pwc-archive/papers-with-abstracts huggingface dataset:
A Deep Latent-Variable Model Application to Select Treatment Intensity in Survival Analysis
Authors: Cédric Beaulac, Jeffrey S. Rosenthal, David Hodgson
Date: 2018-11-29 00:00:00
Tasks: BIG-bench Machine Learning, Decision Making, Missing Values, Small Data Image Classification, Survival Analysis

In the following short article we adapt a new and popular machine learning
model for inference on medical data sets. Our method is based on the
Variational AutoEncoder (VAE) framework that we adapt to survival analysis on
small data sets with missing values. In our model, the true health status
appears as a set of latent variables that affects the observed covariates and
the survival chances. We show that this flexible model allows insightful
decision-making using a predicted distribution and outperforms a classic
survival analysis model.
Incremental Scene Synthesis
Authors: Benjamin Planche, Xuejian Rong, Ziyan Wu, Srikrishna Karanam, Harald Kosch, YingLi Tian, Jan Ernst, Andreas Hutter
Date: 2018-11-29 00:00:00
Proceeding: NeurIPS 2019 12
Tasks: Autonomous Navigation, Hallucination

We present a method to incrementally generate complete 2D or 3D scenes with the following properties: (a) it is globally consistent at each step according to a learned scene prior, (b) real observations of a scene can be incorporated while observing global consistency, (c) unobserved regions can be hallucinated locally in consistence with previous observations, hallucinations and global priors, and (d) hallucinations are statistical in nature, i.e., different scenes can be generated from the same observations. To achieve this, we model the virtual scene, where an active agent at each step can either perceive an observed part of the scene or generate a local hallucination. The latter can be interpreted as the agent's expectation at this step through the scene and can be applied to autonomous navigation. In the limit of observing real data at each point, our method converges to solving the SLAM problem. It can otherwise sample entirely imagined scenes from prior distributions. Besides autonomous agents, applications include problems where large data is required for building robust real-world applications, but few samples are available. We demonstrate efficacy on various 2D as well as 3D data.
Face Detection in the Operating Room: Comparison of State-of-the-art Methods and a Self-supervised Approach
Authors: Thibaut Issenhuth, Vinkle Srivastav, Afshin Gangi, Nicolas Padoy
Date: 2018-11-29 00:00:00
Tasks: Domain Adaptation, Face Detection

Purpose: Face detection is a needed component for the automatic analysis and
assistance of human activities during surgical procedures. Efficient face
detection algorithms can indeed help to detect and identify the persons present
in the room, and also be used to automatically anonymize the data. However,
current algorithms trained on natural images do not generalize well to the
operating room (OR) images. In this work, we provide a comparison of
state-of-the-art face detectors on OR data and also present an approach to
train a face detector for the OR by exploiting non-annotated OR images.
Methods: We propose a comparison of 6 state-of-the-art face detectors on
clinical data using Multi-View Operating Room Faces (MVOR-Faces), a dataset of
operating room images capturing real surgical activities. We then propose to
use self-supervision, a domain adaptation method, for the task of face
detection in the OR. The approach makes use of non-annotated images to
fine-tune a state-of-the-art detector for the OR without using any human
supervision. Results: The results show that the best model, namely the tiny
face detector, yields an average precision of 0.536 at Intersection over Union
(IoU) of 0.5. Our self-supervised model using non-annotated clinical data
outperforms this result by 9.2%. Conclusion: We present the first comparison of
state-of-the-art face detectors on operating room images and show that results
can be significantly improved by using self-supervision on non-annotated data.
Regression by clustering using Metropolis-Hastings
Authors: Adolfo Quiroz, Simón Ramírez-Amaya, Álvaro Riascos
Date: 2018-11-29 00:00:00
Tasks: Clustering, Diagnostic, regression

High quality risk adjustment in health insurance markets weakens insurer incentives to engage in inefficient behavior to attract lower-cost enrollees. We propose a novel methodology based on Markov Chain Monte Carlo methods to improve risk adjustment by clustering diagnostic codes into risk groups optimal for health expenditure prediction. We test the performance of our methodology against common alternatives using panel data from 500 thousand enrollees of the Colombian Healthcare System. Results show that our methodology outperforms common alternatives and suggest that it has potential to improve access to quality healthcare for the chronically ill.
Tuplemax Loss for Language Identification
Authors: Li Wan, Prashant Sridhar, Yang Yu, Quan Wang, Ignacio Lopez Moreno
Date: 2018-11-29 00:00:00
Tasks: Language Identification

