pwc-archive/papers-with-abstracts huggingface dataset:
Data Science in Service of Performing Arts: Applying Machine Learning to Predicting Audience Preferences
Authors: Jacob Abernethy, Cyrus Anderson, Alex Chojnacki, Chengyu Dai, John Dryden, Eric Schwartz, Wenbo Shen, Jonathan Stroud, Laura Wendlandt, Sheng Yang, Daniel Zhang
Date: 2016-09-30 00:00:00
Tasks: BIG-bench Machine Learning, Collaborative Filtering, Marketing

Performing arts organizations aim to enrich their communities through the
arts. To do this, they strive to match their performance offerings to the taste
of those communities. Success relies on understanding audience preference and
predicting their behavior. Similar to most e-commerce or digital entertainment
firms, arts presenters need to recommend the right performance to the right
customer at the right time. As part of the Michigan Data Science Team (MDST),
we partnered with the University Musical Society (UMS), a non-profit performing
arts presenter housed in the University of Michigan, Ann Arbor. We are
providing UMS with analysis and business intelligence, utilizing historical
individual-level sales data. We built a recommendation system based on
collaborative filtering, gaining insights into the artistic preferences of
customers, along with the similarities between performances. To better
understand audience behavior, we used statistical methods from customer-base
analysis. We characterized customer heterogeneity via segmentation, and we
modeled customer cohorts to understand and predict ticket purchasing patterns.
Finally, we combined statistical modeling with natural language processing
(NLP) to explore the impact of wording in program descriptions. These ongoing
efforts provide a platform to launch targeted marketing campaigns, helping UMS
carry out its mission by allocating its resources more efficiently. Celebrating
its 138th season, UMS is a 2014 recipient of the National Medal of Arts, and it
continues to enrich communities by connecting world-renowned artists with
diverse audiences, especially students in their formative years. We aim to
contribute to that mission through data science and customer analytics.
Digitizing Municipal Street Inspections Using Computer Vision
Authors: Varun Adibhatla, Shi Fan, Krystof Litomisky, Patrick Atwater
Date: 2016-09-30 00:00:00
Tasks: Decision Making

"People want an authority to tell them how to value things. But they chose
this authority not based on facts or results. They chose it because it seems
authoritative and familiar." - The Big Short
  The pavement condition index is one such a familiar measure used by many US
cities to measure street quality and justify billions of dollars spent every
year on street repair. These billion-dollar decisions are based on evaluation
criteria that are subjective and not representative. In this paper, we build
upon our initial submission to D4GX 2015 that approaches this problem of
information asymmetry in municipal decision-making.
  We describe a process to identify street-defects using computer vision
techniques on data collected using the Street Quality Identification Device
(SQUID). A User Interface to host a large quantity of image data towards
digitizing the street inspection process and enabling actionable intelligence
for a core public service is also described. This approach of combining device,
data and decision-making around street repair enables cities make targeted
decisions about street repair and could lead to an anticipatory response which
can result in significant cost savings. Lastly, we share lessons learnt from
the deployment of SQUID in the city of Syracuse, NY.
Training Recurrent Answering Units with Joint Loss Minimization for VQA
Authors: Hyeonwoo Noh, Bohyung Han
Date: 2016-06-12 00:00:00
Tasks: Question Answering, Visual Question Answering, Visual Question Answering (VQA)

We propose a novel algorithm for visual question answering based on a
recurrent deep neural network, where every module in the network corresponds to
a complete answering unit with attention mechanism by itself. The network is
optimized by minimizing loss aggregated from all the units, which share model
parameters while receiving different information to compute attention
probability. For training, our model attends to a region within image feature
map, updates its memory based on the question and attended image feature, and
answers the question based on its memory state. This procedure is performed to
compute loss in each step. The motivation of this approach is our observation
that multi-step inferences are often required to answer questions while each
problem may have a unique desirable number of steps, which is difficult to
identify in practice. Hence, we always make the first unit in the network solve
problems, but allow it to learn the knowledge from the rest of units by
backpropagation unless it degrades the model. To implement this idea, we
early-stop training each unit as soon as it starts to overfit. Note that, since
more complex models tend to overfit on easier questions quickly, the last
answering unit in the unfolded recurrent neural network is typically killed
first while the first one remains last. We make a single-step prediction for a
new question using the shared model. This strategy works better than the other
options within our framework since the selected model is trained effectively
from all units without overfitting. The proposed algorithm outperforms other
multi-step attention based approaches using a single step prediction in VQA
dataset.
Referential Uncertainty and Word Learning in High-dimensional, Continuous Meaning Spaces
Authors: Michael Spranger, Katrien Beuls
Date: 2016-09-30 00:00:00
Tasks: BIG-bench Machine Learning, Sensitivity, Vocal Bursts Intensity Prediction

