pwc-archive/papers-with-abstracts huggingface dataset:
Im2Struct: Recovering 3D Shape Structure from a Single RGB Image
Authors: Chengjie Niu, Jun Li, Kai Xu
Date: 2018-04-16 00:00:00
Proceeding: CVPR 2018 6
Tasks: Decoder, Object

We propose to recover 3D shape structures from single RGB images, where
structure refers to shape parts represented by cuboids and part relations
encompassing connectivity and symmetry. Given a single 2D image with an object
depicted, our goal is automatically recover a cuboid structure of the object
parts as well as their mutual relations. We develop a convolutional-recursive
auto-encoder comprised of structure parsing of a 2D image followed by structure
recovering of a cuboid hierarchy. The encoder is achieved by a multi-scale
convolutional network trained with the task of shape contour estimation,
thereby learning to discern object structures in various forms and scales. The
decoder fuses the features of the structure parsing network and the original
image, and recursively decodes a hierarchy of cuboids. Since the decoder
network is learned to recover part relations including connectivity and
symmetry explicitly, the plausibility and generality of part structure recovery
can be ensured. The two networks are jointly trained using the training data of
contour-mask and cuboid structure pairs. Such pairs are generated by rendering
stock 3D CAD models coming with part segmentation. Our method achieves
unprecedentedly faithful and detailed recovery of diverse 3D part structures
from single-view 2D images. We demonstrate two applications of our method
including structure-guided completion of 3D volumes reconstructed from
single-view images and structure-aware interactive editing of 2D images.
On Gradient-Based Learning in Continuous Games
Authors: Eric Mazumdar, Lillian J. Ratliff, S. Shankar Sastry
Date: 2018-04-16 00:00:00
Tasks: Multi-agent Reinforcement Learning, Reinforcement Learning

We formulate a general framework for competitive gradient-based learning that encompasses a wide breadth of multi-agent learning algorithms, and analyze the limiting behavior of competitive gradient-based learning algorithms using dynamical systems theory. For both general-sum and potential games, we characterize a non-negligible subset of the local Nash equilibria that will be avoided if each agent employs a gradient-based learning algorithm. We also shed light on the issue of convergence to non-Nash strategies in general- and zero-sum games, which may have no relevance to the underlying game, and arise solely due to the choice of algorithm. The existence and frequency of such strategies may explain some of the difficulties encountered when using gradient descent in zero-sum games as, e.g., in the training of generative adversarial networks. To reinforce the theoretical contributions, we provide empirical results that highlight the frequency of linear quadratic dynamic games (a benchmark for multi-agent reinforcement learning) that admit global Nash equilibria that are almost surely avoided by policy gradient.
Comparative study of motion detection methods for video surveillance systems
Authors: Kamal Sehairi, Chouireb Fatima, Jean Meunier
Date: 2018-04-16 00:00:00
Tasks: Change Detection, Motion Detection

The objective of this study is to compare several change detection methods
for a mono static camera and identify the best method for different complex
environments and backgrounds in indoor and outdoor scenes. To this end, we used
the CDnet video dataset as a benchmark that consists of many challenging
problems, ranging from basic simple scenes to complex scenes affected by bad
weather and dynamic backgrounds. Twelve change detection methods, ranging from
simple temporal differencing to more sophisticated methods, were tested and
several performance metrics were used to precisely evaluate the results.
Because most of the considered methods have not previously been evaluated on
this recent large scale dataset, this work compares these methods to fill a
lack in the literature, and thus this evaluation joins as complementary
compared with the previous comparative evaluations. Our experimental results
show that there is no perfect method for all challenging cases, each method
performs well in certain cases and fails in others. However, this study enables
the user to identify the most suitable method for his or her needs.
Video Summarization with Attention-Based Encoder-Decoder Networks
Authors: Zhong Ji, Kailin Xiong, Yanwei Pang, Xuelong. Li
Date: 2017-08-31 00:00:00
Tasks: Decoder, Supervised Video Summarization, Video Summarization

