pwc-archive/papers-with-abstracts huggingface dataset:
NeuralFDR: Learning Discovery Thresholds from Hypothesis Features
Authors: Fei Xia, Martin J. Zhang, James Zou, David Tse
Date: 2017-11-03 00:00:00
Proceeding: NeurIPS 2017 12

As datasets grow richer, an important challenge is to leverage the full
features in the data to maximize the number of useful discoveries while
controlling for false positives. We address this problem in the context of
multiple hypotheses testing, where for each hypothesis, we observe a p-value
along with a set of features specific to that hypothesis. For example, in
genetic association studies, each hypothesis tests the correlation between a
variant and the trait. We have a rich set of features for each variant (e.g.
its location, conservation, epigenetics etc.) which could inform how likely the
variant is to have a true association. However popular testing approaches, such
as Benjamini-Hochberg's procedure (BH) and independent hypothesis weighting
(IHW), either ignore these features or assume that the features are categorical
or uni-variate. We propose a new algorithm, NeuralFDR, which automatically
learns a discovery threshold as a function of all the hypothesis features. We
parametrize the discovery threshold as a neural network, which enables flexible
handling of multi-dimensional discrete and continuous features as well as
efficient end-to-end optimization. We prove that NeuralFDR has strong false
discovery rate (FDR) guarantees, and show that it makes substantially more
discoveries in synthetic and real datasets. Moreover, we demonstrate that the
learned discovery threshold is directly interpretable.
Classification without labels: Learning from mixed samples in high energy physics
Authors: Eric M. Metodiev, Benjamin Nachman, Jesse Thaler
Date: 2017-08-09 00:00:00
Tasks: General Classification, Vocal Bursts Intensity Prediction

Modern machine learning techniques can be used to construct powerful models
for difficult collider physics problems. In many applications, however, these
models are trained on imperfect simulations due to a lack of truth-level
information in the data, which risks the model learning artifacts of the
simulation. In this paper, we introduce the paradigm of classification without
labels (CWoLa) in which a classifier is trained to distinguish statistical
mixtures of classes, which are common in collider physics. Crucially, neither
individual labels nor class proportions are required, yet we prove that the
optimal classifier in the CWoLa paradigm is also the optimal classifier in the
traditional fully-supervised case where all label information is available.
After demonstrating the power of this method in an analytical toy example, we
consider a realistic benchmark for collider physics: distinguishing quark-
versus gluon-initiated jets using mixed quark/gluon training samples. More
generally, CWoLa can be applied to any classification problem where labels or
class proportions are unknown or simulations are unreliable, but statistical
mixtures of the classes are available.
Equivalence Between Policy Gradients and Soft Q-Learning
Authors: John Schulman, Xi Chen, Pieter Abbeel
Date: 2017-04-21 00:00:00
Tasks: Policy Gradient Methods, Q-Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Two of the leading approaches for model-free reinforcement learning are
policy gradient methods and $Q$-learning methods. $Q$-learning methods can be
effective and sample-efficient when they work, however, it is not
well-understood why they work, since empirically, the $Q$-values they estimate
are very inaccurate. A partial explanation may be that $Q$-learning methods are
secretly implementing policy gradient updates: we show that there is a precise
equivalence between $Q$-learning and policy gradient methods in the setting of
entropy-regularized reinforcement learning, that "soft" (entropy-regularized)
$Q$-learning is exactly equivalent to a policy gradient method. We also point
out a connection between $Q$-learning methods and natural policy gradient
methods. Experimentally, we explore the entropy-regularized versions of
$Q$-learning and policy gradients, and we find them to perform as well as (or
slightly better than) the standard variants on the Atari benchmark. We also
show that the equivalence holds in practical settings by constructing a
$Q$-learning method that closely matches the learning dynamics of A3C without
using a target network or $\epsilon$-greedy exploration schedule.
Contextual Object Detection with a Few Relevant Neighbors
Authors: Ehud Barnea, Ohad Ben-Shahar
Date: 2017-11-15 00:00:00
Tasks: Object, object-detection, Object Detection

