pwc-archive/papers-with-abstracts huggingface dataset:
AI Fairness for People with Disabilities: Point of View
Authors: Shari Trewin
Date: 2018-11-26 00:00:00
Tasks: Diversity, Fairness

We consider how fair treatment in society for people with disabilities might
be impacted by the rise in the use of artificial intelligence, and especially
machine learning methods. We argue that fairness for people with disabilities
is different to fairness for other protected attributes such as age, gender or
race. One major difference is the extreme diversity of ways disabilities
manifest, and people adapt. Secondly, disability information is highly
sensitive and not always shared, precisely because of the potential for
discrimination. Given these differences, we explore definitions of fairness and
how well they work in the disability space. Finally, we suggest ways of
approaching fairness for people with disabilities in AI applications.
GANsfer Learning: Combining labelled and unlabelled data for GAN based data augmentation
Authors: Christopher Bowles, Roger Gunn, Alexander Hammers, Daniel Rueckert
Date: 2018-11-26 00:00:00
Tasks: Data Augmentation, Segmentation

Medical imaging is a domain which suffers from a paucity of manually
annotated data for the training of learning algorithms. Manually delineating
pathological regions at a pixel level is a time consuming process, especially
in 3D images, and often requires the time of a trained expert. As a result,
supervised machine learning solutions must make do with small amounts of
labelled data, despite there often being additional unlabelled data available.
Whilst of less value than labelled images, these unlabelled images can contain
potentially useful information. In this paper we propose combining both
labelled and unlabelled data within a GAN framework, before using the resulting
network to produce images for use when training a segmentation network. We
explore the task of deep grey matter multi-class segmentation in an AD dataset
and show that the proposed method leads to a significant improvement in
segmentation results, particularly in cases where the amount of labelled data
is restricted. We show that this improvement is largely driven by a greater
ability to segment the structures known to be the most affected by AD, thereby
demonstrating the benefits of exposing the system to more examples of
pathological anatomical variation. We also show how a shift in domain of the
training data from young and healthy towards older and more pathological
examples leads to better segmentations of the latter cases, and that this leads
to a significant improvement in the ability for the computed segmentations to
stratify cases of AD.
Embedding Uncertain Knowledge Graphs
Authors: Xuelu Chen, Muhao Chen, Weijia Shi, Yizhou Sun, Carlo Zaniolo
Date: 2018-11-26 00:00:00
Tasks: Binary Classification, General Classification, Knowledge Graphs, Question Answering, Relation

Embedding models for deterministic Knowledge Graphs (KG) have been
extensively studied, with the purpose of capturing latent semantic relations
between entities and incorporating the structured knowledge into machine
learning. However, there are many KGs that model uncertain knowledge, which
typically model the inherent uncertainty of relations facts with a confidence
score, and embedding such uncertain knowledge represents an unresolved
challenge. The capturing of uncertain knowledge will benefit many
knowledge-driven applications such as question answering and semantic search by
providing more natural characterization of the knowledge. In this paper, we
propose a novel uncertain KG embedding model UKGE, which aims to preserve both
structural and uncertainty information of relation facts in the embedding
space. Unlike previous models that characterize relation facts with binary
classification techniques, UKGE learns embeddings according to the confidence
scores of uncertain relation facts. To further enhance the precision of UKGE,
we also introduce probabilistic soft logic to infer confidence scores for
unseen relation facts during training. We propose and evaluate two variants of
UKGE based on different learning objectives. Experiments are conducted on three
real-world uncertain KGs via three tasks, i.e. confidence prediction, relation
fact ranking, and relation fact classification. UKGE shows effectiveness in
capturing uncertain knowledge by achieving promising results on these tasks,
and consistently outperforms baselines on these tasks.
Art2Real: Unfolding the Reality of Artworks via Semantically-Aware Image-to-Image Translation
Authors: Matteo Tomei, Marcella Cornia, Lorenzo Baraldi, Rita Cucchiara
Date: 2018-11-26 00:00:00
Proceeding: CVPR 2019 6
Tasks: Image-to-Image Translation, Translation