This paper addresses the problem of supervised video summarization by
formulating it as a sequence-to-sequence learning problem, where the input is a
sequence of original video frames, the output is a keyshot sequence. Our key
idea is to learn a deep summarization network with attention mechanism to mimic
the way of selecting the keyshots of human. To this end, we propose a novel
video summarization framework named Attentive encoder-decoder networks for
Video Summarization (AVS), in which the encoder uses a Bidirectional Long
Short-Term Memory (BiLSTM) to encode the contextual information among the input
video frames. As for the decoder, two attention-based LSTM networks are
explored by using additive and multiplicative objective functions,
respectively. Extensive experiments are conducted on three video summarization
benchmark datasets, i.e., SumMe, and TVSum. The results demonstrate the
superiority of the proposed AVS-based approaches against the state-of-the-art
approaches,with remarkable improvements from 0.8% to 3% on two
datasets,respectively..
A refinement of Bennett's inequality with applications to portfolio optimization
Authors: Tony Jebara
Date: 2018-04-16 00:00:00
Tasks: Portfolio Optimization

A refinement of Bennett's inequality is introduced which is strictly tighter
than the classical bound. The new bound establishes the convergence of the
average of independent random variables to its expected value. It also
carefully exploits information about the potentially heterogeneous mean,
variance, and ceiling of each random variable. The bound is strictly sharper in
the homogeneous setting and very often significantly sharper in the
heterogeneous setting. The improved convergence rates are obtained by
leveraging Lambert's W function. We apply the new bound in a portfolio
optimization setting to allocate a budget across investments with heterogeneous
returns.
Watch, Listen, and Describe: Globally and Locally Aligned Cross-Modal Attentions for Video Captioning
Authors: Xin Wang, Yuan-Fang Wang, William Yang Wang
Date: 2018-04-15 00:00:00
Proceeding: NAACL 2018 6
Tasks: Video Captioning, Video Understanding

A major challenge for video captioning is to combine audio and visual cues.
Existing multi-modal fusion methods have shown encouraging results in video
understanding. However, the temporal structures of multiple modalities at
different granularities are rarely explored, and how to selectively fuse the
multi-modal representations at different levels of details remains uncharted.
In this paper, we propose a novel hierarchically aligned cross-modal attention
(HACA) framework to learn and selectively fuse both global and local temporal
dynamics of different modalities. Furthermore, for the first time, we validate
the superior performance of the deep audio features on the video captioning
task. Finally, our HACA model significantly outperforms the previous best
systems and achieves new state-of-the-art results on the widely used MSR-VTT
dataset.
Parsing Speech: A Neural Approach to Integrating Lexical and Acoustic-Prosodic Information
Authors: Trang Tran, Shubham Toshniwal, Mohit Bansal, Kevin Gimpel, Karen Livescu, Mari Ostendorf
Date: 2017-04-24 00:00:00
Proceeding: NAACL 2018 6
Tasks: Sentence

In conversational speech, the acoustic signal provides cues that help
listeners disambiguate difficult parses. For automatically parsing spoken
utterances, we introduce a model that integrates transcribed text and
acoustic-prosodic features using a convolutional neural network over energy and
pitch trajectories coupled with an attention-based recurrent neural network
that accepts text and prosodic features. We find that different types of
acoustic-prosodic features are individually helpful, and together give
statistically significant improvements in parse and disfluency detection F1
scores over a strong text-only baseline. For this study with known sentence
boundaries, error analyses show that the main benefit of acoustic-prosodic
features is in sentences with disfluencies, attachment decisions are most
improved, and transcription errors obscure gains from prosody.
Evolving Event-driven Programs with SignalGP
Authors: Alexander Lalejini, Charles Ofria
Date: 2018-04-15 00:00:00
Tasks: Philosophy, TAG

We present SignalGP, a new genetic programming (GP) technique designed to
incorporate the event-driven programming paradigm into computational
evolution's toolbox. Event-driven programming is a software design philosophy
that simplifies the development of reactive programs by automatically
triggering program modules (event-handlers) in response to external events,
such as signals from the environment or messages from other programs. SignalGP
incorporates these concepts by extending existing tag-based referencing
techniques into an event-driven context. Both events and functions are labeled
with evolvable tags; when an event occurs, the function with the closest
matching tag is triggered. In this work, we apply SignalGP in the context of
linear GP. We demonstrate the value of the event-driven paradigm using two
distinct test problems (an environment coordination problem and a distributed
leader election problem) by comparing SignalGP to variants that are otherwise
identical, but must actively use sensors to process events or messages. In each
of these problems, rapid interaction with the environment or other agents is
critical for maximizing fitness. We also discuss ways in which SignalGP can be
generalized beyond our linear GP implementation.
PacGAN: The power of two samples in generative adversarial networks
Authors: Zinan Lin, Ashish Khetan, Giulia Fanti, Sewoong Oh
Date: 2017-12-12 00:00:00
Proceeding: NeurIPS 2018 12
Tasks: Diversity, Two-sample testing, Vocal Bursts Valence Prediction

