pwc-archive/papers-with-abstracts huggingface dataset:
Effects of Images with Different Levels of Familiarity on EEG
Authors: Ali Saeedi, Ehsan Arbabi
Date: 2017-10-12 00:00:00
Tasks: EEG, Electroencephalogram (EEG), feature selection, General Classification

Evaluating human brain potentials during watching different images can be
used for memory evaluation, information retrieving, guilty-innocent
identification and examining the brain response. In this study, the effects of
watching images, with different levels of familiarity, on subjects'
Electroencephalogram (EEG) have been studied. Three different groups of images
with three familiarity levels of "unfamiliar", "familiar" and "very familiar"
have been considered for this study. EEG signals of 21 subjects (14 men) were
recorded. After signal acquisition, pre-processing, including noise and
artifact removal, were performed on epochs of data. Features, including
spatial-statistical, wavelet, frequency and harmonic parameters, and also
correlation between recording channels, were extracted from the data. Then, we
evaluated the efficiency of the extracted features by using p-value and also an
orthogonal feature selection method (combination of Gram-Schmitt method and
Fisher discriminant ratio) for feature dimensional reduction. As the final step
of feature selection, we used 'add-r take-away l' method for choosing the most
discriminative features. For data classification, including all two-class and
three-class cases, we applied Support Vector Machine (SVM) on the extracted
features. The correct classification rates (CCR) for "unfamiliar-familiar",
"unfamiliar-very familiar" and "familiar-very familiar" cases were 85.6%,
92.6%, and 70.6%, respectively. The best results of classifications were
obtained in pre-frontal and frontal regions of brain. Also, wavelet, frequency
and harmonic features were among the most discriminative features. Finally, in
three-class case, the best CCR was 86.8%.
Arguing Machines: Human Supervision of Black Box AI Systems That Make Life-Critical Decisions
Authors: Lex Fridman, Li Ding, Benedikt Jenik, Bryan Reimer
Date: 2017-10-12 00:00:00
Tasks: Autonomous Driving, General Classification, image-classification, Image Classification

We consider the paradigm of a black box AI system that makes life-critical
decisions. We propose an "arguing machines" framework that pairs the primary AI
system with a secondary one that is independently trained to perform the same
task. We show that disagreement between the two systems, without any knowledge
of underlying system design or operation, is sufficient to arbitrarily improve
the accuracy of the overall decision pipeline given human supervision over
disagreements. We demonstrate this system in two applications: (1) an
illustrative example of image classification and (2) on large-scale real-world
semi-autonomous driving data. For the first application, we apply this
framework to image classification achieving a reduction from 8.0% to 2.8% top-5
error on ImageNet. For the second application, we apply this framework to Tesla
Autopilot and demonstrate the ability to predict 90.4% of system disengagements
that were labeled by human annotators as challenging and needing human
supervision.
Self-Taught Support Vector Machine
Authors: Parvin Razzaghi
Date: 2017-10-12 00:00:00

In this paper, a new approach for classification of target task using limited
labeled target data as well as enormous unlabeled source data is proposed which
is called self-taught learning. The target and source data can be drawn from
different distributions. In the previous approaches, covariate shift assumption
is considered where the marginal distributions p(x) change over domains and the
conditional distributions p(y|x) remain the same. In our approach, we propose a
new objective function which simultaneously learns a common space T(.) where
the conditional distributions over domains p(T(x)|y) remain the same and learns
robust SVM classifiers for target task using both source and target data in the
new representation. Hence, in the proposed objective function, the hidden label
of the source data is also incorporated. We applied the proposed approach on
Caltech-256, MSRC+LMO datasets and compared the performance of our algorithm to
the available competing methods. Our method has a superior performance to the
successful existing algorithms.
Revisiting the Design Issues of Local Models for Japanese Predicate-Argument Structure Analysis
Authors: Yuichiroh Matsubayashi, Kentaro Inui
Date: 2017-10-12 00:00:00
Proceeding: IJCNLP 2017 11

