pwc-archive/papers-with-abstracts huggingface dataset:
Sparse-promoting Full Waveform Inversion based on Online Orthonormal Dictionary Learning
Authors: Lingchen Zhu, Entao Liu, James H. McClellan
Date: 2015-11-16 00:00:00
Tasks: Dictionary Learning

Full waveform inversion (FWI) delivers high-resolution images of the
subsurface by minimizing iteratively the misfit between the recorded and
calculated seismic data. It has been attacked successfully with the
Gauss-Newton method and sparsity promoting regularization based on fixed
multiscale transforms that permit significant subsampling of the seismic data
when the model perturbation at each FWI data-fitting iteration can be
represented with sparse coefficients. Rather than using analytical transforms
with predefined dictionaries to achieve sparse representation, we introduce an
adaptive transform called the Sparse Orthonormal Transform (SOT) whose
dictionary is learned from many small training patches taken from the model
perturbations in previous iterations. The patch-based dictionary is constrained
to be orthonormal and trained with an online approach to provide the best
sparse representation of the complex features and variations of the entire
model perturbation. The complexity of the training method is proportional to
the cube of the number of samples in one small patch. By incorporating both
compressive subsampling and the adaptive SOT-based representation into the
Gauss-Newton least-squares problem for each FWI iteration, the model
perturbation can be recovered after an l1-norm sparsity constraint is applied
on the SOT coefficients. Numerical experiments on synthetic models demonstrate
that the SOT-based sparsity promoting regularization can provide robust FWI
results with reduced computation.
Answering Complicated Question Intents Expressed in Decomposed Question Sequences
Authors: Mohit Iyyer, Wen-tau Yih, Ming-Wei Chang
Date: 2016-11-04 00:00:00
Tasks: Question Answering, Semantic Parsing

Recent work in semantic parsing for question answering has focused on long
and complicated questions, many of which would seem unnatural if asked in a
normal conversation between two humans. In an effort to explore a
conversational QA setting, we present a more realistic task: answering
sequences of simple but inter-related questions. We collect a dataset of 6,066
question sequences that inquire about semi-structured tables from Wikipedia,
with 17,553 question-answer pairs in total. Existing QA systems face two major
problems when evaluated on our dataset: (1) handling questions that contain
coreferences to previous questions or answers, and (2) matching words or
phrases in a question to corresponding entries in the associated table. We
conclude by proposing strategies to handle both of these issues.
Reparameterization trick for discrete variables
Authors: Seiya Tokui, Issei Sato
Date: 2016-11-04 00:00:00

Low-variance gradient estimation is crucial for learning directed graphical
models parameterized by neural networks, where the reparameterization trick is
widely used for those with continuous variables. While this technique gives
low-variance gradient estimates, it has not been directly applicable to
discrete variables, the sampling of which inherently requires discontinuous
operations. We argue that the discontinuity can be bypassed by marginalizing
out the variable of interest, which results in a new reparameterization trick
for discrete variables. This reparameterization greatly reduces the variance,
which is understood by regarding the method as an application of common random
numbers to the estimation. The resulting estimator is theoretically guaranteed
to have a variance not larger than that of the likelihood-ratio method with the
optimal input-dependent baseline. We give empirical results for variational
learning of sigmoid belief networks.
Coin Betting and Parameter-Free Online Learning
Authors: Francesco Orabona, Dávid Pál
Date: 2016-02-12 00:00:00
Proceeding: NeurIPS 2016 12

In the recent years, a number of parameter-free algorithms have been
developed for online linear optimization over Hilbert spaces and for learning
with expert advice. These algorithms achieve optimal regret bounds that depend
on the unknown competitors, without having to tune the learning rates with
oracle choices.
  We present a new intuitive framework to design parameter-free algorithms for
\emph{both} online linear optimization over Hilbert spaces and for learning
with expert advice, based on reductions to betting on outcomes of adversarial
coins. We instantiate it using a betting algorithm based on the
Krichevsky-Trofimov estimator. The resulting algorithms are simple, with no
parameters to be tuned, and they improve or match previous results in terms of
regret guarantee and per-round complexity.
A Self-Driving Robot Using Deep Convolutional Neural Networks on Neuromorphic Hardware
Authors: Tiffany Hwu, Jacob Isbell, Nicolas Oros, Jeffrey Krichmar
Date: 2016-11-04 00:00:00

