pwc-archive/papers-with-abstracts huggingface dataset:
Recovering a Hidden Community Beyond the Kesten-Stigum Threshold in $O(|E| \log^*|V|)$ Time
Authors: Bruce Hajek, Yihong Wu, Jiaming Xu
Date: 2015-10-09 00:00:00
Tasks: Community Detection, Stochastic Block Model

Community detection is considered for a stochastic block model graph of n
vertices, with K vertices in the planted community, edge probability p for
pairs of vertices both in the community, and edge probability q for other pairs
of vertices.
  The main focus of the paper is on weak recovery of the community based on the
graph G, with o(K) misclassified vertices on average, in the sublinear regime
$n^{1-o(1)} \leq K \leq o(n).$ A critical parameter is the effective
signal-to-noise ratio $\lambda=K^2(p-q)^2/((n-K)q)$, with $\lambda=1$
corresponding to the Kesten-Stigum threshold. We show that a belief propagation
algorithm achieves weak recovery if $\lambda>1/e$, beyond the Kesten-Stigum
threshold by a factor of $1/e.$ The belief propagation algorithm only needs to
run for $\log^\ast n+O(1) $ iterations, with the total time complexity $O(|E|
\log^*n)$, where $\log^*n$ is the iterated logarithm of $n.$ Conversely, if
$\lambda \leq 1/e$, no local algorithm can asymptotically outperform trivial
random guessing. Furthermore, a linear message-passing algorithm that
corresponds to applying power iteration to the non-backtracking matrix of the
graph is shown to attain weak recovery if and only if $\lambda>1$. In addition,
the belief propagation algorithm can be combined with a linear-time voting
procedure to achieve the information limit of exact recovery (correctly
classify all vertices with high probability) for all $K \ge \frac{n}{\log n}
\left( \rho_{\rm BP} +o(1) \right),$ where $\rho_{\rm BP}$ is a function of
$p/q$.
Adposition and Case Supersenses v2.6: Guidelines for English
Authors: Nathan Schneider, Jena D. Hwang, Vivek Srikumar, Archna Bhatia, Na-Rae Han, Tim O'Gorman, Sarah R. Moeller, Omri Abend, Adi Shalev, Austin Blodgett, Jakob Prange
Date: 2017-04-07 00:00:00

This document offers a detailed linguistic description of SNACS (Semantic Network of Adposition and Case Supersenses; Schneider et al., 2018), an inventory of 52 semantic labels ("supersenses") that characterize the use of adpositions and case markers at a somewhat coarse level of granularity, as demonstrated in the STREUSLE corpus (https://github.com/nert-nlp/streusle/ ; version 4.5 tracks guidelines version 2.6). Though the SNACS inventory aspires to be universal, this document is specific to English; documentation for other languages will be published separately. Version 2 is a revision of the supersense inventory proposed for English by Schneider et al. (2015, 2016) (henceforth "v1"), which in turn was based on previous schemes. The present inventory was developed after extensive review of the v1 corpus annotations for English, plus previously unanalyzed genitive case possessives (Blodgett and Schneider, 2018), as well as consideration of adposition and case phenomena in Hebrew, Hindi, Korean, and German. Hwang et al. (2017) present the theoretical underpinnings of the v2 scheme. Schneider et al. (2018) summarize the scheme, its application to English corpus data, and an automatic disambiguation task. Liu et al. (2021) offer an English Lexical Semantic Recognition tagger that includes SNACS labels in its output. This documentation can also be browsed alongside corpus data on the Xposition website (Gessler et al., 2022): http://www.xposition.org/
Learning in games with continuous action sets and unknown payoff functions
Authors: Panayotis Mertikopoulos, Zhengyuan Zhou
Date: 2016-08-25 00:00:00

This paper examines the convergence of no-regret learning in games with
continuous action sets. For concreteness, we focus on learning via "dual
averaging", a widely used class of no-regret learning schemes where players
take small steps along their individual payoff gradients and then "mirror" the
output back to their action sets. In terms of feedback, we assume that players
can only estimate their payoff gradients up to a zero-mean error with bounded
variance. To study the convergence of the induced sequence of play, we
introduce the notion of variational stability, and we show that stable
equilibria are locally attracting with high probability whereas globally stable
equilibria are globally attracting with probability 1. We also discuss some
applications to mixed-strategy learning in finite games, and we provide
explicit estimates of the method's convergence speed.
A Comparative Study of Rule Extraction for Recurrent Neural Networks
Authors: Qinglong Wang, Kaixuan Zhang, Alexander G. Ororbia II, Xinyu Xing, Xue Liu, C. Lee Giles
Date: 2018-01-16 00:00:00

