pwc-archive/papers-with-abstracts huggingface dataset:
Multi-feature classifiers for burst detection in single EEG channels from preterm infants
Authors: X. Navarro, F. Porée, M. Kuchenbuch, M. Chavez, A. Beuchée, G. Carrault
Date: 2017-02-08 00:00:00
Tasks: Computational Efficiency, EEG, Electroencephalogram (EEG)

The study of electroencephalographic (EEG) bursts in preterm infants provides
valuable information about maturation or prognostication after perinatal
asphyxia. Over the last two decades, a number of works proposed algorithms to
automatically detect EEG bursts in preterm infants, but they were designed for
populations under 35 weeks of post menstrual age (PMA). However, as the brain
activity evolves rapidly during postnatal life, these solutions might be
under-performing with increasing PMA. In this work we focused on preterm
infants reaching term ages (PMA $\geq$ 36 weeks) using multi-feature
classification on a single EEG channel. Five EEG burst detectors relying on
different machine learning approaches were compared: Logistic regression (LR),
linear discriminant analysis (LDA), k-nearest neighbors (kNN), support vector
machines (SVM) and thresholding (Th). Classifiers were trained by visually
labeled EEG recordings from 14 very preterm infants (born after 28 weeks of
gestation) with 36 - 41 weeks PMA. The most performing classifiers reached
about 95\% accuracy (kNN, SVM and LR) whereas Th obtained 84\%. Compared to
human-automatic agreements, LR provided the highest scores (Cohen's kappa =
0.71) and the best computational efficiency using only three EEG features.
Applying this classifier in a test database of 21 infants $\geq$ 36 weeks PMA,
we show that long EEG bursts and short inter-bust periods are characteristic of
infants with the highest PMA and weights. In view of these results, LR-based
burst detection could be a suitable tool to study maturation in monitoring or
portable devices using a single EEG channel.
Hierarchical Question Answering for Long Documents
Authors: Eunsol Choi, Daniel Hewlett, Alexandre Lacoste, Illia Polosukhin, Jakob Uszkoreit, Jonathan Berant
Date: 2016-11-06 00:00:00
Tasks: Question Answering, Reading Comprehension, Reinforcement Learning, Sentence

We present a framework for question answering that can efficiently scale to
longer documents while maintaining or even improving performance of
state-of-the-art models. While most successful approaches for reading
comprehension rely on recurrent neural networks (RNNs), running them over long
documents is prohibitively slow because it is difficult to parallelize over
sequences. Inspired by how people first skim the document, identify relevant
parts, and carefully read these parts to produce an answer, we combine a
coarse, fast model for selecting relevant sentences and a more expensive RNN
for producing the answer from those sentences. We treat sentence selection as a
latent variable trained jointly from the answer only using reinforcement
learning. Experiments demonstrate the state of the art performance on a
challenging subset of the Wikireading and on a new dataset, while speeding up
the model by 3.5x-6.7x.
An Implementation of Faster RCNN with Study for Region Sampling
Authors: Xinlei Chen, Abhinav Gupta
Date: 2017-02-07 00:00:00
Tasks: General Classification, Object Detection

We adapted the join-training scheme of Faster RCNN framework from Caffe to
TensorFlow as a baseline implementation for object detection. Our code is made
publicly available. This report documents the simplifications made to the
original pipeline, with justifications from ablation analysis on both PASCAL
VOC 2007 and COCO 2014. We further investigated the role of non-maximal
suppression (NMS) in selecting regions-of-interest (RoIs) for region
classification, and found that a biased sampling toward small regions helps
performance and can achieve on-par mAP to NMS-based sampling when converged
sufficiently.
Adversarial Attacks on Neural Network Policies
Authors: Sandy Huang, Nicolas Papernot, Ian Goodfellow, Yan Duan, Pieter Abbeel
Date: 2017-02-08 00:00:00
Tasks: Reinforcement Learning

