pwc-archive/papers-with-abstracts huggingface dataset:
deepTarget: End-to-end Learning Framework for microRNA Target Prediction using Deep Recurrent Neural Networks
Authors: Byunghan Lee, Junghwan Baek, Seunghyun Park, Sungroh Yoon
Date: 2016-03-30 00:00:00
Tasks: Prediction

MicroRNAs (miRNAs) are short sequences of ribonucleic acids that control the
expression of target messenger RNAs (mRNAs) by binding them. Robust prediction
of miRNA-mRNA pairs is of utmost importance in deciphering gene regulations but
has been challenging because of high false positive rates, despite a deluge of
computational tools that normally require laborious manual feature extraction.
This paper presents an end-to-end machine learning framework for miRNA target
prediction. Leveraged by deep recurrent neural networks-based auto-encoding and
sequence-sequence interaction learning, our approach not only delivers an
unprecedented level of accuracy but also eliminates the need for manual feature
extraction. The performance gap between the proposed method and existing
alternatives is substantial (over 25% increase in F-measure), and deepTarget
delivers a quantum leap in the long-standing challenge of robust miRNA target
prediction.
On the Existence of a Projective Reconstruction
Authors: Hon-Leung Lee
Date: 2016-08-19 00:00:00

In this note we study the connection between the existence of a projective reconstruction and the existence of a fundamental matrix satisfying the epipolar constraints.
Critical Points for Two-view Triangulation
Authors: Hon-Leung Lee
Date: 2016-08-19 00:00:00
Tasks: Vocal Bursts Valence Prediction

Two-view triangulation is a problem of minimizing a quadratic polynomial
under an equality constraint. We derive a polynomial that encodes the local
minimizers of this problem using the theory of Lagrange multipliers. This
offers a simpler derivation of the critical points that are given in
Hartley-Sturm [6].
An Efficient Character-Level Neural Machine Translation
Authors: Shenjian Zhao, Zhihua Zhang
Date: 2016-08-16 00:00:00
Tasks: Decoder, Machine Translation, Translation

Neural machine translation aims at building a single large neural network
that can be trained to maximize translation performance. The encoder-decoder
architecture with an attention mechanism achieves a translation performance
comparable to the existing state-of-the-art phrase-based systems on the task of
English-to-French translation. However, the use of large vocabulary becomes the
bottleneck in both training and improving the performance. In this paper, we
propose an efficient architecture to train a deep character-level neural
machine translation by introducing a decimator and an interpolator. The
decimator is used to sample the source sequence before encoding while the
interpolator is used to resample after decoding. Such a deep model has two
major advantages. It avoids the large vocabulary issue radically; at the same
time, it is much faster and more memory-efficient in training than conventional
character-based models. More interestingly, our model is able to translate the
misspelled word like human beings.
Stein Variational Gradient Descent: A General Purpose Bayesian Inference Algorithm
Authors: Qiang Liu, Dilin Wang
Date: 2016-08-16 00:00:00
Proceeding: NeurIPS 2016 12
Tasks: Bayesian Inference, Variational Inference

We propose a general purpose variational inference algorithm that forms a natural counterpart of gradient descent for optimization. Our method iteratively transports a set of particles to match the target distribution, by applying a form of functional gradient descent that minimizes the KL divergence. Empirical studies are performed on various real world models and datasets, on which our method is competitive with existing state-of-the-art methods. The derivation of our method is based on a new theoretical result that connects the derivative of KL divergence under smooth transforms with Stein's identity and a recently proposed kernelized Stein discrepancy, which is of independent interest.
Network Volume Anomaly Detection and Identification in Large-scale Networks based on Online Time-structured Traffic Tensor Tracking
Authors: Hiroyuki Kasai, Wolfgang Kellerer, Martin Kleinsteuber
Date: 2016-08-19 00:00:00
Tasks: Anomaly Detection, Outlier Detection