This paper discusses lexicon word learning in high-dimensional meaning spaces
from the viewpoint of referential uncertainty. We investigate various
state-of-the-art Machine Learning algorithms and discuss the impact of scaling,
representation and meaning space structure. We demonstrate that current Machine
Learning techniques successfully deal with high-dimensional meaning spaces. In
particular, we show that exponentially increasing dimensions linearly impact
learner performance and that referential uncertainty from word sensitivity has
no impact.
Quantifying Urban Traffic Anomalies
Authors: Zhengyi Zhou, Philipp Meerkamp, Chris Volinsky
Date: 2016-09-30 00:00:00

Detecting and quantifying anomalies in urban traffic is critical for
real-time alerting or re-routing in the short run and urban planning in the
long run. We describe a two-step framework that achieves these two goals in a
robust, fast, online, and unsupervised manner. First, we adapt stable principal
component pursuit to detect anomalies for each road segment. This allows us to
pinpoint traffic anomalies early and precisely in space. Then we group the
road-level anomalies across time and space into meaningful anomaly events using
a simple graph expansion procedure. These events can be easily clustered,
visualized, and analyzed by urban planners. We demonstrate the effectiveness of
our system using 7 weeks of anonymized and aggregated cellular location data in
Dallas-Fort Worth. We suggest potential opportunities for urban planners and
policy makers to use our methodology to make informed changes. These
applications include real-time re-routing of traffic in response to abnormally
high traffic, or identifying candidates for high-impact infrastructure
projects.
Asynchronous Multi-Task Learning
Authors: Inci M. Baytas, Ming Yan, Anil K. Jain, Jiayu Zhou
Date: 2016-09-30 00:00:00
Tasks: Multi-Task Learning, Transfer Learning

Many real-world machine learning applications involve several learning tasks
which are inter-related. For example, in healthcare domain, we need to learn a
predictive model of a certain disease for many hospitals. The models for each
hospital may be different because of the inherent differences in the
distributions of the patient populations. However, the models are also closely
related because of the nature of the learning tasks modeling the same disease.
By simultaneously learning all the tasks, multi-task learning (MTL) paradigm
performs inductive knowledge transfer among tasks to improve the generalization
performance. When datasets for the learning tasks are stored at different
locations, it may not always be feasible to transfer the data to provide a
data-centralized computing environment due to various practical issues such as
high data volume and privacy. In this paper, we propose a principled MTL
framework for distributed and asynchronous optimization to address the
aforementioned challenges. In our framework, gradient update does not wait for
collecting the gradient information from all the tasks. Therefore, the proposed
method is very efficient when the communication delay is too high for some task
nodes. We show that many regularized MTL formulations can benefit from this
framework, including the low-rank MTL for shared subspace learning. Empirical
studies on both synthetic and real-world datasets demonstrate the efficiency
and effectiveness of the proposed framework.
Controlling Output Length in Neural Encoder-Decoders
Authors: Yuta Kikuchi, Graham Neubig, Ryohei Sasano, Hiroya Takamura, Manabu Okumura
Date: 2016-09-30 00:00:00
Proceeding: EMNLP 2016 11
Tasks: Decoder, Text Summarization

