pwc-archive/papers-with-abstracts huggingface dataset:
Knowledge Aided Consistency for Weakly Supervised Phrase Grounding
Authors: Kan Chen, Jiyang Gao, Ram Nevatia
Date: 2018-03-11 00:00:00
Proceeding: CVPR 2018 6
Tasks: Phrase Grounding

Given a natural language query, a phrase grounding system aims to localize
mentioned objects in an image. In weakly supervised scenario, mapping between
image regions (i.e., proposals) and language is not available in the training
set. Previous methods address this deficiency by training a grounding system
via learning to reconstruct language information contained in input queries
from predicted proposals. However, the optimization is solely guided by the
reconstruction loss from the language modality, and ignores rich visual
information contained in proposals and useful cues from external knowledge. In
this paper, we explore the consistency contained in both visual and language
modalities, and leverage complementary external knowledge to facilitate weakly
supervised grounding. We propose a novel Knowledge Aided Consistency Network
(KAC Net) which is optimized by reconstructing input query and proposal's
information. To leverage complementary knowledge contained in the visual
features, we introduce a Knowledge Based Pooling (KBP) gate to focus on
query-related proposals. Experiments show that KAC Net provides a significant
improvement on two popular datasets.
On dynamic ensemble selection and data preprocessing for multi-class imbalance learning
Authors: Rafael M. O. Cruz, Robert Sabourin, George D. C. Cavalcanti
Date: 2018-03-11 00:00:00
Tasks: General Classification

Class-imbalance refers to classification problems in which many more
instances are available for certain classes than for others. Such imbalanced
datasets require special attention because traditional classifiers generally
favor the majority class which has a large number of instances. Ensemble of
classifiers have been reported to yield promising results. However, the
majority of ensemble methods applied too imbalanced learning are static ones.
Moreover, they only deal with binary imbalanced problems. Hence, this paper
presents an empirical analysis of dynamic selection techniques and data
preprocessing methods for dealing with multi-class imbalanced problems. We
considered five variations of preprocessing methods and four dynamic selection
methods. Our experiments conducted on 26 multi-class imbalanced problems show
that the dynamic ensemble improves the F-measure and the G-mean as compared to
the static ensemble. Moreover, data preprocessing plays an important role in
such cases.
Calculating the Midsagittal Plane for Symmetrical Bilateral Shapes: Applications to Clinical Facial Surgical Planning
Authors: Aarti Jajoo, Matthew Nicol, Jaime Gateno, Ken-Chung Chen, Zhen Tang, Tasadduk Chowdhury, Jainfu Li, Steve Goufang Shen, James J. Xia
Date: 2018-03-11 00:00:00

It is difficult to estimate the midsagittal plane of human subjects with
craniomaxillofacial (CMF) deformities. We have developed a LAndmark GEometric
Routine (LAGER), which automatically estimates a midsagittal plane for such
subjects. The LAGER algorithm was based on the assumption that the optimal
midsagittal plane of a patient with a deformity is the premorbid midsagittal
plane of the patient (i.e. hypothetically normal without deformity). The LAGER
algorithm consists of three steps. The first step quantifies the asymmetry of
the landmarks using a Euclidean distance matrix analysis and ranks the
landmarks according to their degree of asymmetry. The second step uses a
recursive algorithm to drop outlier landmarks. The third step inputs the
remaining landmarks into an optimization algorithm to determine an optimal
midsaggital plane. We validate LAGER on 20 synthetic models mimicking the
skulls of real patients with CMF deformities. The results indicated that all
the LAGER algorithm-generated midsagittal planes met clinical criteria. Thus it
can be used clinically to determine the midsagittal plane for patients with CMF
deformities.
Learning Large-Scale Bayesian Networks with the sparsebn Package
Authors: Bryon Aragam, Jiaying Gu, Qing Zhou
Date: 2017-03-11 00:00:00

