pwc-archive/papers-with-abstracts huggingface dataset:
PSO-Optimized Hopfield Neural Network-Based Multipath Routing for Mobile Ad-hoc Networks
Authors: Mansour Sheikhan, Ehsan Hemmati
Date: 2017-11-16 00:00:00

Mobile ad-hoc network (MANET) is a dynamic collection of mobile computers
without the need for any existing infrastructure. Nodes in a MANET act as hosts
and routers. Designing of robust routing algorithms for MANETs is a challenging
task. Disjoint multipath routing protocols address this problem and increase
the reliability, security and lifetime of network. However, selecting an
optimal multipath is an NP-complete problem. In this paper, Hopfield neural
network (HNN) which its parameters are optimized by particle swarm optimization
(PSO) algorithm is proposed as multipath routing algorithm. Link expiration
time (LET) between each two nodes is used as the link reliability estimation
metric. This approach can find either node-disjoint or link-disjoint paths in
single phase route discovery. Simulation results confirm that PSO-HNN routing
algorithm has better performance as compared to backup path set selection
algorithm (BPSA) in terms of the path set reliability and number of paths in
the set.
Question Asking as Program Generation
Authors: Anselm Rothe, Brenden M. Lake, Todd M. Gureckis
Date: 2017-11-16 00:00:00
Proceeding: NeurIPS 2017 12
Tasks: Informativeness

A hallmark of human intelligence is the ability to ask rich, creative, and
revealing questions. Here we introduce a cognitive model capable of
constructing human-like questions. Our approach treats questions as formal
programs that, when executed on the state of the world, output an answer. The
model specifies a probability distribution over a complex, compositional space
of programs, favoring concise programs that help the agent learn in the current
context. We evaluate our approach by modeling the types of open-ended questions
generated by humans who were attempting to learn about an ambiguous situation
in a game. We find that our model predicts what questions people will ask, and
can creatively produce novel questions that were not present in the training
set. In addition, we compare a number of model variants, finding that both
question informativeness and complexity are important for producing human-like
questions.
Towards Deep Learning Models for Psychological State Prediction using Smartphone Data: Challenges and Opportunities
Authors: Gatis Mikelsons, Matthew Smith, Abhinav Mehrotra, Mirco Musolesi
Date: 2017-11-16 00:00:00
Tasks: BIG-bench Machine Learning

There is an increasing interest in exploiting mobile sensing technologies and
machine learning techniques for mental health monitoring and intervention.
Researchers have effectively used contextual information, such as mobility,
communication and mobile phone usage patterns for quantifying individuals' mood
and wellbeing. In this paper, we investigate the effectiveness of neural
network models for predicting users' level of stress by using the location
information collected by smartphones. We characterize the mobility patterns of
individuals using the GPS metrics presented in the literature and employ these
metrics as input to the network. We evaluate our approach on the open-source
StudentLife dataset. Moreover, we discuss the challenges and trade-offs
involved in building machine learning models for digital mental health and
highlight potential future work in this direction.
Safe and Nested Subgame Solving for Imperfect-Information Games
Authors: Noam Brown, Tuomas Sandholm
Date: 2017-05-08 00:00:00
Proceeding: NeurIPS 2017 12
Tasks: Translation

In imperfect-information games, the optimal strategy in a subgame may depend
on the strategy in other, unreached subgames. Thus a subgame cannot be solved
in isolation and must instead consider the strategy for the entire game as a
whole, unlike perfect-information games. Nevertheless, it is possible to first
approximate a solution for the whole game and then improve it by solving
individual subgames. This is referred to as subgame solving. We introduce
subgame-solving techniques that outperform prior methods both in theory and
practice. We also show how to adapt them, and past subgame-solving techniques,
to respond to opponent actions that are outside the original action
abstraction; this significantly outperforms the prior state-of-the-art
approach, action translation. Finally, we show that subgame solving can be
repeated as the game progresses down the game tree, leading to far lower
exploitability. These techniques were a key component of Libratus, the first AI
to defeat top humans in heads-up no-limit Texas hold'em poker.
Conditional Markov Chain Search for the Simple Plant Location Problem improves upper bounds on twelve Körkel-Ghosh instances
Authors: Daniel Karapetyan, Boris Goldengorin
Date: 2017-11-16 00:00:00