The research trend in Japanese predicate-argument structure (PAS) analysis is
shifting from pointwise prediction models with local features to global models
designed to search for globally optimal solutions. However, the existing global
models tend to employ only relatively simple local features; therefore, the
overall performance gains are rather limited. The importance of designing a
local model is demonstrated in this study by showing that the performance of a
sophisticated local model can be considerably improved with recent feature
embedding methods and a feature combination learning based on a neural network,
outperforming the state-of-the-art global models in $F_1$ on a common benchmark
dataset.
AMBER: Adaptive Multi-Batch Experience Replay for Continuous Action Control
Authors: Seungyul Han, Youngchul Sung
Date: 2017-10-12 00:00:00
Tasks: continuous-control, Continuous Control

In this paper, a new adaptive multi-batch experience replay scheme is
proposed for proximal policy optimization (PPO) for continuous action control.
On the contrary to original PPO, the proposed scheme uses the batch samples of
past policies as well as the current policy for the update for the next policy,
where the number of the used past batches is adaptively determined based on the
oldness of the past batches measured by the average importance sampling (IS)
weight. The new algorithm constructed by combining PPO with the proposed
multi-batch experience replay scheme maintains the advantages of original PPO
such as random mini-batch sampling and small bias due to low IS weights by
storing the pre-computed advantages and values and adaptively determining the
mini-batch size. Numerical results show that the proposed method significantly
increases the speed and stability of convergence on various continuous control
tasks compared to original PPO.
Quantized Minimum Error Entropy Criterion
Authors: Badong Chen, Lei Xing, Nanning Zheng, Jose C. Príncipe
Date: 2017-10-11 00:00:00
Tasks: Quantization

Comparing with traditional learning criteria, such as mean square error
(MSE), the minimum error entropy (MEE) criterion is superior in nonlinear and
non-Gaussian signal processing and machine learning. The argument of the
logarithm in Renyis entropy estimator, called information potential (IP), is a
popular MEE cost in information theoretic learning (ITL). The computational
complexity of IP is however quadratic in terms of sample number due to double
summation. This creates computational bottlenecks especially for large-scale
datasets. To address this problem, in this work we propose an efficient
quantization approach to reduce the computational burden of IP, which decreases
the complexity from O(N*N) to O (MN) with M << N. The new learning criterion is
called the quantized MEE (QMEE). Some basic properties of QMEE are presented.
Illustrative examples are provided to verify the excellent performance of QMEE.
Adaptive Real-Time Removal of Impulse Noise in Medical Images
Authors: Zohreh HosseinKhani, Mohsen Hajabdollahi, Nader Karimi, Reza Soroushmehr, Shahram Shirani, Kayvan Najarian, Shadrokh Samavi
Date: 2017-09-06 00:00:00

Noise is an important factor that degrades the quality of medical images.
Impulse noise is a common noise, which is caused by malfunctioning of sensor
elements or errors in the transmission of images. In medical images due to
presence of white foreground and black background, many pixels have intensities
similar to impulse noise and distinction between noisy and regular pixels is
difficult. In software techniques, the accuracy of the noise removal is more
important than the algorithm's complexity. But for hardware implementation
having a low complexity algorithm with an acceptable accuracy is essential. In
this paper a low complexity de-noising method is proposed that removes the
noise by local analysis of the image blocks. The proposed method distinguishes
non-noisy pixels that have noise-like intensities. All steps are designed to
have low hardware complexity. Simulation results show that for different
magnetic resonance images, the proposed method removes impulse noise with an
acceptable accuracy.
Provably Fair Representations
Authors: Daniel McNamara, Cheng Soon Ong, Robert C. Williamson
Date: 2017-10-12 00:00:00
Tasks: BIG-bench Machine Learning, Fairness