In many scenarios of a language identification task, the user will specify a
small set of languages which he/she can speak instead of a large set of all
possible languages. We want to model such prior knowledge into the way we train
our neural networks, by replacing the commonly used softmax loss function with
a novel loss function named tuplemax loss. As a matter of fact, a typical
language identification system launched in North America has about 95% users
who could speak no more than two languages. Using the tuplemax loss, our system
achieved a 2.33% error rate, which is a relative 39.4% improvement over the
3.85% error rate of standard softmax loss method.
Improving Hospital Mortality Prediction with Medical Named Entities and Multimodal Learning
Authors: Mengqi Jin, Mohammad Taha Bahadori, Aaron Colak, Parminder Bhatia, Busra Celikkaya, Ram Bhakta, Selvan Senthivel, Mohammed Khalilia, Daniel Navarro, Borui Zhang, Tiberiu Doman, Arun Ravi, Matthieu Liger, Taha Kass-Hout
Date: 2018-11-29 00:00:00
Tasks: Entity Extraction using GAN, Mortality Prediction, Negation, Negation Detection, Time Series, Time Series Analysis

Clinical text provides essential information to estimate the acuity of a
patient during hospital stays in addition to structured clinical data. In this
study, we explore how clinical text can complement a clinical predictive
learning task. We leverage an internal medical natural language processing
service to perform named entity extraction and negation detection on clinical
notes and compose selected entities into a new text corpus to train document
representations. We then propose a multimodal neural network to jointly train
time series signals and unstructured clinical text representations to predict
the in-hospital mortality risk for ICU patients. Our model outperforms the
benchmark by 2% AUC.
On the Transferability of Representations in Neural Networks Between Datasets and Tasks
Authors: Haytham M. Fayek, Lawrence Cavedon, Hong Ren Wu
Date: 2018-11-29 00:00:00
Tasks: Continual Learning, Multi-Task Learning, Transfer Learning

Deep networks, composed of multiple layers of hierarchical distributed
representations, tend to learn low-level features in initial layers and
transition to high-level features towards final layers. Paradigms such as
transfer learning, multi-task learning, and continual learning leverage this
notion of generic hierarchical distributed representations to share knowledge
across datasets and tasks. Herein, we study the layer-wise transferability of
representations in deep networks across a few datasets and tasks and note some
interesting empirical observations.
BCCNet: Bayesian classifier combination neural network
Authors: Olga Isupova, Yunpeng Li, Danil Kuzin, Stephen J. Roberts, Katherine Willis, Steven Reece
Date: 2018-11-29 00:00:00
Tasks: BIG-bench Machine Learning, Decision Making, Disaster Response

Machine learning research for developing countries can demonstrate clear
sustainable impact by delivering actionable and timely information to
in-country government organisations (GOs) and NGOs in response to their
critical information requirements. We co-create products with UK and in-country
commercial, GO and NGO partners to ensure the machine learning algorithms
address appropriate user needs whether for tactical decision making or
evidence-based policy decisions. In one particular case, we developed and
deployed a novel algorithm, BCCNet, to quickly process large quantities of
unstructured data to prevent and respond to natural disasters. Crowdsourcing
provides an efficient mechanism to generate labels from unstructured data to
prime machine learning algorithms for large scale data analysis. However, these
labels are often imperfect with qualities varying among different citizen
scientists, which prohibits their direct use with many state-of-the-art machine
learning techniques. We describe BCCNet, a framework that simultaneously
aggregates biased and contradictory labels from the crowd and trains an
automatic classifier to process new data. Our case studies, mosquito sound
detection for malaria prevention and damage detection for disaster response,
show the efficacy of our method in the challenging context of developing world
applications.
Locally Differentially-Private Randomized Response for Discrete Distribution Learning
Authors: Adriano Pastore, Michael Gastpar
Date: 2018-11-29 00:00:00