We address a family of hard benchmark instances for the Simple Plant Location
Problem (also known as the Uncapacitated Facility Location Problem). The recent
attempt by Fischetti et al. to tackle the K\"orkel-Ghosh instances resulted in
seven new optimal solutions and 22 improved upper bounds. We use automated
generation of heuristics to obtain a new algorithm for the Simple Plant
Location Problem. In our experiments, our new algorithm matched all the
previous best known and optimal solutions, and further improved 12 upper
bounds, all within shorter time budgets compared to the previous efforts.
  Our algorithm design process is split into two phases: (i) development of
algorithmic components such as local search procedures and mutation operators,
and (ii) composition of a metaheuristic from the available components. Phase
(i) requires human expertise and often can be completed by implementing several
simple domain-specific routines known from the literature. Phase (ii) is
entirely automated by employing the Conditional Markov Chain Search (CMCS)
framework. In CMCS, a metaheuristic is flexibly defined by a set of parameters,
called configuration. Then the process of composition of a metaheuristic from
the algorithmic components is reduced to an optimisation problem seeking the
best performing CMCS configuration.
  We discuss the problem of comparing configurations, and propose a new
efficient technique to select the best performing configuration from a large
set. To employ this method, we restrict the original CMCS to a simple
deterministic case that leaves us with a finite and manageable number of
meaningful configurations.
On Pre-Trained Image Features and Synthetic Images for Deep Learning
Authors: Stefan Hinterstoisser, Vincent Lepetit, Paul Wohlhart, Kurt Konolige
Date: 2017-10-29 00:00:00
Tasks: Object, Object Recognition

Deep Learning methods usually require huge amounts of training data to
perform at their full potential, and often require expensive manual labeling.
Using synthetic images is therefore very attractive to train object detectors,
as the labeling comes for free, and several approaches have been proposed to
combine synthetic and real images for training.
  In this paper, we show that a simple trick is sufficient to train very
effectively modern object detectors with synthetic images only: We freeze the
layers responsible for feature extraction to generic layers pre-trained on real
images, and train only the remaining layers with plain OpenGL rendering. Our
experiments with very recent deep architectures for object recognition
(Faster-RCNN, R-FCN, Mask-RCNN) and image feature extractors (InceptionResnet
and Resnet) show this simple approach performs surprisingly well.
Medical Diagnosis From Laboratory Tests by Combining Generative and Discriminative Learning
Authors: Shiyue Zhang, Pengtao Xie, Dong Wang, Eric P. Xing
Date: 2017-11-12 00:00:00
Tasks: Decision Making, Imputation, Medical Diagnosis, Missing Values

A primary goal of computational phenotype research is to conduct medical
diagnosis. In hospital, physicians rely on massive clinical data to make
diagnosis decisions, among which laboratory tests are one of the most important
resources. However, the longitudinal and incomplete nature of laboratory test
data casts a significant challenge on its interpretation and usage, which may
result in harmful decisions by both human physicians and automatic diagnosis
systems. In this work, we take advantage of deep generative models to deal with
the complex laboratory tests. Specifically, we propose an end-to-end
architecture that involves a deep generative variational recurrent neural
networks (VRNN) to learn robust and generalizable features, and a
discriminative neural network (NN) model to learn diagnosis decision making,
and the two models are trained jointly. Our experiments are conducted on a
dataset involving 46,252 patients, and the 50 most frequent tests are used to
predict the 50 most common diagnoses. The results show that our model, VRNN+NN,
significantly (p<0.001) outperforms other baseline models. Moreover, we
demonstrate that the representations learned by the joint training are more
informative than those learned by pure generative models. Finally, we find that
our model offers a surprisingly good imputation for missing values.
Neural Variational Inference and Learning in Undirected Graphical Models
Authors: Volodymyr Kuleshov, Stefano Ermon
Date: 2017-11-07 00:00:00
Proceeding: NeurIPS 2017 12
Tasks: Variational Inference