Understanding recurrent networks through rule extraction has a long history.
This has taken on new interests due to the need for interpreting or verifying
neural networks. One basic form for representing stateful rules is
deterministic finite automata (DFA). Previous research shows that extracting
DFAs from trained second-order recurrent networks is not only possible but also
relatively stable. Recently, several new types of recurrent networks with more
complicated architectures have been introduced. These handle challenging
learning tasks usually involving sequential data. However, it remains an open
problem whether DFAs can be adequately extracted from these models.
Specifically, it is not clear how DFA extraction will be affected when applied
to different recurrent networks trained on data sets with different levels of
complexity. Here, we investigate DFA extraction on several widely adopted
recurrent networks that are trained to learn a set of seven regular Tomita
grammars. We first formally analyze the complexity of Tomita grammars and
categorize these grammars according to that complexity. Then we empirically
evaluate different recurrent networks for their performance of DFA extraction
on all Tomita grammars. Our experiments show that for most recurrent networks,
their extraction performance decreases as the complexity of the underlying
grammar increases. On grammars of lower complexity, most recurrent networks
obtain desirable extraction performance. As for grammars with the highest level
of complexity, while several complicated models fail with only certain
recurrent networks having satisfactory extraction performance.
The "handedness" of language: Directional symmetry breaking of sign usage in words
Authors: Md Izhar Ashraf, Sitabhra Sinha
Date: 2015-09-30 00:00:00
Tasks: valid

Language, which allows complex ideas to be communicated through symbolic
sequences, is a characteristic feature of our species and manifested in a
multitude of forms. Using large written corpora for many different languages
and scripts, we show that the occurrence probability distributions of signs at
the left and right ends of words have a distinct heterogeneous nature.
Characterizing this asymmetry using quantitative inequality measures, viz.
information entropy and the Gini index, we show that the beginning of a word is
less restrictive in sign usage than the end. This property is not simply
attributable to the use of common affixes as it is seen even when only word
roots are considered. We use the existence of this asymmetry to infer the
direction of writing in undeciphered inscriptions that agrees with the
archaeological evidence. Unlike traditional investigations of phonotactic
constraints which focus on language-specific patterns, our study reveals a
property valid across languages and writing systems. As both language and
writing are unique aspects of our species, this universal signature may reflect
an innate feature of the human cognitive phenomenon.
Grounded Language Understanding for Manipulation Instructions Using GAN-Based Classification
Authors: Komei Sugiura, Hisashi Kawai
Date: 2018-01-16 00:00:00
Tasks: Classification, General Classification

The target task of this study is grounded language understanding for domestic
service robots (DSRs). In particular, we focus on instruction understanding for
short sentences where verbs are missing. This task is of critical importance to
build communicative DSRs because manipulation is essential for DSRs. Existing
instruction understanding methods usually estimate missing information only
from non-grounded knowledge; therefore, whether the predicted action is
physically executable or not was unclear.
  In this paper, we present a grounded instruction understanding method to
estimate appropriate objects given an instruction and situation. We extend the
Generative Adversarial Nets (GAN) and build a GAN-based classifier using latent
representations. To quantitatively evaluate the proposed method, we have
developed a data set based on the standard data set used for Visual QA.
Experimental results have shown that the proposed method gives the better
result than baseline methods.
Inferring Semantic Layout for Hierarchical Text-to-Image Synthesis
Authors: Seunghoon Hong, Dingdong Yang, Jongwook Choi, Honglak Lee
Date: 2018-01-16 00:00:00
Proceeding: CVPR 2018 6
Tasks: Image Generation

