pwc-archive/papers-with-abstracts huggingface dataset:
An Efficient Approach to Encoding Context for Spoken Language Understanding
Authors: Raghav Gupta, Abhinav Rastogi, Dilek Hakkani-Tur
Date: 2018-07-01 00:00:00
Tasks: Computational Efficiency, Spoken Language Understanding, Task-Oriented Dialogue Systems

In task-oriented dialogue systems, spoken language understanding, or SLU,
refers to the task of parsing natural language user utterances into semantic
frames. Making use of context from prior dialogue history holds the key to more
effective SLU. State of the art approaches to SLU use memory networks to encode
context by processing multiple utterances from the dialogue at each turn,
resulting in significant trade-offs between accuracy and computational
efficiency. On the other hand, downstream components like the dialogue state
tracker (DST) already keep track of the dialogue state, which can serve as a
summary of the dialogue history. In this work, we propose an efficient approach
to encoding context from prior utterances for SLU. More specifically, our
architecture includes a separate recurrent neural network (RNN) based encoding
module that accumulates dialogue context to guide the frame parsing sub-tasks
and can be shared between SLU and DST. In our experiments, we demonstrate the
effectiveness of our approach on dialogues from two domains.
Accurate Uncertainties for Deep Learning Using Calibrated Regression
Authors: Volodymyr Kuleshov, Nathan Fenner, Stefano Ermon
Date: 2018-07-01 00:00:00
Proceeding: ICML 2018 7
Tasks: Deep Learning, Model-based Reinforcement Learning, regression, Reinforcement Learning, Reinforcement Learning (RL), Time Series, Time Series Analysis, Time Series Forecasting

Methods for reasoning under uncertainty are a key building block of accurate
and reliable machine learning systems. Bayesian methods provide a general
framework to quantify uncertainty. However, because of model misspecification
and the use of approximate inference, Bayesian uncertainty estimates are often
inaccurate -- for example, a 90% credible interval may not contain the true
outcome 90% of the time. Here, we propose a simple procedure for calibrating
any regression algorithm; when applied to Bayesian and probabilistic models, it
is guaranteed to produce calibrated uncertainty estimates given enough data.
Our procedure is inspired by Platt scaling and extends previous work on
classification. We evaluate this approach on Bayesian linear regression,
feedforward, and recurrent neural networks, and find that it consistently
outputs well-calibrated credible intervals while improving performance on time
series forecasting and model-based reinforcement learning tasks.
Stochastic model-based minimization under high-order growth
Authors: Damek Davis, Dmitriy Drusvyatskiy, Kellie J. MacPhee
Date: 2018-07-01 00:00:00
Tasks: model, Vocal Bursts Intensity Prediction

Given a nonsmooth, nonconvex minimization problem, we consider algorithms
that iteratively sample and minimize stochastic convex models of the objective
function. Assuming that the one-sided approximation quality and the variation
of the models is controlled by a Bregman divergence, we show that the scheme
drives a natural stationarity measure to zero at the rate $O(k^{-1/4})$. Under
additional convexity and relative strong convexity assumptions, the function
values converge to the minimum at the rate of $O(k^{-1/2})$ and
$\widetilde{O}(k^{-1})$, respectively. We discuss consequences for stochastic
proximal point, mirror descent, regularized Gauss-Newton, and saddle point
algorithms.
A Shared Attention Mechanism for Interpretation of Neural Automatic Post-Editing Systems
Authors: Inigo Jauregi Unanue, Ehsan Zare Borzeshi, Massimo Piccardi
Date: 2018-07-01 00:00:00
Proceeding: WS 2018 7
Tasks: Automatic Post-Editing, Sentence, Translation

