pwc-archive/papers-with-abstracts huggingface dataset:
The Intelligent ICU Pilot Study: Using Artificial Intelligence Technology for Autonomous Patient Monitoring
Authors: Anis Davoudi, Kumar Rohit Malhotra, Benjamin Shickel, Scott Siegel, Seth Williams, Matthew Ruppert, Emel Bihorac, Tezcan Ozrazgat-Baslanti, Patrick J. Tighe, Azra Bihorac, Parisa Rashidi
Date: 2018-04-25 00:00:00
Tasks: Action Unit Detection, Face Detection, Face Recognition, Facial Action Unit Detection, Facial Expression Recognition, Facial Expression Recognition (FER)

Currently, many critical care indices are repetitively assessed and recorded
by overburdened nurses, e.g. physical function or facial pain expressions of
nonverbal patients. In addition, many essential information on patients and
their environment are not captured at all, or are captured in a non-granular
manner, e.g. sleep disturbance factors such as bright light, loud background
noise, or excessive visitations. In this pilot study, we examined the
feasibility of using pervasive sensing technology and artificial intelligence
for autonomous and granular monitoring of critically ill patients and their
environment in the Intensive Care Unit (ICU). As an exemplar prevalent
condition, we also characterized delirious and non-delirious patients and their
environment. We used wearable sensors, light and sound sensors, and a
high-resolution camera to collected data on patients and their environment. We
analyzed collected data using deep learning and statistical analysis. Our
system performed face detection, face recognition, facial action unit
detection, head pose detection, facial expression recognition, posture
recognition, actigraphy analysis, sound pressure and light level detection, and
visitation frequency detection. We were able to detect patient's face (Mean
average precision (mAP)=0.94), recognize patient's face (mAP=0.80), and their
postures (F1=0.94). We also found that all facial expressions, 11 activity
features, visitation frequency during the day, visitation frequency during the
night, light levels, and sound pressure levels during the night were
significantly different between delirious and non-delirious patients
(p-value<0.05). In summary, we showed that granular and autonomous monitoring
of critically ill patients and their environment is feasible and can be used
for characterizing critical care conditions and related environment factors.
Progressive Neural Networks for Image Classification
Authors: Zhi Zhang, Guanghan Ning, Yigang Cen, Yang Li, Zhiqun Zhao, Hao Sun, Zhihai He
Date: 2018-04-25 00:00:00
Tasks: Classification, General Classification, image-classification, Image Classification

The inference structures and computational complexity of existing deep neural
networks, once trained, are fixed and remain the same for all test images.
However, in practice, it is highly desirable to establish a progressive
structure for deep neural networks which is able to adapt its inference process
and complexity for images with different visual recognition complexity. In this
work, we develop a multi-stage progressive structure with integrated confidence
analysis and decision policy learning for deep neural networks. This new
framework consists of a set of network units to be activated in a sequential
manner with progressively increased complexity and visual recognition power.
Our extensive experimental results on the CIFAR-10 and ImageNet datasets
demonstrate that the proposed progressive deep neural network is able to obtain
more than 10 fold complexity scalability while achieving the state-of-the-art
performance using a single network model satisfying different
complexity-accuracy requirements.
Learning to Map Context-Dependent Sentences to Executable Formal Queries
Authors: Alane Suhr, Srinivasan Iyer, Yoav Artzi
Date: 2018-04-18 00:00:00
Proceeding: NAACL 2018 6

We propose a context-dependent model to map utterances within an interaction
to executable formal queries. To incorporate interaction history, the model
maintains an interaction-level encoder that updates after each turn, and can
copy sub-sequences of previously predicted queries during generation. Our
approach combines implicit and explicit modeling of references between
utterances. We evaluate our model on the ATIS flight planning interactions, and
demonstrate the benefits of modeling context and explicit references.
Multi-Layer Sparse Coding: The Holistic Way
Authors: Aviad Aberdam, Jeremias Sulam, Michael Elad
Date: 2018-04-25 00:00:00

