pwc-archive/papers-with-abstracts huggingface dataset:
Video based Contextual Question Answering
Authors: Akash Ganesan, Divyansh Pal, Karthik Muthuraman, Shubham Dash
Date: 2018-04-19 00:00:00
Tasks: Question Answering

The primary aim of this project is to build a contextual Question-Answering
model for videos. The current methodologies provide a robust model for image
based Question-Answering, but we are aim to generalize this approach to be
videos. We propose a graphical representation of video which is able to handle
several types of queries across the whole video. For example, if a frame has an
image of a man and a cat sitting, it should be able to handle queries like,
where is the cat sitting with respect to the man? or ,what is the man holding
in his hand?. It should be able to answer queries relating to temporal
relationships also.
MR Acquisition-Invariant Representation Learning
Authors: Wouter M. Kouw, Marco Loog, Lambertus W. Bartels, Adriënne M. Mendrik
Date: 2017-09-22 00:00:00
Tasks: Classification, General Classification, Representation Learning

Voxelwise classification approaches are popular and effective methods for
tissue quantification in brain magnetic resonance imaging (MRI) scans. However,
generalization of these approaches is hampered by large differences between
sets of MRI scans such as differences in field strength, vendor or acquisition
protocols. Due to this acquisition related variation, classifiers trained on
data from a specific scanner fail or under-perform when applied to data that
was acquired differently. In order to address this lack of generalization, we
propose a Siamese neural network (MRAI-net) to learn a representation that
minimizes the between-scanner variation, while maintaining the contrast between
brain tissues necessary for brain tissue quantification. The proposed MRAI-net
was evaluated on both simulated and real MRI data. After learning the MR
acquisition invariant representation, any supervised classification model that
uses feature vectors can be applied. In this paper, we provide a proof of
principle, which shows that a linear classifier applied on the MRAI
representation is able to outperform supervised convolutional neural network
classifiers for tissue classification when little target training data is
available.
A Predictive Model for Notional Anaphora in English
Authors: Amir Zeldes
Date: 2018-04-19 00:00:00
Proceeding: WS 2018 6
Tasks: coreference-resolution, Coreference Resolution, model, Referring Expression, Referring expression generation

Notional anaphors are pronouns which disagree with their antecedents'
grammatical categories for notional reasons, such as plural to singular
agreement in: 'the government ... they'. Since such cases are rare and conflict
with evidence from strictly agreeing cases ('the government ... it'), they
present a substantial challenge to both coreference resolution and referring
expression generation. Using the OntoNotes corpus, this paper takes an ensemble
approach to predicting English notional anaphora in context on the basis of the
largest empirical data to date. In addition to state of the art prediction
accuracy, the results suggest that theoretical approaches positing a plural
construal at the antecedent's utterance are insufficient, and that
circumstances at the anaphor's utterance location, as well as global factors
such as genre, have a strong effect on the choice of referring expression.
Minimizing Area and Energy of Deep Learning Hardware Design Using Collective Low Precision and Structured Compression
Authors: Shihui Yin, Gaurav Srivastava, Shreyas K. Venkataramanaiah, Chaitali Chakrabarti, Visar Berisha, Jae-sun Seo
Date: 2018-04-19 00:00:00
Tasks: Binarization

Deep learning algorithms have shown tremendous success in many recognition
tasks; however, these algorithms typically include a deep neural network (DNN)
structure and a large number of parameters, which makes it challenging to
implement them on power/area-constrained embedded platforms. To reduce the
network size, several studies investigated compression by introducing
element-wise or row-/column-/block-wise sparsity via pruning and
regularization. In addition, many recent works have focused on reducing
precision of activations and weights with some reducing down to a single bit.
However, combining various sparsity structures with binarized or
very-low-precision (2-3 bit) neural networks have not been comprehensively
explored. In this work, we present design techniques for minimum-area/-energy
DNN hardware with minimal degradation in accuracy. During training, both
binarization/low-precision and structured sparsity are applied as constraints
to find the smallest memory footprint for a given deep learning algorithm. The
DNN model for CIFAR-10 dataset with weight memory reduction of 50X exhibits
accuracy comparable to that of the floating-point counterpart. Area,
performance and energy results of DNN hardware in 40nm CMOS are reported for
the MNIST dataset. The optimized DNN that combines 8X structured compression
and 3-bit weight precision showed 98.4% accuracy at 20nJ per classification.
Survey of Face Detection on Low-quality Images
Authors: Yuqian Zhou, Ding Liu, Thomas Huang
Date: 2018-04-19 00:00:00
Tasks: Face Detection, Robust Design, Survey

