteers,
or that are expressed in other, more flexible ways. Hence, the approach for
geo-referencing presented in this paper starts from a place graph that contains
the place references as well as spatial relationships extracted from place
descriptions. Spatial relationships are used to constrain the locations of
places and allow the later best-matching process for geo-referencing. The novel
geo-referencing process results in higher precision and recall compared to
state-of-art toponym resolution approaches on several tested place description
datasets.
Iterative PET Image Reconstruction Using Convolutional Neural Network Representation
Authors: Kuang Gong, Jiahui Guan, Kyungsang Kim, Xuezhu Zhang, Georges El Fakhri, Jinyi Qi, Quanzheng Li
Date: 2017-10-09 00:00:00
Tasks: Denoising, Image Reconstruction

PET image reconstruction is challenging due to the ill-poseness of the
inverse problem and limited number of detected photons. Recently deep neural
networks have been widely and successfully used in computer vision tasks and
attracted growing interests in medical imaging. In this work, we trained a deep
residual convolutional neural network to improve PET image quality by using the
existing inter-patient information. An innovative feature of the proposed
method is that we embed the neural network in the iterative reconstruction
framework for image representation, rather than using it as a post-processing
tool. We formulate the objective function as a constraint optimization problem
and solve it using the alternating direction method of multipliers (ADMM)
algorithm. Both simulation data and hybrid real data are used to evaluate the
proposed method. Quantification results show that our proposed iterative neural
network method can outperform the neural network denoising and conventional
penalized maximum likelihood methods.
Deep Laplacian Pyramid Networks for Fast and Accurate Super-Resolution
Authors: Wei-Sheng Lai, Jia-Bin Huang, Narendra Ahuja, Ming-Hsuan Yang
Date: 2017-04-12 00:00:00
Proceeding: CVPR 2017 7
Tasks: Image Super-Resolution, Super-Resolution

Convolutional neural networks have recently demonstrated high-quality
reconstruction for single-image super-resolution. In this paper, we propose the
Laplacian Pyramid Super-Resolution Network (LapSRN) to progressively
reconstruct the sub-band residuals of high-resolution images. At each pyramid
level, our model takes coarse-resolution feature maps as input, predicts the
high-frequency residuals, and uses transposed convolutions for upsampling to
the finer level. Our method does not require the bicubic interpolation as the
pre-processing step and thus dramatically reduces the computational complexity.
We train the proposed LapSRN with deep supervision using a robust Charbonnier
loss function and achieve high-quality reconstruction. Furthermore, our network
generates multi-scale predictions in one feed-forward pass through the
progressive reconstruction, thereby facilitates resource-aware applications.
Extensive quantitative and qualitative evaluations on benchmark datasets show
that the proposed algorithm performs favorably against the state-of-the-art
methods in terms of speed and accuracy.
AffectNet: A Database for Facial Expression, Valence, and Arousal Computing in the Wild
Authors: Ali Mollahosseini, Behzad Hasani, Mohammad H. Mahoor
Date: 2017-08-14 00:00:00
Tasks: Facial Expression Recognition, Facial Expression Recognition (FER)

Automated affective computing in the wild setting is a challenging problem in
computer vision. Existing annotated databases of facial expressions in the wild
are small and mostly cover discrete emotions (aka the categorical model). There
are very limited annotated facial databases for affective computing in the
continuous dimensional model (e.g., valence and arousal). To meet this need, we
collected, annotated, and prepared for public distribution a new database of
facial emotions in the wild (called AffectNet). AffectNet contains more than
1,000,000 facial images from the Internet by querying three major search
engines using 1250 emotion related keywords in six different languages. About
half of the retrieved images were manually annotated for the presence of seven
discrete facial expressions and the intensity of valence and arousal. AffectNet
is by far the largest database of facial expression, valence, and arousal in
the wild enabling research in automated facial expression recognition in two
different emotion models. Two baseline deep neural networks are used to
classify images in the categorical model and predict the intensity of valence
and arousal. Various evaluation metrics show that our deep neural network
baselines can perform better than conventional machine learning methods and
off-the-shelf facial expression recognition systems.
Massive Open Online Courses Temporal Profiling for Dropout Prediction
Authors: Tom Rolandus Hagedoorn, Gerasimos Spanakis
Date: 2017-10-09 00:00:00
Tasks: Prediction, regression

Massive Open Online Courses (MOOCs) are attracting the attention of people
all over the world. Regardless the platform, numbers of registrants for online
courses are impressive but in the same time, completion rates are
disappointing. Understanding the mechanisms of dropping out based on the
learner profile arises as a crucial task in MOOCs, since it will allow
intervening at the right moment in order to assist the learner in completing
the course. In this paper, the dropout behaviour of learners in a MOOC is
thoroughly studied by first extracting features that describe the behavior of
learners within the course and then by comparing three classifiers (Logistic
Regression, Random Forest and AdaBoost) in two tasks: predicting which users
will have dropped out by a certain week and predicting which users will drop
out on a specific week. The former has showed to be considerably easier, with
all three classifiers performing equally well. However, the accuracy for the
second task is lower, and Logistic Regression tends to perform slightly better
than the other two algorithms. We found that features that reflect an active
attitude of the user towards the MOOC, such as submitting their assignment,
posting on the Forum and filling their Profile, are strong indicators of
persistence.
DSOS and SDSOS Optimization: More Tractable Alternatives to Sum of Squares and Semidefinite Optimization
Authors: Amir Ali Ahmadi, Anirudha Majumdar
Date: 2017-06-08 00:00:00
Tasks: valid