Learning graphical models from data is an important problem with wide
applications, ranging from genomics to the social sciences. Nowadays datasets
often have upwards of thousands---sometimes tens or hundreds of thousands---of
variables and far fewer samples. To meet this challenge, we have developed a
new R package called sparsebn for learning the structure of large, sparse
graphical models with a focus on Bayesian networks. While there are many
existing software packages for this task, this package focuses on the unique
setting of learning large networks from high-dimensional data, possibly with
interventions. As such, the methods provided place a premium on scalability and
consistency in a high-dimensional setting. Furthermore, in the presence of
interventions, the methods implemented here achieve the goal of learning a
causal network from data. Additionally, the sparsebn package is fully
compatible with existing software packages for network analysis.
Confidence Intervals for Algorithmic Leveraging in Linear Regression
Authors: Katelyn Gao
Date: 2016-06-05 00:00:00
Tasks: regression

The age of big data has produced data sets that are computationally expensive
to analyze and store. Algorithmic leveraging proposes that we sample
observations from the original data set to generate a representative data set
and then perform analysis on the representative data set. In this paper, we
present efficient algorithms for constructing finite sample confidence
intervals for each algorithmic leveraging estimated regression coefficient,
with asymptotic coverage guarantees. In simulations, we confirm empirically
that the confidence intervals have the desired coverage probabilities, while
bootstrap confidence intervals may not.
Expectation propagation as a way of life: A framework for Bayesian inference on partitioned data
Authors: Aki Vehtari, Andrew Gelman, Tuomas Sivula, Pasi Jylänki, Dustin Tran, Swupnil Sahai, Paul Blomstedt, John P. Cunningham, David Schiminovich, Christian Robert
Date: 2014-12-16 00:00:00
Tasks: Bayesian Inference

A common divide-and-conquer approach for Bayesian computation with big data is to partition the data, perform local inference for each piece separately, and combine the results to obtain a global posterior approximation. While being conceptually and computationally appealing, this method involves the problematic need to also split the prior for the local inferences; these weakened priors may not provide enough regularization for each separate computation, thus eliminating one of the key advantages of Bayesian methods. To resolve this dilemma while still retaining the generalizability of the underlying local inference method, we apply the idea of expectation propagation (EP) as a framework for distributed Bayesian inference. The central idea is to iteratively update approximations to the local likelihoods given the state of the other approximations and the prior. The present paper has two roles: we review the steps that are needed to keep EP algorithms numerically stable, and we suggest a general approach, inspired by EP, for approaching data partitioning problems in a way that achieves the computational benefits of parallelism while allowing each local update to make use of relevant information from the other sites. In addition, we demonstrate how the method can be applied in a hierarchical context to make use of partitioning of both data and parameters. The paper describes a general algorithmic framework, rather than a specific algorithm, and presents an example implementation for it.
Approximating Continuous Functions by ReLU Nets of Minimal Width
Authors: Boris Hanin, Mark Sellke
Date: 2017-10-31 00:00:00

This article concerns the expressive power of depth in deep feed-forward
neural nets with ReLU activations. Specifically, we answer the following
question: for a fixed $d_{in}\geq 1,$ what is the minimal width $w$ so that
neural nets with ReLU activations, input dimension $d_{in}$, hidden layer
widths at most $w,$ and arbitrary depth can approximate any continuous,
real-valued function of $d_{in}$ variables arbitrarily well? It turns out that
this minimal width is exactly equal to $d_{in}+1.$ That is, if all the hidden
layer widths are bounded by $d_{in}$, then even in the infinite depth limit,
ReLU nets can only express a very limited class of functions, and, on the other
hand, any continuous function on the $d_{in}$-dimensional unit cube can be
approximated to arbitrary precision by ReLU nets in which all hidden layers
have width exactly $d_{in}+1.$ Our construction in fact shows that any
continuous function $f:[0,1]^{d_{in}}\to\mathbb R^{d_{out}}$ can be
approximated by a net of width $d_{in}+d_{out}$. We obtain quantitative depth
estimates for such an approximation in terms of the modulus of continuity of
$f$.
Dealing with Unknown Unknowns: Identification and Selection of Minimal Sensing for Fractional Dynamics with Unknown Inputs
Authors: Gaurav Gupta, Sergio Pequito, Paul Bogdan
Date: 2018-03-10 00:00:00
Tasks: EEG, Electroencephalogram (EEG)