Face detection is a well-explored problem. Many challenges on face detectors
like extreme pose, illumination, low resolution and small scales are studied in
the previous work. However, previous proposed models are mostly trained and
tested on good-quality images which are not always the case for practical
applications like surveillance systems. In this paper, we first review the
current state-of-the-art face detectors and their performance on benchmark
dataset FDDB, and compare the design protocols of the algorithms. Secondly, we
investigate their performance degradation while testing on low-quality images
with different levels of blur, noise, and contrast. Our results demonstrate
that both hand-crafted and deep-learning based face detectors are not robust
enough for low-quality images. It inspires researchers to produce more robust
design for face detection in the wild.
On the Complexity of the Weighted Fused Lasso
Authors: Jose Bento, Ralph Furmaniak, Surjyendu Ray
Date: 2018-01-15 00:00:00

The solution path of the 1D fused lasso for an $n$-dimensional input is
piecewise linear with $\mathcal{O}(n)$ segments (Hoefling et al. 2010 and
Tibshirani et al 2011). However, existing proofs of this bound do not hold for
the weighted fused lasso. At the same time, results for the generalized lasso,
of which the weighted fused lasso is a special case, allow $\Omega(3^n)$
segments (Mairal et al. 2012). In this paper, we prove that the number of
segments in the solution path of the weighted fused lasso is
$\mathcal{O}(n^2)$, and that, for some instances, it is $\Omega(n^2)$. We also
give a new, very simple, proof of the $\mathcal{O}(n)$ bound for the fused
lasso.
Unsupervised Representation Adversarial Learning Network: from Reconstruction to Generation
Authors: Yuqian Zhou, Kuangxiao Gu, Thomas Huang
Date: 2018-04-19 00:00:00
Tasks: Clustering, General Classification

A good representation for arbitrarily complicated data should have the
capability of semantic generation, clustering and reconstruction. Previous
research has already achieved impressive performance on either one. This paper
aims at learning a disentangled representation effective for all of them in an
unsupervised way. To achieve all the three tasks together, we learn the forward
and inverse mapping between data and representation on the basis of a symmetric
adversarial process. In theory, we minimize the upper bound of the two
conditional entropy loss between the latent variables and the observations
together to achieve the cycle consistency. The newly proposed RepGAN is tested
on MNIST, fashionMNIST, CelebA, and SVHN datasets to perform unsupervised
classification, generation and reconstruction tasks. The result demonstrates
that RepGAN is able to learn a useful and competitive representation. To the
author's knowledge, our work is the first one to achieve both a high
unsupervised classification accuracy and low reconstruction error on MNIST.
Codes are available at https://github.com/yzhouas/RepGAN-tensorflow.
Sampling-free Uncertainty Estimation in Gated Recurrent Units with Exponential Families
Authors: Seong Jae Hwang, Ronak Mehta, Hyunwoo J. Kim, Vikas Singh
Date: 2018-04-19 00:00:00

There has recently been a concerted effort to derive mechanisms in vision and
machine learning systems to offer uncertainty estimates of the predictions they
make. Clearly, there are enormous benefits to a system that is not only
accurate but also has a sense for when it is not sure. Existing proposals
center around Bayesian interpretations of modern deep architectures -- these
are effective but can often be computationally demanding. We show how classical
ideas in the literature on exponential families on probabilistic networks
provide an excellent starting point to derive uncertainty estimates in Gated
Recurrent Units (GRU). Our proposal directly quantifies uncertainty
deterministically, without the need for costly sampling-based estimation. We
demonstrate how our model can be used to quantitatively and qualitatively
measure uncertainty in unsupervised image sequence prediction. To our
knowledge, this is the first result describing sampling-free uncertainty
estimation for powerful sequential models such as GRUs.
Randomized ICA and LDA Dimensionality Reduction Methods for Hyperspectral Image Classification
Authors: Chippy Jayaprakash, Bharath Bhushan Damodaran, Sowmya V, K. P. Soman
Date: 2018-04-19 00:00:00
Tasks: Dimensionality Reduction, General Classification, Hyperspectral Image Classification, image-classification, Image Classification

