pwc-archive/papers-with-abstracts huggingface dataset:
SafeCity: Understanding Diverse Forms of Sexual Harassment Personal Stories
Authors: Sweta Karlekar, Mohit Bansal
Date: 2018-09-13 00:00:00
Proceeding: EMNLP 2018 10
Tasks: Clustering

With the recent rise of #MeToo, an increasing number of personal stories
about sexual harassment and sexual abuse have been shared online. In order to
push forward the fight against such harassment and abuse, we present the task
of automatically categorizing and analyzing various forms of sexual harassment,
based on stories shared on the online forum SafeCity. For the labels of
groping, ogling, and commenting, our single-label CNN-RNN model achieves an
accuracy of 86.5%, and our multi-label model achieves a Hamming score of 82.5%.
Furthermore, we present analysis using LIME, first-derivative saliency
heatmaps, activation clustering, and embedding visualization to interpret
neural model predictions and demonstrate how this extracts features that can
help automatically fill out incident reports, identify unsafe areas, avoid
unsafe practices, and 'pin the creeps'.
Fairness-aware Classification: Criterion, Convexity, and Bounds
Authors: Yongkai Wu, Lu Zhang, Xintao Wu
Date: 2018-09-13 00:00:00
Tasks: Classification, Computational Efficiency, Fairness, General Classification

Fairness-aware classification is receiving increasing attention in the
machine learning fields. Recently research proposes to formulate the
fairness-aware classification as constrained optimization problems. However,
several limitations exist in previous works due to the lack of a theoretical
framework for guiding the formulation. In this paper, we propose a general
framework for learning fair classifiers which addresses previous limitations.
The framework formulates various commonly-used fairness metrics as convex
constraints that can be directly incorporated into classic classification
models. Within the framework, we propose a constraint-free criterion on the
training data which ensures that any classifier learned from the data is fair.
We also derive the constraints which ensure that the real fairness metric is
satisfied when surrogate functions are used to achieve convexity. Our framework
can be used to for formulating fairness-aware classification with fairness
guarantee and computational efficiency. The experiments using real-world
datasets demonstrate our theoretical results and show the effectiveness of
proposed framework and methods.
DispSegNet: Leveraging Semantics for End-to-End Learning of Disparity Estimation from Stereo Imagery
Authors: Jun-ming Zhang, Katherine A. Skinner, Ram Vasudevan, Matthew Johnson-Roberson
Date: 2018-09-13 00:00:00
Tasks: Disparity Estimation, Segmentation, Semantic Segmentation

Recent work has shown that convolutional neural networks (CNNs) can be
applied successfully in disparity estimation, but these methods still suffer
from errors in regions of low-texture, occlusions and reflections.
Concurrently, deep learning for semantic segmentation has shown great progress
in recent years. In this paper, we design a CNN architecture that combines
these two tasks to improve the quality and accuracy of disparity estimation
with the help of semantic segmentation. Specifically, we propose a network
structure in which these two tasks are highly coupled. One key novelty of this
approach is the two-stage refinement process. Initial disparity estimates are
refined with an embedding learned from the semantic segmentation branch of the
network. The proposed model is trained using an unsupervised approach, in which
images from one half of the stereo pair are warped and compared against images
from the other camera. Another key advantage of the proposed approach is that a
single network is capable of outputting disparity estimates and semantic
labels. These outputs are of great use in autonomous vehicle operation; with
real-time constraints being key, such performance improvements increase the
viability of driving applications. Experiments on KITTI and Cityscapes datasets
show that our model can achieve state-of-the-art results and that leveraging
embedding learned from semantic segmentation improves the performance of
disparity estimation.
Adapting Semantic Segmentation Models for Changes in Illumination and Camera Perspective
Authors: Wei Zhou, Alex Zyner, Stewart Worrall, Eduardo Nebot
Date: 2018-09-13 00:00:00
Tasks: Autonomous Vehicles, Data Augmentation, Segmentation, Semantic Segmentation