Generative adversarial networks (GANs) are innovative techniques for learning
generative models of complex data distributions from samples. Despite
remarkable recent improvements in generating realistic images, one of their
major shortcomings is the fact that in practice, they tend to produce samples
with little diversity, even when trained on diverse datasets. This phenomenon,
known as mode collapse, has been the main focus of several recent advances in
GANs. Yet there is little understanding of why mode collapse happens and why
existing approaches are able to mitigate mode collapse. We propose a principled
approach to handling mode collapse, which we call packing. The main idea is to
modify the discriminator to make decisions based on multiple samples from the
same class, either real or artificially generated. We borrow analysis tools
from binary hypothesis testing---in particular the seminal result of Blackwell
[Bla53]---to prove a fundamental connection between packing and mode collapse.
We show that packing naturally penalizes generators with mode collapse, thereby
favoring generator distributions with less mode collapse during the training
process. Numerical experiments on benchmark datasets suggests that packing
provides significant improvements in practice as well.
Better Fixed-Arity Unbiased Black-Box Algorithms
Authors: Nina Bulanova, Maxim Buzdalov
Date: 2018-04-15 00:00:00

In their GECCO'12 paper, Doerr and Doerr proved that the $k$-ary unbiased
black-box complexity of OneMax on $n$ bits is $O(n/k)$ for $2\le k\le O(\log
n)$. We propose an alternative strategy for achieving this unbiased black-box
complexity when $3\le k\le\log_2 n$. While it is based on the same idea of
block-wise optimization, it uses $k$-ary unbiased operators in a different way.
  For each block of size $2^{k-1}-1$ we set up, in $O(k)$ queries, a virtual
coordinate system, which enables us to use an arbitrary unrestricted algorithm
to optimize this block. This is possible because this coordinate system
introduces a bijection between unrestricted queries and a subset of $k$-ary
unbiased operators. We note that this technique does not depend on OneMax being
solved and can be used in more general contexts.
  This together constitutes an algorithm which is conceptually simpler than the
one by Doerr and Doerr, and at the same time achieves better constant factors
in the asymptotic notation. Our algorithm works in $(2+o(1))\cdot n/(k-1)$,
where $o(1)$ relates to $k$. Our experimental evaluation of this algorithm
shows its efficiency already for $3\le k\le6$.
Hidden Hamiltonian Cycle Recovery via Linear Programming
Authors: Vivek Bagaria, Jian Ding, David Tse, Yihong Wu, Jiaming Xu
Date: 2018-04-15 00:00:00
Tasks: Traveling Salesman Problem

We introduce the problem of hidden Hamiltonian cycle recovery, where there is
an unknown Hamiltonian cycle in an $n$-vertex complete graph that needs to be
inferred from noisy edge measurements. The measurements are independent and
distributed according to $\calP_n$ for edges in the cycle and $\calQ_n$
otherwise. This formulation is motivated by a problem in genome assembly, where
the goal is to order a set of contigs (genome subsequences) according to their
positions on the genome using long-range linking measurements between the
contigs. Computing the maximum likelihood estimate in this model reduces to a
Traveling Salesman Problem (TSP). Despite the NP-hardness of TSP, we show that
a simple linear programming (LP) relaxation, namely the fractional $2$-factor
(F2F) LP, recovers the hidden Hamiltonian cycle with high probability as $n \to
\infty$ provided that $\alpha_n - \log n \to \infty$, where $\alpha_n
\triangleq -2 \log \int \sqrt{d P_n d Q_n}$ is the R\'enyi divergence of order
$\frac{1}{2}$. This condition is information-theoretically optimal in the sense
that, under mild distributional assumptions, $\alpha_n \geq (1+o(1)) \log n$ is
necessary for any algorithm to succeed regardless of the computational cost.
  Departing from the usual proof techniques based on dual witness construction,
the analysis relies on the combinatorial characterization (in particular, the
half-integrality) of the extreme points of the F2F polytope. Represented as
bicolored multi-graphs, these extreme points are further decomposed into
simpler "blossom-type" structures for the large deviation analysis and counting
arguments. Evaluation of the algorithm on real data shows improvements over
existing approaches.
What Happened? Leveraging VerbNet to Predict the Effects of Actions in Procedural Text
Authors: Peter Clark, Bhavana Dalvi, Niket Tandon
Date: 2018-04-15 00:00:00
Tasks: Reading Comprehension

