pwc-archive/papers-with-abstracts huggingface dataset:
Linked Causal Variational Autoencoder for Inferring Paired Spillover Effects
Authors: Vineeth Rakesh, Ruocheng Guo, Raha Moraffah, Nitin Agarwal, Huan Liu
Date: 2018-08-09 00:00:00
Tasks: Variational Inference

Modeling spillover effects from observational data is an important problem in
economics, business, and other fields of research. % It helps us infer the
causality between two seemingly unrelated set of events. For example, if
consumer spending in the United States declines, it has spillover effects on
economies that depend on the U.S. as their largest export market. In this
paper, we aim to infer the causation that results in spillover effects between
pairs of entities (or units), we call this effect as \textit{paired spillover}.
To achieve this, we leverage the recent developments in variational inference
and deep learning techniques to propose a generative model called Linked Causal
Variational Autoencoder (LCVA). Similar to variational autoencoders (VAE), LCVA
incorporates an encoder neural network to learn the latent attributes and a
decoder network to reconstruct the inputs. However, unlike VAE, LCVA treats the
\textit{latent attributes as confounders that are assumed to affect both the
treatment and the outcome of units}. Specifically, given a pair of units $u$
and $\bar{u}$, their individual treatment and outcomes, the encoder network of
LCVA samples the confounders by conditioning on the observed covariates of $u$,
the treatments of both $u$ and $\bar{u}$ and the outcome of $u$. Once inferred,
the latent attributes (or confounders) of $u$ captures the spillover effect of
$\bar{u}$ on $u$. Using a network of users from job training dataset (LaLonde
(1986)) and co-purchase dataset from Amazon e-commerce domain, we show that
LCVA is significantly more robust than existing methods in capturing spillover
effects.
The Effectiveness of Multitask Learning for Phenotyping with Electronic Health Records Data
Authors: Daisy Yi Ding, Chloé Simpson, Stephen Pfohl, Dave C. Kale, Kenneth Jung, Nigam H. Shah
Date: 2018-08-09 00:00:00

Electronic phenotyping is the task of ascertaining whether an individual has
a medical condition of interest by analyzing their medical record and is
foundational in clinical informatics. Increasingly, electronic phenotyping is
performed via supervised learning. We investigate the effectiveness of
multitask learning for phenotyping using electronic health records (EHR) data.
Multitask learning aims to improve model performance on a target task by
jointly learning additional auxiliary tasks and has been used in disparate
areas of machine learning. However, its utility when applied to EHR data has
not been established, and prior work suggests that its benefits are
inconsistent. We present experiments that elucidate when multitask learning
with neural nets improves performance for phenotyping using EHR data relative
to neural nets trained for a single phenotype and to well-tuned logistic
regression baselines. We find that multitask neural nets consistently
outperform single-task neural nets for rare phenotypes but underperform for
relatively more common phenotypes. The effect size increases as more auxiliary
tasks are added. Moreover, multitask learning reduces the sensitivity of neural
nets to hyperparameter settings for rare phenotypes. Last, we quantify
phenotype complexity and find that neural nets trained with or without
multitask learning do not improve on simple baselines unless the phenotypes are
sufficiently complex.
Fuzzy Clustering to Identify Clusters at Different Levels of Fuzziness: An Evolutionary Multi-Objective Optimization Approach
Authors: Avisek Gupta, Shounak Datta, Swagatam Das
Date: 2018-08-09 00:00:00
Tasks: Clustering

Fuzzy clustering methods identify naturally occurring clusters in a dataset,
where the extent to which different clusters are overlapped can differ. Most
methods have a parameter to fix the level of fuzziness. However, the
appropriate level of fuzziness depends on the application at hand. This paper
presents Entropy $c$-Means (ECM), a method of fuzzy clustering that
simultaneously optimizes two contradictory objective functions, resulting in
the creation of fuzzy clusters with different levels of fuzziness. This allows
ECM to identify clusters with different degrees of overlap. ECM optimizes the
two objective functions using two multi-objective optimization methods,
Non-dominated Sorting Genetic Algorithm II (NSGA-II), and Multiobjective
Evolutionary Algorithm based on Decomposition (MOEA/D). We also propose a
method to select a suitable trade-off clustering from the Pareto front.
Experiments on challenging synthetic datasets as well as real-world datasets
show that ECM leads to better cluster detection compared to the conventional
fuzzy clustering methods as well as previously used multi-objective methods for
fuzzy clustering.
Fundamentals of Recurrent Neural Network (RNN) and Long Short-Term Memory (LSTM) Network
Authors: Alex Sherstinsky
Date: 2018-08-09 00:00:00
Tasks: Articles, Rolling Shutter Correction