Neural encoder-decoder models have shown great success in many sequence
generation tasks. However, previous work has not investigated situations in
which we would like to control the length of encoder-decoder outputs. This
capability is crucial for applications such as text summarization, in which we
have to generate concise summaries with a desired length. In this paper, we
propose methods for controlling the output sequence length for neural
encoder-decoder models: two decoding-based methods and two learning-based
methods. Results show that our learning-based methods have the capability to
control length without degrading summary quality in a summarization task.
Two-stage Convolutional Part Heatmap Regression for the 1st 3D Face Alignment in the Wild (3DFAW) Challenge
Authors: Adrian Bulat, Georgios Tzimiropoulos
Date: 2016-09-29 00:00:00
Tasks: 3D Face Alignment, Depth Estimation, Face Alignment, regression

This paper describes our submission to the 1st 3D Face Alignment in the Wild
(3DFAW) Challenge. Our method builds upon the idea of convolutional part
heatmap regression [1], extending it for 3D face alignment. Our method
decomposes the problem into two parts: (a) X,Y (2D) estimation and (b) Z
(depth) estimation. At the first stage, our method estimates the X,Y
coordinates of the facial landmarks by producing a set of 2D heatmaps, one for
each landmark, using convolutional part heatmap regression. Then, these
heatmaps, alongside the input RGB image, are used as input to a very deep
subnetwork trained via residual learning for regressing the Z coordinate. Our
method ranked 1st in the 3DFAW Challenge, surpassing the second best result by
more than 22%.
Algorithms for item categorization based on ordinal ranking data
Authors: Josh Girson, Shuchin Aeron
Date: 2016-09-29 00:00:00
Tasks: Community Detection, Stochastic Block Model, Variational Inference

We present a new method for identifying the latent categorization of items
based on their rankings. Complimenting a recent work that uses a Dirichlet
prior on preference vectors and variational inference, we show that this
problem can be effectively dealt with using existing community detection
algorithms, with the communities corresponding to item categories. In
particular we convert the bipartite ranking data to a unipartite graph of item
affinities, and apply community detection algorithms. In this context we modify
an existing algorithm - namely the label propagation algorithm to a variant
that uses the distance between the nodes for weighting the label propagation -
to identify the categories. We propose and analyze a synthetic ordinal ranking
model and show its relation to the recently much studied stochastic block
model. We test our algorithms on synthetic data and compare performance with
several popular community detection algorithms. We also test the method on real
data sets of movie categorization from the Movie Lens database. In all of the
cases our algorithm is able to identify the categories for a suitable choice of
tuning parameter.
Environmental Noise Embeddings for Robust Speech Recognition
Authors: Suyoun Kim, Bhiksha Raj, Ian Lane
Date: 2016-01-11 00:00:00
Tasks: Management, Multi-Task Learning, Robust Speech Recognition, speech-recognition, Speech Recognition

We propose a novel deep neural network architecture for speech recognition
that explicitly employs knowledge of the background environmental noise within
a deep neural network acoustic model. A deep neural network is used to predict
the acoustic environment in which the system in being used. The discriminative
embedding generated at the bottleneck layer of this network is then
concatenated with traditional acoustic features as input to a deep neural
network acoustic model. Through a series of experiments on Resource Management,
CHiME-3 task, and Aurora4, we show that the proposed approach significantly
improves speech recognition accuracy in noisy and highly reverberant
environments, outperforming multi-condition training, noise-aware training,
i-vector framework, and multi-task learning on both in-domain noise and unseen
noise.
Multi-dimensional signal approximation with sparse structured priors using split Bregman iterations
Authors: Yoann Isaac, Quentin Barthélemy, Cédric Gouy-Pailler, Michèle Sebag, Jamal Atif
Date: 2016-09-29 00:00:00