In recent years, optimization theory has been greatly impacted by the advent
of sum of squares (SOS) optimization. The reliance of this technique on
large-scale semidefinite programs however, has limited the scale of problems to
which it can be applied. In this paper, we introduce DSOS and SDSOS
optimization as linear programming and second-order cone programming-based
alternatives to sum of squares optimization that allow one to trade off
computation time with solution quality. These are optimization problems over
certain subsets of sum of squares polynomials (or equivalently subsets of
positive semidefinite matrices), which can be of interest in general
applications of semidefinite programming where scalability is a limitation. We
show that some basic theorems from SOS optimization which rely on results from
real algebraic geometry are still valid for DSOS and SDSOS optimization.
Furthermore, we show with numerical experiments from diverse application
areas---polynomial optimization, statistics and machine learning, derivative
pricing, and control theory---that with reasonable tradeoffs in accuracy, we
can handle problems at scales that are currently significantly beyond the reach
of traditional sum of squares approaches. Finally, we provide a review of
recent techniques that bridge the gap between our DSOS/SDSOS approach and the
SOS approach at the expense of additional running time. The Supplementary
Material of the paper introduces an accompanying MATLAB package for DSOS and
SDSOS optimization.
Checkpoint Ensembles: Ensemble Methods from a Single Training Process
Authors: Hugh Chen, Scott Lundberg, Su-In Lee
Date: 2017-10-09 00:00:00

We present the checkpoint ensembles method that can learn ensemble models on
a single training process. Although checkpoint ensembles can be applied to any
parametric iterative learning technique, here we focus on neural networks.
Neural networks' composable and simple neurons make it possible to capture many
individual and interaction effects among features. However, small sample sizes
and sampling noise may result in patterns in the training data that are not
representative of the true relationship between the features and the outcome.
As a solution, regularization during training is often used (e.g. dropout).
However, regularization is no panacea -- it does not perfectly address
overfitting. Even with methods like dropout, two methodologies are commonly
used in practice. First is to utilize a validation set independent to the
training set as a way to decide when to stop training. Second is to use
ensemble methods to further reduce overfitting and take advantage of local
optima (i.e. averaging over the predictions of several models). In this paper,
we explore checkpoint ensembles -- a simple technique that combines these two
ideas in one training process. Checkpoint ensembles improve performance by
averaging the predictions from "checkpoints" of the best models within single
training process. We use three real-world data sets -- text, image, and
electronic health record data -- using three prediction models: a vanilla
neural network, a convolutional neural network, and a long short term memory
network to show that checkpoint ensembles outperform existing methods: a method
that selects a model by minimum validation score, and two methods that average
models by weights. Our results also show that checkpoint ensembles capture a
portion of the performance gains that traditional ensembles provide.
Lagged Exact Bayesian Online Changepoint Detection with Parameter Estimation
Authors: Michael Byrd, Linh Nghiem, Jing Cao
Date: 2017-10-09 00:00:00
Tasks: parameter estimation

Identifying changes in the generative process of sequential data, known as
changepoint detection, has become an increasingly important topic for a wide
variety of fields. A recently developed approach, which we call EXact Online
Bayesian Changepoint Detection (EXO), has shown reasonable results with
efficient computation for real time updates. The method is based on a
\textit{forward} recursive message-passing algorithm. However, the detected
changepoints from these methods are unstable. We propose a new algorithm called
Lagged EXact Online Bayesian Changepoint Detection (LEXO) that improves the
accuracy and stability of the detection by incorporating $\ell$-time lags to
the inference. The new algorithm adds a recursive \textit{backward} step to the
forward EXO and has computational complexity linear in the number of added
lags. Estimation of parameters associated with regimes is also developed.
Simulation studies with three common changepoint models show that the detected
changepoints from LEXO are much more stable and parameter estimates from LEXO
have considerably lower MSE than EXO. We illustrate applicability of the
methods with two real world data examples comparing the EXO and LEXO.
Considerations of automated machine learning in clinical metabolic profiling: Altered homocysteine plasma concentration associated with metformin exposure
Authors: Alena Orlenko, Jason H. Moore, Patryk Orzechowski, Randal S. Olson, Junmei Cairns, Pedro J. Caraballo, Richard M. Weinshilboum, Liewei Wang, Matthew K. Breitenstein
Date: 2017-10-09 00:00:00
Tasks: AutoML, BIG-bench Machine Learning, feature selection

With the maturation of metabolomics science and proliferation of biobanks,
clinical metabolic profiling is an increasingly opportunistic frontier for
advancing translational clinical research. Automated Machine Learning (AutoML)
approaches provide exciting opportunity to guide feature selection in agnostic
metabolic profiling endeavors, where potentially thousands of independent data
points must be evaluated. In previous research, AutoML using high-dimensional
data of varying types has been demonstrably robust, outperforming traditional
approaches. However, considerations for application in clinical metabolic
profiling remain to be evaluated. Particularly, regarding the robustness of
AutoML to identify and adjust for common clinical confounders. In this study,
we present a focused case study regarding AutoML considerations for using the
Tree-Based Optimization Tool (TPOT) in metabolic profiling of exposure to
metformin in a biobank cohort. First, we propose a tandem rank-accuracy measure
to guide agnostic feature selection and corresponding threshold determination
in clinical metabolic profiling endeavors. Second, while AutoML, using default
parameters, demonstrated potential to lack sensitivity to low-effect
confounding clinical covariates, we demonstrated residual training and
adjustment of metabolite features as an easily applicable approach to ensure
AutoML adjustment for potential confounding characteristics. Finally, we
present increased homocysteine with long-term exposure to metformin as a
potentially novel, non-replicated metabolite association suggested by TPOT; an
association not identified in parallel clinical metabolic profiling endeavors.
While considerations are recommended, including adjustment approaches for
clinical confounders, AutoML presents an exciting tool to enhance clinical
metabolic profiling and advance translational research endeavors.