pwc-archive/papers-with-abstracts huggingface dataset:
Joint Multimodal Learning with Deep Generative Models
Authors: Masahiro Suzuki, Kotaro Nakayama, Yutaka Matsuo
Date: 2016-11-07 00:00:00

We investigate deep generative models that can exchange multiple modalities
bi-directionally, e.g., generating images from corresponding texts and vice
versa. Recently, some studies handle multiple modalities on deep generative
models, such as variational autoencoders (VAEs). However, these models
typically assume that modalities are forced to have a conditioned relation,
i.e., we can only generate modalities in one direction. To achieve our
objective, we should extract a joint representation that captures high-level
concepts among all modalities and through which we can exchange them
bi-directionally. As described herein, we propose a joint multimodal
variational autoencoder (JMVAE), in which all modalities are independently
conditioned on joint representation. In other words, it models a joint
distribution of modalities. Furthermore, to be able to generate missing
modalities from the remaining modalities properly, we develop an additional
method, JMVAE-kl, that is trained by reducing the divergence between JMVAE's
encoder and prepared networks of respective modalities. Our experiments show
that our proposed method can obtain appropriate joint representation from
multiple modalities and that it can generate and reconstruct them more properly
than conventional VAEs. We further demonstrate that JMVAE can generate multiple
modalities bi-directionally.
Machine Comprehension Using Match-LSTM and Answer Pointer
Authors: Shuohang Wang, Jing Jiang
Date: 2016-08-29 00:00:00
Tasks: Natural Language Inference, Question Answering, Reading Comprehension

Machine comprehension of text is an important problem in natural language
processing. A recently released dataset, the Stanford Question Answering
Dataset (SQuAD), offers a large number of real questions and their answers
created by humans through crowdsourcing. SQuAD provides a challenging testbed
for evaluating machine comprehension algorithms, partly because compared with
previous datasets, in SQuAD the answers do not come from a small set of
candidate answers and they have variable lengths. We propose an end-to-end
neural architecture for the task. The architecture is based on match-LSTM, a
model we proposed previously for textual entailment, and Pointer Net, a
sequence-to-sequence model proposed by Vinyals et al.(2015) to constrain the
output tokens to be from the input sequences. We propose two ways of using
Pointer Net for our task. Our experiments show that both of our two models
substantially outperform the best results obtained by Rajpurkar et al.(2016)
using logistic regression and manually crafted features.
Challenges of Feature Selection for Big Data Analytics
Authors: Jundong Li, Huan Liu
Date: 2016-11-07 00:00:00
Tasks: feature selection

We are surrounded by huge amounts of large-scale high dimensional data. It is
desirable to reduce the dimensionality of data for many learning tasks due to
the curse of dimensionality. Feature selection has shown its effectiveness in
many applications by building simpler and more comprehensive model, improving
learning performance, and preparing clean, understandable data. Recently, some
unique characteristics of big data such as data velocity and data variety
present challenges to the feature selection problem. In this paper, we envision
these challenges of feature selection for big data analytics. In particular, we
first give a brief introduction about feature selection and then detail the
challenges of feature selection for structured, heterogeneous and streaming
data as well as its scalability and stability issues. At last, to facilitate
and promote the feature selection research, we present an open-source feature
selection repository (scikit-feature), which consists of most of current
popular feature selection algorithms.
Action2Activity: Recognizing Complex Activities from Sensor Data
Authors: Ye Liu, Liqiang Nie, Lei Han, Luming Zhang, David S. Rosenblum
Date: 2016-11-07 00:00:00
Tasks: Action Recognition, Activity Recognition, Multi-Task Learning, Temporal Action Localization

As compared to simple actions, activities are much more complex, but
semantically consistent with a human's real life. Techniques for action
recognition from sensor generated data are mature. However, there has been
relatively little work on bridging the gap between actions and activities. To
this end, this paper presents a novel approach for complex activity recognition
comprising of two components. The first component is temporal pattern mining,
which provides a mid-level feature representation for activities, encodes
temporal relatedness among actions, and captures the intrinsic properties of
activities. The second component is adaptive Multi-Task Learning, which
captures relatedness among activities and selects discriminant features.
Extensive experiments on a real-world dataset demonstrate the effectiveness of
our work.
Truth Discovery with Memory Network
Authors: Luyang Li, Bing Qin, Wenjing Ren, Ting Liu
Date: 2016-11-07 00:00:00