Machine learning systems are increasingly used to make decisions about
people's lives, such as whether to give someone a loan or whether to interview
someone for a job. This has led to considerable interest in making such machine
learning systems fair. One approach is to transform the input data used by the
algorithm. This can be achieved by passing each input data point through a
representation function prior to its use in training or testing. Techniques for
learning such representation functions from data have been successful
empirically, but typically lack theoretical fairness guarantees. We show that
it is possible to prove that a representation function is fair according to
common measures of both group and individual fairness, as well as useful with
respect to a target task. These provable properties can be used in a governance
model involving a data producer, a data user and a data regulator, where there
is a separation of concerns between fairness and target task utility to ensure
transparency and prevent perverse incentives. We formally define the 'cost of
mistrust' of using this model compared to the setting where there is a single
trusted party, and provide bounds on this cost in particular cases. We present
a practical approach to learning fair representation functions and apply it to
financial and criminal justice datasets. We evaluate the fairness and utility
of these representation functions using measures motivated by our theoretical
results.
Marginal sequential Monte Carlo for doubly intractable models
Authors: Richard G. Everitt, Dennis Prangle, Philip Maybank, Mark Bell
Date: 2017-10-12 00:00:00
Tasks: Bayesian Inference

Bayesian inference for models that have an intractable partition function is
known as a doubly intractable problem, where standard Monte Carlo methods are
not applicable. The past decade has seen the development of auxiliary variable
Monte Carlo techniques (M{\o}ller et al., 2006; Murray et al., 2006) for
tackling this problem; these approaches being members of the more general class
of pseudo-marginal, or exact-approximate, Monte Carlo algorithms (Andrieu and
Roberts, 2009), which make use of unbiased estimates of intractable posteriors.
Everitt et al. (2017) investigated the use of exact-approximate importance
sampling (IS) and sequential Monte Carlo (SMC) in doubly intractable problems,
but focussed only on SMC algorithms that used data-point tempering. This paper
describes SMC samplers that may use alternative sequences of distributions, and
describes ways in which likelihood estimates may be improved adaptively as the
algorithm progresses, building on ideas from Moores et al. (2015). This
approach is compared with a number of alternative algorithms for doubly
intractable problems, including approximate Bayesian computation (ABC), which
we show is closely related to the method of M{\o}ller et al. (2006).
Sign-Constrained Regularized Loss Minimization
Authors: Tsuyoshi Kato, Misato Kobayashi, Daisuke Sano
Date: 2017-10-12 00:00:00

In practical analysis, domain knowledge about analysis target has often been
accumulated, although, typically, such knowledge has been discarded in the
statistical analysis stage, and the statistical tool has been applied as a
black box. In this paper, we introduce sign constraints that are a handy and
simple representation for non-experts in generic learning problems. We have
developed two new optimization algorithms for the sign-constrained regularized
loss minimization, called the sign-constrained Pegasos (SC-Pega) and the
sign-constrained SDCA (SC-SDCA), by simply inserting the sign correction step
into the original Pegasos and SDCA, respectively. We present theoretical
analyses that guarantee that insertion of the sign correction step does not
degrade the convergence rate for both algorithms. Two applications, where the
sign-constrained learning is effective, are presented. The one is exploitation
of prior information about correlation between explanatory variables and a
target variable. The other is introduction of the sign-constrained to
SVM-Pairwise method. Experimental results demonstrate significant improvement
of generalization performance by introducing sign constraints in both
applications.
Fast, Accurate and Fully Parallelizable Digital Image Correlation
Authors: Peihan Tu
Date: 2017-10-12 00:00:00

Digital image correlation (DIC) is a widely used optical metrology for surface deformation measurements. DIC relies on nonlinear optimization method. Thus an initial guess is quite important due to its influence on the converge characteristics of the algorithm. In order to obtain a reliable, accurate initial guess, a reliability-guided digital image correlation (RG-DIC) method, which is able to intelligently obtain a reliable initial guess without using time-consuming integer-pixel registration, was proposed. However, the RG-DIC and its improved methods are path-dependent and cannot be fully parallelized. Besides, it is highly possible that RG-DIC fails in the full-field analysis of deformation without manual intervention if the deformation fields contain large areas of discontinuous deformation. Feature-based initial guess is highly robust while it is relatively time-consuming. Recently, path-independent algorithm, fast Fourier transform-based cross correlation (FFT-CC) algorithm, was proposed to estimate the initial guess. Complete parallelizability is the major advantage of the FFT-CC algorithm, while it is sensitive to small deformation. Wu et al proposed an efficient integer-pixel search scheme, but the parameters of this algorithm are set by the users empirically. In this technical note, a fully parallelizable DIC method is proposed. Different from RG-DIC method, the proposed method divides DIC algorithm into two parts: full-field initial guess estimation and sub-pixel registration. The proposed method has the following benefits: 1) providing a pre-knowledge of deformation fields; 2) saving computational time; 3) reducing error propagation; 4) integratability with well-established DIC algorithms; 5) fully parallelizability.
Deep Learning in Multiple Multistep Time Series Prediction
Authors: Chuanyun Zang
Date: 2017-10-12 00:00:00
Tasks: Deep Learning, Prediction, Time Series, Time Series Analysis, Time Series Forecasting, Time Series Prediction