Machine learning classifiers are known to be vulnerable to inputs maliciously
constructed by adversaries to force misclassification. Such adversarial
examples have been extensively studied in the context of computer vision
applications. In this work, we show adversarial attacks are also effective when
targeting neural network policies in reinforcement learning. Specifically, we
show existing adversarial example crafting techniques can be used to
significantly degrade test-time performance of trained policies. Our threat
model considers adversaries capable of introducing small perturbations to the
raw input of the policy. We characterize the degree of vulnerability across
tasks and training algorithms, for a subclass of adversarial-example attacks in
white-box and black-box settings. Regardless of the learned task or training
algorithm, we observe a significant drop in performance, even with small
adversarial perturbations that do not interfere with human perception. Videos
are available at http://rll.berkeley.edu/adversarial.
A Historical Review of Forty Years of Research on CMAC
Authors: Frank Z. Xing
Date: 2017-02-08 00:00:00

The Cerebellar Model Articulation Controller (CMAC) is an influential
brain-inspired computing model in many relevant fields. Since its inception in
the 1970s, the model has been intensively studied and many variants of the
prototype, such as Kernel-CMAC, Self-Organizing Map CMAC, and Linguistic CMAC,
have been proposed. This review article focus on how the CMAC model is
gradually developed and refined to meet the demand of fast, adaptive, and
robust control. Two perspective, CMAC as a neural network and CMAC as a table
look-up technique are presented. Three aspects of the model: the architecture,
learning algorithms and applications are discussed. In the end, some potential
future research directions on this model are suggested.
Model-based Classification and Novelty Detection For Point Pattern Data
Authors: Ba-Ngu Vo, Quang N. Tran, Dinh Phung, Ba-Tuong Vo
Date: 2017-01-30 00:00:00
Tasks: Classification, General Classification, Novelty Detection

Point patterns are sets or multi-sets of unordered elements that can be found
in numerous data sources. However, in data analysis tasks such as
classification and novelty detection, appropriate statistical models for point
pattern data have not received much attention. This paper proposes the
modelling of point pattern data via random finite sets (RFS). In particular, we
propose appropriate likelihood functions, and a maximum likelihood estimator
for learning a tractable family of RFS models. In novelty detection, we propose
novel ranking functions based on RFS models, which substantially improve
performance.
Clustering For Point Pattern Data
Authors: Quang N. Tran, Ba-Ngu Vo, Dinh Phung, Ba-Tuong Vo
Date: 2017-02-08 00:00:00
Tasks: Clustering

Clustering is one of the most common unsupervised learning tasks in machine
learning and data mining. Clustering algorithms have been used in a plethora of
applications across several scientific fields. However, there has been limited
research in the clustering of point patterns - sets or multi-sets of unordered
elements - that are found in numerous applications and data sources. In this
paper, we propose two approaches for clustering point patterns. The first is a
non-parametric method based on novel distances for sets. The second is a
model-based approach, formulated via random finite set theory, and solved by
the Expectation-Maximization algorithm. Numerical experiments show that the
proposed methods perform well on both simulated and real data.
Social media mining for identification and exploration of health-related information from pregnant women
Authors: Pramod Bharadwaj Chandrashekar, Arjun Magge, Abeed Sarker, Graciela Gonzalez
Date: 2017-02-08 00:00:00

Widespread use of social media has led to the generation of substantial
amounts of information about individuals, including health-related information.
Social media provides the opportunity to study health-related information about
selected population groups who may be of interest for a particular study. In
this paper, we explore the possibility of utilizing social media to perform
targeted data collection and analysis from a particular population group --
pregnant women. We hypothesize that we can use social media to identify cohorts
of pregnant women and follow them over time to analyze crucial health-related
information. To identify potentially pregnant women, we employ simple
rule-based searches that attempt to detect pregnancy announcements with
moderate precision. To further filter out false positives and noise, we employ
a supervised classifier using a small number of hand-annotated data. We then
collect their posts over time to create longitudinal health timelines and
attempt to divide the timelines into different pregnancy trimesters. Finally,
we assess the usefulness of the timelines by performing a preliminary analysis
to estimate drug intake patterns of our cohort at different trimesters. Our
rule-based cohort identification technique collected 53,820 users over thirty
months from Twitter. Our pregnancy announcement classification technique
achieved an F-measure of 0.81 for the pregnancy class, resulting in 34,895 user
timelines. Analysis of the timelines revealed that pertinent health-related
information, such as drug-intake and adverse reactions can be mined from the
data. Our approach to using user timelines in this fashion has produced very
encouraging results and can be employed for other important tasks where
cohorts, for which health-related information may not be available from other
sources, are required to be followed over time to derive population-based
estimates.
Comparison of machine learning methods for classifying mediastinal lymph node metastasis of non-small cell lung cancer from 18F-FDG PET/CT images
Authors: Hongkai Wang, Zongwei Zhou, Yingci Li, Zhonghua Chen, Peiou Lu, Wenzhi Wang, Wanyu Liu, Lijuan Yu
Date: 2017-02-07 00:00:00
Tasks: Diagnostic, Tumor Segmentation