Truth discovery is to resolve conflicts and find the truth from
multiple-source statements. Conventional methods mostly research based on the
mutual effect between the reliability of sources and the credibility of
statements, however, pay no attention to the mutual effect among the
credibility of statements about the same object. We propose memory network
based models to incorporate these two ideas to do the truth discovery. We use
feedforward memory network and feedback memory network to learn the
representation of the credibility of statements which are about the same
object. Specially, we adopt memory mechanism to learn source reliability and
use it through truth prediction. During learning models, we use multiple types
of data (categorical data and continuous data) by assigning different weights
automatically in the loss function based on their own effect on truth discovery
prediction. The experiment results show that the memory network based models
much outperform the state-of-the-art method and other baseline methods.
Latent Attention For If-Then Program Synthesis
Authors: Xinyun Chen, Chang Liu, Richard Shin, Dawn Song, Mingcheng Chen
Date: 2016-11-07 00:00:00
Proceeding: NeurIPS 2016 12
Tasks: One-Shot Learning, Program Synthesis, Translation

Automatic translation from natural language descriptions into programs is a
longstanding challenging problem. In this work, we consider a simple yet
important sub-problem: translation from textual descriptions to If-Then
programs. We devise a novel neural network architecture for this task which we
train end-to-end. Specifically, we introduce Latent Attention, which computes
multiplicative weights for the words in the description in a two-stage process
with the goal of better leveraging the natural language structures that
indicate the relevant parts for predicting program elements. Our architecture
reduces the error rate by 28.57% compared to prior art. We also propose a
one-shot learning scenario of If-Then program synthesis and simulate it with
our existing dataset. We demonstrate a variation on the training procedure for
this scenario that outperforms the original procedure, significantly closing
the gap to the model trained with all data.
Neuro-Symbolic Program Synthesis
Authors: Emilio Parisotto, Abdel-rahman Mohamed, Rishabh Singh, Lihong Li, Dengyong Zhou, Pushmeet Kohli
Date: 2016-11-06 00:00:00
Tasks: Program induction, Program Synthesis

Recent years have seen the proposal of a number of neural architectures for
the problem of Program Induction. Given a set of input-output examples, these
architectures are able to learn mappings that generalize to new test inputs.
While achieving impressive results, these approaches have a number of important
limitations: (a) they are computationally expensive and hard to train, (b) a
model has to be trained for each task (program) separately, and (c) it is hard
to interpret or verify the correctness of the learnt mapping (as it is defined
by a neural network). In this paper, we propose a novel technique,
Neuro-Symbolic Program Synthesis, to overcome the above-mentioned problems.
Once trained, our approach can automatically construct computer programs in a
domain-specific language that are consistent with a set of input-output
examples provided at test time. Our method is based on two novel neural
modules. The first module, called the cross correlation I/O network, given a
set of input-output examples, produces a continuous representation of the set
of I/O examples. The second module, the Recursive-Reverse-Recursive Neural
Network (R3NN), given the continuous representation of the examples,
synthesizes a program by incrementally expanding partial programs. We
demonstrate the effectiveness of our approach by applying it to the rich and
complex domain of regular expression based string transformations. Experiments
show that the R3NN model is not only able to construct programs from new
input-output examples, but it is also able to construct new programs for tasks
that it had never observed before during training.
Urban MV and LV Distribution Grid Topology Estimation via Group Lasso
Authors: Yizheng Liao, Yang Weng, Guangyi Liu, Ram Rajagopal
Date: 2016-11-06 00:00:00