The recently proposed multi-layer sparse model has raised insightful
connections between sparse representations and convolutional neural networks
(CNN). In its original conception, this model was restricted to a cascade of
convolutional synthesis representations. In this paper, we start by addressing
a more general model, revealing interesting ties to fully connected networks.
We then show that this multi-layer construction admits a brand new
interpretation in a unique symbiosis between synthesis and analysis models:
while the deepest layer indeed provides a synthesis representation, the
mid-layers decompositions provide an analysis counterpart. This new perspective
exposes the suboptimality of previously proposed pursuit approaches, as they do
not fully leverage all the information comprised in the model constraints.
Armed with this understanding, we address fundamental theoretical issues,
revisiting previous analysis and expanding it. Motivated by the limitations of
previous algorithms, we then propose an integrated - holistic - alternative
that estimates all representations in the model simultaneously, and analyze all
these different schemes under stochastic noise assumptions. Inspired by the
synthesis-analysis duality, we further present a Holistic Pursuit algorithm,
which alternates between synthesis and analysis sparse coding steps, eventually
solving for the entire model as a whole, with provable improved performance.
Finally, we present numerical results that demonstrate the practical advantages
of our approach.
Exact and Robust Conformal Inference Methods for Predictive Machine Learning With Dependent Data
Authors: Victor Chernozhukov, Kaspar Wuthrich, Yinchu Zhu
Date: 2018-02-17 00:00:00
Tasks: BIG-bench Machine Learning, Time Series, Time Series Analysis, valid

We extend conformal inference to general settings that allow for time series
data. Our proposal is developed as a randomization method and accounts for
potential serial dependence by including block structures in the permutation
scheme. As a result, the proposed method retains the exact, model-free validity
when the data are i.i.d. or more generally exchangeable, similar to usual
conformal inference methods. When exchangeability fails, as is the case for
common time series data, the proposed approach is approximately valid under
weak assumptions on the conformity score.
Variance Reduction Methods for Sublinear Reinforcement Learning
Authors: Sham Kakade, Mengdi Wang, Lin F. Yang
Date: 2018-02-26 00:00:00
Tasks: Q-Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

There is a technical issue in the analysis that is not easily fixable. We, therefore, withdraw the submission. Sorry for the inconvenience.
Ensemble Validation: Selectivity has a Price, but Variety is Free
Authors: Eric Bax, Farshad Kooti
Date: 2016-10-04 00:00:00

Suppose some classifiers are selected from a set of hypothesis classifiers to
form an equally-weighted ensemble that selects a member classifier at random
for each input example. Then the ensemble has an error bound consisting of the
average error bound for the member classifiers, a term for selectivity that
varies from zero (if all hypothesis classifiers are selected) to a standard
uniform error bound (if only a single classifier is selected), and small
constants. There is no penalty for using a richer hypothesis set if the same
fraction of the hypothesis classifiers are selected for the ensemble.
RULLS: Randomized Union of Locally Linear Subspaces for Feature Engineering
Authors: Namita Lokare, Jorge Silva, Ilknur Kaynar Kabul
Date: 2018-04-25 00:00:00
Tasks: Clustering, Feature Engineering, General Classification

Feature engineering plays an important role in the success of a machine
learning model. Most of the effort in training a model goes into data
preparation and choosing the right representation. In this paper, we propose a
robust feature engineering method, Randomized Union of Locally Linear Subspaces
(RULLS). We generate sparse, non-negative, and rotation invariant features in
an unsupervised fashion. RULLS aggregates features from a random union of
subspaces by describing each point using globally chosen landmarks. These
landmarks serve as anchor points for choosing subspaces. Our method provides a
way to select features that are relevant in the neighborhood around these
chosen landmarks. Distances from each data point to $k$ closest landmarks are
encoded in the feature matrix. The final feature representation is a union of
features from all chosen subspaces.
  The effectiveness of our algorithm is shown on various real-world datasets
for tasks such as clustering and classification of raw data and in the presence
of noise. We compare our method with existing feature generation methods.
Results show a high performance of our method on both classification and
clustering tasks.
TypeSQL: Knowledge-based Type-Aware Neural Text-to-SQL Generation
Authors: Tao Yu, Zifan Li, Zilin Zhang, Rui Zhang, Dragomir Radev
Date: 2018-04-25 00:00:00
Proceeding: NAACL 2018 6
Tasks: slot-filling, Slot Filling, Text to SQL, Text-To-SQL, Vocal Bursts Type Prediction

