pwc-archive/papers-with-abstracts huggingface dataset:
Single-trial P300 Classification using PCA with LDA, QDA and Neural Networks
Authors: Nand Sharma
Date: 2017-12-06 00:00:00
Tasks: Classification, EEG, Electroencephalogram (EEG), ERP, General Classification

The P300 event-related potential (ERP), evoked in scalp-recorded
electroencephalography (EEG) by external stimuli, has proven to be a reliable
response for controlling a BCI. The P300 component of an event related
potential is thus widely used in brain-computer interfaces to translate the
subjects' intent by mere thoughts into commands to control artificial devices.
The main challenge in the classification of P300 trials in
electroencephalographic (EEG) data is the low signal-to-noise ratio (SNR) of
the P300 response. To overcome the low SNR of individual trials, it is common
practice to average together many consecutive trials, which effectively
diminishes the random noise. Unfortunately, when more repeated trials are
required for applications such as the P300 speller, the communication rate is
greatly reduced. This has resulted in a need for better methods to improve
single-trial classification accuracy of P300 response. In this work, we use
Principal Component Analysis (PCA) as a preprocessing method and use Linear
Discriminant Analysis (LDA)and neural networks for classification. The results
show that a combination of PCA with these methods provided as high as 13\%
accuracy gain for single-trial classification while using only 3 to 4 principal
components.
Recurrent Scene Parsing with Perspective Understanding in the Loop
Authors: Shu Kong, Charless Fowlkes
Date: 2017-05-20 00:00:00
Proceeding: CVPR 2018 6
Tasks: Depth Estimation, Monocular Depth Estimation, Scene Parsing, Segmentation, Semantic Segmentation

Objects may appear at arbitrary scales in perspective images of a scene,
posing a challenge for recognition systems that process images at a fixed
resolution. We propose a depth-aware gating module that adaptively selects the
pooling field size in a convolutional network architecture according to the
object scale (inversely proportional to the depth) so that small details are
preserved for distant objects while larger receptive fields are used for those
nearby. The depth gating signal is provided by stereo disparity or estimated
directly from monocular input. We integrate this depth-aware gating into a
recurrent convolutional neural network to perform semantic segmentation. Our
recurrent module iteratively refines the segmentation results, leveraging the
depth and semantic predictions from the previous iterations.
  Through extensive experiments on four popular large-scale RGB-D datasets, we
demonstrate this approach achieves competitive semantic segmentation
performance with a model which is substantially more compact. We carry out
extensive analysis of this architecture including variants that operate on
monocular RGB but use depth as side-information during training, unsupervised
gating as a generic attentional mechanism, and multi-resolution gating. We find
that gated pooling for joint semantic segmentation and depth yields
state-of-the-art results for quantitative monocular depth estimation.
Using Options and Covariance Testing for Long Horizon Off-Policy Policy Evaluation
Authors: Zhaohan Daniel Guo, Philip S. Thomas, Emma Brunskill
Date: 2017-03-09 00:00:00
Proceeding: NeurIPS 2017 12

Evaluating a policy by deploying it in the real world can be risky and
costly. Off-policy policy evaluation (OPE) algorithms use historical data
collected from running a previous policy to evaluate a new policy, which
provides a means for evaluating a policy without requiring it to ever be
deployed. Importance sampling is a popular OPE method because it is robust to
partial observability and works with continuous states and actions. However,
the amount of historical data required by importance sampling can scale
exponentially with the horizon of the problem: the number of sequential
decisions that are made. We propose using policies over temporally extended
actions, called options, and show that combining these policies with importance
sampling can significantly improve performance for long-horizon problems. In
addition, we can take advantage of special cases that arise due to
options-based policies to further improve the performance of importance
sampling. We further generalize these special cases to a general covariance
testing rule that can be used to decide which weights to drop in an IS
estimate, and derive a new IS algorithm called Incremental Importance Sampling
that can provide significantly more accurate estimates for a broad class of
domains.
Feature Squeezing: Detecting Adversarial Examples in Deep Neural Networks
Authors: Weilin Xu, David Evans, Yanjun Qi
Date: 2017-04-04 00:00:00
Proceeding: Network and Distributed System Security Symposium 2018 2