The increasing penetration of distributed energy resources poses numerous
reliability issues to the urban distribution grid. The topology estimation is a
critical step to ensure the robustness of distribution grid operation. However,
the bus connectivity and grid topology estimation are usually hard in
distribution grids. For example, it is technically challenging and costly to
monitor the bus connectivity in urban grids, e.g., underground lines. It is
also inappropriate to use the radial topology assumption exclusively because
the grids of metropolitan cities and regions with dense loads could be with
many mesh structures. To resolve these drawbacks, we propose a data-driven
topology estimation method for MV and LV distribution grids by only utilizing
the historical smart meter measurements. Particularly, a probabilistic
graphical model is utilized to capture the statistical dependencies amongst bus
voltages. We prove that the bus connectivity and grid topology estimation
problems, in radial and mesh structures, can be formulated as a linear
regression with a least absolute shrinkage regularization on grouped variables
(\textit{group lasso}). Simulations show highly accurate results in eight MV
and LV distribution networks at different sizes and 22 topology configurations
using PG\&E residential smart meter data.
Online Multi-view Clustering with Incomplete Views
Authors: Weixiang Shao, Lifang He, Chun-Ta Lu, Philip S. Yu
Date: 2016-11-02 00:00:00
Tasks: Clustering

In the era of big data, it is common to have data with multiple modalities or
coming from multiple sources, known as "multi-view data". Multi-view clustering
provides a natural way to generate clusters from such data. Since different
views share some consistency and complementary information, previous works on
multi-view clustering mainly focus on how to combine various numbers of views
to improve clustering performance. However, in reality, each view may be
incomplete, i.e., instances missing in the view. Furthermore, the size of data
could be extremely huge. It is unrealistic to apply multi-view clustering in
large real-world applications without considering the incompleteness of views
and the memory requirement. None of previous works have addressed all these
challenges simultaneously. In this paper, we propose an online multi-view
clustering algorithm, OMVC, which deals with large-scale incomplete views. We
model the multi-view clustering problem as a joint weighted nonnegative matrix
factorization problem and process the multi-view data chunk by chunk to reduce
the memory requirement. OMVC learns the latent feature matrices for all the
views and pushes them towards a consensus. We further increase the robustness
of the learned latent feature matrices in OMVC via lasso regularization. To
minimize the influence of incompleteness, dynamic weight setting is introduced
to give lower weights to the incoming missing instances in different views.
More importantly, to reduce the computational time, we incorporate a faster
projected gradient descent by utilizing the Hessian matrices in OMVC. Extensive
experiments conducted on four real data demonstrate the effectiveness of the
proposed OMVC method.
Generative Adversarial Networks as Variational Training of Energy Based Models
Authors: Shuangfei Zhai, Yu Cheng, Rogerio Feris, Zhongfei Zhang
Date: 2016-11-06 00:00:00

In this paper, we study deep generative models for effective unsupervised
learning. We propose VGAN, which works by minimizing a variational lower bound
of the negative log likelihood (NLL) of an energy based model (EBM), where the
model density $p(\mathbf{x})$ is approximated by a variational distribution
$q(\mathbf{x})$ that is easy to sample from. The training of VGAN takes a two
step procedure: given $p(\mathbf{x})$, $q(\mathbf{x})$ is updated to maximize
the lower bound; $p(\mathbf{x})$ is then updated one step with samples drawn
from $q(\mathbf{x})$ to decrease the lower bound. VGAN is inspired by the
generative adversarial networks (GANs), where $p(\mathbf{x})$ corresponds to
the discriminator and $q(\mathbf{x})$ corresponds to the generator, but with
several notable differences. We hence name our model variational GANs (VGANs).
VGAN provides a practical solution to training deep EBMs in high dimensional
space, by eliminating the need of MCMC sampling. From this view, we are also
able to identify causes to the difficulty of training GANs and propose viable
solutions. \footnote{Experimental code is available at
https://github.com/Shuangfei/vgan}
Domain Adaptation For Formant Estimation Using Deep Learning
Authors: Yehoshua Dissen, Joseph Keshet, Jacob Goldberger, Cynthia Clopper
Date: 2016-11-06 00:00:00
Tasks: Deep Learning, Domain Adaptation