A natural way to improve the detection of objects is to consider the
contextual constraints imposed by the detection of additional objects in a
given scene. In this work, we exploit the spatial relations between objects in
order to improve detection capacity, as well as analyze various properties of
the contextual object detection problem. To precisely calculate context-based
probabilities of objects, we developed a model that examines the interactions
between objects in an exact probabilistic setting, in contrast to previous
methods that typically utilize approximations based on pairwise interactions.
Such a scheme is facilitated by the realistic assumption that the existence of
an object in any given location is influenced by only few informative locations
in space. Based on this assumption, we suggest a method for identifying these
relevant locations and integrating them into a mostly exact calculation of
probability based on their raw detector responses. This scheme is shown to
improve detection results and provides unique insights about the process of
contextual inference for object detection. We show that it is generally
difficult to learn that a particular object reduces the probability of another,
and that in cases when the context and detector strongly disagree this learning
becomes virtually impossible for the purposes of improving the results of an
object detector. Finally, we demonstrate improved detection results through use
of our approach as applied to the PASCAL VOC and COCO datasets.
Gazing into the Abyss: Real-time Gaze Estimation
Authors: George He, Sami Oueida, Tucker Ward
Date: 2017-11-18 00:00:00
Tasks: Gaze Estimation

Gaze and face tracking algorithms have traditionally battled a compromise
between computational complexity and accuracy; the most accurate neural net
algorithms cannot be implemented in real time, but less complex real-time
algorithms suffer from higher error. This project seeks to better bridge that
gap by improving on real-time eye and facial recognition algorithms in order to
develop accurate, real-time gaze estimation with an emphasis on minimizing
training data and computational complexity. Our goal is to use eye and facial
recognition techniques to enable users to perform limited tasks based on gaze
and facial input using only a standard, low-quality web cam found in most
modern laptops and smart phones and the limited computational power and
training data typical of those scenarios. We therefore identified seven
promising, fundamentally different algorithms based on different user features
and developed one outstanding, one workable, and one honorable mention gaze
tracking pipelines that match the performance of modern gaze trackers while
using no training data.
Adversarial Feature Matching for Text Generation
Authors: Yizhe Zhang, Zhe Gan, Kai Fan, Zhi Chen, Ricardo Henao, Dinghan Shen, Lawrence Carin
Date: 2017-06-12 00:00:00
Proceeding: ICML 2017 8
Tasks: Generative Adversarial Network, Text Generation

The Generative Adversarial Network (GAN) has achieved great success in
generating realistic (real-valued) synthetic data. However, convergence issues
and difficulties dealing with discrete data hinder the applicability of GAN to
text. We propose a framework for generating realistic text via adversarial
training. We employ a long short-term memory network as generator, and a
convolutional network as discriminator. Instead of using the standard objective
of GAN, we propose matching the high-dimensional latent feature distributions
of real and synthetic sentences, via a kernelized discrepancy metric. This
eases adversarial training by alleviating the mode-collapsing problem. Our
experiments show superior performance in quantitative evaluation, and
demonstrate that our model can generate realistic-looking sentences.
Towards Agent-Based Model Specification in Smart Grid: A Cognitive Agent-based Computing Approach
Authors: Waseem Akram, Muaz A. Niazi, Laszlo Barna Iantovics
Date: 2017-10-01 00:00:00

A smart grid can be considered as a complex network where each node
represents a generation unit or a consumer. Whereas links can be used to
represent transmission lines. One way to study complex systems is by using the
agent-based modeling (ABM) paradigm. An ABM is a way of representing a complex
system of autonomous agents interacting with each other. Previously, a number
of studies have been presented in the smart grid domain making use of the ABM
paradigm. However, to the best of our knowledge, none of these studies have
focused on the specification aspect of ABM. An ABM specification is important
not only for understanding but also for replication of the model. In this
study, we focus on development as well as specification of ABM for smart grid.
We propose an ABM by using a combination of agent-based and complex
network-based approaches. For ABM specification, we use ODD and DREAM
specification approaches. We analyze these two specification approaches
qualitatively as well as quantitatively. Extensive experiments demonstrate that
DREAM is a most useful approach as compared with ODD for modeling as well as
for replication of models for smart grid.
Adversarial Symmetric Variational Autoencoder
Authors: Yunchen Pu, Wei-Yao Wang, Ricardo Henao, Liqun Chen, Zhe Gan, Chunyuan Li, Lawrence Carin
Date: 2017-11-14 00:00:00
Proceeding: NeurIPS 2017 12
Tasks: Decoder, Form