Our goal is to answer questions about paragraphs describing processes (e.g.,
photosynthesis). Texts of this genre are challenging because the effects of
actions are often implicit (unstated), requiring background knowledge and
inference to reason about the changing world states. To supply this knowledge,
we leverage VerbNet to build a rulebase (called the Semantic Lexicon) of the
preconditions and effects of actions, and use it along with commonsense
knowledge of persistence to answer questions about change. Our evaluation shows
that our system, ProComp, significantly outperforms two strong reading
comprehension (RC) baselines. Our contributions are two-fold: the Semantic
Lexicon rulebase itself, and a demonstration of how a simulation-based approach
to machine reading can outperform RC methods that rely on surface cues alone.
  Since this work was performed, we have developed neural systems that
outperform ProComp, described elsewhere (Dalvi et al., NAACL'18). However, the
Semantic Lexicon remains a novel and potentially useful resource, and its
integration with neural systems remains a currently unexplored opportunity for
further improvements in machine reading about processes.
Weighted Low-Rank Approximation of Matrices and Background Modeling
Authors: Aritra Dutta, Xin Li, Peter Richtarik
Date: 2018-04-15 00:00:00

We primarily study a special a weighted low-rank approximation of matrices
and then apply it to solve the background modeling problem. We propose two
algorithms for this purpose: one operates in the batch mode on the entire data
and the other one operates in the batch-incremental mode on the data and
naturally captures more background variations and computationally more
effective. Moreover, we propose a robust technique that learns the background
frame indices from the data and does not require any training frames. We
demonstrate through extensive experiments that by inserting a simple weight in
the Frobenius norm, it can be made robust to the outliers similar to the
$\ell_1$ norm. Our methods match or outperform several state-of-the-art online
and batch background modeling methods in virtually all quantitative and
qualitative measures.
Adaptivity for Regularized Kernel Methods by Lepskii's Principle
Authors: Nicole Mücke
Date: 2018-04-15 00:00:00
Tasks: regression

We address the problem of {\it adaptivity} in the framework of reproducing
kernel Hilbert space (RKHS) regression. More precisely, we analyze estimators
arising from a linear regularization scheme $g_\lam$. In practical
applications, an important task is to choose the regularization parameter
$\lam$ appropriately, i.e. based only on the given data and independently on
unknown structural assumptions on the regression function. An attractive
approach avoiding data-splitting is the {\it Lepskii Principle} (LP), also
known as the {\it Balancing Principle} is this setting. We show that a modified
parameter choice based on (LP) is minimax optimal adaptive, up to
$\log\log(n)$. A convenient result is the fact that balancing in $L^2(\nu)-$
norm, which is easiest, automatically gives optimal balancing in all stronger
norms, interpolating between $L^2(\nu)$ and the RKHS. An analogous result is
open for other classical approaches to data dependent choices of the
regularization parameter, e.g. for Hold-Out.
Gnowee: A Hybrid Metaheuristic Optimization Algorithm for Constrained, Black Box, Combinatorial Mixed-Integer Design
Authors: James Bevins, Rachel Slaybaugh
Date: 2018-04-15 00:00:00
Tasks: Metaheuristic Optimization