In this paper we present a domain adaptation technique for formant estimation
using a deep network. We first train a deep learning network on a small read
speech dataset. We then freeze the parameters of the trained network and use
several different datasets to train an adaptation layer that makes the obtained
network universal in the sense that it works well for a variety of speakers and
speech domains with very different characteristics. We evaluated our adapted
network on three datasets, each of which has different speaker characteristics
and speech styles. The performance of our method compares favorably with
alternative methods for formant estimation.
EM Algorithm and Stochastic Control in Economics
Authors: Steven Kou, Xianhua Peng, Xingbo Xu
Date: 2016-11-06 00:00:00

Generalising the idea of the classical EM algorithm that is widely used for
computing maximum likelihood estimates, we propose an EM-Control (EM-C)
algorithm for solving multi-period finite time horizon stochastic control
problems. The new algorithm sequentially updates the control policies in each
time period using Monte Carlo simulation in a forward-backward manner; in other
words, the algorithm goes forward in simulation and backward in optimization in
each iteration. Similar to the EM algorithm, the EM-C algorithm has the
monotonicity of performance improvement in each iteration, leading to good
convergence properties. We demonstrate the effectiveness of the algorithm by
solving stochastic control problems in the monopoly pricing of perishable
assets and in the study of real business cycle.
Deep Convolutional Neural Network Features and the Original Image
Authors: Connor J. Parde, Carlos Castillo, Matthew Q. Hill, Y. Ivette Colon, Swami Sankaranarayanan, Jun-Cheng Chen, Alice J. O'Toole
Date: 2016-11-06 00:00:00
Tasks: Face Recognition

Face recognition algorithms based on deep convolutional neural networks
(DCNNs) have made progress on the task of recognizing faces in unconstrained
viewing conditions. These networks operate with compact feature-based face
representations derived from learning a very large number of face images. While
the learned features produced by DCNNs can be highly robust to changes in
viewpoint, illumination, and appearance, little is known about the nature of
the face code that emerges at the top level of such networks. We analyzed the
DCNN features produced by two face recognition algorithms. In the first set of
experiments we used the top-level features from the DCNNs as input into linear
classifiers aimed at predicting metadata about the images. The results show
that the DCNN features contain surprisingly accurate information about the yaw
and pitch of a face, and about whether the face came from a still image or a
video frame. In the second set of experiments, we measured the extent to which
individual DCNN features operated in a view-dependent or view-invariant manner.
We found that view-dependent coding was a characteristic of the identities
rather than the DCNN features - with some identities coded consistently in a
view-dependent way and others in a view-independent way. In our third analysis,
we visualized the DCNN feature space for over 24,000 images of 500 identities.
Images in the center of the space were uniformly of low quality (e.g., extreme
views, face occlusion, low resolution). Image quality increased monotonically
as a function of distance from the origin. This result suggests that image
quality information is available in the DCNN features, such that consistently
average feature values reflect coding failures that reliably indicate poor or
unusable images. Combined, the results offer insight into the coding mechanisms
that support robust representation of faces in DCNNs.
A Compare-Aggregate Model for Matching Text Sequences
Authors: Shuohang Wang, Jing Jiang
Date: 2016-11-06 00:00:00
Tasks: Answer Selection, model, Reading Comprehension

Many NLP tasks including machine comprehension, answer selection and text
entailment require the comparison between sequences. Matching the important
units between sequences is a key to solve these problems. In this paper, we
present a general "compare-aggregate" framework that performs word-level
matching followed by aggregation using Convolutional Neural Networks. We
particularly focus on the different comparison functions we can use to match
two vectors. We use four different datasets to evaluate the model. We find that
some simple comparison functions based on element-wise operations can work
better than standard neural network and neural tensor network.
Validation of Tsallis Entropy In Inter-Modality Neuroimage Registration
Authors: Henrique Tomaz Amaral-Silva, Luiz Otavio Murta-Jr, Paulo Mazzoncini de Azevedo-Marques, Lauro Wichert-Ana, V. B. Surya Prasath, Colin Studholme
Date: 2016-11-06 00:00:00
Tasks: Computed Tomography (CT), Diagnostic, Image Registration, Medical Image Registration, NMT

