pwc-archive/papers-with-abstracts huggingface dataset:
When do Words Matter? Understanding the Impact of Lexical Choice on Audience Perception using Individual Treatment Effect Estimation
Authors: Zhao Wang, Aron Culotta
Date: 2018-11-12 00:00:00
Tasks: Sentence

Studies across many disciplines have shown that lexical choice can affect
audience perception. For example, how users describe themselves in a social
media profile can affect their perceived socio-economic status. However, we
lack general methods for estimating the causal effect of lexical choice on the
perception of a specific sentence. While randomized controlled trials may
provide good estimates, they do not scale to the potentially millions of
comparisons necessary to consider all lexical choices. Instead, in this paper,
we first offer two classes of methods to estimate the effect on perception of
changing one word to another in a given sentence. The first class of algorithms
builds upon quasi-experimental designs to estimate individual treatment effects
from observational data. The second class treats treatment effect estimation as
a classification problem. We conduct experiments with three data sources (Yelp,
Twitter, and Airbnb), finding that the algorithmic estimates align well with
those produced by randomized-control trials. Additionally, we find that it is
possible to transfer treatment effect classifiers across domains and still
maintain high accuracy.
CQASUMM: Building References for Community Question Answering Summarization Corpora
Authors: Tanya Chowdhury, Tanmoy Chakraborty
Date: 2018-11-12 00:00:00
Tasks: Community Question Answering, Document Summarization, Multi-Document Summarization, Question Answering

Community Question Answering forums such as Quora, Stackoverflow are rich
knowledge resources, often catering to information on topics overlooked by
major search engines. Answers submitted to these forums are often elaborated,
contain spam, are marred by slurs and business promotions. It is difficult for
a reader to go through numerous such answers to gauge community opinion. As a
result summarization becomes a prioritized task for CQA forums. While a number
of efforts have been made to summarize factoid CQA, little work exists in
summarizing non-factoid CQA. We believe this is due to the lack of a
considerably large, annotated dataset for CQA summarization. We create CQASUMM,
the first huge annotated CQA summarization dataset by filtering the 4.4 million
Yahoo! Answers L6 dataset. We sample threads where the best answer can double
up as a reference summary and build hundred word summaries from them. We treat
other answers as candidates documents for summarization. We provide a script to
generate the dataset and introduce the new task of Community Question Answering
Summarization. Multi document summarization has been widely studied with news
article datasets, especially in the DUC and TAC challenges using news corpora.
However documents in CQA have higher variance, contradicting opinion and lesser
amount of overlap. We compare the popular multi document summarization
techniques and evaluate their performance on our CQA corpora. We look into the
state-of-the-art and understand the cases where existing multi document
summarizers (MDS) fail. We find that most MDS workflows are built for the
entirely factual news corpora, whereas our corpus has a fair share of opinion
based instances too. We therefore introduce OpinioSumm, a new MDS which
outperforms the best baseline by 4.6% w.r.t ROUGE-1 score.
Characterizing machine learning process: A maturity framework
Authors: Rama Akkiraju, Vibha Sinha, Anbang Xu, Jalal Mahmud, Pritam Gundecha, Zhe Liu, Xiaotong Liu, John Schumacher
Date: 2018-11-12 00:00:00
Tasks: BIG-bench Machine Learning, Fairness, Management

Academic literature on machine learning modeling fails to address how to make
machine learning models work for enterprises. For example, existing machine
learning processes cannot address how to define business use cases for an AI
application, how to convert business requirements from offering managers into
data requirements for data scientists, and how to continuously improve AI
applications in term of accuracy and fairness, and how to customize general
purpose machine learning models with industry, domain, and use case specific
data to make them more accurate for specific situations etc. Making AI work for
enterprises requires special considerations, tools, methods and processes. In
this paper we present a maturity framework for machine learning model lifecycle
management for enterprises. Our framework is a re-interpretation of the
software Capability Maturity Model (CMM) for machine learning model development
process. We present a set of best practices from our personal experience of
building large scale real-world machine learning models to help organizations
achieve higher levels of maturity independent of their starting point.
A Perceptual Prediction Framework for Self Supervised Event Segmentation
Authors: Sathyanarayanan N. Aakur, Sudeep Sarkar
Date: 2018-11-12 00:00:00
Proceeding: CVPR 2019 6
Tasks: Action Localization, Action Recognition, Event Segmentation, Prediction, Representation Learning, Unsupervised Action Segmentation