The present study shows that the performance of CNN is not significantly
different from the best classical methods and human doctors for classifying
mediastinal lymph node metastasis of NSCLC from PET/CT images. Because CNN does
not need tumor segmentation or feature calculation, it is more convenient and
more objective than the classical methods. However, CNN does not make use of
the import diagnostic features, which have been proved more discriminative than
the texture features for classifying small-sized lymph nodes. Therefore,
incorporating the diagnostic features into CNN is a promising direction for
future research.
On SGD's Failure in Practice: Characterizing and Overcoming Stalling
Authors: Vivak Patel
Date: 2017-02-01 00:00:00
Tasks: Unity

Stochastic Gradient Descent (SGD) is widely used in machine learning problems
to efficiently perform empirical risk minimization, yet, in practice, SGD is
known to stall before reaching the actual minimizer of the empirical risk. SGD
stalling has often been attributed to its sensitivity to the conditioning of
the problem; however, as we demonstrate, SGD will stall even when applied to a
simple linear regression problem with unity condition number for standard
learning rates. Thus, in this work, we numerically demonstrate and
mathematically argue that stalling is a crippling and generic limitation of SGD
and its variants in practice. Once we have established the problem of stalling,
we generalize an existing framework for hedging against its effects, which (1)
deters SGD and its variants from stalling, (2) still provides convergence
guarantees, and (3) makes SGD and its variants more practical methods for
minimization.
Transfer from Multiple Linear Predictive State Representations (PSR)
Authors: Sri Ramana Sekharan, Ramkumar Natarajan, Siddharthan Rajasekaran
Date: 2017-02-07 00:00:00
Tasks: Transfer Learning

In this paper, we tackle the problem of transferring policy from multiple
partially observable source environments to a partially observable target
environment modeled as predictive state representation. This is an entirely new
approach with no previous work, other than the case of transfer in fully
observable domains. We develop algorithms to successfully achieve policy
transfer when we have the model of both the source and target tasks and discuss
in detail their performance and shortcomings. These algorithms could be a
starting point for the field of transfer learning in partial observability.
How to evaluate word embeddings? On importance of data efficiency and simple supervised tasks
Authors: Stanisław Jastrzebski, Damian Leśniak, Wojciech Marian Czarnecki
Date: 2017-02-07 00:00:00
Tasks: Representation Learning, Transfer Learning, Word Embeddings, Word Similarity

Maybe the single most important goal of representation learning is making
subsequent learning faster. Surprisingly, this fact is not well reflected in
the way embeddings are evaluated. In addition, recent practice in word
embeddings points towards importance of learning specialized representations.
We argue that focus of word representation evaluation should reflect those
trends and shift towards evaluating what useful information is easily
accessible. Specifically, we propose that evaluation should focus on data
efficiency and simple supervised tasks, where the amount of available data is
varied and scores of a supervised model are reported for each subset (as
commonly done in transfer learning).
  In order to illustrate significance of such analysis, a comprehensive
evaluation of selected word embeddings is presented. Proposed approach yields a
more complete picture and brings new insight into performance characteristics,
for instance information about word similarity or analogy tends to be
non--linearly encoded in the embedding space, which questions the cosine-based,
unsupervised, evaluation methods. All results and analysis scripts are
available online.
Robust Clustering for Time Series Using Spectral Densities and Functional Data Analysis
Authors: Diego Rivera-García, Luis Angel García-Escudero, Agustín Mayo-Iscar, Joaquín Ortega
Date: 2017-02-07 00:00:00
Tasks: Clustering, Time Series, Time Series Analysis