The applicability of computer vision to real paintings and artworks has been rarely investigated, even though a vast heritage would greatly benefit from techniques which can understand and process data from the artistic domain. This is partially due to the small amount of annotated artistic data, which is not even comparable to that of natural images captured by cameras. In this paper, we propose a semantic-aware architecture which can translate artworks to photo-realistic visualizations, thus reducing the gap between visual features of artistic and realistic data. Our architecture can generate natural images by retrieving and learning details from real photos through a similarity matching strategy which leverages a weakly-supervised semantic understanding of the scene. Experimental results show that the proposed technique leads to increased realism and to a reduction in domain shift, which improves the performance of pre-trained architectures for classification, detection, and segmentation. Code is publicly available at: https://github.com/aimagelab/art2real.
Stepping Stones to Inductive Synthesis of Low-Level Looping Programs
Authors: Christopher D. Rosin
Date: 2018-11-26 00:00:00
Tasks: Program Synthesis

Inductive program synthesis, from input/output examples, can provide an
opportunity to automatically create programs from scratch without presupposing
the algorithmic form of the solution. For induction of general programs with
loops (as opposed to loop-free programs, or synthesis for domain-specific
languages), the state of the art is at the level of introductory programming
assignments. Most problems that require algorithmic subtlety, such as fast
sorting, have remained out of reach without the benefit of significant
problem-specific background knowledge. A key challenge is to identify cues that
are available to guide search towards correct looping programs. We present
MAKESPEARE, a simple delayed-acceptance hillclimbing method that synthesizes
low-level looping programs from input/output examples. During search, delayed
acceptance bypasses small gains to identify significantly-improved stepping
stone programs that tend to generalize and enable further progress. The method
performs well on a set of established benchmarks, and succeeds on the
previously unsolved "Collatz Numbers" program synthesis problem. Additional
benchmarks include the problem of rapidly sorting integer arrays, in which we
observe the emergence of comb sort (a Shell sort variant that is empirically
fast). MAKESPEARE has also synthesized a record-setting program on one of the
puzzles from the TIS-100 assembly language programming game.
HELOC Applicant Risk Performance Evaluation by Topological Hierarchical Decomposition
Authors: Kyle Brown, Derek Doran, Ryan Kramer, Brad Reynolds
Date: 2018-11-26 00:00:00
Tasks: BIG-bench Machine Learning

Strong regulations in the financial industry mean that any decisions based on
machine learning need to be explained. This precludes the use of powerful
supervised techniques such as neural networks. In this study we propose a new
unsupervised and semi-supervised technique known as the topological
hierarchical decomposition (THD). This process breaks a dataset down into ever
smaller groups, where groups are associated with a simplicial complex that
approximate the underlying topology of a dataset. We apply THD to the FICO
machine learning challenge dataset, consisting of anonymized home equity loan
applications using the MAPPER algorithm to build simplicial complexes. We
identify different groups of individuals unable to pay back loans, and
illustrate how the distribution of feature values in a simplicial complex can
be used to explain the decision to grant or deny a loan by extracting
illustrative explanations from two THDs on the dataset.
Abduction-Based Explanations for Machine Learning Models
Authors: Alexey Ignatiev, Nina Narodytska, Joao Marques-Silva
Date: 2018-11-26 00:00:00
Tasks: BIG-bench Machine Learning

The growing range of applications of Machine Learning (ML) in a multitude of
settings motivates the ability of computing small explanations for predictions
made. Small explanations are generally accepted as easier for human decision
makers to understand. Most earlier work on computing explanations is based on
heuristic approaches, providing no guarantees of quality, in terms of how close
such solutions are from cardinality- or subset-minimal explanations. This paper
develops a constraint-agnostic solution for computing explanations for any ML
model. The proposed solution exploits abductive reasoning, and imposes the
requirement that the ML model can be represented as sets of constraints using
some target constraint reasoning system for which the decision problem can be
answered with some oracle. The experimental results, obtained on well-known
datasets, validate the scalability of the proposed approach as well as the
quality of the computed solutions.
Show, Control and Tell: A Framework for Generating Controllable and Grounded Captions
Authors: Marcella Cornia, Lorenzo Baraldi, Rita Cucchiara
Date: 2018-11-26 00:00:00
Proceeding: CVPR 2019 6
Tasks: controllable image captioning, Diversity, Image Captioning