Semantic segmentation using deep neural networks has been widely explored to
generate high-level contextual information for autonomous vehicles. To acquire
a complete $180^\circ$ semantic understanding of the forward surroundings, we
propose to stitch semantic images from multiple cameras with varying
orientations. However, previously trained semantic segmentation models showed
unacceptable performance after significant changes to the camera orientations
and the lighting conditions. To avoid time-consuming hand labeling, we explore
and evaluate the use of data augmentation techniques, specifically skew and
gamma correction, from a practical real-world standpoint to extend the existing
model and provide more robust performance. The presented experimental results
have shown significant improvements with varying illumination and camera
perspective changes.
Sim-to-Real Transfer Learning using Robustified Controllers in Robotic Tasks involving Complex Dynamics
Authors: Jeroen van Baar, Alan Sullivan, Radu Cordorel, Devesh Jha, Diego Romeres, Daniel Nikovski
Date: 2018-09-13 00:00:00
Tasks: Deep Reinforcement Learning, Friction, Reinforcement Learning, Transfer Learning

Learning robot tasks or controllers using deep reinforcement learning has
been proven effective in simulations. Learning in simulation has several
advantages. For example, one can fully control the simulated environment,
including halting motions while performing computations. Another advantage when
robots are involved, is that the amount of time a robot is occupied learning a
task---rather than being productive---can be reduced by transferring the
learned task to the real robot. Transfer learning requires some amount of
fine-tuning on the real robot. For tasks which involve complex (non-linear)
dynamics, the fine-tuning itself may take a substantial amount of time. In
order to reduce the amount of fine-tuning we propose to learn robustified
controllers in simulation. Robustified controllers are learned by exploiting
the ability to change simulation parameters (both appearance and dynamics) for
successive training episodes. An additional benefit for this approach is that
it alleviates the precise determination of physics parameters for the
simulator, which is a non-trivial task. We demonstrate our proposed approach on
a real setup in which a robot aims to solve a maze game, which involves complex
dynamics due to static friction and potentially large accelerations. We show
that the amount of fine-tuning in transfer learning for a robustified
controller is substantially reduced compared to a non-robustified controller.
Linear Algebra and Duality of Neural Networks
Authors: Galin Georgiev
Date: 2018-09-12 00:00:00

Bases, mappings, projections and metrics, natural for Neural network
training, are introduced. Graph-theoretical interpretation is offered.
Non-Gaussianity naturally emerges, even in relatively simple datasets. Training
statistics, hierarchies and energies are analyzed, from physics point of view.
Duality between observables (for example, pixels) and observations is
established. Relationship between exact and numerical solutions is studied.
Physics and financial mathematics interpretations of a key problem are offered.
Examples support all new concepts.
Semantically Enhanced Models for Commonsense Knowledge Acquisition
Authors: Ikhlas Alhussien, Erik Cambria, Zhang NengSheng
Date: 2018-09-12 00:00:00
Tasks: Graph Embedding, Knowledge Base Completion, Knowledge Graph Embedding

Commonsense knowledge is paramount to enable intelligent systems. Typically,
it is characterized as being implicit and ambiguous, hindering thereby the
automation of its acquisition. To address these challenges, this paper presents
semantically enhanced models to enable reasoning through resolving part of
commonsense ambiguity. The proposed models enhance in a knowledge graph
embedding (KGE) framework for knowledge base completion. Experimental results
show the effectiveness of the new semantic models in commonsense reasoning.
Distilled Wasserstein Learning for Word Embedding and Topic Modeling
Authors: Hongteng Xu, Wenlin Wang, Wei Liu, Lawrence Carin
Date: 2018-09-12 00:00:00
Proceeding: NeurIPS 2018 12
Tasks: Mortality Prediction, Word Embeddings

We propose a novel Wasserstein method with a distillation mechanism, yielding
joint learning of word embeddings and topics. The proposed method is based on
the fact that the Euclidean distance between word embeddings may be employed as
the underlying distance in the Wasserstein topic model. The word distributions
of topics, their optimal transports to the word distributions of documents, and
the embeddings of words are learned in a unified framework. When learning the
topic model, we leverage a distilled underlying distance matrix to update the
topic distributions and smoothly calculate the corresponding optimal
transports. Such a strategy provides the updating of word embeddings with
robust guidance, improving the algorithmic convergence. As an application, we
focus on patient admission records, in which the proposed method embeds the
codes of diseases and procedures and learns the topics of admissions, obtaining
superior performance on clinically-meaningful disease network construction,
mortality prediction as a function of admission codes, and procedure
recommendation.
Do-It-Yourself Single Camera 3D Pointer Input Device
Authors: Bernard Llanos, Yee-Hong Yang
Date: 2018-09-12 00:00:00
Tasks: 3D Reconstruction

