pwc-archive/papers-with-abstracts huggingface dataset:
Surface Type Estimation from GPS Tracked Bicycle Activities
Authors: Nitish Nag, Vaibhav Pandey, Aishwarya Manjunath, Avinash Vaka, Ramesh Jain
Date: 2018-09-25 00:00:00
Tasks: Recommendation Systems, Vocal Bursts Type Prediction

Road conditions affect both machine and human powered modes of
transportation. In the case of human powered transportation, poor road
conditions increase the work for the individual to travel. Previous estimates
for these parameters have used computationally expensive analysis of satellite
images. In this work, we use a computationally inexpensive and simple method by
using only GPS data from a human powered cyclist. By estimating if the road
taken by the user has high or low variations in their directional vector, we
classify if the user is on a paved road or on an unpaved trail. In order to do
this, three methods were adopted, changes in frequency of the direction of
slope in a given path segment, fitting segments of the path, and finding the
first derivative and the number of points of zero crossings of each segment.
Machine learning models such as support vector machines, K-nearest neighbors,
and decision trees were used for the classification of the path. We show in our
methods, the decision trees performed the best with an accuracy of 86\%.
Estimation of the type of surface can be used for many applications such as
understanding rolling resistance for power estimation estimation or building
exercise recommendation systems by user profiling as described in detail in the
paper.
A Gradient-Based Split Criterion for Highly Accurate and Transparent Model Trees
Authors: Klaus Broelemann, Gjergji Kasneci
Date: 2018-09-25 00:00:00

Machine learning algorithms aim at minimizing the number of false decisions and increasing the accuracy of predictions. However, the high predictive power of advanced algorithms comes at the costs of transparency. State-of-the-art methods, such as neural networks and ensemble methods, often result in highly complex models that offer little transparency. We propose shallow model trees as a way to combine simple and highly transparent predictive models for higher predictive power without losing the transparency of the original models. We present a novel split criterion for model trees that allows for significantly higher predictive power than state-of-the-art model trees while maintaining the same level of simplicity. This novel approach finds split points which allow the underlying simple models to make better predictions on the corresponding data. In addition, we introduce multiple mechanisms to increase the transparency of the resulting trees.
BanditSum: Extractive Summarization as a Contextual Bandit
Authors: Yue Dong, Yikang Shen, Eric Crawford, Herke van Hoof, Jackie Chi Kit Cheung
Date: 2018-09-25 00:00:00
Proceeding: EMNLP 2018 10
Tasks: Extractive Summarization, Extractive Text Summarization, Reinforcement Learning

In this work, we propose a novel method for training neural networks to perform single-document extractive summarization without heuristically-generated extractive labels. We call our approach BanditSum as it treats extractive summarization as a contextual bandit (CB) problem, where the model receives a document to summarize (the context), and chooses a sequence of sentences to include in the summary (the action). A policy gradient reinforcement learning algorithm is used to train the model to select sequences of sentences that maximize ROUGE score. We perform a series of experiments demonstrating that BanditSum is able to achieve ROUGE scores that are better than or comparable to the state-of-the-art for extractive summarization, and converges using significantly fewer update steps than competing approaches. In addition, we show empirically that BanditSum performs significantly better than competing approaches when good summary sentences appear late in the source document.
Non-native children speech recognition through transfer learning
Authors: Marco Matassoni, Roberto Gretter, Daniele Falavigna, Diego Giuliani
Date: 2018-09-25 00:00:00
Tasks: speech-recognition, Speech Recognition, Transfer Learning

This work deals with non-native children's speech and investigates both
multi-task and transfer learning approaches to adapt a multi-language Deep
Neural Network (DNN) to speakers, specifically children, learning a foreign
language. The application scenario is characterized by young students learning
English and German and reading sentences in these second-languages, as well as
in their mother language. The paper analyzes and discusses techniques for
training effective DNN-based acoustic models starting from children native
speech and performing adaptation with limited non-native audio material. A
multi-lingual model is adopted as baseline, where a common phonetic lexicon,
defined in terms of the units of the International Phonetic Alphabet (IPA), is
shared across the three languages at hand (Italian, German and English); DNN
adaptation methods based on transfer learning are evaluated on significant
non-native evaluation sets. Results show that the resulting non-native models
allow a significant improvement with respect to a mono-lingual system adapted
to speakers of the target language.
Deep Neural Networks for Pattern Recognition
Authors: Kyongsik Yun, Alexander Huyen, Thomas Lu
Date: 2018-09-25 00:00:00
Tasks: General Classification, image-classification, Image Classification, object-detection, Object Detection