In this work a robust clustering algorithm for stationary time series is
proposed. The algorithm is based on the use of estimated spectral densities,
which are considered as functional data, as the basic characteristic of
stationary time series for clustering purposes. A robust algorithm for
functional data is then applied to the set of spectral densities. Trimming
techniques and restrictions on the scatter within groups reduce the effect of
noise in the data and help to prevent the identification of spurious clusters.
The procedure is tested in a simulation study, and is also applied to a real
data set.
BundleFusion: Real-time Globally Consistent 3D Reconstruction using On-the-fly Surface Re-integration
Authors: Angela Dai, Matthias Nießner, Michael Zollhöfer, Shahram Izadi, Christian Theobalt
Date: 2016-04-05 00:00:00
Tasks: 3D Reconstruction, Mixed Reality, Pose Estimation

Real-time, high-quality, 3D scanning of large-scale scenes is key to mixed
reality and robotic applications. However, scalability brings challenges of
drift in pose estimation, introducing significant errors in the accumulated
model. Approaches often require hours of offline processing to globally correct
model errors. Recent online methods demonstrate compelling results, but suffer
from: (1) needing minutes to perform online correction preventing true
real-time use; (2) brittle frame-to-frame (or frame-to-model) pose estimation
resulting in many tracking failures; or (3) supporting only unstructured
point-based representations, which limit scan quality and applicability. We
systematically address these issues with a novel, real-time, end-to-end
reconstruction framework. At its core is a robust pose estimation strategy,
optimizing per frame for a global set of camera poses by considering the
complete history of RGB-D input with an efficient hierarchical approach. We
remove the heavy reliance on temporal tracking, and continually localize to the
globally optimized frames instead. We contribute a parallelizable optimization
framework, which employs correspondences based on sparse features and dense
geometric and photometric matching. Our approach estimates globally optimized
(i.e., bundle adjusted) poses in real-time, supports robust tracking with
recovery from gross tracking failures (i.e., relocalization), and re-estimates
the 3D model in real-time to ensure global consistency; all within a single
framework. Our approach outperforms state-of-the-art online systems with
quality on par to offline methods, but with unprecedented speed and scan
completeness. Our framework leads to a comprehensive online scanning solution
for large indoor environments, enabling ease of use and high-quality results.
Playing SNES in the Retro Learning Environment
Authors: Nadav Bhonker, Shai Rozenberg, Itay Hubara
Date: 2016-11-07 00:00:00
Tasks: Atari Games, Reinforcement Learning, SNES Games

Mastering a video game requires skill, tactics and strategy. While these
attributes may be acquired naturally by human players, teaching them to a
computer program is a far more challenging task. In recent years, extensive
research was carried out in the field of reinforcement learning and numerous
algorithms were introduced, aiming to learn how to perform human tasks such as
playing video games. As a result, the Arcade Learning Environment (ALE)
(Bellemare et al., 2013) has become a commonly used benchmark environment
allowing algorithms to train on various Atari 2600 games. In many games the
state-of-the-art algorithms outperform humans. In this paper we introduce a new
learning environment, the Retro Learning Environment --- RLE, that can run
games on the Super Nintendo Entertainment System (SNES), Sega Genesis and
several other gaming consoles. The environment is expandable, allowing for more
video games and consoles to be easily added to the environment, while
maintaining the same interface as ALE. Moreover, RLE is compatible with Python
and Torch. SNES games pose a significant challenge to current algorithms due to
their higher level of complexity and versatility.
Estimation of classrooms occupancy using a multi-layer perceptron
Authors: Eugénio Rodrigues, Luísa Dias Pereira, Adélio Rodrigues Gaspar, Álvaro Gomes, Manuel Carlos Gameiro da Silva
Date: 2017-02-07 00:00:00