Temporal segmentation of long videos is an important problem, that has
largely been tackled through supervised learning, often requiring large amounts
of annotated training data. In this paper, we tackle the problem of
self-supervised temporal segmentation of long videos that alleviate the need
for any supervision. We introduce a self-supervised, predictive learning
framework that draws inspiration from cognitive psychology to segment long,
visually complex videos into individual, stable segments that share the same
semantics. We also introduce a new adaptive learning paradigm that helps reduce
the effect of catastrophic forgetting in recurrent neural networks. Extensive
experiments on three publicly available datasets - Breakfast Actions, 50
Salads, and INRIA Instructional Videos datasets show the efficacy of the
proposed approach. We show that the proposed approach is able to outperform
weakly-supervised and other unsupervised learning approaches by up to 24% and
have competitive performance compared to fully supervised approaches. We also
show that the proposed approach is able to learn highly discriminative features
that help improve action recognition when used in a representation learning
paradigm.
A Framework of Transfer Learning in Object Detection for Embedded Systems
Authors: Ioannis Athanasiadis, Panagiotis Mousouliotis, Loukas Petrou
Date: 2018-11-12 00:00:00
Tasks: Hyperparameter Optimization, Object, object-detection, Object Detection, Object Recognition, Transfer Learning

Transfer learning is one of the subjects undergoing intense study in the area
of machine learning. In object recognition and object detection there are known
experiments for the transferability of parameters, but not for neural networks
which are suitable for object detection in real time embedded applications,
such as the SqueezeDet neural network. We use transfer learning to accelerate
the training of SqueezeDet to a new group of classes. Also, experiments are
conducted to study the transferability and co-adaptation phenomena introduced
by the transfer learning process. To accelerate training, we propose a new
implementation of the SqueezeDet training which provides a faster pipeline for
data processing and achieves 1.8 times speedup compared to the initial
implementation. Finally, we created a mechanism for automatic hyperparameter
optimization using an empirical method.
Bio-YODIE: A Named Entity Linking System for Biomedical Text
Authors: Genevieve Gorrell, Xingyi Song, Angus Roberts
Date: 2018-11-12 00:00:00
Tasks: Entity Linking

Ever-expanding volumes of biomedical text require automated semantic
annotation techniques to curate and put to best use. An established field of
research seeks to link mentions in text to knowledge bases such as those
included in the UMLS (Unified Medical Language System), in order to enable a
more sophisticated understanding. This work has yielded good results for tasks
such as curating literature, but increasingly, annotation systems are more
broadly applied. Medical vocabularies are expanding in size, and with them the
extent of term ambiguity. Document collections are increasing in size and
complexity, creating a greater need for speed and robustness. Furthermore, as
the technologies are turned to new tasks, requirements change; for example
greater coverage of expressions may be required in order to annotate patient
records, and greater accuracy may be needed for applications that affect
patients. This places new demands on the approaches currently in use. In this
work, we present a new system, Bio-YODIE, and compare it to two other popular
systems in order to give guidance about suitable approaches in different
scenarios and how systems might be designed to accommodate future needs.
Generative Dual Adversarial Network for Generalized Zero-shot Learning
Authors: He Huang, Changhu Wang, Philip S. Yu, Chang-Dong Wang
Date: 2018-11-12 00:00:00
Proceeding: CVPR 2019 6
Tasks: Generalized Zero-Shot Learning, Metric Learning, Zero-Shot Learning