This paper focuses on analysis and design of time-varying complex networks
having fractional order dynamics. These systems are key in modeling the complex
dynamical processes arising in several natural and man made systems. Notably,
examples include neurophysiological signals such as electroencephalogram (EEG)
that captures the variation in potential fields, and blood oxygenation level
dependent (BOLD) signal, which serves as a proxy for neuronal activity.
Notwithstanding, the complex networks originated by locally measuring EEG and
BOLD are often treated as isolated networks and do not capture the dependency
from external stimuli, e.g., originated in subcortical structures such as the
thalamus and the brain stem. Therefore, we propose a paradigm-shift towards the
analysis of such complex networks under unknown unknowns (i.e., excitations).
Consequently, the main contributions of the present paper are threefold: (i) we
present an alternating scheme that enables to determine the best estimate of
the model parameters and unknown stimuli; (ii) we provide necessary and
sufficient conditions to ensure that it is possible to retrieve the state and
unknown stimuli; and (iii) upon these conditions we determine a small subset of
variables that need to be measured to ensure that both state and input can be
recovered, while establishing sub-optimality guarantees with respect to the
smallest possible subset. Finally, we present several pedagogical examples of
the main results using real data collected from an EEG wearable device.
Language Identification of Bengali-English Code-Mixed data using Character & Phonetic based LSTM Models
Authors: Soumil Mandal, Sourya Dipta Das, Dipankar Das
Date: 2018-03-10 00:00:00
Tasks: Language Identification

Language identification of social media text still remains a challenging task
due to properties like code-mixing and inconsistent phonetic transliterations.
In this paper, we present a supervised learning approach for language
identification at the word level of low resource Bengali-English code-mixed
data taken from social media. We employ two methods of word encoding, namely
character based and root phone based to train our deep LSTM models. Utilizing
these two models we created two ensemble models using stacking and threshold
technique which gave 91.78% and 92.35% accuracies respectively on our testing
data.
Fast Threshold Tests for Detecting Discrimination
Authors: Emma Pierson, Sam Corbett-Davies, Sharad Goel
Date: 2017-02-27 00:00:00

Threshold tests have recently been proposed as a useful method for detecting
bias in lending, hiring, and policing decisions. For example, in the case of
credit extensions, these tests aim to estimate the bar for granting loans to
white and minority applicants, with a higher inferred threshold for minorities
indicative of discrimination. This technique, however, requires fitting a
complex Bayesian latent variable model for which inference is often
computationally challenging. Here we develop a method for fitting threshold
tests that is two orders of magnitude faster than the existing approach,
reducing computation from hours to minutes. To achieve these performance gains,
we introduce and analyze a flexible family of probability distributions on the
interval [0, 1] -- which we call discriminant distributions -- that is
computationally efficient to work with. We demonstrate our technique by
analyzing 2.7 million police stops of pedestrians in New York City.
Algorithmically probable mutations reproduce aspects of evolution such as convergence rate, genetic memory, and modularity
Authors: Santiago Hernández-Orozco, Narsis A. Kiani, Hector Zenil
Date: 2017-09-01 00:00:00
Tasks: Diversity, Evolutionary Algorithms