This paper introduces Gnowee, a modular, Python-based, open-source hybrid
metaheuristic optimization algorithm (Available from
https://github.com/SlaybaughLab/Gnowee). Gnowee is designed for rapid
convergence to nearly globally optimum solutions for complex, constrained
nuclear engineering problems with mixed-integer and combinatorial design
vectors and high-cost, noisy, discontinuous, black box objective function
evaluations. Gnowee's hybrid metaheuristic framework is a new combination of a
set of diverse, robust heuristics that appropriately balance diversification
and intensification strategies across a wide range of optimization problems.
  This novel algorithm was specifically developed to optimize complex nuclear
design problems; the motivating research problem was the design of material
stack-ups to modify neutron energy spectra to specific targeted spectra for
applications in nuclear medicine, technical nuclear forensics, nuclear physics,
etc. However, there are a wider range of potential applications for this
algorithm both within the nuclear community and beyond. To demonstrate Gnowee's
behavior for a variety of problem types, comparisons between Gnowee and several
well-established metaheuristic algorithms are made for a set of eighteen
continuous, mixed-integer, and combinatorial benchmarks. These results
demonstrate Gnoweee to have superior flexibility and convergence
characteristics over a wide range of design spaces. We anticipate this wide
range of applicability will make this algorithm desirable for many complex
engineering applications.
White matter fiber analysis using kernel dictionary learning and sparsity priors
Authors: Kuldeep Kumar, Kaleem Siddiqi, Christian Desrosiers
Date: 2018-04-15 00:00:00
Tasks: Dictionary Learning

Diffusion magnetic resonance imaging, a non-invasive tool to infer white
matter fiber connections, produces a large number of streamlines containing a
wealth of information on structural connectivity. The size of these
tractography outputs makes further analyses complex, creating a need for
methods to group streamlines into meaningful bundles. In this work, we address
this by proposing a set of kernel dictionary learning and sparsity priors based
methods. Proposed frameworks include L-0 norm, group sparsity, as well as
manifold regularization prior. The proposed methods allow streamlines to be
assigned to more than one bundle, making it more robust to overlapping bundles
and inter-subject variations. We evaluate the performance of our method on a
labeled set and data from Human Connectome Project. Results highlight the
ability of our method to group streamlines into plausible bundles and
illustrate the impact of sparsity priors on the performance of the proposed
methods.
FDMO: Feature Assisted Direct Monocular Odometry
Authors: Georges Younes, Daniel Asmar, John Zelek
Date: 2018-04-15 00:00:00
Tasks: Visual Odometry

Visual Odometry (VO) can be categorized as being either direct or feature
based. When the system is calibrated photometrically, and images are captured
at high rates, direct methods have shown to outperform feature-based ones in
terms of accuracy and processing time; they are also more robust to failure in
feature-deprived environments. On the downside, Direct methods rely on
heuristic motion models to seed the estimation of camera motion between frames;
in the event that these models are violated (e.g., erratic motion), Direct
methods easily fail. This paper proposes a novel system entitled FDMO (Feature
assisted Direct Monocular Odometry), which complements the advantages of both
direct and featured based techniques. FDMO bootstraps indirect feature tracking
upon the sub-pixel accurate localized direct keyframes only when failure modes
(e.g., large baselines) of direct tracking occur. Control returns back to
direct odometry when these conditions are no longer violated. Efficiencies are
introduced to help FDMO perform in real time. FDMO shows significant drift
(alignment, rotation & scale) reduction when compared to DSO & ORB SLAM when
evaluated using the TumMono and EuroC datasets.
Are Automatic Methods for Cognate Detection Good Enough for Phylogenetic Reconstruction in Historical Linguistics?
Authors: Taraka Rama, Johann-Mattis List, Johannes Wahle, Gerhard Jäger
Date: 2018-04-15 00:00:00
Proceeding: NAACL 2018 6

We evaluate the performance of state-of-the-art algorithms for automatic
cognate detection by comparing how useful automatically inferred cognates are
for the task of phylogenetic inference compared to classical manually annotated
cognate sets. Our findings suggest that phylogenies inferred from automated
cognate sets come close to phylogenies inferred from expert-annotated ones,
although on average, the latter are still superior. We conclude that future
work on phylogenetic reconstruction can profit much from automatic cognate
detection. Especially where scholars are merely interested in exploring the
bigger picture of a language family's phylogeny, algorithms for automatic
cognate detection are a useful complement for current research on language
phylogenies.