This paper studies the problem of generalized zero-shot learning which requires the model to train on image-label pairs from some seen classes and test on the task of classifying new images from both seen and unseen classes. Most previous models try to learn a fixed one-directional mapping between visual and semantic space, while some recently proposed generative methods try to generate image features for unseen classes so that the zero-shot learning problem becomes a traditional fully-supervised classification problem. In this paper, we propose a novel model that provides a unified framework for three different approaches: visual-> semantic mapping, semantic->visual mapping, and metric learning. Specifically, our proposed model consists of a feature generator that can generate various visual features given class embeddings as input, a regressor that maps each visual feature back to its corresponding class embedding, and a discriminator that learns to evaluate the closeness of an image feature and a class embedding. All three components are trained under the combination of cyclic consistency loss and dual adversarial loss. Experimental results show that our model not only preserves higher accuracy in classifying images from seen classes, but also performs better than existing state-of-the-art models in in classifying images from unseen classes.
On the practice of classification learning for clinical diagnosis and therapy advice in oncology
Authors: Flavio S Correa da Silva, Frederico P Costa, Antonio F Iemma
Date: 2018-11-12 00:00:00
Tasks: BIG-bench Machine Learning, General Classification

Artificial intelligence and medicine have a longstanding and proficuous
relationship. In the present work we develop a brief assessment of this
relationship with specific focus on machine learning, in which we highlight
some critical points which may hinder the use of machine learning techniques
for clinical diagnosis and therapy advice in practice. We then suggest a
conceptual framework to build successful systems to aid clinical diagnosis and
therapy advice, grounded on a novel concept we have coined drifting domains. We
focus on oncology to build our arguments, as this area of medicine furnishes
strong evidence for the critical points we take into account here.
Quantum-inspired sublinear classical algorithms for solving low-rank linear systems
Authors: Nai-Hui Chia, Han-Hsuan Lin, Chunhao Wang
Date: 2018-11-12 00:00:00
Tasks: Recommendation Systems

We present classical sublinear-time algorithms for solving low-rank linear
systems of equations. Our algorithms are inspired by the HHL quantum algorithm
for solving linear systems and the recent breakthrough by Tang of dequantizing
the quantum algorithm for recommendation systems. Let $A \in \mathbb{C}^{m
\times n}$ be a rank-$k$ matrix, and $b \in \mathbb{C}^m$ be a vector. We
present two algorithms: a "sampling" algorithm that provides a sample from
$A^{-1}b$ and a "query" algorithm that outputs an estimate of an entry of
$A^{-1}b$, where $A^{-1}$ denotes the Moore-Penrose pseudo-inverse. Both of our
algorithms have query and time complexity $O(\mathrm{poly}(k, \kappa, \|A\|_F,
1/\epsilon)\,\mathrm{polylog}(m, n))$, where $\kappa$ is the condition number
of $A$ and $\epsilon$ is the precision parameter. Note that the algorithms we
consider are sublinear time, so they cannot write and read the whole matrix or
vectors. In this paper, we assume that $A$ and $b$ come with well-known
low-overhead data structures such that entries of $A$ and $b$ can be sampled
according to some natural probability distributions. Alternatively, when $A$ is
positive semidefinite, our algorithms can be adapted so that the sampling
assumption on $b$ is not required.
Learning from positive and unlabeled data: a survey
Authors: Jessa Bekker, Jesse Davis
Date: 2018-11-12 00:00:00
Tasks: BIG-bench Machine Learning, Knowledge Base Completion, Medical Diagnosis, Survey

Learning from positive and unlabeled data or PU learning is the setting where a learner only has access to positive examples and unlabeled data. The assumption is that the unlabeled data can contain both positive and negative examples. This setting has attracted increasing interest within the machine learning literature as this type of data naturally arises in applications such as medical diagnosis and knowledge base completion. This article provides a survey of the current state of the art in PU learning. It proposes seven key research questions that commonly arise in this field and provides a broad overview of how the field has tried to address them.
A test case for application of convolutional neural networks to spatio-temporal climate data: Re-identifying clustered weather patterns
Authors: Ashesh Chattopadhyay, Pedram Hassanzadeh, Saba Pasha
Date: 2018-11-12 00:00:00
Tasks: Clustering