Because of their effectiveness in broad practical applications, LSTM networks have received a wealth of coverage in scientific journals, technical blogs, and implementation guides. However, in most articles, the inference formulas for the LSTM network and its parent, RNN, are stated axiomatically, while the training formulas are omitted altogether. In addition, the technique of "unrolling" an RNN is routinely presented without justification throughout the literature. The goal of this paper is to explain the essential RNN and LSTM fundamentals in a single document. Drawing from concepts in signal processing, we formally derive the canonical RNN formulation from differential equations. We then propose and prove a precise statement, which yields the RNN unrolling technique. We also review the difficulties with training the standard RNN and address them by transforming the RNN into the "Vanilla LSTM" network through a series of logical arguments. We provide all equations pertaining to the LSTM system together with detailed descriptions of its constituent entities. Albeit unconventional, our choice of notation and the method for presenting the LSTM system emphasizes ease of understanding. As part of the analysis, we identify new opportunities to enrich the LSTM system and incorporate these extensions into the Vanilla LSTM network, producing the most general LSTM variant to date. The target reader has already been exposed to RNNs and LSTM networks through numerous available resources and is open to an alternative pedagogical approach. A Machine Learning practitioner seeking guidance for implementing our new augmented LSTM model in software for experimentation and research will find the insights and derivations in this tutorial valuable as well.
The Elephant in the Room
Authors: Amir Rosenfeld, Richard Zemel, John K. Tsotsos
Date: 2018-08-09 00:00:00
Tasks: Object, object-detection, Object Detection, Position

We showcase a family of common failures of state-of-the art object detectors.
These are obtained by replacing image sub-regions by another sub-image that
contains a trained object. We call this "object transplanting". Modifying an
image in this manner is shown to have a non-local impact on object detection.
Slight changes in object position can affect its identity according to an
object detector as well as that of other objects in the image. We provide some
analysis and suggest possible reasons for the reported phenomena.
$α$-Approximation Density-based Clustering of Multi-valued Objects
Authors: Zhilin Zhang
Date: 2018-08-09 00:00:00
Tasks: Clustering

Multi-valued data are commonly found in many real applications. During the
process of clustering multi-valued data, most existing methods use sampling or
aggregation mechanisms that cannot reflect the real distribution of objects and
their instances and thus fail to obtain high-quality clusters. In this paper, a
concept of $\alpha$-approximation distance is introduced to measure the
connectivity between multi-valued objects by taking account of the distribution
of the instances. An $\alpha$-approximation density-based clustering algorithm
(DBCMO) is proposed to efficiently cluster the multi-valued objects by using
global and local R* tree structures. To speed up the algorithm, four pruning
rules on the tree structures are implemented. Empirical studies on synthetic
and real datasets demonstrate that DBCMO can efficiently and effectively
discover the multi-valued object clusters. A comparison with two existing
methods further shows that DBCMO can better handle a continuous decrease in the
cluster density and detect clusters of varying density.
Code-Mixed Sentiment Analysis Using Machine Learning and Neural Network Approaches
Authors: Pruthwik Mishra, Prathyusha Danda, Pranav Dhakras
Date: 2018-08-09 00:00:00
Tasks: BIG-bench Machine Learning, Sentence, Sentiment Analysis