Medical image registration plays an important role in determining topographic
and morphological changes for functional diagnostic and therapeutic purposes.
Manual alignment and semi-automated software still have been used; however they
are subjective and make specialists spend precious time. Fully automated
methods are faster and user-independent, but the critical point is registration
reliability. Similarity measurement using Mutual Information (MI) with Shannon
entropy (MIS) is the most common automated method that is being currently
applied in medical images, although more reliable algorithms have been proposed
over the last decade, suggesting improvements and different entropies; such as
Studholme et al, (1999), who demonstrated that the normalization of Mutual
Information (NMI) provides an invariant entropy measure for 3D medical image
registration. In this paper, we described a set of experiments to evaluate the
applicability of Tsallis entropy in the Mutual Information (MIT) and in the
Normalized Mutual Information (NMIT) as cost functions for Magnetic Resonance
Imaging (MRI), Positron Emission Tomography (PET) and Computed Tomography (CT)
exams registration. The effect of changing overlap in a simple image model and
clinical experiments on current entropies (Entropy Correlation Coefficient -
ECC, MIS and NMI) and the proposed ones (MIT and NMT) showed NMI and NMIT with
Tsallis parameter close to 1 as the best options (confidence and accuracy) for
CT to MRI and PET to MRI automatic neuroimaging registration.
LSTM-Based System-Call Language Modeling and Robust Ensemble Method for Designing Host-Based Intrusion Detection Systems
Authors: Gyuwan Kim, Hayoon Yi, Jangho Lee, Yunheung Paek, Sungroh Yoon
Date: 2016-11-06 00:00:00
Tasks: Computer Security, Intrusion Detection, Language Modeling, Language Modelling

In computer security, designing a robust intrusion detection system is one of
the most fundamental and important problems. In this paper, we propose a
system-call language-modeling approach for designing anomaly-based host
intrusion detection systems. To remedy the issue of high false-alarm rates
commonly arising in conventional methods, we employ a novel ensemble method
that blends multiple thresholding classifiers into a single one, making it
possible to accumulate 'highly normal' sequences. The proposed system-call
language model has various advantages leveraged by the fact that it can learn
the semantic meaning and interactions of each system call that existing methods
cannot effectively consider. Through diverse experiments on public benchmark
datasets, we demonstrate the validity and effectiveness of the proposed method.
Moreover, we show that our model possesses high portability, which is one of
the key aspects of realizing successful intrusion detection systems.
Beyond Fine Tuning: A Modular Approach to Learning on Small Data
Authors: Ark Anderson, Kyle Shaffer, Artem Yankov, Court D. Corley, Nathan O. Hodas
Date: 2016-11-06 00:00:00
Tasks: Small Data Image Classification, Transfer Learning

In this paper we present a technique to train neural network models on small
amounts of data. Current methods for training neural networks on small amounts
of rich data typically rely on strategies such as fine-tuning a pre-trained
neural network or the use of domain-specific hand-engineered features. Here we
take the approach of treating network layers, or entire networks, as modules
and combine pre-trained modules with untrained modules, to learn the shift in
distributions between data sets. The central impact of using a modular approach
comes from adding new representations to a network, as opposed to replacing
representations via fine-tuning. Using this technique, we are able surpass
results using standard fine-tuning transfer learning approaches, and we are
also able to significantly increase performance over such approaches when using
smaller amounts of data.
Communication-Efficient Distributed Statistical Inference
Authors: Michael. I. Jordan, Jason D. Lee, Yun Yang
Date: 2016-05-25 00:00:00
Tasks: Bayesian Inference, Computational Efficiency

We present a Communication-efficient Surrogate Likelihood (CSL) framework for
solving distributed statistical inference problems. CSL provides a
communication-efficient surrogate to the global likelihood that can be used for
low-dimensional estimation, high-dimensional regularized estimation and
Bayesian inference. For low-dimensional estimation, CSL provably improves upon
naive averaging schemes and facilitates the construction of confidence
intervals. For high-dimensional regularized estimation, CSL leads to a
minimax-optimal estimator with controlled communication cost. For Bayesian
inference, CSL can be used to form a communication-efficient quasi-posterior
distribution that converges to the true posterior. This quasi-posterior
procedure significantly improves the computational efficiency of MCMC
algorithms even in a non-distributed setting. We present both theoretical
analysis and experiments to explore the properties of the CSL approximation.