Automatic post-editing (APE) systems aim to correct the systematic errors
made by machine translators. In this paper, we propose a neural APE system that
encodes the source (src) and machine translated (mt) sentences with two
separate encoders, but leverages a shared attention mechanism to better
understand how the two inputs contribute to the generation of the post-edited
(pe) sentences. Our empirical observations have showed that when the mt is
incorrect, the attention shifts weight toward tokens in the src sentence to
properly edit the incorrect translation. The model has been trained and
evaluated on the official data from the WMT16 and WMT17 APE IT domain
English-German shared tasks. Additionally, we have used the extra 500K
artificial data provided by the shared task. Our system has been able to
reproduce the accuracies of systems trained with the same data, while at the
same time providing better interpretability.
Automatic Identification of Twin Zygosity in Resting-State Functional MRI
Authors: Andrey Gritsenko, Martin A. Lindquist, Gregory R. Kirk, Moo. K. Chung
Date: 2018-06-30 00:00:00
Tasks: Variable Selection

A key strength of twin studies arises from the fact that there are two types
of twins, monozygotic and dizygotic, that share differing amounts of genetic
information. Accurate differentiation of twin types allows efficient inference
on genetic influences in a population. However, identification of zygosity is
often prone to errors without genotying. In this study, we propose a novel
pairwise feature representation to classify the zygosity of twin pairs of
resting state functional magnetic resonance images (rs-fMRI). For this, we
project an fMRI signal to a set of basis functions and use the projection
coefficients as the compact and discriminative feature representation of noisy
fMRI. We encode the relationship between twins as the correlation between the
new feature representations across brain regions. We employ hill climbing
variable selection to identify brain regions that are the most genetically
affected. The proposed framework was applied to 208 twin pairs and achieved
94.19% classification accuracy in automatically identifying the zygosity of
paired images.
chemmodlab: A Cheminformatics Modeling Laboratory for Fitting and Assessing Machine Learning Models
Authors: Jeremy R. Ash Jacqueline M. Hughes-Oliver
Date: 2018-06-30 00:00:00
Tasks: BIG-bench Machine Learning, Blocking

The goal of chemmodlab is to streamline the fitting and assessment pipeline
for many machine learning models in R, making it easy for researchers to
compare the utility of new models. While focused on implementing methods for
model fitting and assessment that have been accepted by experts in the
cheminformatics field, all of the methods in chemmodlab have broad utility for
the machine learning community. chemmodlab contains several assessment
utilities including a plotting function that constructs accumulation curves and
a function that computes many performance measures. The most novel feature of
chemmodlab is the ease with which statistically significant performance
differences for many machine learning models is presented by means of the
multiple comparisons similarity plot. Differences are assessed using repeated
k-fold cross validation where blocking increases precision and multiplicity
adjustments are applied.
Cooperative Learning of Audio and Video Models from Self-Supervised Synchronization
Authors: Bruno Korbar, Du Tran, Lorenzo Torresani
Date: 2018-06-30 00:00:00
Proceeding: NeurIPS 2018 12
Tasks: Action Recognition, Audio Classification, Self-Supervised Action Recognition, Self-Supervised Audio Classification, Temporal Action Localization

There is a natural correlation between the visual and auditive elements of a
video. In this work we leverage this connection to learn general and effective
models for both audio and video analysis from self-supervised temporal
synchronization. We demonstrate that a calibrated curriculum learning scheme, a
careful choice of negative examples, and the use of a contrastive loss are
critical ingredients to obtain powerful multi-sensory representations from
models optimized to discern temporal synchronization of audio-video pairs.
Without further finetuning, the resulting audio features achieve performance
superior or comparable to the state-of-the-art on established audio
classification benchmarks (DCASE2014 and ESC-50). At the same time, our visual
subnet provides a very effective initialization to improve the accuracy of
video-based action recognition models: compared to learning from scratch, our
self-supervised pretraining yields a remarkable gain of +19.9% in action
recognition accuracy on UCF101 and a boost of +17.7% on HMDB51.
Embedding Models for Episodic Knowledge Graphs
Authors: Yunpu Ma, Volker Tresp, Erik Daxberger
Date: 2018-06-30 00:00:00
Tasks: Inductive Learning, Knowledge Graph Embeddings, Knowledge Graphs