We consider a setup in which confidential i.i.d. samples $X_1,\dotsc,X_n$
from an unknown finite-support distribution $\boldsymbol{p}$ are passed through
$n$ copies of a discrete privatization channel (a.k.a. mechanism) producing
outputs $Y_1,\dotsc,Y_n$. The channel law guarantees a local differential
privacy of $\epsilon$. Subject to a prescribed privacy level $\epsilon$, the
optimal channel should be designed such that an estimate of the source
distribution based on the channel outputs $Y_1,\dotsc,Y_n$ converges as fast as
possible to the exact value $\boldsymbol{p}$. For this purpose we study the
convergence to zero of three distribution distance metrics: $f$-divergence,
mean-squared error and total variation. We derive the respective normalized
first-order terms of convergence (as $n\to\infty$), which for a given target
privacy $\epsilon$ represent a rule-of-thumb factor by which the sample size
must be augmented so as to achieve the same estimation accuracy as that of a
non-randomizing channel. We formulate the privacy-fidelity trade-off problem as
being that of minimizing said first-order term under a privacy constraint
$\epsilon$. We further identify a scalar quantity that captures the essence of
this trade-off, and prove bounds and data-processing inequalities on this
quantity. For some specific instances of the privacy-fidelity trade-off
problem, we derive inner and outer bounds on the optimal trade-off curve.
The Effect of Heterogeneous Data for Alzheimer's Disease Detection from Speech
Authors: Aparna Balagopalan, Jekaterina Novikova, Frank Rudzicz, Marzyeh Ghassemi
Date: 2018-11-29 00:00:00
Tasks: Alzheimer's Disease Detection, Fairness, Inductive Bias

Speech datasets for identifying Alzheimer's disease (AD) are generally
restricted to participants performing a single task, e.g. describing an image
shown to them. As a result, models trained on linguistic features derived from
such datasets may not be generalizable across tasks. Building on prior work
demonstrating that same-task data of healthy participants helps improve AD
detection on a single-task dataset of pathological speech, we augment an
AD-specific dataset consisting of subjects describing a picture with multi-task
healthy data. We demonstrate that normative data from multiple speech-based
tasks helps improve AD detection by up to 9%. Visualization of decision
boundaries reveals that models trained on a combination of structured picture
descriptions and unstructured conversational speech have the least out-of-task
error and show the most potential to generalize to multiple tasks. We analyze
the impact of age of the added samples and if they affect fairness in
classification. We also provide explanations for a possible inductive bias
effect across tasks using model-agnostic feature anchors. This work highlights
the need for heterogeneous datasets for encoding changes in multiple facets of
cognition and for developing a task-independent AD detection model.
Discovering Spatio-Temporal Action Tubes
Authors: Yuancheng Ye, Xiaodong Yang, YingLi Tian
Date: 2018-11-29 00:00:00
Tasks: Action Detection

In this paper, we address the challenging problem of spatial and temporal
action detection in videos. We first develop an effective approach to localize
frame-level action regions through integrating static and kinematic information
by the early- and late-fusion detection scheme. With the intention of exploring
important temporal connections among the detected action regions, we propose a
tracking-by-point-matching algorithm to stitch the discrete action regions into
a continuous spatio-temporal action tube. Recurrent 3D convolutional neural
network is used to predict action categories and determine temporal boundaries
of the generated tubes. We then introduce an action footprint map to refine the
candidate tubes based on the action-specific spatial characteristics preserved
in the convolutional layers of R3DCNN. In the extensive experiments, our method
achieves superior detection results on the three public benchmark datasets:
UCFSports, J-HMDB and UCF101.
Counterfactual Learning from Human Proofreading Feedback for Semantic Parsing
Authors: Carolin Lawrence, Stefan Riezler
Date: 2018-11-29 00:00:00
Tasks: counterfactual, Question Answering, Semantic Parsing