Dimensionality reduction is an important step in processing the hyperspectral
images (HSI) to overcome the curse of dimensionality problem. Linear
dimensionality reduction methods such as Independent component analysis (ICA)
and Linear discriminant analysis (LDA) are commonly employed to reduce the
dimensionality of HSI. These methods fail to capture non-linear dependency in
the HSI data, as data lies in the nonlinear manifold. To handle this, nonlinear
transformation techniques based on kernel methods were introduced for
dimensionality reduction of HSI. However, the kernel methods involve cubic
computational complexity while computing the kernel matrix, and thus its
potential cannot be explored when the number of pixels (samples) are large. In
literature a fewer number of pixels are randomly selected to partial to
overcome this issue, however this sub-optimal strategy might neglect important
information in the HSI. In this paper, we propose randomized solutions to the
ICA and LDA dimensionality reduction methods using Random Fourier features, and
we label them as RFFICA and RFFLDA. Our proposed method overcomes the
scalability issue and to handle the non-linearities present in the data more
efficiently. Experiments conducted with two real-world hyperspectral datasets
demonstrates that our proposed randomized methods outperform the conventional
kernel ICA and kernel LDA in terms overall, per-class accuracies and
computational time.
Weakly Supervised Representation Learning for Unsynchronized Audio-Visual Events
Authors: Sanjeel Parekh, Slim Essid, Alexey Ozerov, Ngoc Q. K. Duong, Patrick Pérez, Gaël Richard
Date: 2018-04-19 00:00:00
Tasks: Multiple Instance Learning, Representation Learning

Audio-visual representation learning is an important task from the
perspective of designing machines with the ability to understand complex
events. To this end, we propose a novel multimodal framework that instantiates
multiple instance learning. We show that the learnt representations are useful
for classifying events and localizing their characteristic audio-visual
elements. The system is trained using only video-level event labels without any
timing information. An important feature of our method is its capacity to learn
from unsynchronized audio-visual events. We achieve state-of-the-art results on
a large-scale dataset of weakly-labeled audio event videos. Visualizations of
localized visual regions and audio segments substantiate our system's efficacy,
especially when dealing with noisy situations where modality-specific cues
appear asynchronously.
A comparative study of feature selection methods for stress hotspot classification in materials
Authors: Ankita Mangal, Elizabeth A. Holm
Date: 2018-04-19 00:00:00
Tasks: BIG-bench Machine Learning, feature selection, General Classification

The first step in constructing a machine learning model is defining the
features of the data set that can be used for optimal learning. In this work we
discuss feature selection methods, which can be used to build better models, as
well as achieve model interpretability. We applied these methods in the context
of stress hotspot classification problem, to determine what microstructural
characteristics can cause stress to build up in certain grains during uniaxial
tensile deformation. The results show how some feature selection techniques are
biased and demonstrate a preferred technique to get feature rankings for
physical interpretations.
Effects of sampling skewness of the importance-weighted risk estimator on model selection
Authors: Wouter M. Kouw, Marco Loog
Date: 2018-04-19 00:00:00
Tasks: Model Selection, Selection bias

Importance-weighting is a popular and well-researched technique for dealing
with sample selection bias and covariate shift. It has desirable
characteristics such as unbiasedness, consistency and low computational
complexity. However, weighting can have a detrimental effect on an estimator as
well. In this work, we empirically show that the sampling distribution of an
importance-weighted estimator can be skewed. For sample selection bias
settings, and for small sample sizes, the importance-weighted risk estimator
produces overestimates for datasets in the body of the sampling distribution,
i.e. the majority of cases, and large underestimates for data sets in the tail
of the sampling distribution. These over- and underestimates of the risk lead
to suboptimal regularization parameters when used for importance-weighted
validation.
Mathematical Analysis on Out-of-Sample Extensions
Authors: Jianzhong Wang
Date: 2018-04-19 00:00:00
Tasks: Dimensionality Reduction