Although deep neural networks (DNNs) have achieved great success in many
tasks, they can often be fooled by \emph{adversarial examples} that are
generated by adding small but purposeful distortions to natural examples.
Previous studies to defend against adversarial examples mostly focused on
refining the DNN models, but have either shown limited success or required
expensive computation. We propose a new strategy, \emph{feature squeezing},
that can be used to harden DNN models by detecting adversarial examples.
Feature squeezing reduces the search space available to an adversary by
coalescing samples that correspond to many different feature vectors in the
original space into a single sample. By comparing a DNN model's prediction on
the original input with that on squeezed inputs, feature squeezing detects
adversarial examples with high accuracy and few false positives. This paper
explores two feature squeezing methods: reducing the color bit depth of each
pixel and spatial smoothing. These simple strategies are inexpensive and
complementary to other defenses, and can be combined in a joint detection
framework to achieve high detection rates against state-of-the-art attacks.
What's in my closet?: Image classification using fuzzy logic
Authors: Amina E. Hussein
Date: 2017-12-05 00:00:00
Tasks: Classification, General Classification, image-classification, Image Classification

A fuzzy system was created in MATLAB to identify an item of clothing as a
dress, shirt, or pair of pants from a series of input images. The system was
initialized using a high-contrast vector-image of each item of clothing as the
state closest to a direct solution. Nine other user-input images (three of each
item) were also used to determine the characteristic function of each item and
recognize each pattern. Mamdani inference systems were used for edge location
and identification of characteristic regions of interest for each item of
clothing. Based on these non-dimensional trends, a second Mamdani fuzzy
inference system was used to characterize each image as containing a shirt, a
dress, or a pair of pants. An outline of the fuzzy inference system and image
processing techniques used for creating an image pattern recognition system are
discussed.
Robust Photometric Stereo via Dictionary Learning
Authors: Andrew J. Wagenmaker, Brian E. Moore, Raj Rao Nadakuditi
Date: 2017-10-24 00:00:00
Tasks: Dictionary Learning

Photometric stereo is a method that seeks to reconstruct the normal vectors
of an object from a set of images of the object illuminated under different
light sources. While effective in some situations, classical photometric stereo
relies on a diffuse surface model that cannot handle objects with complex
reflectance patterns, and it is sensitive to non-idealities in the images. In
this work, we propose a novel approach to photometric stereo that relies on
dictionary learning to produce robust normal vector reconstructions.
Specifically, we develop two formulations for applying dictionary learning to
photometric stereo. We propose a model that applies dictionary learning to
regularize and reconstruct the normal vectors from the images under the classic
Lambertian reflectance model. We then generalize this model to explicitly model
non-Lambertian objects. We investigate both approaches through extensive
experimentation on synthetic and real benchmark datasets and observe
state-of-the-art performance compared to existing robust photometric stereo
methods.
Anatomically Constrained Neural Networks (ACNN): Application to Cardiac Image Enhancement and Segmentation
Authors: Ozan Oktay, Enzo Ferrante, Konstantinos Kamnitsas, Mattias Heinrich, Wenjia Bai, Jose Caballero, Stuart Cook, Antonio de Marvao, Timothy Dawes, Declan O'Regan, Bernhard Kainz, Ben Glocker, Daniel Rueckert
Date: 2017-05-22 00:00:00
Tasks: Anatomy, Image Enhancement