Current captioning approaches can describe images using black-box architectures whose behavior is hardly controllable and explainable from the exterior. As an image can be described in infinite ways depending on the goal and the context at hand, a higher degree of controllability is needed to apply captioning algorithms in complex scenarios. In this paper, we introduce a novel framework for image captioning which can generate diverse descriptions by allowing both grounding and controllability. Given a control signal in the form of a sequence or set of image regions, we generate the corresponding caption through a recurrent architecture which predicts textual chunks explicitly grounded on regions, following the constraints of the given control. Experiments are conducted on Flickr30k Entities and on COCO Entities, an extended version of COCO in which we add grounding annotations collected in a semi-automatic manner. Results demonstrate that our method achieves state of the art performances on controllable image captioning, in terms of caption quality and diversity. Code and annotations are publicly available at: https://github.com/aimagelab/show-control-and-tell.
Noisy Computations during Inference: Harmful or Helpful?
Authors: Minghai Qin, Dejan Vucinic
Date: 2018-11-26 00:00:00
Tasks: CPU, GPU

We study two aspects of noisy computations during inference. The first aspect
is how to mitigate their side effects for naturally trained deep learning
systems. One of the motivations for looking into this problem is to reduce the
high power cost of conventional computing of neural networks through the use of
analog neuromorphic circuits. Traditional GPU/CPU-centered deep learning
architectures exhibit bottlenecks in power-restricted applications (e.g.,
embedded systems). The use of specialized neuromorphic circuits, where analog
signals passed through memory-cell arrays are sensed to accomplish
matrix-vector multiplications, promises large power savings and speed gains but
brings with it the problems of limited precision of computations and
unavoidable analog noise. We manage to improve inference accuracy from 21.1% to
99.5% for MNIST images, from 29.9% to 89.1% for CIFAR10, and from 15.5% to
89.6% for MNIST stroke sequences with the presence of strong noise (with
signal-to-noise power ratio being 0 dB) by noise-injected training and a voting
method. This observation promises neural networks that are insensitive to
inference noise, which reduces the quality requirements on neuromorphic
circuits and is crucial for their practical usage. The second aspect is how to
utilize the noisy inference as a defensive architecture against black-box
adversarial attacks. During inference, by injecting proper noise to signals in
the neural networks, the robustness of adversarially-trained neural networks
against black-box attacks has been further enhanced by 0.5% and 1.13% for two
adversarially trained models for MNIST and CIFAR10, respectively.
Understanding Image Quality and Trust in Peer-to-Peer Marketplaces
Authors: Xiao Ma, Lina Mezghani, Kimberly Wilber, Hui Hong, Robinson Piramuthu, Mor Naaman, Serge Belongie
Date: 2018-11-26 00:00:00

As any savvy online shopper knows, second-hand peer-to-peer marketplaces are
filled with images of mixed quality. How does image quality impact marketplace
outcomes, and can quality be automatically predicted? In this work, we
conducted a large-scale study on the quality of user-generated images in
peer-to-peer marketplaces. By gathering a dataset of common second-hand
products (~75,000 images) and annotating a subset with human-labeled quality
judgments, we were able to model and predict image quality with decent accuracy
(~87%). We then conducted two studies focused on understanding the relationship
between these image quality scores and two marketplace outcomes: sales and
perceived trustworthiness. We show that image quality is associated with higher
likelihood that an item will be sold, though other factors such as view count
were better predictors of sales. Nonetheless, we show that high quality
user-generated images selected by our models outperform stock imagery in
eliciting perceptions of trust from users. Our findings can inform the design
of future marketplaces and guide potential sellers to take better product
images.
Evolving Space-Time Neural Architectures for Videos
Authors: AJ Piergiovanni, Anelia Angelova, Alexander Toshev, Michael S. Ryoo
Date: 2018-11-26 00:00:00
Proceeding: ICCV 2019 10
Tasks: Action Classification, Action Recognition, Action Recognition In Videos