Sentiment Analysis for Indian Languages (SAIL)-Code Mixed tools contest aimed
at identifying the sentence level sentiment polarity of the code-mixed dataset
of Indian languages pairs (Hi-En, Ben-Hi-En). Hi-En dataset is henceforth
referred to as HI-EN and Ben-Hi-En dataset as BN-EN respectively. For this, we
submitted four models for sentiment analysis of code-mixed HI-EN and BN-EN
datasets. The first model was an ensemble voting classifier consisting of three
classifiers - linear SVM, logistic regression and random forests while the
second one was a linear SVM. Both the models used TF-IDF feature vectors of
character n-grams where n ranged from 2 to 6. We used scikit-learn (sklearn)
machine learning library for implementing both the approaches. Run1 was
obtained from the voting classifier and Run2 used the linear SVM model for
producing the results. Out of the four submitted outputs Run2 outperformed Run1
in both the datasets. We finished first in the contest for both HI-EN with an
F-score of 0.569 and BN-EN with an F-score of 0.526.
VerIDeep: Verifying Integrity of Deep Neural Networks through Sensitive-Sample Fingerprinting
Authors: Zecheng He, Tianwei Zhang, Ruby B. Lee
Date: 2018-08-09 00:00:00
Tasks: Deep Learning

Deep learning has become popular, and numerous cloud-based services are
provided to help customers develop and deploy deep learning applications.
Meanwhile, various attack techniques have also been discovered to stealthily
compromise the model's integrity. When a cloud customer deploys a deep learning
model in the cloud and serves it to end-users, it is important for him to be
able to verify that the deployed model has not been tampered with, and the
model's integrity is protected.
  We propose a new low-cost and self-served methodology for customers to verify
that the model deployed in the cloud is intact, while having only black-box
access (e.g., via APIs) to the deployed model. Customers can detect arbitrary
changes to their deep learning models. Specifically, we define
\texttt{Sensitive-Sample} fingerprints, which are a small set of transformed
inputs that make the model outputs sensitive to the model's parameters. Even
small weight changes can be clearly reflected in the model outputs, and
observed by the customer. Our experiments on different types of model integrity
attacks show that we can detect model integrity breaches with high accuracy
($>$99\%) and low overhead ($<$10 black-box model accesses).
A Hybrid Recommender System for Patient-Doctor Matchmaking in Primary Care
Authors: Qiwei Han, Mengxin Ji, Inigo Martinez de Rituerto de Troya, Manas Gaur, Leid Zejnilovic
Date: 2018-08-09 00:00:00
Tasks: Collaborative Filtering, Recommendation Systems

We partner with a leading European healthcare provider and design a mechanism
to match patients with family doctors in primary care. We define the
matchmaking process for several distinct use cases given different levels of
available information about patients. Then, we adopt a hybrid recommender
system to present each patient a list of family doctor recommendations. In
particular, we model patient trust of family doctors using a large-scale
dataset of consultation histories, while accounting for the temporal dynamics
of their relationships. Our proposed approach shows higher predictive accuracy
than both a heuristic baseline and a collaborative filtering approach, and the
proposed trust measure further improves model performance.
Self-Adaptive Systems in Organic Computing: Strategies for Self-Improvement
Authors: Andreas Niederquell
Date: 2018-08-08 00:00:00

With the intensified use of intelligent things, the demands on the
technological systems are increasing permanently. A possible approach to meet
the continuously changing challenges is to shift the system integration from
design to run-time by using adaptive systems. Diverse adaptivity properties,
so-called self-* properties, form the basis of these systems and one of the
properties is self-improvement. It describes the ability of a system not only
to adapt to a changing environment according to a predefined model, but also
the capability to adapt the adaptation logic of the whole system. In this
paper, a closer look is taken at the structure of self-adaptive systems.
Additionally, the systems' ability to improve themselves during run-time is
described from the perspective of Organic Computing. Furthermore, four
different strategies for self-improvement are presented, following the taxonomy
of self-adaptation suggested by Christian Krupitzer et al.
Fast Image Scanning with Deep Max-Pooling Convolutional Neural Networks
Authors: Alessandro Giusti, Dan C. Cireşan, Jonathan Masci, Luca M. Gambardella, Jürgen Schmidhuber
Date: 2013-02-07 00:00:00
Tasks: General Classification, image-classification, Image Classification