In the field of pattern recognition research, the method of using deep neural
networks based on improved computing hardware recently attracted attention
because of their superior accuracy compared to conventional methods. Deep
neural networks simulate the human visual system and achieve human equivalent
accuracy in image classification, object detection, and segmentation. This
chapter introduces the basic structure of deep neural networks that simulate
human neural networks. Then we identify the operational processes and
applications of conditional generative adversarial networks, which are being
actively researched based on the bottom-up and top-down mechanisms, the most
important functions of the human visual perception process. Finally, recent
developments in training strategies for effective learning of complex deep
neural networks are addressed.
MPRAD: A Multiparametric Radiomics Framework
Authors: Vishwa S. Parekh, Michael A. Jacobs
Date: 2018-09-25 00:00:00
Tasks: Specificity

Multiparametric radiological imaging is vital for detection, characterization
and diagnosis of many different diseases. The use of radiomics for quantitative
extraction of textural features from radiological imaging is increasing moving
towards clinical decision support. However, current methods in radiomics are
limited to using single images for the extraction of these textural features
and may limit the applicable scope of radiomics in different clinical settings.
Thus, in the current form, they are not capable of capturing the true
underlying tissue characteristics in high dimensional multiparametric imaging
space. To overcome this challenge, we have developed a multiparametric imaging
radiomic framework termed MPRAD for extraction of radiomic features from high
dimensional datasets. MPRAD was tested on two different organs and diseases;
breast cancer and cerebrovascular accidents in brain, commonly referred to as
stroke. The MPRAD framework classified malignant from benign breast lesions
with excellent sensitivity and specificity of 87% and 80.5% respectively with
an AUC of 0.88 providing a 9%-28% increase in AUC over single radiomic
parameters. More importantly, in breast, the glandular tissue MPRAD were
similar between each group with no significance differences. Similarly, the
MPRAD features in brain stroke demonstrated increased performance in
distinguishing the perfusion-diffusion mismatch compared to single parameter
radiomics and there were no differences within the white and gray matter
tissue. In conclusion, we have introduced the use of multiparametric radiomics
into a clinical setting
Inferring Complementary Products from Baskets and Browsing Sessions
Authors: Ilya Trofimov
Date: 2018-09-25 00:00:00

Complementary products recommendation is an important problem in e-commerce.
Such recommendations increase the average order price and the number of
products in baskets. Complementary products are typically inferred from basket
data. In this study, we propose the BB2vec model. The BB2vec model learns
vector representations of products by analyzing jointly two types of data -
Baskets and Browsing sessions (visiting web pages of products). These vector
representations are used for making complementary products recommendation. The
proposed model alleviates the cold start problem by delivering better
recommendations for products having few or no purchases. We show that the
BB2vec model has better performance than other models which use only basket
data.
RAFP-Pred: Robust Prediction of Antifreeze Proteins using Localized Analysis of n-Peptide Compositions
Authors: Shujaat Khan, Imran Naseem, Roberto Togneri, Mohammed Bennamoun
Date: 2018-09-25 00:00:00
Tasks: Specificity

In extreme cold weather, living organisms produce Antifreeze Proteins (AFPs)
to counter the otherwise lethal intracellular formation of ice. Structures and
sequences of various AFPs exhibit a high degree of heterogeneity, consequently
the prediction of the AFPs is considered to be a challenging task. In this
research, we propose to handle this arduous manifold learning task using the
notion of localized processing. In particular an AFP sequence is segmented into
two sub-segments each of which is analyzed for amino acid and di-peptide
compositions. We propose to use only the most significant features using the
concept of information gain (IG) followed by a random forest classification
approach. The proposed RAFP-Pred achieved an excellent performance on a number
of standard datasets. We report a high Youden's index
(sensitivity+specificity-1) value of 0.75 on the standard independent test data
set outperforming the AFP-PseAAC, AFP\_PSSM, AFP-Pred and iAFP by a margin of
0.05, 0.06, 0.14 and 0.68 respectively. The verification rate on the UniProKB
dataset is found to be 83.19\% which is substantially superior to the 57.18\%
reported for the iAFP method.
Satellite Imagery Multiscale Rapid Detection with Windowed Networks
Authors: Adam Van Etten
Date: 2018-09-25 00:00:00
Tasks: object-detection, Object Detection

Detecting small objects over large areas remains a significant challenge in
satellite imagery analytics. Among the challenges is the sheer number of pixels
and geographical extent per image: a single DigitalGlobe satellite image
encompasses over 64 km2 and over 250 million pixels. Another challenge is that
objects of interest are often minuscule (~pixels in extent even for the highest
resolution imagery), which complicates traditional computer vision techniques.
To address these issues, we propose a pipeline (SIMRDWN) that evaluates
satellite images of arbitrarily large size at native resolution at a rate of >
0.2 km2/s. Building upon the tensorflow object detection API paper, this
pipeline offers a unified approach to multiple object detection frameworks that
can run inference on images of arbitrary size. The SIMRDWN pipeline includes a
modified version of YOLO (known as YOLT), along with the models of the
tensorflow object detection API: SSD, Faster R-CNN, and R-FCN. The proposed
approach allows comparison of the performance of these four frameworks, and can
rapidly detect objects of vastly different scales with relatively little
training data over multiple sensors. For objects of very different scales (e.g.
airplanes versus airports) we find that using two different detectors at
different scales is very effective with negligible runtime cost.We evaluate
large test images at native resolution and find mAP scores of 0.2 to 0.8 for
vehicle localization, with the YOLT architecture achieving both the highest mAP
and fastest inference speed.
Personalized Education at Scale
Authors: Sam Saarinen, Evan Cater, Michael Littman
Date: 2018-09-24 00:00:00
Tasks: Reinforcement Learning, Reinforcement Learning (RL)