We present a new algorithm for single camera 3D reconstruction, or 3D input
for human-computer interfaces, based on precise tracking of an elongated
object, such as a pen, having a pattern of colored bands. To configure the
system, the user provides no more than one labelled image of a handmade
pointer, measurements of its colored bands, and the camera's pinhole projection
matrix. Other systems are of much higher cost and complexity, requiring
combinations of multiple cameras, stereocameras, and pointers with sensors and
lights. Instead of relying on information from multiple devices, we examine our
single view more closely, integrating geometric and appearance constraints to
robustly track the pointer in the presence of occlusion and distractor objects.
By probing objects of known geometry with the pointer, we demonstrate
acceptable accuracy of 3D localization.
Learning to Summarize Radiology Findings
Authors: Yuhao Zhang, Daisy Yi Ding, Tianpei Qian, Christopher D. Manning, Curtis P. Langlotz
Date: 2018-09-12 00:00:00
Proceeding: WS 2018 10

The Impression section of a radiology report summarizes crucial radiology
findings in natural language and plays a central role in communicating these
findings to physicians. However, the process of generating impressions by
summarizing findings is time-consuming for radiologists and prone to errors. We
propose to automate the generation of radiology impressions with neural
sequence-to-sequence learning. We further propose a customized neural model for
this task which learns to encode the study background information and use this
information to guide the decoding process. On a large dataset of radiology
reports collected from actual hospital studies, our model outperforms existing
non-neural and neural baselines under the ROUGE metrics. In a blind experiment,
a board-certified radiologist indicated that 67% of sampled system summaries
are at least as good as the corresponding human-written summaries, suggesting
significant clinical validity. To our knowledge our work represents the first
attempt in this direction.
Geometric Image Synthesis
Authors: Hassan Abu Alhaija, Siva Karthik Mustikovela, Andreas Geiger, Carsten Rother
Date: 2018-09-12 00:00:00
Tasks: Image Generation, Instance Segmentation, Semantic Segmentation

The task of generating natural images from 3D scenes has been a long standing
goal in computer graphics. On the other hand, recent developments in deep
neural networks allow for trainable models that can produce natural-looking
images with little or no knowledge about the scene structure. While the
generated images often consist of realistic looking local patterns, the overall
structure of the generated images is often inconsistent. In this work we
propose a trainable, geometry-aware image generation method that leverages
various types of scene information, including geometry and segmentation, to
create realistic looking natural images that match the desired scene structure.
Our geometrically-consistent image synthesis method is a deep neural network,
called Geometry to Image Synthesis (GIS) framework, which retains the
advantages of a trainable method, e.g., differentiability and adaptiveness,
but, at the same time, makes a step towards the generalizability, control and
quality output of modern graphics rendering engines. We utilize the GIS
framework to insert vehicles in outdoor driving scenes, as well as to generate
novel views of objects from the Linemod dataset. We qualitatively show that our
network is able to generalize beyond the training set to novel scene
geometries, object shapes and segmentations. Furthermore, we quantitatively
show that the GIS framework can be used to synthesize large amounts of training
data which proves beneficial for training instance segmentation models.
An Online Plug-and-Play Algorithm for Regularized Image Reconstruction
Authors: Yu Sun, Brendt Wohlberg, Ulugbek S. Kamilov
Date: 2018-09-12 00:00:00
Tasks: Image Reconstruction