We propose a novel hierarchical approach for text-to-image synthesis by
inferring semantic layout. Instead of learning a direct mapping from text to
image, our algorithm decomposes the generation process into multiple steps, in
which it first constructs a semantic layout from the text by the layout
generator and converts the layout to an image by the image generator. The
proposed layout generator progressively constructs a semantic layout in a
coarse-to-fine manner by generating object bounding boxes and refining each box
by estimating object shapes inside the box. The image generator synthesizes an
image conditioned on the inferred semantic layout, which provides a useful
semantic structure of an image matching with the text description. Our model
not only generates semantically more meaningful images, but also allows
automatic annotation of generated images and user-controlled generation process
by modifying the generated scene layout. We demonstrate the capability of the
proposed model on challenging MS-COCO dataset and show that the model can
substantially improve the image quality, interpretability of output and
semantic alignment to input text over existing approaches.
Real-time Road Traffic Information Detection Through Social Media
Authors: Chandra Khatri
Date: 2018-01-16 00:00:00

In current study, a mechanism to extract traffic related information such as
congestion and incidents from textual data from the internet is proposed. The
current source of data is Twitter. As the data being considered is extremely
large in size automated models are developed to stream, download, and mine the
data in real-time. Furthermore, if any tweet has traffic related information
then the models should be able to infer and extract this data.
  Currently, the data is collected only for United States and a total of
120,000 geo-tagged traffic related tweets are extracted, while six million
geo-tagged non-traffic related tweets are retrieved and classification models
are trained. Furthermore, this data is used for various kinds of spatial and
temporal analysis. A mechanism to calculate level of traffic congestion,
safety, and traffic perception for cities in U.S. is proposed. Traffic
congestion and safety rankings for the various urban areas are obtained and
then they are statistically validated with existing widely adopted rankings.
Traffic perception depicts the attitude and perception of people towards the
traffic.
  It is also seen that traffic related data when visualized spatially and
temporally provides the same pattern as the actual traffic flows for various
urban areas. When visualized at the city level, it is clearly visible that the
flow of tweets is similar to flow of vehicles and that the traffic related
tweets are representative of traffic within the cities. With all the findings
in current study, it is shown that significant amount of traffic related
information can be extracted from Twitter and other sources on internet.
Furthermore, Twitter and these data sources are freely available and are not
bound by spatial and temporal limitations. That is, wherever there is a user
there is a potential for data.
A large scale study of SVM based methods for abstract screening in systematic reviews
Authors: Tanay Kumar Saha, Mourad Ouzzani, Hossam M. Hammady, Ahmed K. Elmagarmid, Wajdi Dhifli, Mohammad Al Hasan
Date: 2016-10-01 00:00:00

A major task in systematic reviews is abstract screening, i.e., excluding,
often hundreds or thousand of, irrelevant citations returned from a database
search based on titles and abstracts. Thus, a systematic review platform that
can automate the abstract screening process is of huge importance. Several
methods have been proposed for this task. However, it is very hard to clearly
understand the applicability of these methods in a systematic review platform
because of the following challenges: (1) the use of non-overlapping metrics for
the evaluation of the proposed methods, (2) usage of features that are very
hard to collect, (3) using a small set of reviews for the evaluation, and (4)
no solid statistical testing or equivalence grouping of the methods. In this
paper, we use feature representation that can be extracted per citation. We
evaluate SVM-based methods (commonly used) on a large set of reviews ($61$) and
metrics ($11$) to provide equivalence grouping of methods based on a solid
statistical test. Our analysis also includes a strong variability of the
metrics using $500$x$2$ cross validation. While some methods shine for
different metrics and for different datasets, there is no single method that
dominates the pack. Furthermore, we observe that in some cases relevant
(included) citations can be found after screening only 15-20% of them via a
certainty based sampling. A few included citations present outlying
characteristics and can only be found after a very large number of screening
steps. Finally, we present an ensemble algorithm for producing a $5$-star
rating of citations based on their relevance. Such algorithm combines the best
methods from our evaluation and through its $5$-star rating outputs a more
easy-to-consume prediction.
A Human-Grounded Evaluation Benchmark for Local Explanations of Machine Learning
Authors: Sina Mohseni, Jeremy E. Block, Eric D. Ragan
Date: 2018-01-16 00:00:00
Tasks: BIG-bench Machine Learning, Decision Making, Interpretable Machine Learning, Segmentation, Semantic Segmentation