This paper addresses the structurally-constrained sparse decomposition of
multi-dimensional signals onto overcomplete families of vectors, called
dictionaries. The contribution of the paper is threefold. Firstly, a generic
spatio-temporal regularization term is designed and used together with the
standard $\ell_1$ regularization term to enforce a sparse decomposition
preserving the spatio-temporal structure of the signal. Secondly, an
optimization algorithm based on the split Bregman approach is proposed to
handle the associated optimization problem, and its convergence is analyzed.
Our well-founded approach yields same accuracy as the other algorithms at the
state-of-the-art, with significant gains in terms of convergence speed.
Thirdly, the empirical validation of the approach on artificial and real-world
problems demonstrates the generality and effectiveness of the method. On
artificial problems, the proposed regularization subsumes the Total Variation
minimization and recovers the expected decomposition. On the real-world problem
of electro-encephalography brainwave decomposition, the approach outperforms
similar approaches in terms of P300 evoked potentials detection, using
structured spatial priors to guide the decomposition.
Max-plus statistical leverage scores
Authors: James Hook
Date: 2016-09-29 00:00:00

The statistical leverage scores of a complex matrix $A\in\mathbb{C}^{n\times
d}$ record the degree of alignment between col$(A)$ and the coordinate axes in
$\mathbb{C}^n$. These score are used in random sampling algorithms for solving
certain numerical linear algebra problems. In this paper we present a max-plus
algebraic analogue for statistical leverage scores. We show that max-plus
statistical leverage scores can be used to calculate the exact asymptotic
behavior of the conventional statistical leverage scores of a generic matrices
of Puiseux series and also provide a novel way to approximate the conventional
statistical leverage scores of a fixed or complex matrix. The advantage of
approximating a complex matrices scores with max-plus scores is that the
max-plus scores can be computed very quickly. This approximation is typically
accurate to within an order or magnitude and should be useful in practical
problems where the true scores are known to vary widely.
Fast learning rates with heavy-tailed losses
Authors: Vu Dinh, Lam Si Tung Ho, Duy Nguyen, Binh T. Nguyen
Date: 2016-09-29 00:00:00
Proceeding: NeurIPS 2016 12
Tasks: Clustering, Quantization

We study fast learning rates when the losses are not necessarily bounded and
may have a distribution with heavy tails. To enable such analyses, we introduce
two new conditions: (i) the envelope function $\sup_{f \in \mathcal{F}}|\ell
\circ f|$, where $\ell$ is the loss function and $\mathcal{F}$ is the
hypothesis class, exists and is $L^r$-integrable, and (ii) $\ell$ satisfies the
multi-scale Bernstein's condition on $\mathcal{F}$. Under these assumptions, we
prove that learning rate faster than $O(n^{-1/2})$ can be obtained and,
depending on $r$ and the multi-scale Bernstein's powers, can be arbitrarily
close to $O(n^{-1})$. We then verify these assumptions and derive fast learning
rates for the problem of vector quantization by $k$-means clustering with
heavy-tailed distributions. The analyses enable us to obtain novel learning
rates that extend and complement existing results in the literature from both
theoretical and practical viewpoints.
Reconstructing Vechicles from a Single Image: Shape Priors for Road Scene Understanding
Authors: J. Krishna Murthy, G. V. Sai Krishna, Falak Chhaya, K. Madhava Krishna
Date: 2016-09-29 00:00:00
Tasks: Autonomous Driving, road scene understanding, Scene Understanding

We present an approach for reconstructing vehicles from a single (RGB) image,
in the context of autonomous driving. Though the problem appears to be
ill-posed, we demonstrate that prior knowledge about how 3D shapes of vehicles
project to an image can be used to reason about the reverse process, i.e., how
shapes (back-)project from 2D to 3D. We encode this knowledge in \emph{shape
priors}, which are learnt over a small keypoint-annotated dataset. We then
formulate a shape-aware adjustment problem that uses the learnt shape priors to
recover the 3D pose and shape of a query object from an image. For shape
representation and inference, we leverage recent successes of Convolutional
Neural Networks (CNNs) for the task of object and keypoint localization, and
train a novel cascaded fully-convolutional architecture to localize vehicle
\emph{keypoints} in images. The shape-aware adjustment then robustly recovers
shape (3D locations of the detected keypoints) while simultaneously filling in
occluded keypoints. To tackle estimation errors incurred due to erroneously
detected keypoints, we use an Iteratively Re-weighted Least Squares (IRLS)
scheme for robust optimization, and as a by-product characterize noise models
for each predicted keypoint. We evaluate our approach on autonomous driving
benchmarks, and present superior results to existing monocular, as well as
stereo approaches.
Maximum Entropy Vector Kernels for MIMO system identification
Authors: Giulia Prando, Gianluigi Pillonetto, Alessandro Chiuso
Date: 2015-08-12 00:00:00