Convolutional neural networks (CNNs) can potentially provide powerful tools
for classifying and identifying patterns in climate and environmental data.
However, because of the inherent complexities of such data, which are often
spatio-temporal, chaotic, and non-stationary, the CNN algorithms must be
designed/evaluated for each specific dataset and application. Yet to start,
CNN, a supervised technique, requires a large labeled dataset. Labeling demands
(human) expert time, which combined with the limited number of relevant
examples in this area, can discourage using CNNs for new problems. To address
these challenges, here we (1) Propose an effective auto-labeling strategy based
on using an unsupervised clustering algorithm and evaluating the performance of
CNNs in re-identifying these clusters; (2) Use this approach to label thousands
of daily large-scale weather patterns over North America in the outputs of a
fully-coupled climate model and show the capabilities of CNNs in re-identifying
the 4 clustered regimes. The deep CNN trained with $1000$ samples or more per
cluster has an accuracy of $90\%$ or better. Accuracy scales monotonically but
nonlinearly with the size of the training set, e.g. reaching $94\%$ with $3000$
training samples per cluster. Effects of architecture and hyperparameters on
the performance of CNNs are examined and discussed.
Automatic kidney segmentation in ultrasound images using subsequent boundary distance regression and pixelwise classification networks
Authors: Shi Yin, Qinmu Peng, Hongming Li, Zhengqiang Zhang, Xinge You, Susan L. Furth, Gregory E. Tasian, Yong Fan
Date: 2018-11-12 00:00:00
Tasks: Classification, Data Augmentation, Distance regression, General Classification, regression

It remains challenging to automatically segment kidneys in clinical
ultrasound (US) images due to the kidneys' varied shapes and image intensity
distributions, although semi-automatic methods have achieved promising
performance. In this study, we propose subsequent boundary distance regression
and pixel classification networks to segment the kidneys, informed by the fact
that the kidney boundaries have relatively homogenous texture patterns across
images. Particularly, we first use deep neural networks pre-trained for
classification of natural images to extract high-level image features from US
images, then these features are used as input to learn kidney boundary distance
maps using a boundary distance regression network, and finally the predicted
boundary distance maps are classified as kidney pixels or non-kidney pixels
using a pixel classification network in an end-to-end learning fashion. We also
adopted a data-augmentation method based on kidney shape registration to
generate enriched training data from a small number of US images with manually
segmented kidney labels. Experimental results have demonstrated that our method
could effectively improve the performance of automatic kidney segmentation,
significantly better than deep learning-based pixel classification networks.
Random Dictators with a Random Referee: Constant Sample Complexity Mechanisms for Social Choice
Authors: Brandon Fain, Ashish Goel, Kamesh Munagala, Nina Prabhu
Date: 2018-11-12 00:00:00

We study social choice mechanisms in an implicit utilitarian framework with a metric constraint, where the goal is to minimize \textit{Distortion}, the worst case social cost of an ordinal mechanism relative to underlying cardinal utilities. We consider two additional desiderata: Constant sample complexity and Squared Distortion. Constant sample complexity means that the mechanism (potentially randomized) only uses a constant number of ordinal queries regardless of the number of voters and alternatives. Squared Distortion is a measure of variance of the Distortion of a randomized mechanism. Our primary contribution is the first social choice mechanism with constant sample complexity \textit{and} constant Squared Distortion (which also implies constant Distortion). We call the mechanism Random Referee, because it uses a random agent to compare two alternatives that are the favorites of two other random agents. We prove that the use of a comparison query is necessary: no mechanism that only elicits the top-k preferred alternatives of voters (for constant k) can have Squared Distortion that is sublinear in the number of alternatives. We also prove that unlike any top-k only mechanism, the Distortion of Random Referee meaningfully improves on benign metric spaces, using the Euclidean plane as a canonical example. Finally, among top-1 only mechanisms, we introduce Random Oligarchy. The mechanism asks just 3 queries and is essentially optimal among the class of such mechanisms with respect to Distortion. In summary, we demonstrate the surprising power of constant sample complexity mechanisms generally, and just three random voters in particular, to provide some of the best known results in the implicit utilitarian framework.
Improving Generalization for Abstract Reasoning Tasks Using Disentangled Feature Representations
Authors: Xander Steenbrugge, Sam Leroux, Tim Verbelen, Bart Dhoedt
Date: 2018-11-12 00:00:00
Tasks: Relational Reasoning, Representation Learning