Tailoring the presentation of information to the needs of individual students
leads to massive gains in student outcomes~\cite{bloom19842}. This finding is
likely due to the fact that different students learn differently, perhaps as a
result of variation in ability, interest or other
factors~\cite{schiefele1992interest}. Adapting presentations to the educational
needs of an individual has traditionally been the domain of experts, making it
expensive and logistically challenging to do at scale, and also leading to
inequity in educational outcomes. Increased course sizes and large MOOC
enrollments provide an unprecedented access to student data. We propose that
emerging technologies in reinforcement learning (RL), as well as
semi-supervised learning, natural language processing, and computer vision are
critical to leveraging this data to provide personalized education at scale.
Computational and informatics advances for reproducible data analysis in neuroimaging
Authors: Russell A. Poldrack, Krzysztof J. Gorgolewski, Gael Varoquaux
Date: 2018-09-24 00:00:00

The reproducibility of scientific research has become a point of critical
concern. We argue that openness and transparency are critical for
reproducibility, and we outline an ecosystem for open and transparent science
that has emerged within the human neuroimaging community. We discuss the range
of open data sharing resources that have been developed for neuroimaging data,
and the role of data standards (particularly the Brain Imaging Data Structure)
in enabling the automated sharing, processing, and reuse of large neuroimaging
datasets. We outline how the open-source Python language has provided the basis
for a data science platform that enables reproducible data analysis and
visualization. We also discuss how new advances in software engineering, such
as containerization, provide the basis for greater reproducibility in data
analysis. The emergence of this new ecosystem provides an example for many
areas of science that are currently struggling with reproducibility.
Self-Adaptive Surrogate-Assisted Covariance Matrix Adaptation Evolution Strategy
Authors: Ilya Loshchilov, Marc Schoenauer, Michèle Sebag
Date: 2012-04-11 00:00:00

This paper presents a novel mechanism to adapt surrogate-assisted
population-based algorithms. This mechanism is applied to ACM-ES, a recently
proposed surrogate-assisted variant of CMA-ES. The resulting algorithm,
saACM-ES, adjusts online the lifelength of the current surrogate model (the
number of CMA-ES generations before learning a new surrogate) and the surrogate
hyper-parameters. Both heuristics significantly improve the quality of the
surrogate model, yielding a significant speed-up of saACM-ES compared to the
ACM-ES and CMA-ES baselines. The empirical validation of saACM-ES on the
BBOB-2012 noiseless testbed demonstrates the efficiency and the scalability
w.r.t the problem dimension and the population size of the proposed approach,
that reaches new best results on some of the benchmark problems.
Generative replay with feedback connections as a general strategy for continual learning
Authors: Gido M. van de Ven, Andreas S. Tolias
Date: 2018-09-27 00:00:00
Tasks: Continual Learning, Lifelong learning, Permuted-MNIST

A major obstacle to developing artificial intelligence applications capable
of true lifelong learning is that artificial neural networks quickly or
catastrophically forget previously learned tasks when trained on a new one.
Numerous methods for alleviating catastrophic forgetting are currently being
proposed, but differences in evaluation protocols make it difficult to directly
compare their performance. To enable more meaningful comparisons, here we
identified three distinct scenarios for continual learning based on whether
task identity is known and, if it is not, whether it needs to be inferred.
Performing the split and permuted MNIST task protocols according to each of
these scenarios, we found that regularization-based approaches (e.g., elastic
weight consolidation) failed when task identity needed to be inferred. In
contrast, generative replay combined with distillation (i.e., using class
probabilities as "soft targets") achieved superior performance in all three
scenarios. Addressing the issue of efficiency, we reduced the computational
cost of generative replay by integrating the generative model into the main
model by equipping it with generative feedback or backward connections. This
Replay-through-Feedback approach substantially shortened training time with no
or negligible loss in performance. We believe this to be an important first
step towards making the powerful technique of generative replay scalable to
real-world continual learning applications.
Monge-Ampère Flow for Generative Modeling
Authors: Linfeng Zhang, Weinan E, Lei Wang
Date: 2018-09-26 00:00:00
Tasks: Density Estimation