Many problems in machine learning are naturally expressed in the language of
undirected graphical models. Here, we propose black-box learning and inference
algorithms for undirected models that optimize a variational approximation to
the log-likelihood of the model. Central to our approach is an upper bound on
the log-partition function parametrized by a function q that we express as a
flexible neural network. Our bound makes it possible to track the partition
function during learning, to speed-up sampling, and to train a broad class of
hybrid directed/undirected models via a unified variational inference
framework. We empirically demonstrate the effectiveness of our method on
several popular generative modeling datasets.
Poverty Mapping Using Convolutional Neural Networks Trained on High and Medium Resolution Satellite Images, With an Application in Mexico
Authors: Boris Babenko, Jonathan Hersh, David Newhouse, Anusha Ramakrishnan, Tom Swartz
Date: 2017-11-16 00:00:00

Mapping the spatial distribution of poverty in developing countries remains
an important and costly challenge. These "poverty maps" are key inputs for
poverty targeting, public goods provision, political accountability, and impact
evaluation, that are all the more important given the geographic dispersion of
the remaining bottom billion severely poor individuals. In this paper we train
Convolutional Neural Networks (CNNs) to estimate poverty directly from high and
medium resolution satellite images. We use both Planet and Digital Globe
imagery with spatial resolutions of 3-5 sq. m. and 50 sq. cm. respectively,
covering all 2 million sq. km. of Mexico. Benchmark poverty estimates come from
the 2014 MCS-ENIGH combined with the 2015 Intercensus and are used to estimate
poverty rates for 2,456 Mexican municipalities. CNNs are trained using the 896
municipalities in the 2014 MCS-ENIGH. We experiment with several architectures
(GoogleNet, VGG) and use GoogleNet as a final architecture where weights are
fine-tuned from ImageNet. We find that 1) the best models, which incorporate
satellite-estimated land use as a predictor, explain approximately 57% of the
variation in poverty in a validation sample of 10 percent of MCS-ENIGH
municipalities; 2) Across all MCS-ENIGH municipalities explanatory power
reduces to 44% in a CNN prediction and landcover model; 3) Predicted poverty
from the CNN predictions alone explains 47% of the variation in poverty in the
validation sample, and 37% over all MCS-ENIGH municipalities; 4) In urban areas
we see slight improvements from using Digital Globe versus Planet imagery,
which explain 61% and 54% of poverty variation respectively. We conclude that
CNNs can be trained end-to-end on satellite imagery to estimate poverty,
although there is much work to be done to understand how the training process
influences out of sample validation.
InterpNET: Neural Introspection for Interpretable Deep Learning
Authors: Shane Barratt
Date: 2017-10-26 00:00:00
Tasks: Classification, Deep Learning, General Classification, image-classification, Image Classification

Humans are able to explain their reasoning. On the contrary, deep neural
networks are not. This paper attempts to bridge this gap by introducing a new
way to design interpretable neural networks for classification, inspired by
physiological evidence of the human visual system's inner-workings. This paper
proposes a neural network design paradigm, termed InterpNET, which can be
combined with any existing classification architecture to generate natural
language explanations of the classifications. The success of the module relies
on the assumption that the network's computation and reasoning is represented
in its internal layer activations. While in principle InterpNET could be
applied to any existing classification architecture, it is evaluated via an
image classification and explanation task. Experiments on a CUB bird
classification and explanation dataset show qualitatively and quantitatively
that the model is able to generate high-quality explanations. While the current
state-of-the-art METEOR score on this dataset is 29.2, InterpNET achieves a
much higher METEOR score of 37.9.
A Sequential Thinning Algorithm For Multi-Dimensional Binary Patterns
Authors: Himanshu Jain, Archana Praveen Kumar
Date: 2017-10-09 00:00:00

Thinning is the removal of contour pixels/points of connected components in
an image to produce their skeleton with retained connectivity and structural
properties. The output requirements of a thinning procedure often vary with
application. This paper proposes a sequential algorithm that is very easy to
understand and modify based on application to perform the thinning of
multi-dimensional binary patterns. The algorithm was tested on 2D and 3D
patterns and showed very good results. Moreover, comparisons were also made
with two of the state-of-the-art methods used for 2D patterns. The results
obtained prove the validity of the procedure.
GA-PSO-Optimized Neural-Based Control Scheme for Adaptive Congestion Control to Improve Performance in Multimedia Applications
Authors: Mansour Sheikhan, Ehsan Hemmati, Reza Shahnazi
Date: 2017-11-16 00:00:00