Interacting with relational databases through natural language helps users of
any background easily query and analyze a vast amount of data. This requires a
system that understands users' questions and converts them to SQL queries
automatically. In this paper we present a novel approach, TypeSQL, which views
this problem as a slot filling task. Additionally, TypeSQL utilizes type
information to better understand rare entities and numbers in natural language
questions. We test this idea on the WikiSQL dataset and outperform the prior
state-of-the-art by 5.5% in much less time. We also show that accessing the
content of databases can significantly improve the performance when users'
queries are not well-formed. TypeSQL gets 82.6% accuracy, a 17.5% absolute
improvement compared to the previous content-sensitive model.
Removing Confounding Factors Associated Weights in Deep Neural Networks Improves the Prediction Accuracy for Healthcare Applications
Authors: Haohan Wang, Zhenglin Wu, Eric P. Xing
Date: 2018-03-20 00:00:00
Tasks: EEG, Electroencephalogram (EEG)

The proliferation of healthcare data has brought the opportunities of
applying data-driven approaches, such as machine learning methods, to assist
diagnosis. Recently, many deep learning methods have been shown with impressive
successes in predicting disease status with raw input data. However, the
"black-box" nature of deep learning and the high-reliability requirement of
biomedical applications have created new challenges regarding the existence of
confounding factors. In this paper, with a brief argument that inappropriate
handling of confounding factors will lead to models' sub-optimal performance in
real-world applications, we present an efficient method that can remove the
influences of confounding factors such as age or gender to improve the
across-cohort prediction accuracy of neural networks. One distinct advantage of
our method is that it only requires minimal changes of the baseline model's
architecture so that it can be plugged into most of the existing neural
networks. We conduct experiments across CT-scan, MRA, and EEG brain wave with
convolutional neural networks and LSTM to verify the efficiency of our method.
Analysis of a Canonical Labeling Algorithm for the Alignment of Correlated Erdős-Rényi Graphs
Authors: Osman Emre Dai, Daniel Cullina, Negar Kiyavash, Matthias Grossglauser
Date: 2018-04-25 00:00:00
Tasks: Graph Matching

Graph alignment in two correlated random graphs refers to the task of identifying the correspondence between vertex sets of the graphs. Recent results have characterized the exact information-theoretic threshold for graph alignment in correlated Erd\H{o}s-R\'enyi graphs. However, very little is known about the existence of efficient algorithms to achieve graph alignment without seeds. In this work we identify a region in which a straightforward $O(n^{11/5} \log n )$-time canonical labeling algorithm, initially introduced in the context of graph isomorphism, succeeds in aligning correlated Erd\H{o}s-R\'enyi graphs. The algorithm has two steps. In the first step, all vertices are labeled by their degrees and a trivial minimum distance alignment (i.e., sorting vertices according to their degrees) matches a fixed number of highest degree vertices in the two graphs. Having identified this subset of vertices, the remaining vertices are matched using a alignment algorithm for bipartite graphs.
The phase transition for the existence of the maximum likelihood estimate in high-dimensional logistic regression
Authors: Emmanuel J. Candes, Pragya Sur
Date: 2018-04-25 00:00:00
Tasks: regression

This paper rigorously establishes that the existence of the maximum
likelihood estimate (MLE) in high-dimensional logistic regression models with
Gaussian covariates undergoes a sharp `phase transition'. We introduce an
explicit boundary curve $h_{\text{MLE}}$, parameterized by two scalars
measuring the overall magnitude of the unknown sequence of regression
coefficients, with the following property: in the limit of large sample sizes
$n$ and number of features $p$ proportioned in such a way that $p/n \rightarrow
\kappa$, we show that if the problem is sufficiently high dimensional in the
sense that $\kappa > h_{\text{MLE}}$, then the MLE does not exist with
probability one. Conversely, if $\kappa < h_{\text{MLE}}$, the MLE
asymptotically exists with probability one.
Cheap Non-standard Analysis and Computability
Authors: Olivier Bournez, Sabrina Ouazzani
Date: 2018-04-25 00:00:00