We present a new method for finding video CNN architectures that capture rich spatio-temporal information in videos. Previous work, taking advantage of 3D convolutions, obtained promising results by manually designing video CNN architectures. We here develop a novel evolutionary search algorithm that automatically explores models with different types and combinations of layers to jointly learn interactions between spatial and temporal aspects of video representations. We demonstrate the generality of this algorithm by applying it to two meta-architectures, obtaining new architectures superior to manually designed architectures. Further, we propose a new component, the iTGM layer, which more efficiently utilizes its parameters to allow learning of space-time interactions over longer time horizons. The iTGM layer is often preferred by the evolutionary algorithm and allows building cost-efficient networks. The proposed approach discovers new and diverse video architectures that were previously unknown. More importantly they are both more accurate and faster than prior models, and outperform the state-of-the-art results on multiple datasets we test, including HMDB, Kinetics, and Moments in Time. We will open source the code and models, to encourage future model development.
Recovery guarantees for polynomial approximation from dependent data with outliers
Authors: Lam Si Tung Ho, Hayden Schaeffer, Giang Tran, Rachel Ward
Date: 2018-11-25 00:00:00

Learning non-linear systems from noisy, limited, and/or dependent data is an
important task across various scientific fields including statistics,
engineering, computer science, mathematics, and many more. In general, this
learning task is ill-posed; however, additional information about the data's
structure or on the behavior of the unknown function can make the task
well-posed. In this work, we study the problem of learning nonlinear functions
from corrupted and dependent data. The learning problem is recast as a sparse
robust linear regression problem where we incorporate both the unknown
coefficients and the corruptions in a basis pursuit framework. The main
contribution of our paper is to provide a reconstruction guarantee for the
associated $\ell_1$-optimization problem where the sampling matrix is formed
from dependent data. Specifically, we prove that the sampling matrix satisfies
the null space property and the stable null space property, provided that the
data is compact and satisfies a suitable concentration inequality. We show that
our recovery results are applicable to various types of dependent data such as
exponentially strongly $\alpha$-mixing data, geometrically $\mathcal{C}$-mixing
data, and uniformly ergodic Markov chain. Our theoretical results are verified
via several numerical simulations.
Arena Model: Inference About Competitions
Authors: Chenhe Zhang, Peiyuan Sun
Date: 2018-11-25 00:00:00
Tasks: model

The authors propose a parametric model called the arena model for prediction
in paired competitions, i.e. paired comparisons with eliminations and
bifurcations. The arena model has a number of appealing advantages. First, it
predicts the results of competitions without rating many individuals. Second,
it takes full advantage of the structure of competitions. Third, the model
provides an easy method to quantify the uncertainty in competitions. Fourth,
some of our methods can be directly generalized for comparisons among three or
more individuals. Furthermore, the authors identify an invariant Bayes
estimator with regard to the prior distribution and prove the consistency of
the estimations of uncertainty. Currently, the arena model is not effective in
tracking the change of strengths of individuals, but its basic framework
provides a solid foundation for future study of such cases.
Sentiment Analysis of Financial News Articles using Performance Indicators
Authors: Srikumar Krishnamoorthy
Date: 2018-11-25 00:00:00
Tasks: Articles, Sentiment Analysis