A new form of variational autoencoder (VAE) is developed, in which the joint
distribution of data and codes is considered in two (symmetric) forms: ($i$)
from observed data fed through the encoder to yield codes, and ($ii$) from
latent codes drawn from a simple prior and propagated through the decoder to
manifest data. Lower bounds are learned for marginal log-likelihood fits
observed data and latent codes. When learning with the variational bound, one
seeks to minimize the symmetric Kullback-Leibler divergence of joint density
functions from ($i$) and ($ii$), while simultaneously seeking to maximize the
two marginal log-likelihoods. To facilitate learning, a new form of adversarial
training is developed. An extensive set of experiments is performed, in which
we demonstrate state-of-the-art data reconstruction and generation on several
image benchmark datasets.
One-Sided Unsupervised Domain Mapping
Authors: Sagie Benaim, Lior Wolf
Date: 2017-06-02 00:00:00
Proceeding: NeurIPS 2017 12
Tasks: Style Transfer, Unsupervised Image-To-Image Translation

In unsupervised domain mapping, the learner is given two unmatched datasets
$A$ and $B$. The goal is to learn a mapping $G_{AB}$ that translates a sample
in $A$ to the analog sample in $B$. Recent approaches have shown that when
learning simultaneously both $G_{AB}$ and the inverse mapping $G_{BA}$,
convincing mappings are obtained. In this work, we present a method of learning
$G_{AB}$ without learning $G_{BA}$. This is done by learning a mapping that
maintains the distance between a pair of samples. Moreover, good mappings are
obtained, even by maintaining the distance between different parts of the same
sample before and after mapping. We present experimental results that the new
method not only allows for one sided mapping learning, but also leads to
preferable numerical results over the existing circularity-based constraint.
Our entire code is made publicly available at
https://github.com/sagiebenaim/DistanceGAN .
The Cultural Evolution of National Constitutions
Authors: Daniel N. Rockmore, Chen Fang, Nicholas J. Foti, Tom Ginsburg, David C. Krakauer
Date: 2017-11-18 00:00:00

We explore how ideas from infectious disease and genetics can be used to
uncover patterns of cultural inheritance and innovation in a corpus of 591
national constitutions spanning 1789 - 2008. Legal "Ideas" are encoded as
"topics" - words statistically linked in documents - derived from topic
modeling the corpus of constitutions. Using these topics we derive a diffusion
network for borrowing from ancestral constitutions back to the US Constitution
of 1789 and reveal that constitutions are complex cultural recombinants. We
find systematic variation in patterns of borrowing from ancestral texts and
"biological"-like behavior in patterns of inheritance with the distribution of
"offspring" arising through a bounded preferential-attachment process. This
process leads to a small number of highly innovative (influential)
constitutions some of which have yet to have been identified as so in the
current literature. Our findings thus shed new light on the critical nodes of
the constitution-making network. The constitutional network structure reflects
periods of intense constitution creation, and systematic patterns of variation
in constitutional life-span and temporal influence.
Is China Entering WTO or shijie maoyi zuzhi--a Corpus Study of English Acronyms in Chinese Newspapers
Authors: Hai Hu
Date: 2017-11-18 00:00:00
Tasks: Translation