Non standard analysis is an area of Mathematics dealing with notions of
infinitesimal and infinitely large numbers, in which many statements from
classical analysis can be expressed very naturally. Cheap non-standard analysis
introduced by Terence Tao in 2012 is based on the idea that considering that a
property holds eventually is sufficient to give the essence of many of its
statements. This provides constructivity but at some (acceptable) price. We
consider computability in cheap non-standard analysis. We prove that many
concepts from computable analysis as well as several concepts from
computability can be very elegantly and alternatively presented in this
framework. It provides a dual view and dual proofs to several statements
already known in these fields.
JUNIPR: a Framework for Unsupervised Machine Learning in Particle Physics
Authors: Anders Andreassen, Ilya Feige, Christopher Frye, Matthew D. Schwartz
Date: 2018-04-25 00:00:00
Tasks: BIG-bench Machine Learning

In applications of machine learning to particle physics, a persistent
challenge is how to go beyond discrimination to learn about the underlying
physics. To this end, a powerful tool would be a framework for unsupervised
learning, where the machine learns the intricate high-dimensional contours of
the data upon which it is trained, without reference to pre-established labels.
In order to approach such a complex task, an unsupervised network must be
structured intelligently, based on a qualitative understanding of the data. In
this paper, we scaffold the neural network's architecture around a
leading-order model of the physics underlying the data. In addition to making
unsupervised learning tractable, this design actually alleviates existing
tensions between performance and interpretability. We call the framework
JUNIPR: "Jets from UNsupervised Interpretable PRobabilistic models". In this
approach, the set of particle momenta composing a jet are clustered into a
binary tree that the neural network examines sequentially. Training is
unsupervised and unrestricted: the network could decide that the data bears
little correspondence to the chosen tree structure. However, when there is a
correspondence, the network's output along the tree has a direct physical
interpretation. JUNIPR models can perform discrimination tasks, through the
statistically optimal likelihood-ratio test, and they permit visualizations of
discrimination power at each branching in a jet's tree. Additionally, JUNIPR
models provide a probability distribution from which events can be drawn,
providing a data-driven Monte Carlo generator. As a third application, JUNIPR
models can reweight events from one (e.g. simulated) data set to agree with
distributions from another (e.g. experimental) data set.
Factors Influencing the Surprising Instability of Word Embeddings
Authors: Laura Wendlandt, Jonathan K. Kummerfeld, Rada Mihalcea
Date: 2018-04-25 00:00:00
Proceeding: NAACL 2018 6
Tasks: Word Embeddings

Despite the recent popularity of word embedding methods, there is only a
small body of work exploring the limitations of these representations. In this
paper, we consider one aspect of embedding spaces, namely their stability. We
show that even relatively high frequency words (100-200 occurrences) are often
unstable. We provide empirical evidence for how various factors contribute to
the stability of word embeddings, and we analyze the effects of stability on
downstream tasks.
Cross-Modal Retrieval with Implicit Concept Association
Authors: Yale Song, Mohammad Soleymani
Date: 2018-04-12 00:00:00
Tasks: Cross-Modal Retrieval, Image Retrieval, Multiple Instance Learning, Retrieval, Sentence