In recent years a number of large-scale triple-oriented knowledge graphs have
been generated and various models have been proposed to perform learning in
those graphs. Most knowledge graphs are static and reflect the world in its
current state. In reality, of course, the state of the world is changing: a
healthy person becomes diagnosed with a disease and a new president is
inaugurated. In this paper, we extend models for static knowledge graphs to
temporal knowledge graphs. This enables us to store episodic data and to
generalize to new facts (inductive learning). We generalize leading learning
models for static knowledge graphs (i.e., Tucker, RESCAL, HolE, ComplEx,
DistMult) to temporal knowledge graphs. In particular, we introduce a new
tensor model, ConT, with superior generalization performance. The performances
of all proposed models are analyzed on two different datasets: the Global
Database of Events, Language, and Tone (GDELT) and the database for Integrated
Conflict Early Warning System (ICEWS). We argue that temporal knowledge graph
embeddings might be models also for cognitive episodic memory (facts we
remember and can recollect) and that a semantic memory (current facts we know)
can be generated from episodic memory by a marginalization operation. We
validate this episodic-to-semantic projection hypothesis with the ICEWS
dataset.
Advanced Methods for the Optical Quality Assurance of Silicon Sensors
Authors: E. Lavrik, I. Panasenko, H. R. Schmidt
Date: 2018-06-30 00:00:00
Tasks: Classification, General Classification

We describe a setup for optical quality assurance of silicon microstrip
sensors. Pattern recognition algorithms were developed to analyze microscopic
scans of the sensors for defects. It is shown that the software has a
recognition and classification rate of $>$~90\% for defects like scratches,
shorts, broken metal lines etc. We have demonstrated that advanced image
processing based on neural network techniques is able to further improve the
recognition and defect classification rate.
Improved Techniques for Learning to Dehaze and Beyond: A Collective Study
Authors: Yu Liu, Guanlong Zhao, Boyuan Gong, Yang Li, Ritu Raj, Niraj Goel, Satya Kesav, Sandeep Gottimukkala, Zhangyang Wang, Wenqi Ren, DaCheng Tao
Date: 2018-06-30 00:00:00
Tasks: Image Dehazing, Image Restoration, Object, object-detection, Object Detection, Single Image Dehazing

Here we explore two related but important tasks based on the recently
released REalistic Single Image DEhazing (RESIDE) benchmark dataset: (i) single
image dehazing as a low-level image restoration problem; and (ii) high-level
visual understanding (e.g., object detection) of hazy images. For the first
task, we investigated a variety of loss functions and show that
perception-driven loss significantly improves dehazing performance. In the
second task, we provide multiple solutions including using advanced modules in
the dehazing-detection cascade and domain-adaptive object detectors. In both
tasks, our proposed solutions significantly improve performance. GitHub
repository URL is: https://github.com/guanlongzhao/dehaze
Achieving Fairness through Adversarial Learning: an Application to Recidivism Prediction
Authors: Christina Wadsworth, Francesca Vera, Chris Piech
Date: 2018-06-30 00:00:00
Tasks: Fairness, Management, Prediction

Recidivism prediction scores are used across the USA to determine sentencing
and supervision for hundreds of thousands of inmates. One such generator of
recidivism prediction scores is Northpointe's Correctional Offender Management
Profiling for Alternative Sanctions (COMPAS) score, used in states like
California and Florida, which past research has shown to be biased against
black inmates according to certain measures of fairness. To counteract this
racial bias, we present an adversarially-trained neural network that predicts
recidivism and is trained to remove racial bias. When comparing the results of
our model to COMPAS, we gain predictive accuracy and get closer to achieving
two out of three measures of fairness: parity and equality of odds. Our model
can be generalized to any prediction and demographic. This piece of research
contributes an example of scientific replication and simplification in a
high-stakes real-world application like recidivism prediction.
Modeling Friends and Foes
Authors: Pedro A. Ortega, Shane Legg
Date: 2018-06-30 00:00:00