This is one of the first studies that quantitatively examine the usage of
English acronyms (e.g. WTO) in Chinese texts. Using newspaper corpora, I try to
answer 1) for all instances of a concept that has an English acronym (e.g.
World Trade Organization), what percentage is expressed in the English acronym
(WTO), and what percentage in its Chinese translation (shijie maoyi zuzhi), and
2) what factors are at play in language users' choice between the English and
Chinese forms? Results show that different concepts have different percentage
for English acronyms (PercentOfEn), ranging from 2% to 98%. Linear models show
that PercentOfEn for individual concepts can be predicted by language economy
(how long the Chinese translation is), concept frequency, and whether the first
appearance of the concept in Chinese newspapers is the English acronym or its
Chinese translation (all p < .05).
Anonymous Hedonic Game for Task Allocation in a Large-Scale Multiple Agent System
Authors: Inmo Jang, Hyo-Sang Shin, Antonios Tsourdos
Date: 2017-11-18 00:00:00
Tasks: Decision Making

This paper proposes a novel game-theoretical autonomous decision-making
framework to address a task allocation problem for a swarm of multiple agents.
We consider cooperation of self-interested agents, and show that our proposed
decentralized algorithm guarantees convergence of agents with social inhibition
to a Nash stable partition (i.e., social agreement) within polynomial time. The
algorithm is simple and executable based on local interactions with neighbor
agents under a strongly-connected communication network and even in
asynchronous environments. We analytically present a mathematical formulation
for computing the lower bound of suboptimality of the solution, and
additionally show that 50% of suboptimality can be at least guaranteed if
social utilities are non-decreasing functions with respect to the number of
co-working agents. The results of numerical experiments confirm that the
proposed framework is scalable, fast adaptable against dynamical environments,
and robust even in a realistic situation.
From Common to Special: When Multi-Attribute Learning Meets Personalized Opinions
Authors: Zhiyong Yang, Qianqian Xu, Xiaochun Cao, Qingming Huang
Date: 2017-11-18 00:00:00
Tasks: Attribute, Diversity, feature selection

Visual attributes, which refer to human-labeled semantic annotations, have
gained increasing popularity in a wide range of real world applications.
Generally, the existing attribute learning methods fall into two categories:
one focuses on learning user-specific labels separately for different
attributes, while the other one focuses on learning crowd-sourced global labels
jointly for multiple attributes. However, both categories ignore the joint
effect of the two mentioned factors: the personal diversity with respect to the
global consensus; and the intrinsic correlation among multiple attributes. To
overcome this challenge, we propose a novel model to learn user-specific
predictors across multiple attributes. In our proposed model, the diversity of
personalized opinions and the intrinsic relationship among multiple attributes
are unified in a common-to-special manner. To this end, we adopt a
three-component decomposition. Specifically, our model integrates a common
cognition factor, an attribute-specific bias factor and a user-specific bias
factor. Meanwhile Lasso and group Lasso penalties are adopted to leverage
efficient feature selection. Furthermore, theoretical analysis is conducted to
show that our proposed method could reach reasonable performance. Eventually,
the empirical study carried out in this paper demonstrates the effectiveness of
our proposed method.
3D Shape Classification Using Collaborative Representation based Projections
Authors: F. Fotopoulou, S. Oikonomou, A. Papathanasiou, G. Economou, S. Fotopoulos
Date: 2017-11-13 00:00:00
Tasks: 3D Shape Classification, Classification, General Classification, Multi-class Classification, Representation Learning

A novel 3D shape classification scheme, based on collaborative representation
learning, is investigated in this work. A data-driven feature-extraction
procedure, taking the form of a simple projection operator, is in the core of
our methodology. Provided a shape database, a graph encapsulating the
structural relationships among all the available shapes, is first constructed
and then employed in defining low-dimensional sparse projections. The recently
introduced method of CRPs (collaborative representation based projections),
which is based on L2-Graph, is the first variant that is included towards this
end. A second algorithm, that particularizes the CRPs to shape descriptors that
are inherently nonnegative, is also introduced as potential alternative. In
both cases, the weights in the graph reflecting the database structure are
calculated so as to approximate each shape as a sparse linear combination of
the remaining dataset objects. By way of solving a generalized eigenanalysis
problem, a linear matrix operator is designed that will act as the feature
extractor. Two popular, inherently high dimensional descriptors, namely
ShapeDNA and Global Point Signature (GPS), are employed in our experimentations
with SHREC10, SHREC11 and SCHREC 15 datasets, where shape recognition is cast
as a multi-class classification problem that is tackled by means of an SVM
(support vector machine) acting within the reduced dimensional space of the
crafted projections. The results are very promising and outperform state of the
art methods, providing evidence about the highly discriminative nature of the
introduced 3D shape representations.
Transfer Learning based Dynamic Multiobjective Optimization Algorithms
Authors: Min Jiang, Zhongqiang Huang, Liming Qiu, Wenzhen Huang, Gary G. Yen
Date: 2016-12-19 00:00:00
Tasks: BIG-bench Machine Learning, Multiobjective Optimization, Transfer Learning