This paper addresses network anomography, that is, the problem of inferring
network-level anomalies from indirect link measurements. This problem is cast
as a low-rank subspace tracking problem for normal flows under incomplete
observations, and an outlier detection problem for abnormal flows. Since
traffic data is large-scale time-structured data accompanied with noise and
outliers under partial observations, an efficient modeling method is essential.
To this end, this paper proposes an online subspace tracking of a Hankelized
time-structured traffic tensor for normal flows based on the Candecomp/PARAFAC
decomposition exploiting the recursive least squares (RLS) algorithm. We
estimate abnormal flows as outlier sparse flows via sparsity maximization in
the underlying under-constrained linear-inverse problem. A major advantage is
that our algorithm estimates normal flows by low-dimensional matrices with
time-directional features as well as the spatial correlation of multiple links
without using the past observed measurements and the past model parameters.
Extensive numerical evaluations show that the proposed algorithm achieves
faster convergence per iteration of model approximation, and better volume
anomaly detection performance compared to state-of-the-art algorithms.
High-dimensional Mixed Graphical Models
Authors: Jie Cheng, Tianxi Li, Elizaveta Levina, Ji Zhu
Date: 2013-04-09 00:00:00
Tasks: Computational Efficiency, Vocal Bursts Intensity Prediction

While graphical models for continuous data (Gaussian graphical models) and
discrete data (Ising models) have been extensively studied, there is little
work on graphical models linking both continuous and discrete variables (mixed
data), which are common in many scientific applications. We propose a novel
graphical model for mixed data, which is simple enough to be suitable for
high-dimensional data, yet flexible enough to represent all possible graph
structures. We develop a computationally efficient regression-based algorithm
for fitting the model by focusing on the conditional log-likelihood of each
variable given the rest. The parameters have a natural group structure, and
sparsity in the fitted graph is attained by incorporating a group lasso
penalty, approximated by a weighted $\ell_1$ penalty for computational
efficiency. We demonstrate the effectiveness of our method through an extensive
simulation study and apply it to a music annotation data set (CAL500),
obtaining a sparse and interpretable graphical model relating the continuous
features of the audio signal to categorical variables such as genre, emotions,
and usage associated with particular songs. While we focus on binary discrete
variables, we also show that the proposed methodology can be easily extended to
general discrete variables.
A heuristic scheme for the Cooperative Team Orienteering Problem with Time Windows
Authors: Iman Roozbeh, Melih Ozlen, John W. Hearne
Date: 2016-08-19 00:00:00
Tasks: Attribute

The Cooperative Orienteering Problem with Time Windows (COPTW)is a class of
problems with some important applications and yet has received relatively
little attention. In the COPTW a certain number of team members are required to
collect the associated reward from each customer simultaneously and
cooperatively. This requirement to have one or more team members simultaneously
available at a vertex to collect the reward, poses a challenging OR task. Exact
methods are not able to handle large scale instances of the COPTW and no
heuristic schemes have been developed for this problem so far. In this paper, a
new modification to the classical Clarke and Wright saving heuristic is
proposed to handle this problem. A new benchmark set generated by adding the
resource requirement attribute to the existing benchmarks. The heuristic
algorithm followed by boosting operators achieves optimal solutions for 64.5%
of instances for which the optimal results are known. The proposed solution
approach attains an optimality gap of 2.61% for the same instances and solves
benchmarks with realistic size within short computational times.
A novel learning-based frame pooling method for Event Detection
Authors: Lan Wang, Chenqiang Gao, Jiang Liu, Deyu Meng
Date: 2016-03-07 00:00:00
Tasks: Event Detection

Detecting complex events in a large video collection crawled from video
websites is a challenging task. When applying directly good image-based feature
representation, e.g., HOG, SIFT, to videos, we have to face the problem of how
to pool multiple frame feature representations into one feature representation.
In this paper, we propose a novel learning-based frame pooling method. We
formulate the pooling weight learning as an optimization problem and thus our
method can automatically learn the best pooling weight configuration for each
specific event category. Experimental results conducted on TRECVID MED 2011
reveal that our method outperforms the commonly used average pooling and max
pooling strategies on both high-level and low-level 2D image features.
Who did What: A Large-Scale Person-Centered Cloze Dataset
Authors: Takeshi Onishi, Hai Wang, Mohit Bansal, Kevin Gimpel, David Mcallester
Date: 2016-08-19 00:00:00
Proceeding: EMNLP 2016 11
Tasks: Articles, Multiple-choice, Reading Comprehension