Active queue control aims to improve the overall communication network
throughput while providing lower delay and small packet loss rate. The basic
idea is to actively trigger packet dropping (or marking provided by explicit
congestion notification (ECN)) before buffer overflow. In this paper, two
artificial neural networks (ANN)-based control schemes are proposed for
adaptive queue control in TCP communication networks. The structure of these
controllers is optimized using genetic algorithm (GA) and the output weights of
ANNs are optimized using particle swarm optimization (PSO) algorithm. The
controllers are radial bias function (RBF)-based, but to improve the robustness
of RBF controller, an error-integral term is added to RBF equation in the
second scheme. Experimental results show that GA- PSO-optimized improved RBF
(I-RBF) model controls network congestion effectively in terms of link
utilization with a low packet loss rate and outperform Drop Tail,
proportional-integral (PI), random exponential marking (REM), and adaptive
random early detection (ARED) controllers.
Grammatical facial expression recognition using customized deep neural network architecture
Authors: Devesh Walawalkar
Date: 2017-11-16 00:00:00
Tasks: Facial Expression Recognition, Facial Expression Recognition (FER)

This paper proposes to expand the visual understanding capacity of computers
by helping it recognize human sign language more efficiently. This is carried
out through recognition of facial expressions, which accompany the hand signs
used in this language. This paper specially focuses on the popular Brazilian
sign language (LIBRAS). While classifying different hand signs into their
respective word meanings has already seen much literature dedicated to it, the
emotions or intention with which the words are expressed haven't primarily been
taken into consideration. As from our normal human experience, words expressed
with different emotions or mood can have completely different meanings attached
to it. Lending computers the ability of classifying these facial expressions,
can help add another level of deep understanding of what the deaf person
exactly wants to communicate. The proposed idea is implemented through a deep
neural network having a customized architecture. This helps learning specific
patterns in individual expressions much better as compared to a generic
approach. With an overall accuracy of 98.04%, the implemented deep network
performs excellently well and thus is fit to be used in any given practical
scenario.
Multi-style Generative Network for Real-time Transfer
Authors: Hang Zhang, Kristin Dana
Date: 2017-03-20 00:00:00
Tasks: Style Transfer

Despite the rapid progress in style transfer, existing approaches using
feed-forward generative network for multi-style or arbitrary-style transfer are
usually compromised of image quality and model flexibility. We find it is
fundamentally difficult to achieve comprehensive style modeling using
1-dimensional style embedding. Motivated by this, we introduce CoMatch Layer
that learns to match the second order feature statistics with the target
styles. With the CoMatch Layer, we build a Multi-style Generative Network
(MSG-Net), which achieves real-time performance. We also employ an specific
strategy of upsampled convolution which avoids checkerboard artifacts caused by
fractionally-strided convolution. Our method has achieved superior image
quality comparing to state-of-the-art approaches. The proposed MSG-Net as a
general approach for real-time style transfer is compatible with most existing
techniques including content-style interpolation, color-preserving, spatial
control and brush stroke size control. MSG-Net is the first to achieve
real-time brush-size control in a purely feed-forward manner for style
transfer. Our implementations and pre-trained models for Torch, PyTorch and
MXNet frameworks will be publicly available.
A New Method for Performance Analysis in Nonlinear Dimensionality Reduction
Authors: Jiaxi Liang, Shojaeddin Chenouri, Christopher G. Small
Date: 2017-11-16 00:00:00
Tasks: Dimensionality Reduction

In this paper, we develop a local rank correlation measure which quantifies
the performance of dimension reduction methods. The local rank correlation is
easily interpretable, and robust against the extreme skewness of nearest
neighbor distributions in high dimensions. Some benchmark datasets are studied.
We find that the local rank correlation closely corresponds to our visual
interpretation of the quality of the output. In addition, we demonstrate that
the local rank correlation is useful in estimating the intrinsic dimensionality
of the original data, and in selecting a suitable value of tuning parameters
used in some algorithms.
LDMNet: Low Dimensional Manifold Regularized Neural Networks
Authors: Wei Zhu, Qiang Qiu, Jiaji Huang, Robert Calderbank, Guillermo Sapiro, Ingrid Daubechies
Date: 2017-11-16 00:00:00
Proceeding: CVPR 2018 6
Tasks: Face Recognition, Small Data Image Classification