One of the major distinguishing features of the dynamic multiobjective
optimization problems (DMOPs) is the optimization objectives will change over
time, thus tracking the varying Pareto-optimal front becomes a challenge. One
of the promising solutions is reusing the "experiences" to construct a
prediction model via statistical machine learning approaches. However most of
the existing methods ignore the non-independent and identically distributed
nature of data used to construct the prediction model. In this paper, we
propose an algorithmic framework, called Tr-DMOEA, which integrates transfer
learning and population-based evolutionary algorithm for solving the DMOPs.
This approach takes the transfer learning method as a tool to help reuse the
past experience for speeding up the evolutionary process, and at the same time,
any population based multiobjective algorithms can benefit from this
integration without any extensive modifications. To verify this, we incorporate
the proposed approach into the development of three well-known algorithms,
nondominated sorting genetic algorithm II (NSGA-II), multiobjective particle
swarm optimization (MOPSO), and the regularity model-based multiobjective
estimation of distribution algorithm (RM-MEDA), and then employ twelve
benchmark functions to test these algorithms as well as compare with some
chosen state-of-the-art designs. The experimental results confirm the
effectiveness of the proposed method through exploiting machine learning
technology.
DLTK: State of the Art Reference Implementations for Deep Learning on Medical Images
Authors: Nick Pawlowski, Sofia Ira Ktena, Matthew C. H. Lee, Bernhard Kainz, Daniel Rueckert, Ben Glocker, Martin Rajchl
Date: 2017-11-18 00:00:00
Tasks: Image Segmentation, Semantic Segmentation

We present DLTK, a toolkit providing baseline implementations for efficient
experimentation with deep learning methods on biomedical images. It builds on
top of TensorFlow and its high modularity and easy-to-use examples allow for a
low-threshold access to state-of-the-art implementations for typical medical
imaging problems. A comparison of DLTK's reference implementations of popular
network architectures for image segmentation demonstrates new top performance
on the publicly available challenge data "Multi-Atlas Labeling Beyond the
Cranial Vault". The average test Dice similarity coefficient of $81.5$ exceeds
the previously best performing CNN ($75.7$) and the accuracy of the challenge
winning method ($79.0$).
PSF : Introduction to R Package for Pattern Sequence Based Forecasting Algorithm
Authors: Neeraj Bokde, Gualberto Asencio-Cortés, Francisco Martínez-Álvarez, Kishore Kulat
Date: 2016-06-17 00:00:00
Tasks: Clustering, Prediction, Time Series, Time Series Analysis, Time Series Forecasting, Univariate Time Series Forecasting

This paper discusses about an R package that implements the Pattern Sequence
based Forecasting (PSF) algorithm, which was developed for univariate time
series forecasting. This algorithm has been successfully applied to many
different fields. The PSF algorithm consists of two major parts: clustering and
prediction. The clustering part includes selection of the optimum number of
clusters. It labels time series data with reference to such clusters. The
prediction part includes functions like optimum window size selection for
specific patterns and prediction of future values with reference to past
pattern sequences. The PSF package consists of various functions to implement
the PSF algorithm. It also contains a function which automates all other
functions to obtain optimized prediction results. The aim of this package is to
promote the PSF algorithm and to ease its implementation with minimum efforts.
This paper describes all the functions in the PSF package with their syntax. It
also provides a simple example of usage. Finally, the usefulness of this
package is discussed by comparing it to auto.arima and ets, well-known time
series forecasting functions available on CRAN repository.