We have constructed a new "Who-did-What" dataset of over 200,000
fill-in-the-gap (cloze) multiple choice reading comprehension problems
constructed from the LDC English Gigaword newswire corpus. The WDW dataset has
a variety of novel features. First, in contrast with the CNN and Daily Mail
datasets (Hermann et al., 2015) we avoid using article summaries for question
formation. Instead, each problem is formed from two independent articles --- an
article given as the passage to be read and a separate article on the same
events used to form the question. Second, we avoid anonymization --- each
choice is a person named entity. Third, the problems have been filtered to
remove a fraction that are easily solved by simple baselines, while remaining
84% solvable by humans. We report performance benchmarks of standard systems
and propose the WDW dataset as a challenge task for the community.
Semantic Understanding of Scenes through the ADE20K Dataset
Authors: Bolei Zhou, Hang Zhao, Xavier Puig, Tete Xiao, Sanja Fidler, Adela Barriuso, Antonio Torralba
Date: 2016-08-18 00:00:00
Tasks: Scene Parsing, Segmentation, Semantic Segmentation

Scene parsing, or recognizing and segmenting objects and stuff in an image,
is one of the key problems in computer vision. Despite the community's efforts
in data collection, there are still few image datasets covering a wide range of
scenes and object categories with dense and detailed annotations for scene
parsing. In this paper, we introduce and analyze the ADE20K dataset, spanning
diverse annotations of scenes, objects, parts of objects, and in some cases
even parts of parts. A generic network design called Cascade Segmentation
Module is then proposed to enable the segmentation networks to parse a scene
into stuff, objects, and object parts in a cascade. We evaluate the proposed
module integrated within two existing semantic segmentation networks, yielding
significant improvements for scene parsing. We further show that the scene
parsing networks trained on ADE20K can be applied to a wide variety of scenes
and objects.
Distributed Optimization of Convex Sum of Non-Convex Functions
Authors: Shripad Gade, Nitin H. Vaidya
Date: 2016-08-18 00:00:00
Tasks: Distributed Optimization

We present a distributed solution to optimizing a convex function composed of
several non-convex functions. Each non-convex function is privately stored with
an agent while the agents communicate with neighbors to form a network. We show
that coupled consensus and projected gradient descent algorithm proposed in [1]
can optimize convex sum of non-convex functions under an additional assumption
on gradient Lipschitzness. We further discuss the applications of this analysis
in improving privacy in distributed optimization.
DNN-based Speech Synthesis for Indian Languages from ASCII text
Authors: Srikanth Ronanki, Siva Reddy, Bajibabu Bollepalli, Simon King
Date: 2016-08-18 00:00:00
Tasks: Speech Synthesis, text-to-speech, Text to Speech, Text-To-Speech Synthesis

Text-to-Speech synthesis in Indian languages has a seen lot of progress over
the decade partly due to the annual Blizzard challenges. These systems assume
the text to be written in Devanagari or Dravidian scripts which are nearly
phonemic orthography scripts. However, the most common form of computer
interaction among Indians is ASCII written transliterated text. Such text is
generally noisy with many variations in spelling for the same word. In this
paper we evaluate three approaches to synthesize speech from such noisy ASCII
text: a naive Uni-Grapheme approach, a Multi-Grapheme approach, and a
supervised Grapheme-to-Phoneme (G2P) approach. These methods first convert the
ASCII text to a phonetic script, and then learn a Deep Neural Network to
synthesize speech from that. We train and test our models on Blizzard Challenge
datasets that were transliterated to ASCII using crowdsourcing. Our experiments
on Hindi, Tamil and Telugu demonstrate that our models generate speech of
competetive quality from ASCII text compared to the speech synthesized from the
native scripts. All the accompanying transliterated datasets are released for
public access.
Probabilistic Data Analysis with Probabilistic Programming
Authors: Feras Saad, Vikash Mansinghka
Date: 2016-08-18 00:00:00
Tasks: Clustering, Dimensionality Reduction, Probabilistic Programming