Let $X=\mathbf{X}\cup\mathbf{Z}$ be a data set in $\mathbb{R}^D$, where
$\mathbf{X}$ is the training set and $\mathbf{Z}$ is the test one. Many
unsupervised learning algorithms based on kernel methods have been developed to
provide dimensionality reduction (DR) embedding for a given training set $\Phi:
\mathbf{X} \to \mathbb{R}^d$ ( $d\ll D$) that maps the high-dimensional data
$\mathbf{X}$ to its low-dimensional feature representation
$\mathbf{Y}=\Phi(\mathbf{X})$. However, these algorithms do not
straightforwardly produce DR of the test set $\mathbf{Z}$. An out-of-sample
extension method provides DR of $\mathbf{Z}$ using an extension of the existent
embedding $\Phi$, instead of re-computing the DR embedding for the whole set
$X$. Among various out-of-sample DR extension methods, those based on
Nystr\"{o}m approximation are very attractive. Many papers have developed such
out-of-extension algorithms and shown their validity by numerical experiments.
However, the mathematical theory for the DR extension still need further
consideration. Utilizing the reproducing kernel Hilbert space (RKHS) theory,
this paper develops a preliminary mathematical analysis on the out-of-sample DR
extension operators. It treats an out-of-sample DR extension operator as an
extension of the identity on the RKHS defined on $\mathbf{X}$. Then the
Nystr\"{o}m-type DR extension turns out to be an orthogonal projection. In the
paper, we also present the conditions for the exact DR extension and give the
estimate for the error of the extension.
Stylistic Variation in Social Media Part-of-Speech Tagging
Authors: Murali Raghu Babu Balusu, Taha Merghani, Jacob Eisenstein
Date: 2018-04-19 00:00:00
Proceeding: WS 2018 6
Tasks: Entity Linking, Mixture-of-Experts, Part-Of-Speech Tagging, Sentiment Analysis

Social media features substantial stylistic variation, raising new challenges
for syntactic analysis of online writing. However, this variation is often
aligned with author attributes such as age, gender, and geography, as well as
more readily-available social network metadata. In this paper, we report new
evidence on the link between language and social networks in the task of
part-of-speech tagging. We find that tagger error rates are correlated with
network structure, with high accuracy in some parts of the network, and lower
accuracy elsewhere. As a result, tagger accuracy depends on training from a
balanced sample of the network, rather than training on texts from a narrow
subcommunity. We also describe our attempts to add robustness to stylistic
variation, by building a mixture-of-experts model in which each expert is
associated with a region of the social network. While prior work found that
similar approaches yield performance improvements in sentiment analysis and
entity linking, we were unable to obtain performance improvements in
part-of-speech tagging, despite strong evidence for the link between
part-of-speech error rates and social network structure.
Nonparametric Stochastic Compositional Gradient Descent for Q-Learning in Continuous Markov Decision Problems
Authors: Alec Koppel, Ekaterina Tolstaya, Ethan Stump, Alejandro Ribeiro
Date: 2018-04-19 00:00:00
Tasks: Q-Learning, Stochastic Optimization