Neuromorphic computing is a promising solution for reducing the size, weight
and power of mobile embedded systems. In this paper, we introduce a realization
of such a system by creating the first closed-loop battery-powered
communication system between an IBM TrueNorth NS1e and an autonomous
Android-Based Robotics platform. Using this system, we constructed a dataset of
path following behavior by manually driving the Android-Based robot along steep
mountain trails and recording video frames from the camera mounted on the robot
along with the corresponding motor commands. We used this dataset to train a
deep convolutional neural network implemented on the TrueNorth NS1e. The NS1e,
which was mounted on the robot and powered by the robot's battery, resulted in
a self-driving robot that could successfully traverse a steep mountain path in
real time. To our knowledge, this represents the first time the TrueNorth NS1e
neuromorphic chip has been embedded on a mobile platform under closed-loop
control.
Bayesian Optical Flow with Uncertainty Quantification
Authors: Jie Sun, Fernando J. Quevedo, Erik Bollt
Date: 2016-11-04 00:00:00
Tasks: Optical Flow Estimation, Uncertainty Quantification

Optical flow refers to the visual motion observed between two consecutive
images. Since the degree of freedom is typically much larger than the
constraints imposed by the image observations, the straightforward formulation
of optical flow as an inverse problem is ill-posed. Standard approaches to
determine optical flow rely on formulating and solving an optimization problem
that contains both a data fidelity term and a regularization term, the latter
effectively resolves the otherwise ill-posedness of the inverse problem. In
this work, we depart from the deterministic formalism, and instead treat
optical flow as a statistical inverse problem. We discuss how a classical
optical flow solution can be interpreted as a point estimate in this more
general framework. The statistical approach, whose "solution" is a distribution
of flow fields, which we refer to as Bayesian optical flow, allows not only
"point" estimates (e.g., the computation of average flow field), but also
statistical estimates (e.g., quantification of uncertainty) that are beyond any
standard method for optical flow. As application, we benchmark Bayesian optical
flow together with uncertainty quantification using several types of prescribed
ground-truth flow fields and images.
TorchCraft: a Library for Machine Learning Research on Real-Time Strategy Games
Authors: Gabriel Synnaeve, Nantas Nardelli, Alex Auvolat, Soumith Chintala, Timothée Lacroix, Zeming Lin, Florian Richoux, Nicolas Usunier
Date: 2016-11-01 00:00:00
Tasks: BIG-bench Machine Learning, Real-Time Strategy Games, Starcraft

We present TorchCraft, a library that enables deep learning research on
Real-Time Strategy (RTS) games such as StarCraft: Brood War, by making it
easier to control these games from a machine learning framework, here Torch.
This white paper argues for using RTS games as a benchmark for AI research, and
describes the design and components of TorchCraft.
Integrating Atlas and Graph Cut Methods for LV Segmentation from Cardiac Cine MRI
Authors: Shusil Dangi, Nathan Cahill, Cristian A. Linte
Date: 2016-11-03 00:00:00
Tasks: LV Segmentation, Segmentation

Magnetic Resonance Imaging (MRI) has evolved as a clinical standard-of-care
imaging modality for cardiac morphology, function assessment, and guidance of
cardiac interventions. All these applications rely on accurate extraction of
the myocardial tissue and blood pool from the imaging data. Here we propose a
framework for left ventricle (LV) segmentation from cardiac cine-MRI. First, we
segment the LV blood pool using iterative graph cuts, and subsequently use this
information to segment the myocardium. We formulate the segmentation procedure
as an energy minimization problem in a graph subject to the shape prior
obtained by label propagation from an average atlas using affine registration.
The proposed framework has been validated on 30 patient cardiac cine-MRI
datasets available through the STACOM LV segmentation challenge and yielded
fast, robust, and accurate segmentation results.
Conspiracies between Learning Algorithms, Circuit Lower Bounds and Pseudorandomness
Authors: Igor C. Oliveira, Rahul Santhanam
Date: 2016-11-03 00:00:00
Tasks: LEMMA