Probabilistic techniques are central to data analysis, but different
approaches can be difficult to apply, combine, and compare. This paper
introduces composable generative population models (CGPMs), a computational
abstraction that extends directed graphical models and can be used to describe
and compose a broad class of probabilistic data analysis techniques. Examples
include hierarchical Bayesian models, multivariate kernel methods,
discriminative machine learning, clustering algorithms, dimensionality
reduction, and arbitrary probabilistic programs. We also demonstrate the
integration of CGPMs into BayesDB, a probabilistic programming platform that
can express data analysis tasks using a modeling language and a structured
query language. The practical value is illustrated in two ways. First, CGPMs
are used in an analysis that identifies satellite data records which probably
violate Kepler's Third Law, by composing causal probabilistic programs with
non-parametric Bayes in under 50 lines of probabilistic code. Second, for
several representative data analysis tasks, we report on lines of code and
accuracy measurements of various CGPMs, plus comparisons with standard baseline
solutions from Python and MATLAB libraries.
Molecular Graph Convolutions: Moving Beyond Fingerprints
Authors: Steven Kearnes, Kevin McCloskey, Marc Berndl, Vijay Pande, Patrick Riley
Date: 2016-03-02 00:00:00
Tasks: BIG-bench Machine Learning, Drug Discovery, Graph Regression

Molecular "fingerprints" encoding structural information are the workhorse of
cheminformatics and machine learning in drug discovery applications. However,
fingerprint representations necessarily emphasize particular aspects of the
molecular structure while ignoring others, rather than allowing the model to
make data-driven decisions. We describe molecular "graph convolutions", a
machine learning architecture for learning from undirected graphs, specifically
small molecules. Graph convolutions use a simple encoding of the molecular
graph---atoms, bonds, distances, etc.---which allows the model to take greater
advantage of information in the graph structure. Although graph convolutions do
not outperform all fingerprint-based methods, they (along with other
graph-based methods) represent a new paradigm in ligand-based virtual screening
with exciting opportunities for future improvement.
A Tight Convex Upper Bound on the Likelihood of a Finite Mixture
Authors: Elad Mezuman, Yair Weiss
Date: 2016-08-18 00:00:00

The likelihood function of a finite mixture model is a non-convex function
with multiple local maxima and commonly used iterative algorithms such as EM
will converge to different solutions depending on initial conditions. In this
paper we ask: is it possible to assess how far we are from the global maximum
of the likelihood? Since the likelihood of a finite mixture model can grow
unboundedly by centering a Gaussian on a single datapoint and shrinking the
covariance, we constrain the problem by assuming that the parameters of the
individual models are members of a large discrete set (e.g. estimating a
mixture of two Gaussians where the means and variances of both Gaussians are
members of a set of a million possible means and variances). For this setting
we show that a simple upper bound on the likelihood can be computed using
convex optimization and we analyze conditions under which the bound is
guaranteed to be tight. This bound can then be used to assess the quality of
solutions found by EM (where the final result is projected on the discrete set)
or any other mixture estimation algorithm. For any dataset our method allows us
to find a finite mixture model together with a dataset-specific bound on how
far the likelihood of this mixture is from the global optimum of the likelihood
Locally Adaptive Dynamic Networks
Authors: Daniele Durante, David B. Dunson
Date: 2015-05-21 00:00:00
Tasks: Data Augmentation, Position

Our focus is on realistically modeling and forecasting dynamic networks of
face-to-face contacts among individuals. Important aspects of such data that
lead to problems with current methods include the tendency of the contacts to
move between periods of slow and rapid changes, and the dynamic heterogeneity
in the actors' connectivity behaviors. Motivated by this application, we
develop a novel method for Locally Adaptive DYnamic (LADY) network inference.
The proposed model relies on a dynamic latent space representation in which
each actor's position evolves in time via stochastic differential equations.
Using a state space representation for these stochastic processes and
P\'olya-gamma data augmentation, we develop an efficient MCMC algorithm for
posterior inference along with tractable procedures for online updating and
forecasting of future networks. We evaluate performance in simulation studies,
and consider an application to face-to-face contacts among individuals in a
primary school.
Parameter Learning for Log-supermodular Distributions
Authors: Tatiana Shpakova, Francis Bach
Date: 2016-08-18 00:00:00
Proceeding: NeurIPS 2016 12
Tasks: Combinatorial Optimization, Denoising, Image Denoising, Image Segmentation, parameter estimation, Semantic Segmentation