The project aims to research on combining deep learning specifically
Long-Short Memory (LSTM) and basic statistics in multiple multistep time series
prediction. LSTM can dive into all the pages and learn the general trends of
variation in a large scope, while the well selected medians for each page can
keep the special seasonality of different pages so that the future trend will
not fluctuate too much from the reality. A recent Kaggle competition on 145K
Web Traffic Time Series Forecasting [1] is used to thoroughly illustrate and
test this idea.
Restricted Strong Convexity Implies Weak Submodularity
Authors: Ethan R. Elenberg, Rajiv Khanna, Alexandros G. Dimakis, Sahand Negahban
Date: 2016-12-02 00:00:00
Tasks: feature selection

We connect high-dimensional subset selection and submodular maximization. Our
results extend the work of Das and Kempe (2011) from the setting of linear
regression to arbitrary objective functions. For greedy feature selection, this
connection allows us to obtain strong multiplicative performance bounds on
several methods without statistical modeling assumptions. We also derive
recovery guarantees of this form under standard assumptions. Our work shows
that greedy algorithms perform within a constant factor from the best possible
subset-selection solution for a broad class of general objective functions. Our
methods allow a direct control over the number of obtained features as opposed
to regularization parameters that only implicitly control sparsity. Our proof
technique uses the concept of weak submodularity initially defined by Das and
Kempe. We draw a connection between convex analysis and submodular set function
theory which may be of independent interest for other statistical learning
applications that have combinatorial structure.
Fast initial guess estimation for digital image correlation
Authors: Peihan Tu
Date: 2017-10-12 00:00:00

Digital image correlation (DIC) is a widely used optical metrology for quantitative deformation measurement due to its non-contact, low-cost, highly precise feature. DIC relies on nonlinear optimization algorithm. Thus it is quite important to efficiently obtain a reliable initial guess. The most widely used method for obtaining initial guess is reliability-guided digital image correlation (RG-DIC) method, which is reliable but path-dependent. This path-dependent method limits the further improvement of computation speed of DIC using parallel computing technology, and error of calculation may be spread out along the calculation path. Therefore, a reliable and path-independent algorithm which is able to provide reliable initial guess is desirable to reach full potential of the ability of parallel computing. In this paper, an algorithm used for initial guess estimation is proposed. Numerical and real experiments show that the proposed algorithm, adaptive incremental dissimilarity approximations algorithm (A-IDA), has the following characteristics: 1) Compared with inverse compositional Gauss-Newton (IC-GN) sub-pixel registration algorithm, the computational time required by A-IDA algorithm is negligible, especially when subset size is relatively large; 2) the efficiency of A-IDA algorithm is less influenced by search range; 3) the efficiency is less influenced by subset size; 4) it is easy to select the threshold for the proposed algorithm.
Capturing Long-range Contextual Dependencies with Memory-enhanced Conditional Random Fields
Authors: Fei Liu, Timothy Baldwin, Trevor Cohn
Date: 2017-09-12 00:00:00
Proceeding: IJCNLP 2017 11