Natural selection explains how life has evolved over millions of years from
more primitive forms. The speed at which this happens, however, has sometimes
defied formal explanations when based on random (uniformly distributed)
mutations. Here we investigate the application of a simplicity bias based on a
natural but algorithmic distribution of mutations (no recombination) in various
examples, particularly binary matrices in order to compare evolutionary
convergence rates. Results both on synthetic and on small biological examples
indicate an accelerated rate when mutations are not statistical uniform but
\textit{algorithmic uniform}. We show that algorithmic distributions can evolve
modularity and genetic memory by preservation of structures when they first
occur sometimes leading to an accelerated production of diversity but also
population extinctions, possibly explaining naturally occurring phenomena such
as diversity explosions (e.g. the Cambrian) and massive extinctions (e.g. the
End Triassic) whose causes are currently a cause for debate. The natural
approach introduced here appears to be a better approximation to biological
evolution than models based exclusively upon random uniform mutations, and it
also approaches a formal version of open-ended evolution based on previous
formal results. These results validate some suggestions in the direction that
computation may be an equally important driver of evolution. We also show that
inducing the method on problems of optimization, such as genetic algorithms,
has the potential to accelerate convergence of artificial evolutionary
algorithms.
Optimizing Human Learning
Authors: Behzad Tabibian, Utkarsh Upadhyay, Abir De, Ali Zarezade, Bernhard Schoelkopf, Manuel Gomez-Rodriguez
Date: 2017-12-05 00:00:00
Tasks: Memorization, Point Processes

Spaced repetition is a technique for efficient memorization which uses
repeated, spaced review of content to improve long-term retention. Can we find
the optimal reviewing schedule to maximize the benefits of spaced repetition?
In this paper, we introduce a novel, flexible representation of spaced
repetition using the framework of marked temporal point processes and then
address the above question as an optimal control problem for stochastic
differential equations with jumps. For two well-known human memory models, we
show that the optimal reviewing schedule is given by the recall probability of
the content to be learned. As a result, we can then develop a simple, scalable
online algorithm, Memorize, to sample the optimal reviewing times. Experiments
on both synthetic and real data gathered from Duolingo, a popular
language-learning online platform, show that our algorithm may be able to help
learners memorize more effectively than alternatives.
A Deep Learning Approach for Pose Estimation from Volumetric OCT Data
Authors: Nils Gessert, Matthias Schlüter, Alexander Schlaefer
Date: 2018-03-10 00:00:00
Tasks: Deep Learning, Pose Estimation

Tracking the pose of instruments is a central problem in image-guided
surgery. For microscopic scenarios, optical coherence tomography (OCT) is
increasingly used as an imaging modality. OCT is suitable for accurate pose
estimation due to its micrometer range resolution and volumetric field of view.
However, OCT image processing is challenging due to speckle noise and
reflection artifacts in addition to the images' 3D nature. We address pose
estimation from OCT volume data with a new deep learning-based tracking
framework. For this purpose, we design a new 3D convolutional neural network
(CNN) architecture to directly predict the 6D pose of a small marker geometry
from OCT volumes. We use a hexapod robot to automatically acquire labeled data
points which we use to train 3D CNN architectures for multi-output regression.
We use this setup to provide an in-depth analysis on deep learning-based pose
estimation from volumes. Specifically, we demonstrate that exploiting volume
information for pose estimation yields higher accuracy than relying on 2D
representations with depth information. Supporting this observation, we provide
quantitative and qualitative results that 3D CNNs effectively exploit the depth
structure of marker objects. Regarding the deep learning aspect, we present
efficient design principles for 3D CNNs, making use of insights from the 2D
deep learning community. In particular, we present Inception3D as a new
architecture which performs best for our application. We show that our deep
learning approach reaches errors at our ground-truth label's resolution. We
achieve a mean average error of $\SI{14.89 \pm 9.3}{\micro\metre}$ and
$\SI{0.096 \pm 0.072}{\degree}$ for position and orientation learning,
respectively.
Revisiting Decomposable Submodular Function Minimization with Incidence Relations
Authors: Pan Li, Olgica Milenkovic
Date: 2018-03-10 00:00:00
Proceeding: NeurIPS 2018 12

We introduce a new approach to decomposable submodular function minimization
(DSFM) that exploits incidence relations. Incidence relations describe which
variables effectively influence the component functions, and when properly
utilized, they allow for improving the convergence rates of DSFM solvers. Our
main results include the precise parametrization of the DSFM problem based on
incidence relations, the development of new scalable alternative projections
and parallel coordinate descent methods and an accompanying rigorous analysis
of their convergence rates.
Learning to Localize Sound Source in Visual Scenes
Authors: Arda Senocak, Tae-Hyun Oh, Junsik Kim, Ming-Hsuan Yang, In So Kweon
Date: 2018-03-10 00:00:00
Proceeding: CVPR 2018 6
Tasks: Sound Source Localization