Plug-and-play priors (PnP) is a powerful framework for regularizing imaging
inverse problems by using advanced denoisers within an iterative algorithm.
Recent experimental evidence suggests that PnP algorithms achieve
state-of-the-art performance in a range of imaging applications. In this paper,
we introduce a new online PnP algorithm based on the iterative
shrinkage/thresholding algorithm (ISTA). The proposed algorithm uses only a
subset of measurements at every iteration, which makes it scalable to very
large datasets. We present a new theoretical convergence analysis, for both
batch and online variants of PnP-ISTA, for denoisers that do not necessarily
correspond to proximal operators. We also present simulations illustrating the
applicability of the algorithm to image reconstruction in diffraction
tomography. The results in this paper have the potential to expand the
applicability of the PnP framework to very large and redundant datasets.
Zero-Shot Cross-lingual Classification Using Multilingual Neural Machine Translation
Authors: Akiko Eriguchi, Melvin Johnson, Orhan Firat, Hideto Kazawa, Wolfgang Macherey
Date: 2018-09-12 00:00:00
Tasks: Classification, Cross-Lingual Transfer, General Classification, Machine Translation, NMT, Transfer Learning, Translation, zero-shot-classification, Zero-Shot Learning

Transferring representations from large supervised tasks to downstream tasks
has shown promising results in AI fields such as Computer Vision and Natural
Language Processing (NLP). In parallel, the recent progress in Machine
Translation (MT) has enabled one to train multilingual Neural MT (NMT) systems
that can translate between multiple languages and are also capable of
performing zero-shot translation. However, little attention has been paid to
leveraging representations learned by a multilingual NMT system to enable
zero-shot multilinguality in other NLP tasks. In this paper, we demonstrate a
simple framework, a multilingual Encoder-Classifier, for cross-lingual transfer
learning by reusing the encoder from a multilingual NMT system and stitching it
with a task-specific classifier component. Our proposed model achieves
significant improvements in the English setup on three benchmark tasks - Amazon
Reviews, SST and SNLI. Further, our system can perform classification in a new
language for which no classification data was seen during training, showing
that zero-shot classification is possible and remarkably competitive. In order
to understand the underlying factors contributing to this finding, we conducted
a series of analyses on the effect of the shared vocabulary, the training data
type for NMT, classifier complexity, encoder representation power, and model
generalization on zero-shot performance. Our results provide strong evidence
that the representations learned from multilingual NMT systems are widely
applicable across languages and tasks.
Fair lending needs explainable models for responsible recommendation
Authors: Jiahao Chen
Date: 2018-09-12 00:00:00
Tasks: BIG-bench Machine Learning, Explainable Models, Fairness

The financial services industry has unique explainability and fairness
challenges arising from compliance and ethical considerations in credit
decisioning. These challenges complicate the use of model machine learning and
artificial intelligence methods in business decision processes.
SAFE: A Neural Survival Analysis Model for Fraud Early Detection
Authors: Panpan Zheng, Shuhan Yuan, Xintao Wu
Date: 2018-09-12 00:00:00
Tasks: Survival Analysis

Many online platforms have deployed anti-fraud systems to detect and prevent
fraudulent activities. However, there is usually a gap between the time that a
user commits a fraudulent action and the time that the user is suspended by the
platform. How to detect fraudsters in time is a challenging problem. Most of
the existing approaches adopt classifiers to predict fraudsters given their
activity sequences along time. The main drawback of classification models is
that the prediction results between consecutive timestamps are often
inconsistent. In this paper, we propose a survival analysis based fraud early
detection model, SAFE, which maps dynamic user activities to survival
probabilities that are guaranteed to be monotonically decreasing along time.
SAFE adopts recurrent neural network (RNN) to handle user activity sequences
and directly outputs hazard values at each timestamp, and then, survival
probability derived from hazard values is deployed to achieve consistent
predictions. Because we only observe the user suspended time instead of the
fraudulent activity time in the training data, we revise the loss function of
the regular survival model to achieve fraud early detection. Experimental
results on two real world datasets demonstrate that SAFE outperforms both the
survival analysis model and recurrent neural network model alone as well as
state-of-the-art fraud early detection approaches.
Automatic Program Synthesis of Long Programs with a Learned Garbage Collector
Authors: Amit Zohar, Lior Wolf
Date: 2018-09-12 00:00:00
Proceeding: NeurIPS 2018 12
Tasks: Program Synthesis