Mining financial text documents and understanding the sentiments of
individual investors, institutions and markets is an important and challenging
problem in the literature. Current approaches to mine sentiments from financial
texts largely rely on domain specific dictionaries. However, dictionary based
methods often fail to accurately predict the polarity of financial texts. This
paper aims to improve the state-of-the-art and introduces a novel sentiment
analysis approach that employs the concept of financial and non-financial
performance indicators. It presents an association rule mining based
hierarchical sentiment classifier model to predict the polarity of financial
texts as positive, neutral or negative. The performance of the proposed model
is evaluated on a benchmark financial dataset. The model is also compared
against other state-of-the-art dictionary and machine learning based approaches
and the results are found to be quite promising. The novel use of performance
indicators for financial sentiment analysis offers interesting and useful
insights.
Latent Dirichlet Allocation with Residual Convolutional Neural Network Applied in Evaluating Credibility of Chinese Listed Companies
Authors: Mohan Zhang, Zhichao Luo, Hai Lu
Date: 2018-11-24 00:00:00
Tasks: Articles

This project demonstrated a methodology to estimating cooperate credibility
with a Natural Language Processing approach. As cooperate transparency impacts
both the credibility and possible future earnings of the firm, it is an
important factor to be considered by banks and investors on risk assessments of
listed firms. This approach of estimating cooperate credibility can bypass
human bias and inconsistency in the risk assessment, the use of large
quantitative data and neural network models provides more accurate estimation
in a more efficient manner compare to manual assessment. At the beginning, the
model will employs Latent Dirichlet Allocation and THU Open Chinese Lexicon
from Tsinghua University to classify topics in articles which are potentially
related to corporate credibility. Then with the keywords related to each
topics, we trained a residual convolutional neural network with data labeled
according to surveys of fund manager and accountant's opinion on corporate
credibility. After the training, we run the model with preprocessed news
reports regarding to all of the 3065 listed companies, the model is supposed to
give back companies ranking based on the level of their transparency.
HCqa: Hybrid and Complex Question Answering on Textual Corpus and Knowledge Graph
Authors: Somayeh Asadifar, Mohsen Kahani, Saeedeh Shekarpour
Date: 2018-11-24 00:00:00
Tasks: Knowledge Graphs, named-entity-recognition, Named Entity Recognition, Named Entity Recognition (NER), Question Answering, Relation, Relation Extraction

Question Answering (QA) systems provide easy access to the vast amount of knowledge without having to know the underlying complex structure of the knowledge. The research community has provided ad hoc solutions to the key QA tasks, including named entity recognition and disambiguation, relation extraction and query building. Furthermore, some have integrated and composed these components to implement many tasks automatically and efficiently. However, in general, the existing solutions are limited to simple and short questions and still do not address complex questions composed of several sub-questions. Exploiting the answer to complex questions is further challenged if it requires integrating knowledge from unstructured data sources, i.e., textual corpus, as well as structured data sources, i.e., knowledge graphs. In this paper, an approach (HCqa) is introduced for dealing with complex questions requiring federating knowledge from a hybrid of heterogeneous data sources (structured and unstructured). We contribute in developing (i) a decomposition mechanism which extracts sub-questions from potentially long and complex input questions, (ii) a novel comprehensive schema, first of its kind, for extracting and annotating relations, and (iii) an approach for executing and aggregating the answers of sub-questions. The evaluation of HCqa showed a superior accuracy in the fundamental tasks, such as relation extraction, as well as the federation task.
Application of Clinical Concept Embeddings for Heart Failure Prediction in UK EHR data
Authors: Spiros Denaxas, Pontus Stenetorp, Sebastian Riedel, Maria Pikoula, Richard Dobson, Harry Hemingway
Date: 2018-11-23 00:00:00
Tasks: Feature Engineering, Word Embeddings

Electronic health records (EHR) are increasingly being used for constructing
disease risk prediction models. Feature engineering in EHR data however is
challenging due to their highly dimensional and heterogeneous nature.
Low-dimensional representations of EHR data can potentially mitigate these
challenges. In this paper, we use global vectors (GloVe) to learn word
embeddings for diagnoses and procedures recorded using 13 million ontology
terms across 2.7 million hospitalisations in national UK EHR. We demonstrate
the utility of these embeddings by evaluating their performance in identifying
patients which are at higher risk of being hospitalised for congestive heart
failure. Our findings indicate that embeddings can enable the creation of
robust EHR-derived disease risk prediction models and address some the
limitations associated with manual clinical feature engineering.