We prove several results giving new and stronger connections between
learning, circuit lower bounds and pseudorandomness. Among other results, we
show a generic learning speedup lemma, equivalences between various learning
models in the exponential time and subexponential time regimes, a dichotomy
between learning and pseudorandomness, consequences of non-trivial learning for
circuit lower bounds, Karp-Lipton theorems for probabilistic exponential time,
and NC$^1$-hardness for the Minimum Circuit Size Problem.
PrivLogit: Efficient Privacy-preserving Logistic Regression by Tailoring Numerical Optimizers
Authors: Wei Xie, Yang Wang, Steven M. Boker, Donald E. Brown
Date: 2016-11-03 00:00:00
Tasks: BIG-bench Machine Learning, Privacy Preserving, regression

Safeguarding privacy in machine learning is highly desirable, especially in
collaborative studies across many organizations. Privacy-preserving distributed
machine learning (based on cryptography) is popular to solve the problem.
However, existing cryptographic protocols still incur excess computational
overhead. Here, we make a novel observation that this is partially due to naive
adoption of mainstream numerical optimization (e.g., Newton method) and failing
to tailor for secure computing. This work presents a contrasting perspective:
customizing numerical optimization specifically for secure settings. We propose
a seemingly less-favorable optimization method that can in fact significantly
accelerate privacy-preserving logistic regression. Leveraging this new method,
we propose two new secure protocols for conducting logistic regression in a
privacy-preserving and distributed manner. Extensive theoretical and empirical
evaluations prove the competitive performance of our two secure proposals while
without compromising accuracy or privacy: with speedup up to 2.3x and 8.1x,
respectively, over state-of-the-art; and even faster as data scales up. Such
drastic speedup is on top of and in addition to performance improvements from
existing (and future) state-of-the-art cryptography. Our work provides a new
way towards efficient and practical privacy-preserving logistic regression for
large-scale studies which are common for modern science.
Using a Deep Reinforcement Learning Agent for Traffic Signal Control
Authors: Wade Genders, Saiedeh Razavi
Date: 2016-11-03 00:00:00
Tasks: Deep Reinforcement Learning, Q-Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL), Traffic Signal Control

Ensuring transportation systems are efficient is a priority for modern
society. Technological advances have made it possible for transportation
systems to collect large volumes of varied data on an unprecedented scale. We
propose a traffic signal control system which takes advantage of this new, high
quality data, with minimal abstraction compared to other proposed systems. We
apply modern deep reinforcement learning methods to build a truly adaptive
traffic signal control agent in the traffic microsimulator SUMO. We propose a
new state space, the discrete traffic state encoding, which is information
dense. The discrete traffic state encoding is used as input to a deep
convolutional neural network, trained using Q-learning with experience replay.
Our agent was compared against a one hidden layer neural network traffic signal
control agent and reduces average cumulative delay by 82%, average queue length
by 66% and average travel time by 20%.
Cross: Efficient Low-rank Tensor Completion
Authors: Anru Zhang
Date: 2016-11-03 00:00:00

The completion of tensors, or high-order arrays, attracts significant
attention in recent research. Current literature on tensor completion primarily
focuses on recovery from a set of uniformly randomly measured entries, and the
required number of measurements to achieve recovery is not guaranteed to be
optimal. In addition, the implementation of some previous methods is NP-hard.
In this article, we propose a framework for low-rank tensor completion via a
novel tensor measurement scheme we name Cross. The proposed procedure is
efficient and easy to implement. In particular, we show that a third order
tensor of Tucker rank-$(r_1, r_2, r_3)$ in $p_1$-by-$p_2$-by-$p_3$ dimensional
space can be recovered from as few as $r_1r_2r_3 + r_1(p_1-r_1) + r_2(p_2-r_2)
+ r_3(p_3-r_3)$ noiseless measurements, which matches the sample complexity
lower-bound. In the case of noisy measurements, we also develop a theoretical
upper bound and the matching minimax lower bound for recovery error over
certain classes of low-rank tensors for the proposed procedure. The results can
be further extended to fourth or higher-order tensors. Simulation studies show
that the method performs well under a variety of settings. Finally, the
procedure is illustrated through a real dataset in neuroimaging.
Analyzing Games with Ambiguous Player Types using the ${\rm MINthenMAX}$ Decision Model
Authors: Ilan Nehama
Date: 2016-03-04 00:00:00
Tasks: Vocal Bursts Type Prediction