We consider the problem of generating automatic code given sample
input-output pairs. We train a neural network to map from the current state and
the outputs to the program's next statement. The neural network optimizes
multiple tasks concurrently: the next operation out of a set of high level
commands, the operands of the next statement, and which variables can be
dropped from memory. Using our method we are able to create programs that are
more than twice as long as existing state-of-the-art solutions, while improving
the success rate for comparable lengths, and cutting the run-time by two orders
of magnitude. Our code, including an implementation of various literature
baselines, is publicly available at https://github.com/amitz25/PCCoder
A Unified Batch Online Learning Framework for Click Prediction
Authors: Rishabh Iyer, Nimit Acharya, Tanuja Bompada, Denis Charles, Eren Manavoglu
Date: 2018-09-12 00:00:00

We present a unified framework for Batch Online Learning (OL) for Click
Prediction in Search Advertisement. Machine Learning models once deployed, show
non-trivial accuracy and calibration degradation over time due to model
staleness. It is therefore necessary to regularly update models, and do so
automatically. This paper presents two paradigms of Batch Online Learning, one
which incrementally updates the model parameters via an early stopping
mechanism, and another which does so through a proximal regularization. We
argue how both these schemes naturally trade-off between old and new data. We
then theoretically and empirically show that these two seemingly different
schemes are closely related. Through extensive experiments, we demonstrate the
utility of of our OL framework; how the two OL schemes relate to each other and
how they trade-off between the new and historical data. We then compare batch
OL to full model retrains, and show how online learning is more robust to data
issues. We also demonstrate the long term impact of Online Learning, the role
of the initial Models in OL, the impact of delays in the update, and finally
conclude with some implementation details and challenges in deploying a real
world online learning system in production. While this paper mostly focuses on
application of click prediction for search advertisement, we hope that the
lessons learned here can be carried over to other problem domains.
PARyOpt: A software for Parallel Asynchronous Remote Bayesian Optimization
Authors: Balaji Sesha Sarath Pokuri, Alec Lofquist, Chad M Risko, Baskar Ganapathysubramanian
Date: 2018-09-12 00:00:00
Tasks: Bayesian Optimization, Distributed Computing, Surface Reconstruction

PARyOpt is a python based implementation of the Bayesian optimization routine
designed for remote and asynchronous function evaluations. Bayesian
optimization is especially attractive for computational optimization due to its
low cost function footprint as well as the ability to account for uncertainties
in data. A key challenge to efficiently deploy any optimization strategy on
distributed computing systems is the synchronization step, where data from
multiple function calls is assimilated to identify the next campaign of
function calls. Bayesian optimization provides an elegant approach to overcome
this issue via asynchronous updates. We formulate, develop and implement a
parallel, asynchronous variant of Bayesian optimization. The framework is
robust and resilient to external failures. We show how such asynchronous
evaluations help reduce the total optimization wall clock time for a suite of
test problems. Additionally, we show how the software design of the framework
allows easy extension to response surface reconstruction (Kriging), providing a
high performance software for autonomous exploration. The software is available
on PyPI, with examples and documentation.
Creating Fair Models of Atherosclerotic Cardiovascular Disease Risk
Authors: Stephen Pfohl, Ben Marafino, Adrien Coulet, Fatima Rodriguez, Latha Palaniappan, Nigam H. Shah
Date: 2018-09-12 00:00:00
Tasks: Fairness, Management

Guidelines for the management of atherosclerotic cardiovascular disease (ASCVD) recommend the use of risk stratification models to identify patients most likely to benefit from cholesterol-lowering and other therapies. These models have differential performance across race and gender groups with inconsistent behavior across studies, potentially resulting in an inequitable distribution of beneficial therapy. In this work, we leverage adversarial learning and a large observational cohort extracted from electronic health records (EHRs) to develop a "fair" ASCVD risk prediction model with reduced variability in error rates across groups. We empirically demonstrate that our approach is capable of aligning the distribution of risk predictions conditioned on the outcome across several groups simultaneously for models built from high-dimensional EHR data. We also discuss the relevance of these results in the context of the empirical trade-off between fairness and model performance.