In semantic parsing for question-answering, it is often too expensive to
collect gold parses or even gold answers as supervision signals. We propose to
convert model outputs into a set of human-understandable statements which allow
non-expert users to act as proofreaders, providing error markings as learning
signals to the parser. Because model outputs were suggested by a historic
system, we operate in a counterfactual, or off-policy, learning setup. We
introduce new estimators which can effectively leverage the given feedback and
which avoid known degeneracies in counterfactual learning, while still being
applicable to stochastic gradient optimization for neural semantic parsing.
Furthermore, we discuss how our feedback collection method can be seamlessly
integrated into deployed virtual personal assistants that embed a semantic
parser. Our work is the first to show that semantic parsers can be improved
significantly by counterfactual learning from logged human feedback data.
Perceiving Physical Equation by Observing Visual Scenarios
Authors: Siyu Huang, Zhi-Qi Cheng, Xi Li, Xiao Wu, Zhongfei Zhang, Alexander Hauptmann
Date: 2018-11-29 00:00:00

Inferring universal laws of the environment is an important ability of human
intelligence as well as a symbol of general AI. In this paper, we take a step
toward this goal such that we introduce a new challenging problem of inferring
invariant physical equation from visual scenarios. For instance, teaching a
machine to automatically derive the gravitational acceleration formula by
watching a free-falling object. To tackle this challenge, we present a novel
pipeline comprised of an Observer Engine and a Physicist Engine by respectively
imitating the actions of an observer and a physicist in the real world.
Generally, the Observer Engine watches the visual scenarios and then extracting
the physical properties of objects. The Physicist Engine analyses these data
and then summarizing the inherent laws of object dynamics. Specifically, the
learned laws are expressed by mathematical equations such that they are more
interpretable than the results given by common probabilistic models.
Experiments on synthetic videos have shown that our pipeline is able to
discover physical equations on various physical worlds with different visual
appearances.
Machine Learning on Electronic Health Records: Models and Features Usages to predict Medication Non-Adherence
Authors: Thomas Janssoone, Clémence Bic, Dorra Kanoun, Pierre Hornus, Pierre Rinder
Date: 2018-11-29 00:00:00
Tasks: BIG-bench Machine Learning

Adherence can be defined as "the extent to which patients take their
medications as prescribed by their healthcare providers"[Osterberg and
Blaschke, 2005]. World Health Organization's reports point out that, in
developed countries, only about 50% of patients with chronic diseases correctly
follow their treatments. This severely compromises the efficiency of long-term
therapy and increases the cost of health services. We propose in this paper
different models of patient drug consumption in breast cancer treatments. The
aim of these different approaches is to predict medication non-adherence while
giving insights to doctors of the underlying reasons of these illegitimate
drop-outs. Working with oncologists, we show the interest of Machine- Learning
algorithms fined tune by the feedback of experts to estimate a risk score of a
patient's non-adherence and thus improve support throughout their care path.
ImageNet-trained CNNs are biased towards texture; increasing shape bias improves accuracy and robustness
Authors: Robert Geirhos, Patricia Rubisch, Claudio Michaelis, Matthias Bethge, Felix A. Wichmann, Wieland Brendel
Date: 2018-11-29 00:00:00
Proceeding: ICLR 2019 5
Tasks: Domain Generalization, Image Classification, object-detection, Object Detection, Object Recognition, Out-of-Distribution Generalization

Convolutional Neural Networks (CNNs) are commonly thought to recognise objects by learning increasingly complex representations of object shapes. Some recent studies suggest a more important role of image textures. We here put these conflicting hypotheses to a quantitative test by evaluating CNNs and human observers on images with a texture-shape cue conflict. We show that ImageNet-trained CNNs are strongly biased towards recognising textures rather than shapes, which is in stark contrast to human behavioural evidence and reveals fundamentally different classification strategies. We then demonstrate that the same standard architecture (ResNet-50) that learns a texture-based representation on ImageNet is able to learn a shape-based representation instead when trained on "Stylized-ImageNet", a stylized version of ImageNet. This provides a much better fit for human behavioural performance in our well-controlled psychophysical lab setting (nine experiments totalling 48,560 psychophysical trials across 97 observers) and comes with a number of unexpected emergent benefits such as improved object detection performance and previously unseen robustness towards a wide range of image distortions, highlighting advantages of a shape-based representation.
Early Stratification of Patients at Risk for Postoperative Complications after Elective Colectomy
Authors: Wen Wang, Rema Padman, Nirav Shah
Date: 2018-11-29 00:00:00