Traditional cross-modal retrieval assumes explicit association of concepts
across modalities, where there is no ambiguity in how the concepts are linked
to each other, e.g., when we do the image search with a query "dogs", we expect
to see dog images. In this paper, we consider a different setting for
cross-modal retrieval where data from different modalities are implicitly
linked via concepts that must be inferred by high-level reasoning; we call this
setting implicit concept association. To foster future research in this
setting, we present a new dataset containing 47K pairs of animated GIFs and
sentences crawled from the web, in which the GIFs depict physical or emotional
reactions to the scenarios described in the text (called "reaction GIFs"). We
report on a user study showing that, despite the presence of implicit concept
association, humans are able to identify video-sentence pairs with matching
concepts, suggesting the feasibility of our task. Furthermore, we propose a
novel visual-semantic embedding network based on multiple instance learning.
Unlike traditional approaches, we compute multiple embeddings from each
modality, each representing different concepts, and measure their similarity by
considering all possible combinations of visual-semantic embeddings in the
framework of multiple instance learning. We evaluate our approach on two
video-sentence datasets with explicit and implicit concept association and
report competitive results compared to existing approaches on cross-modal
retrieval.
DeepIM: Deep Iterative Matching for 6D Pose Estimation
Authors: Yi Li, Gu Wang, Xiangyang Ji, Yu Xiang, Dieter Fox
Date: 2018-03-31 00:00:00
Proceeding: ECCV 2018 9
Tasks: 6D Pose Estimation, 6D Pose Estimation using RGB, Pose Estimation, Robot Manipulation

Estimating the 6D pose of objects from images is an important problem in various applications such as robot manipulation and virtual reality. While direct regression of images to object poses has limited accuracy, matching rendered images of an object against the observed image can produce accurate results. In this work, we propose a novel deep neural network for 6D pose matching named DeepIM. Given an initial pose estimation, our network is able to iteratively refine the pose by matching the rendered image against the observed image. The network is trained to predict a relative pose transformation using an untangled representation of 3D location and 3D orientation and an iterative training process. Experiments on two commonly used benchmarks for 6D pose estimation demonstrate that DeepIM achieves large improvements over state-of-the-art methods. We furthermore show that DeepIM is able to match previously unseen objects.
Personalized Language Model for Query Auto-Completion
Authors: Aaron Jaech, Mari Ostendorf
Date: 2018-04-25 00:00:00
Proceeding: ACL 2018 7
Tasks: Language Modeling, Language Modelling, model

Query auto-completion is a search engine feature whereby the system suggests
completed queries as the user types. Recently, the use of a recurrent neural
network language model was suggested as a method of generating query
completions. We show how an adaptable language model can be used to generate
personalized completions and how the model can use online updating to make
predictions for users not seen during training. The personalized predictions
are significantly better than a baseline that uses no user information.
Automatic Latent Fingerprint Segmentation
Authors: Dinh-Luan Nguyen, Kai Cao, Anil K. Jain
Date: 2018-04-25 00:00:00
Tasks: Friction, Segmentation

We present a simple but effective method for automatic latent fingerprint
segmentation, called SegFinNet. SegFinNet takes a latent image as an input and
outputs a binary mask highlighting the friction ridge pattern. Our algorithm
combines fully convolutional neural network and detection-based approaches to
process the entire input latent image in one shot instead of using latent
patches. Experimental results on three different latent databases (i.e. NIST
SD27, WVU, and an operational forensic database) show that SegFinNet
outperforms both human markup for latents and the state-of-the-art latent
segmentation algorithms. We further show that this improved cropping boosts the
hit rate of a latent fingerprint matcher.
3D-PhysNet: Learning the Intuitive Physics of Non-Rigid Object Deformations
Authors: Zhihua Wang, Stefano Rosa, Bo Yang, Sen Wang, Niki Trigoni, Andrew Markham
Date: 2018-04-25 00:00:00

The ability to interact and understand the environment is a fundamental
prerequisite for a wide range of applications from robotics to augmented
reality. In particular, predicting how deformable objects will react to applied
forces in real time is a significant challenge. This is further confounded by
the fact that shape information about encountered objects in the real world is
often impaired by occlusions, noise and missing regions e.g. a robot
manipulating an object will only be able to observe a partial view of the
entire solid. In this work we present a framework, 3D-PhysNet, which is able to
predict how a three-dimensional solid will deform under an applied force using
intuitive physics modelling. In particular, we propose a new method to encode
the physical properties of the material and the applied force, enabling
generalisation over materials. The key is to combine deep variational
autoencoders with adversarial training, conditioned on the applied force and
the material properties. We further propose a cascaded architecture that takes
a single 2.5D depth view of the object and predicts its deformation. Training
data is provided by a physics simulator. The network is fast enough to be used
in real-time applications from partial views. Experimental results show the
viability and the generalisation properties of the proposed architecture.