Visual events are usually accompanied by sounds in our daily lives. We pose
the question: Can the machine learn the correspondence between visual scene and
the sound, and localize the sound source only by observing sound and visual
scene pairs like human? In this paper, we propose a novel unsupervised
algorithm to address the problem of localizing the sound source in visual
scenes. A two-stream network structure which handles each modality, with
attention mechanism is developed for sound source localization. Moreover,
although our network is formulated within the unsupervised learning framework,
it can be extended to a unified architecture with a simple modification for the
supervised and semi-supervised learning settings as well. Meanwhile, a new
sound source dataset is developed for performance evaluation. Our empirical
evaluation shows that the unsupervised method eventually go through false
conclusion in some cases. We show that even with a few supervision, false
conclusion is able to be corrected and the source of sound in a visual scene
can be localized effectively.
3D Reconstruction of Incomplete Archaeological Objects Using a Generative Adversarial Network
Authors: Renato Hermoza, Ivan Sipiran
Date: 2017-11-17 00:00:00
Tasks: 3D Reconstruction, Cultural Vocal Bursts Intensity Prediction, Decoder, Generative Adversarial Network, Object, Object Reconstruction

We introduce a data-driven approach to aid the repairing and conservation of
archaeological objects: ORGAN, an object reconstruction generative adversarial
network (GAN). By using an encoder-decoder 3D deep neural network on a GAN
architecture, and combining two loss objectives: a completion loss and an
Improved Wasserstein GAN loss, we can train a network to effectively predict
the missing geometry of damaged objects. As archaeological objects can greatly
differ between them, the network is conditioned on a variable, which can be a
culture, a region or any metadata of the object. In our results, we show that
our method can recover most of the information from damaged objects, even in
cases where more than half of the voxels are missing, without producing many
errors.
Multi-Goal Reinforcement Learning: Challenging Robotics Environments and Request for Research
Authors: Matthias Plappert, Marcin Andrychowicz, Alex Ray, Bob McGrew, Bowen Baker, Glenn Powell, Jonas Schneider, Josh Tobin, Maciek Chociej, Peter Welinder, Vikash Kumar, Wojciech Zaremba
Date: 2018-02-26 00:00:00
Tasks: continuous-control, Continuous Control, Multi-Goal Reinforcement Learning, OpenAI Gym, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

The purpose of this technical report is two-fold. First of all, it introduces
a suite of challenging continuous control tasks (integrated with OpenAI Gym)
based on currently existing robotics hardware. The tasks include pushing,
sliding and pick & place with a Fetch robotic arm as well as in-hand object
manipulation with a Shadow Dexterous Hand. All tasks have sparse binary rewards
and follow a Multi-Goal Reinforcement Learning (RL) framework in which an agent
is told what to do using an additional input.
  The second part of the paper presents a set of concrete research ideas for
improving RL algorithms, most of which are related to Multi-Goal RL and
Hindsight Experience Replay.
Sample-Relaxed Two-Dimensional Color Principal Component Analysis for Face Recognition and Image Reconstruction
Authors: Mei-Xiang Zhao, Zhigang Jia, Dunwei Gong
Date: 2018-03-10 00:00:00
Tasks: Face Recognition, Image Reconstruction

A sample-relaxed two-dimensional color principal component analysis
(SR-2DCPCA) approach is presented for face recognition and image reconstruction
based on quaternion models. A relaxation vector is automatically generated
according to the variances of training color face images with the same label. A
sample-relaxed, low-dimensional covariance matrix is constructed based on all
the training samples relaxed by a relaxation vector, and its eigenvectors
corresponding to the $r$ largest eigenvalues are defined as the optimal
projection. The SR-2DCPCA aims to enlarge the global variance rather than to
maximize the variance of the projected training samples. The numerical results
based on real face data sets validate that SR-2DCPCA has a higher recognition
rate than state-of-the-art methods and is efficient in image reconstruction.