This paper presents a multi-layer perceptron model for the estimation of
classrooms number of occupants from sensed indoor environmental data-relative
humidity, air temperature, and carbon dioxide concentration. The modelling
datasets were collected from two classrooms in the Secondary School of Pombal,
Portugal. The number of occupants and occupation periods were obtained from
class attendance reports. However, post-class occupancy was unknown and the
developed model is used to reconstruct the classrooms occupancy by filling the
unreported periods. Different model structure and environment variables
combination were tested. The model with best accuracy had as input vector 10
variables of five averaged time intervals of relative humidity and carbon
dioxide concentration. The model presented a mean square error of 1.99,
coefficient of determination of 0.96 with a significance of p-value < 0.001,
and a mean absolute error of 1 occupant. These results show promising
estimation capabilities in uncertain indoor environment conditions.
Lower Bounds on Active Learning for Graphical Model Selection
Authors: Jonathan Scarlett, Volkan Cevher
Date: 2016-07-08 00:00:00
Tasks: Active Learning, Model Selection

We consider the problem of estimating the underlying graph associated with a
Markov random field, with the added twist that the decoding algorithm can
iteratively choose which subsets of nodes to sample based on the previous
samples, resulting in an active learning setting. Considering both Ising and
Gaussian models, we provide algorithm-independent lower bounds for
high-probability recovery within the class of degree-bounded graphs. Our main
results are minimax lower bounds for the active setting that match the best
known lower bounds for the passive setting, which in turn are known to be tight
in several cases of interest. Our analysis is based on Fano's inequality, along
with novel mutual information bounds for the active learning setting, and the
application of restricted graph ensembles. While we consider ensembles that are
similar or identical to those used in the passive setting, we require different
analysis techniques, with a key challenge being bounding a mutual information
quantity associated with observed subsets of nodes, as opposed to full
observations.
NewsQA: A Machine Comprehension Dataset
Authors: Adam Trischler, Tong Wang, Xingdi Yuan, Justin Harris, Alessandro Sordoni, Philip Bachman, Kaheer Suleman
Date: 2016-11-29 00:00:00
Proceeding: WS 2017 8
Tasks: Articles, Natural Language Inference, Reading Comprehension

We present NewsQA, a challenging machine comprehension dataset of over
100,000 human-generated question-answer pairs. Crowdworkers supply questions
and answers based on a set of over 10,000 news articles from CNN, with answers
consisting of spans of text from the corresponding articles. We collect this
dataset through a four-stage process designed to solicit exploratory questions
that require reasoning. A thorough analysis confirms that NewsQA demands
abilities beyond simple word matching and recognizing textual entailment. We
measure human performance on the dataset and compare it to several strong
neural models. The performance gap between humans and machines (0.198 in F1)
indicates that significant progress can be made on NewsQA through future
research. The dataset is freely available at
https://datasets.maluuba.com/NewsQA.
Latent Sequence Decompositions
Authors: William Chan, Yu Zhang, Quoc Le, Navdeep Jaitly
Date: 2016-10-10 00:00:00
Tasks: speech-recognition, Speech Recognition, valid

We present the Latent Sequence Decompositions (LSD) framework. LSD decomposes
sequences with variable lengthed output units as a function of both the input
sequence and the output sequence. We present a training algorithm which samples
valid extensions and an approximate decoding algorithm. We experiment with the
Wall Street Journal speech recognition task. Our LSD model achieves 12.9% WER
compared to a character baseline of 14.8% WER. When combined with a
convolutional network on the encoder, we achieve 9.6% WER.
Propagation via Kernelization: The Vertex Cover Constraint
Authors: Clément Carbonnel, Emmanuel Hébrard
Date: 2017-02-07 00:00:00

The technique of kernelization consists in extracting, from an instance of a
problem, an essentially equivalent instance whose size is bounded in a
parameter k. Besides being the basis for efficient param-eterized algorithms,
this method also provides a wealth of information to reason about in the
context of constraint programming. We study the use of kernelization for
designing propagators through the example of the Vertex Cover constraint. Since
the classic kernelization rules often correspond to dominance rather than
consistency, we introduce the notion of "loss-less" kernel. While our
preliminary experimental results show the potential of the approach, they also
show some of its limits. In particular, this method is more effective for
vertex covers of large and sparse graphs, as they tend to have, relatively,
smaller kernels.