Research in interpretable machine learning proposes different computational and human subject approaches to evaluate model saliency explanations. These approaches measure different qualities of explanations to achieve diverse goals in designing interpretable machine learning systems. In this paper, we propose a human attention benchmark for image and text domains using multi-layer human attention masks aggregated from multiple human annotators. We then present an evaluation study to evaluate model saliency explanations obtained using Grad-cam and LIME techniques. We demonstrate our benchmark's utility for quantitative evaluation of model explanations by comparing it with human subjective ratings and ground-truth single-layer segmentation masks evaluations. Our study results show that our threshold agnostic evaluation method with the human attention baseline is more effective than single-layer object segmentation masks to ground truth. Our experiments also reveal user biases in the subjective rating of model saliency explanations.
Circular Antenna Array Design for Breast Cancer Detection
Authors: Kalthoum Ouerghi, Najib Fadlallah, Amor Smida, Ridha Ghayoula, Jaouhar Fattahi, Noureddine Boulejfen
Date: 2018-01-15 00:00:00
Tasks: Breast Cancer Detection

Microwave imaging for breast cancer detection is based on the contrast in the
electrical properties of healthy fatty breast tissues. This paper presents an
industrial, scientific and medical (ISM) bands comparative study of five
microstrip patch antennas for microwave imaging at a frequency of 2.45 GHz. The
choice of one antenna is made for an antenna array composed of 8 antennas for a
microwave breast imaging system. Each antenna element is arranged in a circular
configuration so that it can be directly faced to the breast phantom for better
tumor detection. This choice is made by putting each antenna alone on the
Breast skin to study the electric field, magnetic fields and current density in
the healthy tissue of the breast phantom designed and simulated in Ansoft High
Frequency Simulation Software (HFSS).
Multi-Label Learning from Medical Plain Text with Convolutional Residual Models
Authors: Xinyuan Zhang, Ricardo Henao, Zhe Gan, Yitong Li, Lawrence Carin
Date: 2018-01-15 00:00:00
Tasks: General Classification, Multi-Label Classification, MUlTI-LABEL-ClASSIFICATION, Multi-Label Learning, Sentence, Sentence Embedding, Sentence-Embedding

Predicting diagnoses from Electronic Health Records (EHRs) is an important
medical application of multi-label learning. We propose a convolutional
residual model for multi-label classification from doctor notes in EHR data. A
given patient may have multiple diagnoses, and therefore multi-label learning
is required. We employ a Convolutional Neural Network (CNN) to encode plain
text into a fixed-length sentence embedding vector. Since diagnoses are
typically correlated, a deep residual network is employed on top of the CNN
encoder, to capture label (diagnosis) dependencies and incorporate information
directly from the encoded sentence vector. A real EHR dataset is considered,
and we compare the proposed model with several well-known baselines, to predict
diagnoses based on doctor notes. Experimental results demonstrate the
superiority of the proposed convolutional residual model.
Generating Adversarial Examples with Adversarial Networks
Authors: Chaowei Xiao, Bo Li, Jun-Yan Zhu, Warren He, Mingyan Liu, Dawn Song
Date: 2018-01-08 00:00:00
Proceeding: ICLR 2018 1
Tasks: Adversarial Robustness

A challenge to explore adversarial robustness of neural networks on MNIST.
Global Convergence of Policy Gradient Methods for the Linear Quadratic Regulator
Authors: Maryam Fazel, Rong Ge, Sham M. Kakade, Mehran Mesbahi
Date: 2018-01-15 00:00:00
Proceeding: ICML 2018 7
Tasks: continuous-control, Continuous Control, Policy Gradient Methods, Reinforcement Learning