Deep neural networks have proved very successful on archetypal tasks for
which large training sets are available, but when the training data are scarce,
their performance suffers from overfitting. Many existing methods of reducing
overfitting are data-independent, and their efficacy is often limited when the
training set is very small. Data-dependent regularizations are mostly motivated
by the observation that data of interest lie close to a manifold, which is
typically hard to parametrize explicitly and often requires human input of
tangent vectors. These methods typically only focus on the geometry of the
input data, and do not necessarily encourage the networks to produce
geometrically meaningful features. To resolve this, we propose a new framework,
the Low-Dimensional-Manifold-regularized neural Network (LDMNet), which
incorporates a feature regularization method that focuses on the geometry of
both the input data and the output features. In LDMNet, we regularize the
network by encouraging the combination of the input data and the output
features to sample a collection of low dimensional manifolds, which are
searched efficiently without explicit parametrization. To achieve this, we
directly use the manifold dimension as a regularization term in a variational
functional. The resulting Euler-Lagrange equation is a Laplace-Beltrami
equation over a point cloud, which is solved by the point integral method
without increasing the computational complexity. We demonstrate two benefits of
LDMNet in the experiments. First, we show that LDMNet significantly outperforms
widely-used network regularizers such as weight decay and DropOut. Second, we
show that LDMNet can be designed to extract common features of an object imaged
via different modalities, which proves to be very useful in real-world
applications such as cross-spectral face recognition.
A Generative Approach to Question Answering
Authors: Rajarshee Mitra
Date: 2017-11-16 00:00:00
Tasks: Decoder, Generative Question Answering, Question Answering, Sentence

Question Answering has come a long way from answer sentence selection,
relational QA to reading and comprehension. We shift our attention to
generative question answering (gQA) by which we facilitate machine to read
passages and answer questions by learning to generate the answers. We frame the
problem as a generative task where the encoder being a network that models the
relationship between question and passage and encoding them to a vector thus
facilitating the decoder to directly form an abstraction of the answer. Not
being able to retain facts and making repetitions are common mistakes that
affect the overall legibility of answers. To counter these issues, we employ
copying mechanism and maintenance of coverage vector in our model respectively.
Our results on MS-MARCO demonstrate it's superiority over baselines and we also
show qualitative examples where we improved in terms of correctness and
readability
A Forward-Backward Approach for Visualizing Information Flow in Deep Networks
Authors: Aditya Balu, Thanh V. Nguyen, Apurva Kokate, Chinmay Hegde, Soumik Sarkar
Date: 2017-11-16 00:00:00

We introduce a new, systematic framework for visualizing information flow in
deep networks. Specifically, given any trained deep convolutional network model
and a given test image, our method produces a compact support in the image
domain that corresponds to a (high-resolution) feature that contributes to the
given explanation. Our method is both computationally efficient as well as
numerically robust. We present several preliminary numerical results that
support the benefits of our framework over existing methods.
Beyond Sparsity: Tree Regularization of Deep Models for Interpretability
Authors: Mike Wu, Michael C. Hughes, Sonali Parbhoo, Maurizio Zazzi, Volker Roth, Finale Doshi-Velez
Date: 2017-11-16 00:00:00
Tasks: Time Series, Time Series Analysis

The lack of interpretability remains a key barrier to the adoption of deep
models in many applications. In this work, we explicitly regularize deep models
so human users might step through the process behind their predictions in
little time. Specifically, we train deep time-series models so their
class-probability predictions have high accuracy while being closely modeled by
decision trees with few nodes. Using intuitive toy examples as well as medical
tasks for treating sepsis and HIV, we demonstrate that this new tree
regularization yields models that are easier for humans to simulate than
simpler L1 or L2 penalties without sacrificing predictive power.