Deep Neural Networks now excel at image classification, detection and
segmentation. When used to scan images by means of a sliding window, however,
their high computational complexity can bring even the most powerful hardware
to its knees. We show how dynamic programming can speedup the process by orders
of magnitude, even when max-pooling layers are present.
Large-Scale Study of Curiosity-Driven Learning
Authors: Yuri Burda, Harri Edwards, Deepak Pathak, Amos Storkey, Trevor Darrell, Alexei A. Efros
Date: 2018-08-13 00:00:00
Proceeding: ICLR 2019 5
Tasks: Atari Games, Prediction, Reinforcement Learning, SNES Games

Reinforcement learning algorithms rely on carefully engineering environment
rewards that are extrinsic to the agent. However, annotating each environment
with hand-designed, dense rewards is not scalable, motivating the need for
developing reward functions that are intrinsic to the agent. Curiosity is a
type of intrinsic reward function which uses prediction error as reward signal.
In this paper: (a) We perform the first large-scale study of purely
curiosity-driven learning, i.e. without any extrinsic rewards, across 54
standard benchmark environments, including the Atari game suite. Our results
show surprisingly good performance, and a high degree of alignment between the
intrinsic curiosity objective and the hand-designed extrinsic rewards of many
game environments. (b) We investigate the effect of using different feature
spaces for computing prediction error and show that random features are
sufficient for many popular RL game benchmarks, but learned features appear to
generalize better (e.g. to novel game levels in Super Mario Bros.). (c) We
demonstrate limitations of the prediction-based rewards in stochastic setups.
Game-play videos and code are at
https://pathak22.github.io/large-scale-curiosity/
REGMAPR - Text Matching Made Easy
Authors: Siddhartha Brahma
Date: 2018-08-13 00:00:00
Tasks: Natural Language Inference, Sentence, Text Matching

Text matching is a fundamental problem in natural language processing. Neural
models using bidirectional LSTMs for sentence encoding and inter-sentence
attention mechanisms perform remarkably well on several benchmark datasets. We
propose REGMAPR - a simple and general architecture for text matching that does
not use inter-sentence attention. Starting from a Siamese architecture, we
augment the embeddings of the words with two features based on exact and para-
phrase match between words in the two sentences. We train the model using three
types of regularization on datasets for textual entailment, paraphrase
detection and semantic related- ness. REGMAPR performs comparably or better
than more complex neural models or models using a large number of handcrafted
features. REGMAPR achieves state-of-the-art results for paraphrase detection on
the SICK dataset and for textual entailment on the SNLI dataset among models
that do not use inter-sentence attention.
Disentangled Representation Learning for Non-Parallel Text Style Transfer
Authors: Vineet John, Lili Mou, Hareesh Bahuleyan, Olga Vechtomova
Date: 2018-08-13 00:00:00
Proceeding: ACL 2019 7
Tasks: Prediction, Representation Learning, Style Transfer, Text Style Transfer

This paper tackles the problem of disentangling the latent variables of style
and content in language models. We propose a simple yet effective approach,
which incorporates auxiliary multi-task and adversarial objectives, for label
prediction and bag-of-words prediction, respectively. We show, both
qualitatively and quantitatively, that the style and content are indeed
disentangled in the latent space. This disentangled latent representation
learning method is applied to style transfer on non-parallel corpora. We
achieve substantially better results in terms of transfer accuracy, content
preservation and language fluency, in comparison to previous state-of-the-art
approaches.
Overarching Computation Model (OCM)
Authors: Henok Ghebrechristos, Drew Miller
Date: 2018-08-01 00:00:00
Tasks: model

Existing models of computation, such as a Turing machine (hereafter, TM), do
not consider the agent involved in interpreting the outcome of the computation.
We argue that a TM, or any other computation model, has no significance if its
output is not interpreted by some agent. Furthermore, we argue that including
the interpreter in the model definition sheds light on some of the difficult
problems faced in computation and mathematics. We provide an analytic process
framework to address this limitation. The framework can be overlaid on existing
concepts of computation to address many practical and philosophical concerns
such as the P vs NP problem. In addition, we argue that the P vs NP problem is
reminiscent of existing computation model which does not account for the person
that initiates the computation and interprets the intermediate and final
output. We utilize the observation that deterministic computational procedures
lack fundamental capacity to fully simulate their non-deterministic variant to
conclude that the set NP cannot be fully contained in P. Deterministic
procedure can approximate non-deterministic variant to some degree. However,
the logical implication of the fundamental differences between determinism and
non-determinism is that equivalence of the two classes is impossible to
establish.
Vision-Based Preharvest Yield Mapping for Apple Orchards
Authors: Pravakar Roy, Abhijeet Kislay, Patrick A. Plonski, James Luby, Volkan Isler
Date: 2018-08-13 00:00:00
Tasks: Clustering