Incorporation of prior knowledge about organ shape and location is key to
improve performance of image analysis approaches. In particular, priors can be
useful in cases where images are corrupted and contain artefacts due to
limitations in image acquisition. The highly constrained nature of anatomical
objects can be well captured with learning based techniques. However, in most
recent and promising techniques such as CNN based segmentation it is not
obvious how to incorporate such prior knowledge. State-of-the-art methods
operate as pixel-wise classifiers where the training objectives do not
incorporate the structure and inter-dependencies of the output. To overcome
this limitation, we propose a generic training strategy that incorporates
anatomical prior knowledge into CNNs through a new regularisation model, which
is trained end-to-end. The new framework encourages models to follow the global
anatomical properties of the underlying anatomy (e.g. shape, label structure)
via learned non-linear representations of the shape. We show that the proposed
approach can be easily adapted to different analysis tasks (e.g. image
enhancement, segmentation) and improve the prediction accuracy of the
state-of-the-art models. The applicability of our approach is shown on
multi-modal cardiac datasets and public benchmarks. Additionally, we
demonstrate how the learned deep models of 3D shapes can be interpreted and
used as biomarkers for classification of cardiac pathologies.
Learning to Forecast Videos of Human Activity with Multi-granularity Models and Adaptive Rendering
Authors: Mengyao Zhai, Jiacheng Chen, Ruizhi Deng, Lei Chen, Ligeng Zhu, Greg Mori
Date: 2017-12-05 00:00:00
Tasks: Decoder, Video Forecasting

We propose an approach for forecasting video of complex human activity
involving multiple people. Direct pixel-level prediction is too simple to
handle the appearance variability in complex activities. Hence, we develop
novel intermediate representations. An architecture combining a hierarchical
temporal model for predicting human poses and encoder-decoder convolutional
neural networks for rendering target appearances is proposed. Our hierarchical
model captures interactions among people by adopting a dynamic group-based
interaction mechanism. Next, our appearance rendering network encodes the
targets' appearances by learning adaptive appearance filters using a fully
convolutional network. Finally, these filters are placed in encoder-decoder
neural networks to complete the rendering. We demonstrate that our model can
generate videos that are superior to state-of-the-art methods, and can handle
complex human activity scenarios in video forecasting.
Recognizing Plans by Learning Embeddings from Observed Action Distributions
Authors: Yantian Zha, Yikang Li, Sriram Gopalakrishnan, Baoxin Li, Subbarao Kambhampati
Date: 2017-12-05 00:00:00
Tasks: Activity Recognition, Word Embeddings

Recent advances in visual activity recognition have raised the possibility of
applications such as automated video surveillance. Effective approaches for
such problems however require the ability to recognize the plans of agents from
video information. Although traditional plan recognition algorithms depend on
access to sophisticated planning domain models, one recent promising direction
involves learning approximated (or shallow) domain models directly from the
observed activity sequences DUP. One limitation is that such approaches expect
observed action sequences as inputs. In many cases involving vision/sensing
from raw data, there is considerable uncertainty about the specific action at
any given time point. The most we can expect in such cases is probabilistic
information about the action at that point. The input will then be sequences of
such observed action distributions. In this work, we address the problem of
constructing an effective data-interface that allows a plan recognition module
to directly handle such observation distributions. Such an interface works like
a bridge between the low-level perception module, and the high-level plan
recognition module. We propose two approaches. The first involves resampling
the distribution sequences to single action sequences, from which we could
learn an action affinity model based on learned action (word) embeddings for
plan recognition. The second is to directly learn action distribution
embeddings by our proposed Distr2vec (distribution to vector) model, to
construct an affinity model for plan recognition.
Blind Image Deblurring Using Row-Column Sparse Representations
Authors: Mohammad Tofighi, Yuelong Li, Vishal Monga
Date: 2017-12-05 00:00:00
Tasks: Blind Image Deblurring, Deblurring, Image Deblurring

Blind image deblurring is a particularly challenging inverse problem where
the blur kernel is unknown and must be estimated en route to recover the
deblurred image. The problem is of strong practical relevance since many
imaging devices such as cellphone cameras, must rely on deblurring algorithms
to yield satisfactory image quality. Despite significant research effort,
handling large motions remains an open problem. In this paper, we develop a new
method called Blind Image Deblurring using Row-Column Sparsity (BD-RCS) to
address this issue. Specifically, we model the outer product of kernel and
image coefficients in certain transformation domains as a rank-one matrix, and
recover it by solving a rank minimization problem. Our central contribution
then includes solving {\em two new} optimization problems involving row and
column sparsity to automatically determine blur kernel and image support
sequentially. The kernel and image can then be recovered through a singular
value decomposition (SVD). Experimental results on linear motion deblurring
demonstrate that BD-RCS can yield better results than state of the art,
particularly when the blur is caused by large motion. This is confirmed both
visually and through quantitative measures.
How to Learn a Model Checker
Authors: Dung Phan, Radu Grosu, Nicola Paoletti, Scott A. Smolka, Scott D. Stoller
Date: 2017-12-05 00:00:00
Tasks: BIG-bench Machine Learning, model

