pwc-archive/papers-with-abstracts huggingface dataset:
A Genetic Algorithm-Based Solver for Very Large Jigsaw Puzzles
Authors: Dror Sholomon, Eli David, Nathan S. Netanyahu
Date: 2017-11-17 00:00:00
Proceeding: CVPR 2013 6

In this paper we propose the first effective automated, genetic algorithm
(GA)-based jigsaw puzzle solver. We introduce a novel procedure of merging two
"parent" solutions to an improved "child" solution by detecting, extracting,
and combining correctly assembled puzzle segments. The solver proposed exhibits
state-of-the-art performance solving previously attempted puzzles faster and
far more accurately, and also puzzles of size never before attempted. Other
contributions include the creation of a benchmark of large images, previously
unavailable. We share the data sets and all of our results for future testing
and comparative evaluation of jigsaw puzzle solvers.
A Generalized Genetic Algorithm-Based Solver for Very Large Jigsaw Puzzles of Complex Types
Authors: Dror Sholomon, Eli David, Nathan S. Netanyahu
Date: 2017-11-17 00:00:00

In this paper we introduce new types of square-piece jigsaw puzzles, where in
addition to the unknown location and orientation of each piece, a piece might
also need to be flipped. These puzzles, which are associated with a number of
real world problems, are considerably harder, from a computational standpoint.
Specifically, we present a novel generalized genetic algorithm (GA)-based
solver that can handle puzzle pieces of unknown location and orientation (Type
2 puzzles) and (two-sided) puzzle pieces of unknown location, orientation, and
face (Type 4 puzzles). To the best of our knowledge, our solver provides a new
state-of-the-art, solving previously attempted puzzles faster and far more
accurately, handling puzzle sizes that have never been attempted before, and
assembling the newly introduced two-sided puzzles automatically and
effectively. This paper also presents, among other results, the most extensive
set of experimental results, compiled as of yet, on Type 2 puzzles.
An Automatic Solver for Very Large Jigsaw Puzzles Using Genetic Algorithms
Authors: Dror Sholomon, Eli David, Nathan S. Netanyahu
Date: 2017-11-17 00:00:00

In this paper we propose the first effective genetic algorithm (GA)-based
jigsaw puzzle solver. We introduce a novel crossover procedure that merges two
"parent" solutions to an improved "child" configuration by detecting,
extracting, and combining correctly assembled puzzle segments. The solver
proposed exhibits state-of-the-art performance, as far as handling previously
attempted puzzles more accurately and efficiently, as well puzzle sizes that
have not been attempted before. The extended experimental results provided in
this paper include, among others, a thorough inspection of up to 30,745-piece
puzzles (compared to previous attempts on 22,755-piece puzzles), using a
considerably faster concurrent implementation of the algorithm. Furthermore, we
explore the impact of different phases of the novel crossover operator by
experimenting with several variants of the GA. Finally, we compare different
fitness functions and their effect on the overall results of the GA-based
solver.
Genetic Algorithm-Based Solver for Very Large Multiple Jigsaw Puzzles of Unknown Dimensions and Piece Orientation
Authors: Dror Sholomon, Eli David, Nathan S. Netanyahu
Date: 2017-11-17 00:00:00

In this paper we propose the first genetic algorithm (GA)-based solver for
jigsaw puzzles of unknown puzzle dimensions and unknown piece location and
orientation. Our solver uses a novel crossover technique, and sets a new
state-of-the-art in terms of the puzzle sizes solved and the accuracy obtained.
The results are significantly improved, even when compared to previous solvers
assuming known puzzle dimensions. Moreover, the solver successfully contends
with a mixed bag of multiple puzzle pieces, assembling simultaneously all
puzzles.
A Two-Phase Genetic Algorithm for Image Registration
Authors: Sarit Chicotay, Eli David, Nathan S. Netanyahu
Date: 2017-11-17 00:00:00
Tasks: Image Registration, Vocal Bursts Valence Prediction