How can one detect friendly and adversarial behavior from raw data? Detecting
whether an environment is a friend, a foe, or anything in between, remains a
poorly understood yet desirable ability for safe and robust agents. This paper
proposes a definition of these environmental "attitudes" based on an
characterization of the environment's ability to react to the agent's private
strategy. We define an objective function for a one-shot game that allows
deriving the environment's probability distribution under friendly and
adversarial assumptions alongside the agent's optimal strategy. Furthermore, we
present an algorithm to compute these equilibrium strategies, and show
experimentally that both friendly and adversarial environments possess
non-trivial optimal strategies.
Benchmarking the Hill-Valley Evolutionary Algorithm for the GECCO 2018 Competition on Niching Methods Multimodal Optimization
Authors: S. C. Maree, T. Alderliesten, D. Thierens, P. A. N. Bosman
Date: 2018-06-30 00:00:00
Tasks: Benchmarking

This report presents benchmarking results of the latest version of the
Hill-Valley Evolutionary Algorithm (HillVallEA) on the CEC2013 niching
benchmark suite. The benchmarking follows restrictions required by the GECCO
2018 competition on Niching methods for Multimodal Optimization. In particular,
no problem dependent parameter tuning is performed. A number of adjustments
have been made to original publication of HillVallEA that are discussed in this
report.
The Historical Significance of Textual Distances
Authors: Ted Underwood
Date: 2018-06-30 00:00:00
Proceeding: COLING 2018 8
Tasks: Information Retrieval, Retrieval

Measuring similarity is a basic task in information retrieval, and now often
a building-block for more complex arguments about cultural change. But do
measures of textual similarity and distance really correspond to evidence about
cultural proximity and differentiation? To explore that question empirically,
this paper compares textual and social measures of the similarities between
genres of English-language fiction. Existing measures of textual similarity
(cosine similarity on tf-idf vectors or topic vectors) are also compared to new
strategies that use supervised learning to anchor textual measurement in a
social context.
AI in Education needs interpretable machine learning: Lessons from Open Learner Modelling
Authors: Cristina Conati, Kaska Porayska-Pomsta, Manolis Mavrikis
Date: 2018-06-30 00:00:00
Tasks: BIG-bench Machine Learning, Interpretable Machine Learning

Interpretability of the underlying AI representations is a key raison
d'\^{e}tre for Open Learner Modelling (OLM) -- a branch of Intelligent Tutoring
Systems (ITS) research. OLMs provide tools for 'opening' up the AI models of
learners' cognition and emotions for the purpose of supporting human learning
and teaching. Over thirty years of research in ITS (also known as AI in
Education) produced important work, which informs about how AI can be used in
Education to best effects and, through the OLM research, what are the necessary
considerations to make it interpretable and explainable for the benefit of
learning. We argue that this work can provide a valuable starting point for a
framework of interpretable AI, and as such is of relevance to the application
of both knowledge-based and machine learning systems in other high-stakes
contexts, beyond education.
Fractional Wavelet Scattering Network and Applications
Authors: Li Liu, Jiasong Wu, Dengwang Li, Lotfi Senhadji, Huazhong Shu
Date: 2018-06-30 00:00:00
Tasks: General Classification, image-classification, Image Classification, Segmentation

Objective: The present study introduces a fractional wavelet scattering
network (FrScatNet), which is a generalized translation invariant version of
the classical wavelet scattering network (ScatNet). Methods: In our approach,
the FrScatNet is constructed based on the fractional wavelet transform (FRWT).
The fractional scattering coefficients are iteratively computed using FRWTs and
modulus operators. The feature vectors constructed by fractional scattering
coefficients are usually used for signal classification. In this work, an
application example of FrScatNet is provided in order to assess its performance
on pathological images. Firstly, the FrScatNet extracts feature vectors from
patches of the original histological images under different orders. Then we
classify those patches into target (benign or malignant) and background groups.
And the FrScatNet property is analyzed by comparing error rates computed from
different fractional orders respectively. Based on the above pathological image
classification, a gland segmentation algorithm is proposed by combining the
boundary information and the gland location. Results: The error rates for
different fractional orders of FrScatNet are examined and show that the
classification accuracy is significantly improved in fractional scattering
domain. We also compare the FrScatNet based gland segmentation method with
those proposed in the 2015 MICCAI Gland Segmentation Challenge and our method
achieves comparable results. Conclusion: The FrScatNet is shown to achieve
accurate and robust results. More stable and discriminative fractional
scattering coefficients are obtained by the FrScatNet in this work.
Significance: The added fractional order parameter is able to analyze the image
in the fractional scattering domain.
Game-Theoretic Interpretability for Temporal Modeling
Authors: Guang-He Lee, David Alvarez-Melis, Tommi S. Jaakkola
Date: 2018-06-30 00:00:00