We show how machine-learning techniques, particularly neural networks, offer
a very effective and highly efficient solution to the approximate
model-checking problem for continuous and hybrid systems, a solution where the
general-purpose model checker is replaced by a model-specific classifier
trained by sampling model trajectories. To the best of our knowledge, we are
the first to establish this link from machine learning to model checking. Our
method comprises a pipeline of analysis techniques for estimating and obtaining
statistical guarantees on the classifier's prediction performance, as well as
tuning techniques to improve such performance. Our experimental evaluation
considers the time-bounded reachability problem for three well-established
benchmarks in the hybrid systems community. On these examples, we achieve an
accuracy of 99.82% to 100% and a false-negative rate (incorrectly predicting
that unsafe states are not reachable from a given state) of 0.0007 to 0. We
believe that this level of accuracy is acceptable in many practical
applications and we show how the approximate model checker can be made more
conservative by tuning the classifier through further training and selection of
the classification threshold.
Concentration of weakly dependent Banach-valued sums and applications to statistical learning methods
Authors: Gilles Blanchard, Oleksandr Zadorozhnyi
Date: 2017-12-05 00:00:00
Tasks: Vocal Bursts Type Prediction

We obtain a Bernstein-type inequality for sums of Banach-valued random
variables satisfying a weak dependence assumption of general type and under
certain smoothness assumptions of the underlying Banach norm. We use this
inequality in order to investigate in the asymptotical regime the error upper
bounds for the broad family of spectral regularization methods for reproducing
kernel decision rules, when trained on a sample coming from a $\tau-$mixing
process.
FlagIt: A System for Minimally Supervised Human Trafficking Indicator Mining
Authors: Mayank Kejriwal, Jiayuan Ding, Runqi Shao, Anoop Kumar, Pedro Szekely
Date: 2017-12-05 00:00:00
Tasks: TAG

In this paper, we describe and study the indicator mining problem in the
online sex advertising domain. We present an in-development system, FlagIt
(Flexible and adaptive generation of Indicators from text), which combines the
benefits of both a lightweight expert system and classical semi-supervision
(heuristic re-labeling) with recently released state-of-the-art unsupervised
text embeddings to tag millions of sentences with indicators that are highly
correlated with human trafficking. The FlagIt technology stack is open source.
On preliminary evaluations involving five indicators, FlagIt illustrates
promising performance compared to several alternatives. The system is being
actively developed, refined and integrated into a domain-specific search system
used by over 200 law enforcement agencies to combat human trafficking, and is
being aggressively extended to mine at least six more indicators with minimal
programming effort. FlagIt is a good example of a system that operates in
limited label settings, and that requires creative combinations of established
machine learning techniques to produce outputs that could be used by real-world
non-technical analysts.
Stochastic Cubic Regularization for Fast Nonconvex Optimization
Authors: Nilesh Tripuraneni, Mitchell Stern, Chi Jin, Jeffrey Regier, Michael. I. Jordan
Date: 2017-11-08 00:00:00
Proceeding: NeurIPS 2018 12

This paper proposes a stochastic variant of a classic algorithm---the
cubic-regularized Newton method [Nesterov and Polyak 2006]. The proposed
algorithm efficiently escapes saddle points and finds approximate local minima
for general smooth, nonconvex functions in only
$\mathcal{\tilde{O}}(\epsilon^{-3.5})$ stochastic gradient and stochastic
Hessian-vector product evaluations. The latter can be computed as efficiently
as stochastic gradients. This improves upon the
$\mathcal{\tilde{O}}(\epsilon^{-4})$ rate of stochastic gradient descent. Our
rate matches the best-known result for finding local minima without requiring
any delicate acceleration or variance-reduction techniques.
Co-domain Embedding using Deep Quadruplet Networks for Unseen Traffic Sign Recognition
Authors: Junsik Kim, Seokju Lee, Tae-Hyun Oh, In So Kweon
Date: 2017-12-05 00:00:00
Tasks: General Classification, Traffic Sign Recognition