In many common interactive scenarios, participants lack information about
other participants, and specifically about the preferences of other
participants. In this work, we model an extreme case of incomplete information,
which we term games with type ambiguity, where a participant lacks even
information enabling him to form a belief on the preferences of others. Under
type ambiguity, one cannot analyze the scenario using the commonly used
Bayesian framework, and therefore he needs to model the participants using a
different decision model.
  In this work, we present the ${\rm MINthenMAX}$ decision model under
ambiguity. This model is a refinement of Wald's MiniMax principle, which we
show to be too coarse for games with type ambiguity. We characterize ${\rm
MINthenMAX}$ as the finest refinement of the MiniMax principle that satisfies
three properties we claim are necessary for games with type ambiguity. This
prior-less approach we present her also follows the common practice in computer
science of worst-case analysis.
  Finally, we define and analyze the corresponding equilibrium concept assuming
all players follow ${\rm MINthenMAX}$. We demonstrate this equilibrium by
applying it to two common economic scenarios: coordination games and bilateral
trade. We show that in both scenarios, an equilibrium in pure strategies always
exists and we analyze the equilibria.
CogALex-V Shared Task: ROOT18
Authors: Emmanuele Chersoni, Giulia Rambelli, Enrico Santus
Date: 2016-11-03 00:00:00
Proceeding: WS 2016 12

In this paper, we describe ROOT 18, a classifier using the scores of several
unsupervised distributional measures as features to discriminate between
semantically related and unrelated words, and then to classify the related
pairs according to their semantic relation (i.e. synonymy, antonymy, hypernymy,
part-whole meronymy). Our classifier participated in the CogALex-V Shared Task,
showing a solid performance on the first subtask, but a poor performance on the
second subtask. The low scores reported on the second subtask suggest that
distributional measures are not sufficient to discriminate between multiple
semantic relations at once.
Spectral community detection in heterogeneous large networks
Authors: Hafiz Tiomoko Ali, Romain Couillet
Date: 2016-11-03 00:00:00
Tasks: Clustering, Community Detection

In this article, we study spectral methods for community detection based on $
\alpha$-parametrized normalized modularity matrix hereafter called $ {\bf
L}_\alpha $ in heterogeneous graph models. We show, in a regime where community
detection is not asymptotically trivial, that $ {\bf L}_\alpha $ can be well
approximated by a more tractable random matrix which falls in the family of
spiked random matrices. The analysis of this equivalent spiked random matrix
allows us to improve spectral methods for community detection and assess their
performances in the regime under study. In particular, we prove the existence
of an optimal value $ \alpha_{\rm opt} $ of the parameter $ \alpha $ for which
the detection of communities is best ensured and we provide an on-line
estimation of $ \alpha_{\rm opt} $ only based on the knowledge of the graph
adjacency matrix. Unlike classical spectral methods for community detection
where clustering is performed on the eigenvectors associated with extreme
eigenvalues, we show through our theoretical analysis that a regularization
should instead be performed on those eigenvectors prior to clustering in
heterogeneous graphs. Finally, through a deeper study of the regularized
eigenvectors used for clustering, we assess the performances of our new
algorithm for community detection. Numerical simulations in the course of the
article show that our methods outperform state-of-the-art spectral methods on
dense heterogeneous graphs.
Audio-Visual Speaker Diarization Based on Spatiotemporal Bayesian Fusion
Authors: Israel D. Gebru, Silèye Ba, Xiaofei Li, Radu Horaud
Date: 2016-03-31 00:00:00
Tasks: Clustering, speaker-diarization, Speaker Diarization, Visual Tracking

Speaker diarization consists of assigning speech signals to people engaged in
a dialogue. An audio-visual spatiotemporal diarization model is proposed. The
model is well suited for challenging scenarios that consist of several
participants engaged in multi-party interaction while they move around and turn
their heads towards the other participants rather than facing the cameras and
the microphones. Multiple-person visual tracking is combined with multiple
speech-source localization in order to tackle the speech-to-person association
problem. The latter is solved within a novel audio-visual fusion method on the
following grounds: binaural spectral features are first extracted from a
microphone pair, then a supervised audio-visual alignment technique maps these
features onto an image, and finally a semi-supervised clustering method assigns
binaural spectral features to visible persons. The main advantage of this
method over previous work is that it processes in a principled way speech
signals uttered simultaneously by multiple persons. The diarization itself is
cast into a latent-variable temporal graphical model that infers speaker
identities and speech turns, based on the output of an audio-visual association
process, executed at each time slice, and on the dynamics of the diarization
variable itself. The proposed formulation yields an efficient exact inference
procedure. A novel dataset, that contains audio-visual training data as well as
a number of scenarios involving several participants engaged in formal and
informal dialogue, is introduced. The proposed method is thoroughly tested and
benchmarked with respect to several state-of-the art diarization algorithms.
Probabilistic Modeling of Progressive Filtering
Authors: Giuliano Armano
Date: 2016-11-03 00:00:00
Tasks: General Classification, Text Categorization