Despite successful applications across a broad range of NLP tasks,
conditional random fields ("CRFs"), in particular the linear-chain variant, are
only able to model local features. While this has important benefits in terms
of inference tractability, it limits the ability of the model to capture
long-range dependencies between items. Attempts to extend CRFs to capture
long-range dependencies have largely come at the cost of computational
complexity and approximate inference. In this work, we propose an extension to
CRFs by integrating external memory, taking inspiration from memory networks,
thereby allowing CRFs to incorporate information far beyond neighbouring steps.
Experiments across two tasks show substantial improvements over strong CRF and
LSTM baselines.
Joint Image Filtering with Deep Convolutional Networks
Authors: Yijun Li, Jia-Bin Huang, Narendra Ahuja, Ming-Hsuan Yang
Date: 2017-10-11 00:00:00

Joint image filters leverage the guidance image as a prior and transfer the
structural details from the guidance image to the target image for suppressing
noise or enhancing spatial resolution. Existing methods either rely on various
explicit filter constructions or hand-designed objective functions, thereby
making it difficult to understand, improve, and accelerate these filters in a
coherent framework. In this paper, we propose a learning-based approach for
constructing joint filters based on Convolutional Neural Networks. In contrast
to existing methods that consider only the guidance image, the proposed
algorithm can selectively transfer salient structures that are consistent with
both guidance and target images. We show that the model trained on a certain
type of data, e.g., RGB and depth images, generalizes well to other modalities,
e.g., flash/non-Flash and RGB/NIR images. We validate the effectiveness of the
proposed joint filter through extensive experimental evaluations with
state-of-the-art methods.
A Unified Neural Network Approach for Estimating Travel Time and Distance for a Taxi Trip
Authors: Ishan Jindal, Tony, Qin, Xue-wen Chen, Matthew Nokleby, Jieping Ye
Date: 2017-10-12 00:00:00
Tasks: Feature Engineering, Management, Travel Time Estimation

In building intelligent transportation systems such as taxi or rideshare
services, accurate prediction of travel time and distance is crucial for
customer experience and resource management. Using the NYC taxi dataset, which
contains taxi trips data collected from GPS-enabled taxis [23], this paper
investigates the use of deep neural networks to jointly predict taxi trip time
and distance. We propose a model, called ST-NN (Spatio-Temporal Neural
Network), which first predicts the travel distance between an origin and a
destination GPS coordinate, then combines this prediction with the time of day
to predict the travel time. The beauty of ST-NN is that it uses only the raw
trips data without requiring further feature engineering and provides a joint
estimate of travel time and distance. We compare the performance of ST-NN to
that of state-of-the-art travel time estimation methods, and we observe that
the proposed approach generalizes better than state-of-the-art methods. We show
that ST-NN approach significantly reduces the mean absolute error for both
predicted travel time and distance, about 17% for travel time prediction. We
also observe that the proposed approach is more robust to outliers present in
the dataset by testing the performance of ST-NN on the datasets with and
without outliers.
A Finite Element Computational Framework for Active Contours on Graphs
Authors: Nikolaos Kolotouros, Petros Maragos
Date: 2017-10-12 00:00:00
Tasks: Image Segmentation, Semantic Segmentation

In this paper we present a new framework for the solution of active contour
models on graphs. With the use of the Finite Element Method we generalize
active contour models on graphs and reduce the problem from a partial
differential equation to the solution of a sparse non-linear system.
Additionally, we extend the proposed framework to solve models where the curve
evolution is locally constrained around its current location. Based on the
previous extension, we propose a fast algorithm for the solution of a wide
range active contour models. Last, we present a supervised extension of
Geodesic Active Contours for image segmentation and provide experimental
evidence for the effectiveness of our framework.
Using Context Events in Neural Network Models for Event Temporal Status Identification
Authors: Zeyu Dai, Wenlin Yao, Ruihong Huang
Date: 2017-10-12 00:00:00
Proceeding: IJCNLP 2017 11

Focusing on the task of identifying event temporal status, we find that
events directly or indirectly governing the target event in a dependency tree
are most important contexts. Therefore, we extract dependency chains containing
context events and use them as input in neural network models, which
consistently outperform previous models using local context words as input.
Visualization verifies that the dependency chain representation can effectively
capture the context events which are closely related to the target event and
play key roles in predicting event temporal status.