Recent advances in visual recognition show overarching success by virtue of
large amounts of supervised data. However,the acquisition of a large supervised
dataset is often challenging. This is also true for intelligent transportation
applications, i.e., traffic sign recognition. For example, a model trained with
data of one country may not be easily generalized to another country without
much data. We propose a novel feature embedding scheme for unseen class
classification when the representative class template is given. Traffic signs,
unlike other objects, have official images. We perform co-domain embedding
using a quadruple relationship from real and synthetic domains. Our quadruplet
network fully utilizes the explicit pairwise similarity relationships among
samples from different domains. We validate our method on three datasets with
two experiments involving one-shot classification and feature generalization.
The results show that the proposed method outperforms competing approaches on
both seen and unseen classes.
Online Learning with Gated Linear Networks
Authors: Joel Veness, Tor Lattimore, Avishkar Bhoopchand, Agnieszka Grabska-Barwinska, Christopher Mattern, Peter Toth
Date: 2017-12-05 00:00:00

This paper describes a family of probabilistic architectures designed for
online learning under the logarithmic loss. Rather than relying on non-linear
transfer functions, our method gains representational power by the use of data
conditioning. We state under general conditions a learnable capacity theorem
that shows this approach can in principle learn any bounded Borel-measurable
function on a compact subset of euclidean space; the result is stronger than
many universality results for connectionist architectures because we provide
both the model and the learning procedure for which convergence is guaranteed.
RACE: Large-scale ReAding Comprehension Dataset From Examinations
Authors: Guokun Lai, Qizhe Xie, Hanxiao Liu, Yiming Yang, Eduard Hovy
Date: 2017-04-15 00:00:00
Proceeding: EMNLP 2017 9
Tasks: Reading Comprehension

We present RACE, a new dataset for benchmark evaluation of methods in the
reading comprehension task. Collected from the English exams for middle and
high school Chinese students in the age range between 12 to 18, RACE consists
of near 28,000 passages and near 100,000 questions generated by human experts
(English instructors), and covers a variety of topics which are carefully
designed for evaluating the students' ability in understanding and reasoning.
In particular, the proportion of questions that requires reasoning is much
larger in RACE than that in other benchmark datasets for reading comprehension,
and there is a significant gap between the performance of the state-of-the-art
models (43%) and the ceiling human performance (95%). We hope this new dataset
can serve as a valuable resource for research and evaluation in machine
comprehension. The dataset is freely available at
http://www.cs.cmu.edu/~glai1/data/race/ and the code is available at
https://github.com/qizhex/RACE_AR_baselines.
Sum of previous inpatient serum creatinine measurements predicts acute kidney injury in rehospitalized patients
Authors: Sam Weisenthal, Haofu Liao, Philip Ng, Martin Zand
Date: 2017-12-05 00:00:00
Tasks: Kidney Function

Acute Kidney Injury (AKI), the abrupt decline in kidney function due to
temporary or permanent injury, is associated with increased mortality,
morbidity, length of stay, and hospital cost. Sometimes, simple interventions
such as medication review or hydration can prevent AKI. There is therefore
interest in estimating risk of AKI at hospitalization. To gain insight into
this task, we employ multilayer perceptron (MLP) and recurrent neural networks
(RNNs) using serum creatinine (sCr) as a lone feature. We explore different
feature input structures, including variable-length look-backs and a nested
formulation for rehospitalized patients with previous sCr measurements.
Experimental results show that the simplest model, MLP processing the sum of
sCr, had best performance: AUROC 0.92 and AUPRC 0.70. Such a simple model could
be easily integrated into an EHR. Preliminary results also suggest that
inpatient data streams with missing outpatient measurements---common in the
medical setting---might be best modeled with a tailored architecture.