Progressive filtering is a simple way to perform hierarchical classification,
inspired by the behavior that most humans put into practice while attempting to
categorize an item according to an underlying taxonomy. Each node of the
taxonomy being associated with a different category, one may visualize the
categorization process by looking at the item going downwards through all the
nodes that accept it as belonging to the corresponding category. This paper is
aimed at modeling the progressive filtering technique from a probabilistic
perspective, in a hierarchical text categorization setting. As a result, the
designer of a system based on progressive filtering should be facilitated in
the task of devising, training, and testing it.
Homotopy Smoothing for Non-Smooth Problems with Lower Complexity than $O(1/ε)$
Authors: Yi Xu, Yan Yan, Qihang Lin, Tianbao Yang
Date: 2016-07-13 00:00:00
Proceeding: NeurIPS 2016

In this paper, we develop a novel {\bf ho}moto{\bf p}y {\bf s}moothing (HOPS)
algorithm for solving a family of non-smooth problems that is composed of a
non-smooth term with an explicit max-structure and a smooth term or a simple
non-smooth term whose proximal mapping is easy to compute. The best known
iteration complexity for solving such non-smooth optimization problems is
$O(1/\epsilon)$ without any assumption on the strong convexity. In this work,
we will show that the proposed HOPS achieved a lower iteration complexity of
$\widetilde O(1/\epsilon^{1-\theta})$\footnote{$\widetilde O()$ suppresses a
logarithmic factor.} with $\theta\in(0,1]$ capturing the local sharpness of the
objective function around the optimal solutions. To the best of our knowledge,
this is the lowest iteration complexity achieved so far for the considered
non-smooth optimization problems without strong convexity assumption. The HOPS
algorithm employs Nesterov's smoothing technique and Nesterov's accelerated
gradient method and runs in stages, which gradually decreases the smoothing
parameter in a stage-wise manner until it yields a sufficiently good
approximation of the original function. We show that HOPS enjoys a linear
convergence for many well-known non-smooth problems (e.g., empirical risk
minimization with a piece-wise linear loss function and $\ell_1$ norm
regularizer, finding a point in a polyhedron, cone programming, etc).
Experimental results verify the effectiveness of HOPS in comparison with
Nesterov's smoothing algorithm and the primal-dual style of first-order
methods.
A-Ward_p\b{eta}: Effective hierarchical clustering using the Minkowski metric and a fast k -means initialisation
Authors: Renato Cordeiro de Amorim, Vladimir Makarenkov, Boris Mirkin
Date: 2016-11-03 00:00:00
Tasks: Clustering

In this paper we make two novel contributions to hierarchical clustering.
First, we introduce an anomalous pattern initialisation method for hierarchical
clustering algorithms, called A-Ward, capable of substantially reducing the
time they take to converge. This method generates an initial partition with a
sufficiently large number of clusters. This allows the cluster merging process
to start from this partition rather than from a trivial partition composed
solely of singletons. Our second contribution is an extension of the Ward and
Ward p algorithms to the situation where the feature weight exponent can differ
from the exponent of the Minkowski distance. This new method, called A-Ward
p\b{eta} , is able to generate a much wider variety of clustering solutions. We
also demonstrate that its parameters can be estimated reasonably well by using
a cluster validity index. We perform numerous experiments using data sets with
two types of noise, insertion of noise features and blurring within-cluster
values of some features. These experiments allow us to conclude: (i) our
anomalous pattern initialisation method does indeed reduce the time a
hierarchical clustering algorithm takes to complete, without negatively
impacting its cluster recovery ability; (ii) A-Ward p\b{eta} provides better
cluster recovery than both Ward and Ward p.