We present a deep generative model, named Monge-Amp\`ere flow, which builds
on continuous-time gradient flow arising from the Monge-Amp\`ere equation in
optimal transport theory. The generative map from the latent space to the data
space follows a dynamical system, where a learnable potential function guides a
compressible fluid to flow towards the target density distribution. Training of
the model amounts to solving an optimal control problem. The Monge-Amp\`ere
flow has tractable likelihoods and supports efficient sampling and inference.
One can easily impose symmetry constraints in the generative model by designing
suitable scalar potential functions. We apply the approach to unsupervised
density estimation of the MNIST dataset and variational calculation of the
two-dimensional Ising model at the critical point. This approach brings
insights and techniques from Monge-Amp\`ere equation, optimal transport, and
fluid dynamics into reversible flow-based generative models.
Learning to Coordinate Multiple Reinforcement Learning Agents for Diverse Query Reformulation
Authors: Rodrigo Nogueira, Jannis Bulian, Massimiliano Ciaramita
Date: 2018-09-27 00:00:00
Proceeding: ICLR 2019 5
Tasks: Diversity, Question Answering, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL), Retrieval

We propose a method to efficiently learn diverse strategies in reinforcement
learning for query reformulation in the tasks of document retrieval and
question answering. In the proposed framework an agent consists of multiple
specialized sub-agents and a meta-agent that learns to aggregate the answers
from sub-agents to produce a final answer. Sub-agents are trained on disjoint
partitions of the training data, while the meta-agent is trained on the full
training set. Our method makes learning faster, because it is highly
parallelizable, and has better generalization performance than strong
baselines, such as an ensemble of agents trained on the full data. We show that
the improved performance is due to the increased diversity of reformulation
strategies.
Inference for Individual Mediation Effects and Interventional Effects in Sparse High-Dimensional Causal Graphical Models
Authors: Abhishek Chakrabortty, Preetam Nandy, Hongzhe Li
Date: 2018-09-27 00:00:00

We consider the problem of identifying intermediate variables (or mediators) that regulate the effect of a treatment on a response variable. While there has been significant research on this classical topic, little work has been done when the set of potential mediators is high-dimensional (HD). A further complication arises when these mediators are interrelated (with unknown dependencies). In particular, we assume that the causal structure of the treatment, the confounders, the potential mediators and the response is a (possibly unknown) directed acyclic graph (DAG). HD DAG models have previously been used for the estimation of causal effects from observational data. In particular, methods called IDA and joint-IDA have been developed for estimating the effects of single and multiple simultaneous interventions, respectively. In this paper, we propose an IDA-type method called MIDA for estimating so-called individual mediation effects from HD observational data. Although IDA and joint-IDA estimators have been shown to be consistent in certain sparse HD settings, their asymptotic properties such as convergence in distribution and inferential tools in such settings have remained unknown. In this paper, we prove HD consistency of MIDA for linear structural equation models with sub-Gaussian errors. More importantly, we derive distributional convergence results for MIDA in similar HD settings, which are applicable to IDA and joint-IDA estimators as well. To our knowledge, these are the first such distributional convergence results facilitating inference for IDA-type estimators. These are built on our novel theoretical results regarding uniform bounds for linear regression estimators over varying subsets of HD covariates which may be of independent interest. Finally, we empirically validate our asymptotic theory for MIDA and demonstrate its usefulness via simulations and a real data application.
Predictive Embeddings for Hate Speech Detection on Twitter
Authors: Rohan Kshirsagar, Tyus Cukuvac, Kathleen McKeown, Susan McGregor
Date: 2018-09-27 00:00:00
Proceeding: WS 2018 10
Tasks: Hate Speech Detection, Word Embeddings

We present a neural-network based approach to classifying online hate speech
in general, as well as racist and sexist speech in particular. Using
pre-trained word embeddings and max/mean pooling from simple, fully-connected
transformations of these embeddings, we are able to predict the occurrence of
hate speech on three commonly used publicly available datasets. Our models
match or outperform state of the art F1 performance on all three datasets using
significantly fewer parameters and minimal feature preprocessing compared to
previous methods.
Conditional WaveGAN
Authors: Chae Young Lee, Anoop Toffy, Gue Jun Jung, Woo-Jin Han
Date: 2018-09-27 00:00:00
Tasks: Audio Generation

Generative models are successfully used for image synthesis in the recent
years. But when it comes to other modalities like audio, text etc little
progress has been made. Recent works focus on generating audio from a
generative model in an unsupervised setting. We explore the possibility of
using generative models conditioned on class labels. Concatenation based
conditioning and conditional scaling were explored in this work with various
hyper-parameter tuning methods. In this paper we introduce Conditional WaveGANs
(cWaveGAN). Find our implementation at https://github.com/acheketa/cwavegan