We consider log-supermodular models on binary variables, which are
probabilistic models with negative log-densities which are submodular. These
models provide probabilistic interpretations of common combinatorial
optimization tasks such as image segmentation. In this paper, we focus
primarily on parameter estimation in the models from known upper-bounds on the
intractable log-partition function. We show that the bound based on separable
optimization on the base polytope of the submodular function is always inferior
to a bound based on "perturb-and-MAP" ideas. Then, to learn parameters, given
that our approximation of the log-partition function is an expectation (over
our own randomization), we use a stochastic subgradient technique to maximize a
lower-bound on the log-likelihood. This can also be extended to conditional
maximum likelihood. We illustrate our new results in a set of experiments in
binary image denoising, where we highlight the flexibility of a probabilistic
model to learn with missing data.
How Image Degradations Affect Deep CNN-based Face Recognition?
Authors: Samil Karahan, Merve Kilinc Yildirim, Kadir Kirtac, Ferhat Sukru Rende, Gultekin Butun, Hazim Kemal Ekenel
Date: 2016-08-18 00:00:00
Tasks: Face Recognition

Face recognition approaches that are based on deep convolutional neural
networks (CNN) have been dominating the field. The performance improvements
they have provided in the so called in-the-wild datasets are significant,
however, their performance under image quality degradations have not been
assessed, yet. This is particularly important, since in real-world face
recognition applications, images may contain various kinds of degradations due
to motion blur, noise, compression artifacts, color distortions, and occlusion.
In this work, we have addressed this problem and analyzed the influence of
these image degradations on the performance of deep CNN-based face recognition
approaches using the standard LFW closed-set identification protocol. We have
evaluated three popular deep CNN models, namely, the AlexNet, VGG-Face, and
GoogLeNet. Results have indicated that blur, noise, and occlusion cause a
significant decrease in performance, while deep CNN models are found to be
robust to distortions, such as color distortions and change in color balance.
Multilingual Modal Sense Classification using a Convolutional Neural Network
Authors: Ana Marasović, Anette Frank
Date: 2016-08-18 00:00:00
Proceeding: WS 2016 8
Tasks: Classification, General Classification

Modal sense classification (MSC) is a special WSD task that depends on the
meaning of the proposition in the modal's scope. We explore a CNN architecture
for classifying modal sense in English and German. We show that CNNs are
superior to manually designed feature-based classifiers and a standard NN
classifier. We analyze the feature maps learned by the CNN and identify known
and previously unattested linguistic features. We benchmark the CNN on a
standard WSD task, where it compares favorably to models using
sense-disambiguated target vectors.
Multi-task and Lifelong Learning of Kernels
Authors: Anastasia Pentina, Shai Ben-David
Date: 2016-02-21 00:00:00
Tasks: General Classification, Generalization Bounds, Lifelong learning

We consider a problem of learning kernels for use in SVM classification in
the multi-task and lifelong scenarios and provide generalization bounds on the
error of a large margin classifier. Our results show that, under mild
conditions on the family of kernels used for learning, solving several related
tasks simultaneously is beneficial over single task learning. In particular, as
the number of observed tasks grows, assuming that in the considered family of
kernels there exists one that yields low approximation error on all tasks, the
overhead associated with learning such a kernel vanishes and the complexity
converges to that of learning when this good kernel is given to the learner.
Active Learning for Approximation of Expensive Functions with Normal Distributed Output Uncertainty
Authors: Joachim van der Herten, Ivo Couckuyt, Dirk Deschrijver, Tom Dhaene
Date: 2016-08-18 00:00:00
Tasks: Active Learning

When approximating a black-box function, sampling with active learning
focussing on regions with non-linear responses tends to improve accuracy. We
present the FLOLA-Voronoi method introduced previously for deterministic
responses, and theoretically derive the impact of output uncertainty. The
algorithm automatically puts more emphasis on exploration to provide more
information to the models.