We consider Markov Decision Problems defined over continuous state and action
spaces, where an autonomous agent seeks to learn a map from its states to
actions so as to maximize its long-term discounted accumulation of rewards. We
address this problem by considering Bellman's optimality equation defined over
action-value functions, which we reformulate into a nested non-convex
stochastic optimization problem defined over a Reproducing Kernel Hilbert Space
(RKHS). We develop a functional generalization of stochastic quasi-gradient
method to solve it, which, owing to the structure of the RKHS, admits a
parameterization in terms of scalar weights and past state-action pairs which
grows proportionately with the algorithm iteration index. To ameliorate this
complexity explosion, we apply Kernel Orthogonal Matching Pursuit to the
sequence of kernel weights and dictionaries, which yields a controllable error
in the descent direction of the underlying optimization method. We prove that
the resulting algorithm, called KQ-Learning, converges with probability 1 to a
stationary point of this problem, yielding a fixed point of the Bellman
optimality operator under the hypothesis that it belongs to the RKHS. Under
constant learning rates, we further obtain convergence to a small Bellman error
that depends on the chosen learning rates. Numerical evaluation on the
Continuous Mountain Car and Inverted Pendulum tasks yields convergent
parsimonious learned action-value functions, policies that are competitive with
the state of the art, and exhibit reliable, reproducible learning behavior.
Automatic Prediction of Building Age from Photographs
Authors: Matthias Zeppelzauer, Miroslav Despotovic, Muntaha Sakeena, David Koch, Mario Döller
Date: 2018-04-06 00:00:00
Tasks: Age Estimation, Prediction

We present a first method for the automated age estimation of buildings from
unconstrained photographs. To this end, we propose a two-stage approach that
firstly learns characteristic visual patterns for different building epochs at
patch-level and then globally aggregates patch-level age estimates over the
building. We compile evaluation datasets from different sources and perform an
detailed evaluation of our approach, its sensitivity to parameters, and the
capabilities of the employed deep networks to learn characteristic visual
age-related patterns. Results show that our approach is able to estimate
building age at a surprisingly high level that even outperforms human
evaluators and thereby sets a new performance baseline. This work represents a
first step towards the automated assessment of building parameters for
automated price prediction.
Cluster Analysis on Locally Asymptotically Self-similar Processes with Known Number of Clusters
Authors: Qidi Peng, Nan Rao, Ran Zhao
Date: 2018-04-13 00:00:00
Tasks: Clustering

We conduct cluster analysis on a class of locally asymptotically self-similar stochastic processes, which includes multifractional Brownian motion as a representative. When the true number of clusters is supposed to be known, a new covariance-based dissimilarity measure is introduced, from which we obtain the approximately asymptotically consistent clustering algorithms. In simulation studies, clustering data sampled from multifractional Brownian motions with distinct functional Hurst parameters illustrates the approximated asymptotic consistency of the proposed algorithms. Clustering global financial markets' equity indexes returns and sovereign CDS spreads provides a successful real world application.
I/O Logic in HOL --- First Steps
Authors: Christoph Benzmüller, Xavier Parent
Date: 2018-03-26 00:00:00

A semantical embedding of input/output logic in classical higher-order logic
is presented. This embedding enables the mechanisation and automation of
reasoning tasks in input/output logic with off-the-shelf higher-order theorem
provers and proof assistants. The key idea for the solution presented here
results from the analysis of an inaccurate previous embedding attempt, which we
will discuss as well.
Functional Generative Design: An Evolutionary Approach to 3D-Printing
Authors: Cem C. Tutum, Supawit Chockchowwat, Etienne Vouga, Risto Miikkulainen
Date: 2018-04-19 00:00:00

Consumer-grade printers are widely available, but their ability to print
complex objects is limited. Therefore, new designs need to be discovered that
serve the same function, but are printable. A representative such problem is to
produce a working, reliable mechanical spring. The proposed methodology for
discovering solutions to this problem consists of three components: First, an
effective search space is learned through a variational autoencoder (VAE);
second, a surrogate model for functional designs is built; and third, a genetic
algorithm is used to simultaneously update the hyperparameters of the surrogate
and to optimize the designs using the updated surrogate. Using a car-launcher
mechanism as a test domain, spring designs were 3D-printed and evaluated to
update the surrogate model. Two experiments were then performed: First, the
initial set of designs for the surrogate-based optimizer was selected randomly
from the training set that was used for training the VAE model, which resulted
in an exploitative search behavior. On the other hand, in the second
experiment, the initial set was composed of more uniformly selected designs
from the same training set and a more explorative search behavior was observed.
Both of the experiments showed that the methodology generates interesting,
successful, and reliable spring geometries robust to the noise inherent in the
3D printing process. The methodology can be generalized to other functional
design problems, thus making consumer-grade 3D printing more versatile.