Recent contributions have framed linear system identification as a
nonparametric regularized inverse problem. Relying on $\ell_2$-type
regularization which accounts for the stability and smoothness of the impulse
response to be estimated, these approaches have been shown to be competitive
w.r.t classical parametric methods. In this paper, adopting Maximum Entropy
arguments, we derive a new $\ell_2$ penalty deriving from a vector-valued
kernel; to do so we exploit the structure of the Hankel matrix, thus
controlling at the same time complexity, measured by the McMillan degree,
stability and smoothness of the identified models. As a special case we recover
the nuclear norm penalty on the squared block Hankel matrix. In contrast with
previous literature on reweighted nuclear norm penalties, our kernel is
described by a small number of hyper-parameters, which are iteratively updated
through marginal likelihood maximization; constraining the structure of the
kernel acts as a (hyper)regularizer which helps controlling the effective
degrees of freedom of our estimator. To optimize the marginal likelihood we
adapt a Scaled Gradient Projection (SGP) algorithm which is proved to be
significantly computationally cheaper than other first and second order
off-the-shelf optimization methods. The paper also contains an extensive
comparison with many state-of-the-art methods on several Monte-Carlo studies,
which confirms the effectiveness of our procedure.
Redefining Binarization and the Visual Archetype
Authors: Anguelos Nicolaou, Liwicki Marcus
Date: 2016-09-29 00:00:00
Tasks: Binarization

Although binarization is considered passe, it still remains a highly popular
research topic. In this paper we propose a rethinking of what binarization is.
We introduce the notion of the visual archetype as the ideal form of any one
document. Binarization can be defined as the restoration of the visual
archetype for a class of images. This definition broadens the scope of what
binarization means but also suggests ground-truth should focus on the
foreground.
A Searchlight Factor Model Approach for Locating Shared Information in Multi-Subject fMRI Analysis
Authors: Hejia Zhang, Po-Hsuan Chen, Janice Chen, Xia Zhu, Javier S. Turek, Theodore L. Willke, Uri Hasson, Peter J. Ramadge
Date: 2016-09-29 00:00:00
Tasks: General Classification

There is a growing interest in joint multi-subject fMRI analysis. The
challenge of such analysis comes from inherent anatomical and functional
variability across subjects. One approach to resolving this is a shared
response factor model. This assumes a shared and time synchronized stimulus
across subjects. Such a model can often identify shared information, but it may
not be able to pinpoint with high resolution the spatial location of this
information. In this work, we examine a searchlight based shared response model
to identify shared information in small contiguous regions (searchlights)
across the whole brain. Validation using classification tasks demonstrates that
we can pinpoint informative local regions.
Inducing Multilingual Text Analysis Tools Using Bidirectional Recurrent Neural Networks
Authors: Othman Zennaki, Nasredine Semmar, Laurent Besacier
Date: 2016-09-29 00:00:00
Proceeding: COLING 2016 12
Tasks: POS, Translation, Word Alignment

This work focuses on the rapid development of linguistic annotation tools for
resource-poor languages. We experiment several cross-lingual annotation
projection methods using Recurrent Neural Networks (RNN) models. The
distinctive feature of our approach is that our multilingual word
representation requires only a parallel corpus between the source and target
language. More precisely, our method has the following characteristics: (a) it
does not use word alignment information, (b) it does not assume any knowledge
about foreign languages, which makes it applicable to a wide range of
resource-poor languages, (c) it provides truly multilingual taggers. We
investigate both uni- and bi-directional RNN models and propose a method to
include external information (for instance low level information from POS) in
the RNN to train higher level taggers (for instance, super sense taggers). We
demonstrate the validity and genericity of our model by using parallel corpora
(obtained by manual or automatic translation). Our experiments are conducted to
induce cross-lingual POS and super sense taggers.