We present an end-to-end computer vision system for mapping yield in an apple
orchard using images captured from a single camera. Our proposed system is
platform independent and does not require any specific lighting conditions. Our
main technical contributions are 1)~a semi-supervised clustering algorithm that
utilizes colors to identify apples and 2)~an unsupervised clustering method
that utilizes spatial properties to estimate fruit counts from apple clusters
having arbitrarily complex geometry. Additionally, we utilize camera motion to
merge the counts across multiple views. We verified the performance of our
algorithms by conducting multiple field trials on three tree rows consisting of
$252$ trees at the University of Minnesota Horticultural Research Center.
Results indicate that the detection method achieves $F_1$-measure $.95 -.97$
for multiple color varieties and lighting conditions. The counting method
achieves an accuracy of $89\%-98\%$. Additionally, we report merged fruit
counts from both sides of the tree rows. Our yield estimation method achieves
an overall accuracy of $91.98\% - 94.81\%$ across different datasets.
Angular-Based Word Meta-Embedding Learning
Authors: James O' Neill, Danushka Bollegala
Date: 2018-08-13 00:00:00
Tasks: Meta-Learning, Word Embeddings, Word Similarity

Ensembling word embeddings to improve distributed word representations has
shown good success for natural language processing tasks in recent years. These
approaches either carry out straightforward mathematical operations over a set
of vectors or use unsupervised learning to find a lower-dimensional
representation. This work compares meta-embeddings trained for different
losses, namely loss functions that account for angular distance between the
reconstructed embedding and the target and those that account normalized
distances based on the vector length. We argue that meta-embeddings are better
to treat the ensemble set equally in unsupervised learning as the respective
quality of each embedding is unknown for upstream tasks prior to
meta-embedding. We show that normalization methods that account for this such
as cosine and KL-divergence objectives outperform meta-embedding trained on
standard $\ell_1$ and $\ell_2$ loss on \textit{defacto} word similarity and
relatedness datasets and find it outperforms existing meta-learning strategies.
Hidden Fluid Mechanics: A Navier-Stokes Informed Deep Learning Framework for Assimilating Flow Visualization Data
Authors: Maziar Raissi, Alireza Yazdani, George Em. Karniadakis
Date: 2018-08-13 00:00:00

We present hidden fluid mechanics (HFM), a physics informed deep learning
framework capable of encoding an important class of physical laws governing
fluid motions, namely the Navier-Stokes equations. In particular, we seek to
leverage the underlying conservation laws (i.e., for mass, momentum, and
energy) to infer hidden quantities of interest such as velocity and pressure
fields merely from spatio-temporal visualizations of a passive scaler (e.g.,
dye or smoke), transported in arbitrarily complex domains (e.g., in human
arteries or brain aneurysms). Our approach towards solving the aforementioned
data assimilation problem is unique as we design an algorithm that is agnostic
to the geometry or the initial and boundary conditions. This makes HFM highly
flexible in choosing the spatio-temporal domain of interest for data
acquisition as well as subsequent training and predictions. Consequently, the
predictions made by HFM are among those cases where a pure machine learning
strategy or a mere scientific computing approach simply cannot reproduce. The
proposed algorithm achieves accurate predictions of the pressure and velocity
fields in both two and three dimensional flows for several benchmark problems
motivated by real-world applications. Our results demonstrate that this
relatively simple methodology can be used in physical and biomedical problems
to extract valuable quantitative information (e.g., lift and drag forces or
wall shear stresses in arteries) for which direct measurements may not be
possible.