In this work we explore the generalization characteristics of unsupervised
representation learning by leveraging disentangled VAE's to learn a useful
latent space on a set of relational reasoning problems derived from Raven
Progressive Matrices. We show that the latent representations, learned by
unsupervised training using the right objective function, significantly
outperform the same architectures trained with purely supervised learning,
especially when it comes to generalization.
Syntax Helps ELMo Understand Semantics: Is Syntax Still Relevant in a Deep Neural Architecture for SRL?
Authors: Emma Strubell, Andrew McCallum
Date: 2018-11-12 00:00:00
Proceeding: WS 2018 7
Tasks: Semantic Role Labeling, Word Embeddings

Do unsupervised methods for learning rich, contextualized token
representations obviate the need for explicit modeling of linguistic structure
in neural network models for semantic role labeling (SRL)? We address this
question by incorporating the massively successful ELMo embeddings (Peters et
al., 2018) into LISA (Strubell et al., 2018), a strong, linguistically-informed
neural network architecture for SRL. In experiments on the CoNLL-2005 shared
task we find that though ELMo out-performs typical word embeddings, beginning
to close the gap in F1 between LISA with predicted and gold syntactic parses,
syntactically-informed models still out-perform syntax-free models when both
use ELMo, especially on out-of-domain data. Our results suggest that linguistic
structures are indeed still relevant in this golden age of deep learning for
NLP.
Adaptive Target Recognition: A Case Study Involving Airport Baggage Screening
Authors: Ankit Manerikar, Tanmay Prakash, Avinash C. Kak
Date: 2018-11-12 00:00:00

This work addresses the question whether it is possible to design a
computer-vision based automatic threat recognition (ATR) system so that it can
adapt to changing specifications of a threat without having to create a new ATR
each time. The changes in threat specifications, which may be warranted by
intelligence reports and world events, are typically regarding the physical
characteristics of what constitutes a threat: its material composition, its
shape, its method of concealment, etc. Here we present our design of an AATR
system (Adaptive ATR) that can adapt to changing specifications in materials
characterization (meaning density, as measured by its x-ray attenuation
coefficient), its mass, and its thickness. Our design uses a two-stage cascaded
approach, in which the first stage is characterized by a high recall rate over
the entire range of possibilities for the threat parameters that are allowed to
change. The purpose of the second stage is to then fine-tune the performance of
the overall system for the current threat specifications. The computational
effort for this fine-tuning for achieving a desired PD/PFA rate is far less
than what it would take to create a new classifier with the same overall
performance for the new set of threat specifications.
Learning data augmentation policies using augmented random search
Authors: Mingyang Geng, Kele Xu, Bo Ding, Huaimin Wang, Lei Zhang
Date: 2018-11-12 00:00:00
Tasks: Data Augmentation, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Previous attempts for data augmentation are designed manually, and the
augmentation policies are dataset-specific. Recently, an automatic data
augmentation approach, named AutoAugment, is proposed using reinforcement
learning. AutoAugment searches for the augmentation polices in the discrete
search space, which may lead to a sub-optimal solution. In this paper, we
employ the Augmented Random Search method (ARS) to improve the performance of
AutoAugment. Our key contribution is to change the discrete search space to
continuous space, which will improve the searching performance and maintain the
diversities between sub-policies. With the proposed method, state-of-the-art
accuracies are achieved on CIFAR-10, CIFAR-100, and ImageNet (without
additional data). Our code is available at https://github.com/gmy2013/ARS-Aug.
Markov Property in Generative Classifiers
Authors: Gherardo Varando, Concha Bielza, Pedro Larrañaga, Eva Riccomagno
Date: 2018-11-12 00:00:00

We show that, for generative classifiers, conditional independence
corresponds to linear constraints for the induced discrimination functions.
Discrimination functions of undirected Markov network classifiers can thus be
characterized by sets of linear constraints. These constraints are represented
by a second order finite difference operator over functions of categorical
variables. As an application we study the expressive power of generative
classifiers under the undirected Markov property and we present a general
method to combine discriminative and generative classifiers.