Interpretability has arisen as a key desideratum of machine learning models
alongside performance. Approaches so far have been primarily concerned with
fixed dimensional inputs emphasizing feature relevance or selection. In
contrast, we focus on temporal modeling and the problem of tailoring the
predictor, functionally, towards an interpretable family. To this end, we
propose a co-operative game between the predictor and an explainer without any
a priori restrictions on the functional class of the predictor. The goal of the
explainer is to highlight, locally, how well the predictor conforms to the
chosen interpretable family of temporal models. Our co-operative game is setup
asymmetrically in terms of information sets for efficiency reasons. We develop
and illustrate the framework in the context of temporal sequence models with
examples.
A New Benchmark and Progress Toward Improved Weakly Supervised Learning
Authors: Jason Ramapuram, Russ Webb
Date: 2018-06-30 00:00:00
Tasks: All, Weakly-supervised Learning

Knowledge Matters: Importance of Prior Information for Optimization [7], by
Gulcehre et. al., sought to establish the limits of current black-box, deep
learning techniques by posing problems which are difficult to learn without
engineering knowledge into the model or training procedure. In our work, we
completely solve the previous Knowledge Matters problem using a generic model,
pose a more difficult and scalable problem, All-Pairs, and advance this new
problem by introducing a new learned, spatially-varying histogram model called
TypeNet which outperforms conventional models on the problem. We present
results on All-Pairs where our model achieves 100% test accuracy while the best
ResNet models achieve 79% accuracy. In addition, our model is more than an
order of magnitude smaller than Resnet-34. The challenge of solving
larger-scale All-Pairs problems with high accuracy is presented to the
community for investigation.
Modeling Mistrust in End-of-Life Care
Authors: Willie Boag, Harini Suresh, Leo Anthony Celi, Peter Szolovits, Marzyeh Ghassemi
Date: 2018-06-30 00:00:00
Tasks: BIG-bench Machine Learning, Sentiment Analysis

In this work, we characterize the doctor-patient relationship using a machine learning-derived trust score. We show that this score has statistically significant racial associations, and that by modeling trust directly we find stronger disparities in care than by stratifying on race. We further demonstrate that mistrust is indicative of worse outcomes, but is only weakly associated with physiologically-created severity scores. Finally, we describe sentiment analysis experiments indicating patients with higher levels of mistrust have worse experiences and interactions with their caregivers. This work is a step towards measuring fairer machine learning in the healthcare domain.
Machine Learning for Integrating Data in Biology and Medicine: Principles, Practice, and Opportunities
Authors: Marinka Zitnik, Francis Nguyen, Bo wang, Jure Leskovec, Anna Goldenberg, Michael M. Hoffman
Date: 2018-06-30 00:00:00
Tasks: BIG-bench Machine Learning, Data Integration

New technologies have enabled the investigation of biology and human health
at an unprecedented scale and in multiple dimensions. These dimensions include
a myriad of properties describing genome, epigenome, transcriptome, microbiome,
phenotype, and lifestyle. No single data type, however, can capture the
complexity of all the factors relevant to understanding a phenomenon such as a
disease. Integrative methods that combine data from multiple technologies have
thus emerged as critical statistical and computational approaches. The key
challenge in developing such approaches is the identification of effective
models to provide a comprehensive and relevant systems view. An ideal method
can answer a biological or medical question, identifying important features and
predicting outcomes, by harnessing heterogeneous data across several dimensions
of biological variation. In this Review, we describe the principles of data
integration and discuss current methods and available implementations. We
provide examples of successful data integration in biology and medicine.
Finally, we discuss current challenges in biomedical integrative methods and
our perspective on the future development of the field.