Direct policy gradient methods for reinforcement learning and continuous
control problems are a popular approach for a variety of reasons: 1) they are
easy to implement without explicit knowledge of the underlying model 2) they
are an "end-to-end" approach, directly optimizing the performance metric of
interest 3) they inherently allow for richly parameterized policies. A notable
drawback is that even in the most basic continuous control problem (that of
linear quadratic regulators), these methods must solve a non-convex
optimization problem, where little is understood about their efficiency from
both computational and statistical perspectives. In contrast, system
identification and model based planning in optimal control theory have a much
more solid theoretical footing, where much is known with regards to their
computational and statistical properties. This work bridges this gap showing
that (model free) policy gradient methods globally converge to the optimal
solution and are efficient (polynomially so in relevant problem dependent
quantities) with regards to their sample and computational complexities.
An octree cells occupancy geometric dimensionality descriptor for massive on-server point cloud visualisation and classification
Authors: Remi Cura, Julien Perret, Nicolas Paparoditis
Date: 2018-01-15 00:00:00
Tasks: General Classification, object-detection, Object Detection, Surface Reconstruction

Lidar datasets are becoming more and more common. They are appreciated for
their precise 3D nature, and have a wide range of applications, such as surface
reconstruction, object detection, visualisation, etc. For all this
applications, having additional semantic information per point has potential of
increasing the quality and the efficiency of the application. In the last
decade the use of Machine Learning and more specifically classification methods
have proved to be successful to create this semantic information. In this
paradigm, the goal is to classify points into a set of given classes (for
instance tree, building, ground, other). Some of these methods use descriptors
(also called feature) of a point to learn and predict its class. Designing the
descriptors is then the heart of these methods. Descriptors can be based on
points geometry and attributes, use contextual information, etc. Furthermore,
descriptors can be used by humans for easier visual understanding and sometimes
filtering. In this work we propose a new simple geometric descriptor that gives
information about the implicit local dimensionality of the point cloud at
various scale. For instance a tree seen from afar is more volumetric in nature
(3D), yet locally each leaves is rather planar (2D). To do so we build an
octree centred on the point to consider, and compare the variation of the
occupancy of the cells across the levels of the octree. We compare this
descriptor with the state of the art dimensionality descriptor and show its
interest. We further test the descriptor for classification within the Point
Cloud Server, and demonstrate efficiency and correctness results.
Training Gaussian Mixture Models at Scale via Coresets
Authors: Mario Lucic, Matthew Faulkner, Andreas Krause, Dan Feldman
Date: 2017-03-23 00:00:00

How can we train a statistical mixture model on a massive data set? In this
work we show how to construct coresets for mixtures of Gaussians. A coreset is
a weighted subset of the data, which guarantees that models fitting the coreset
also provide a good fit for the original data set. We show that, perhaps
surprisingly, Gaussian mixtures admit coresets of size polynomial in dimension
and the number of mixture components, while being independent of the data set
size. Hence, one can harness computationally intensive algorithms to compute a
good approximation on a significantly smaller data set. More importantly, such
coresets can be efficiently constructed both in distributed and streaming
settings and do not impose restrictions on the data generating process. Our
results rely on a novel reduction of statistical estimation to problems in
computational geometry and new combinatorial complexity results for mixtures of
Gaussians. Empirical evaluation on several real-world datasets suggests that
our coreset-based approach enables significant reduction in training-time with
negligible approximation error.
A Gaussian mixture model representation of endmember variability in hyperspectral unmixing
Authors: Yuan Zhou, Anand Rangarajan, Paul D. Gader
Date: 2017-09-29 00:00:00
Tasks: Hyperspectral Unmixing

Hyperspectral unmixing while considering endmember variability is usually
performed by the normal compositional model (NCM), where the endmembers for
each pixel are assumed to be sampled from unimodal Gaussian distributions.
However, in real applications, the distribution of a material is often not
Gaussian. In this paper, we use Gaussian mixture models (GMM) to represent the
endmember variability. We show, given the GMM starting premise, that the
distribution of the mixed pixel (under the linear mixing model) is also a GMM
(and this is shown from two perspectives). The first perspective originates
from the random variable transformation and gives a conditional density
function of the pixels given the abundances and GMM parameters. With proper
smoothness and sparsity prior constraints on the abundances, the conditional
density function leads to a standard maximum a posteriori (MAP) problem which
can be solved using generalized expectation maximization. The second
perspective originates from marginalizing over the endmembers in the GMM, which
provides us with a foundation to solve for the endmembers at each pixel. Hence,
our model can not only estimate the abundances and distribution parameters, but
also the distinct endmember set for each pixel. We tested the proposed GMM on
several synthetic and real datasets, and showed its potential by comparing it
to current popular methods.