Stratifying patients at risk for postoperative complications may facilitate
timely and accurate workups and reduce the burden of adverse events on patients
and the health system. Currently, a widely-used surgical risk calculator
created by the American College of Surgeons, NSQIP, uses 21 preoperative
covariates to assess risk of postoperative complications, but lacks dynamic,
real-time capabilities to accommodate postoperative information. We propose a
new Hidden Markov Model sequence classifier for analyzing patients'
postoperative temperature sequences that incorporates their time-invariant
characteristics in both transition probability and initial state probability in
order to develop a postoperative "real-time" complication detector. Data from
elective Colectomy surgery indicate that our method has improved classification
performance compared to 8 other machine learning classifiers when using the
full temperature sequence associated with the patients' length of stay.
Additionally, within 44 hours after surgery, the performance of the model is
close to that of full-length temperature sequence.
ApolloCar3D: A Large 3D Car Instance Understanding Benchmark for Autonomous Driving
Authors: Xibin Song, Peng Wang, Dingfu Zhou, Rui Zhu, Chenye Guan, Yuchao Dai, Hao Su, Hongdong Li, Ruigang Yang
Date: 2018-11-29 00:00:00
Proceeding: CVPR 2019 6
Tasks: 3D Car Instance Understanding, Autonomous Driving

Autonomous driving has attracted remarkable attention from both industry and
academia. An important task is to estimate 3D properties(e.g.translation,
rotation and shape) of a moving or parked vehicle on the road. This task, while
critical, is still under-researched in the computer vision community -
partially owing to the lack of large scale and fully-annotated 3D car database
suitable for autonomous driving research. In this paper, we contribute the
first large-scale database suitable for 3D car instance understanding -
ApolloCar3D. The dataset contains 5,277 driving images and over 60K car
instances, where each car is fitted with an industry-grade 3D CAD model with
absolute model size and semantically labelled keypoints. This dataset is above
20 times larger than PASCAL3D+ and KITTI, the current state-of-the-art. To
enable efficient labelling in 3D, we build a pipeline by considering 2D-3D
keypoint correspondences for a single instance and 3D relationship among
multiple instances. Equipped with such dataset, we build various baseline
algorithms with the state-of-the-art deep convolutional neural networks.
Specifically, we first segment each car with a pre-trained Mask R-CNN, and then
regress towards its 3D pose and shape based on a deformable 3D car model with
or without using semantic keypoints. We show that using keypoints significantly
improves fitting performance. Finally, we develop a new 3D metric jointly
considering 3D pose and 3D shape, allowing for comprehensive evaluation and
ablation study. By comparing with human performance we suggest several future
directions for further improvements.
Iterative Residual CNNs for Burst Photography Applications
Authors: Filippos Kokkinos, Stamatios Lefkimmiatis
Date: 2018-11-29 00:00:00
Proceeding: CVPR 2019 6
Tasks: Demosaicking, Denoising, Image Restoration

Modern inexpensive imaging sensors suffer from inherent hardware constraints
which often result in captured images of poor quality. Among the most common
ways to deal with such limitations is to rely on burst photography, which
nowadays acts as the backbone of all modern smartphone imaging applications. In
this work, we focus on the fact that every frame of a burst sequence can be
accurately described by a forward (physical) model. This in turn allows us to
restore a single image of higher quality from a sequence of low quality images
as the solution of an optimization problem. Inspired by an extension of the
gradient descent method that can handle non-smooth functions, namely the
proximal gradient descent, and modern deep learning techniques, we propose a
convolutional iterative network with a transparent architecture. Our network,
uses a burst of low quality image frames and is able to produce an output of
higher image quality recovering fine details which are not distinguishable in
any of the original burst frames. We focus both on the burst photography
pipeline as a whole, i.e. burst demosaicking and denoising, as well as on the
traditional Gaussian denoising task. The developed method demonstrates
consistent state-of-the art performance across the two tasks and as opposed to
other recent deep learning approaches does not have any inherent restrictions
either to the number of frames or their ordering. Code can be found at
https://fkokkinos.github.io/deep_burst/