Image Registration (IR) is the process of aligning two (or more) images of
the same scene taken at different times, different viewpoints and/or by
different sensors. It is an important, crucial step in various image analysis
tasks where multiple data sources are integrated/fused, in order to extract
high-level information.
  Registration methods usually assume a relevant transformation model for a
given problem domain. The goal is to search for the "optimal" instance of the
transformation model assumed with respect to a similarity measure in question.
  In this paper we present a novel genetic algorithm (GA)-based approach for
IR. Since GA performs effective search in various optimization problems, it
could prove useful also for IR. Indeed, various GAs have been proposed for IR.
However, most of them assume certain constraints, which simplify the
transformation model, restrict the search space or make additional
preprocessing requirements. In contrast, we present a generalized GA-based
solution for an almost fully affine transformation model, which achieves
competitive results without such limitations using a two-phase method and a
multi-objective optimization (MOO) approach.
  We present good results for multiple dataset and demonstrate the robustness
of our method in the presence of noisy data.
Addressing Expensive Multi-objective Games with Postponed Preference Articulation via Memetic Co-evolution
Authors: Adam Żychowski, Abhishek Gupta, Jacek Mańdziuk, Yew Soon Ong
Date: 2017-11-17 00:00:00
Tasks: Lifelong learning

This paper presents algorithmic and empirical contributions demonstrating
that the convergence characteristics of a co-evolutionary approach to tackle
Multi-Objective Games (MOGs) with postponed preference articulation can often
be hampered due to the possible emergence of the so-called Red Queen effect.
Accordingly, it is hypothesized that the convergence characteristics can be
significantly improved through the incorporation of memetics (local solution
refinements as a form of lifelong learning), as a promising means of mitigating
(or at least suppressing) the Red Queen phenomenon by providing a guiding hand
to the purely genetic mechanisms of co-evolution. Our practical motivation is
to address MOGs of a time-sensitive nature that are characterized by
computationally expensive evaluations, wherein there is a natural need to
reduce the total number of true function evaluations consumed in achieving good
quality solutions. To this end, we propose novel enhancements to
co-evolutionary approaches for tackling MOGs, such that memetic local
refinements can be efficiently applied on evolved candidate strategies by
searching on computationally cheap surrogate payoff landscapes (that preserve
postponed preference conditions). The efficacy of the proposal is demonstrated
on a suite of test MOGs that have been designed.
Deep supervised learning using local errors
Authors: Hesham Mostafa, Vishwajith Ramesh, Gert Cauwenberghs
Date: 2017-11-17 00:00:00

Error backpropagation is a highly effective mechanism for learning
high-quality hierarchical features in deep networks. Updating the features or
weights in one layer, however, requires waiting for the propagation of error
signals from higher layers. Learning using delayed and non-local errors makes
it hard to reconcile backpropagation with the learning mechanisms observed in
biological neural networks as it requires the neurons to maintain a memory of
the input long enough until the higher-layer errors arrive. In this paper, we
propose an alternative learning mechanism where errors are generated locally in
each layer using fixed, random auxiliary classifiers. Lower layers could thus
be trained independently of higher layers and training could either proceed
layer by layer, or simultaneously in all layers using local error information.
We address biological plausibility concerns such as weight symmetry
requirements and show that the proposed learning mechanism based on fixed,
broad, and random tuning of each neuron to the classification categories
outperforms the biologically-motivated feedback alignment learning technique on
the MNIST, CIFAR10, and SVHN datasets, approaching the performance of standard
backpropagation. Our approach highlights a potential biological mechanism for
the supervised, or task-dependent, learning of feature hierarchies. In
addition, we show that it is well suited for learning deep networks in custom
hardware where it can drastically reduce memory traffic and data communication
overheads.
Iterative Machine Teaching
Authors: Weiyang Liu, Bo Dai, Ahmad Humayun, Charlene Tay, Chen Yu, Linda B. Smith, James M. Rehg, Le Song
Date: 2017-05-30 00:00:00
Proceeding: ICML 2017 8

In this paper, we consider the problem of machine teaching, the inverse
problem of machine learning. Different from traditional machine teaching which
views the learners as batch algorithms, we study a new paradigm where the
learner uses an iterative algorithm and a teacher can feed examples
sequentially and intelligently based on the current performance of the learner.
We show that the teaching complexity in the iterative case is very different
from that in the batch case. Instead of constructing a minimal training set for
learners, our iterative machine teaching focuses on achieving fast convergence
in the learner model. Depending on the level of information the teacher has
from the learner model, we design teaching algorithms which can provably reduce
the number of teaching examples and achieve faster convergence than learning
without teachers. We also validate our theoretical findings with extensive
experiments on different data distribution and real image datasets.
Identifying Patterns of Associated-Conditions through Topic Models of Electronic Medical Records
Authors: Moumita Bhattacharya, Claudine Jurkovitz, Hagit Shatkay
Date: 2017-11-17 00:00:00
Tasks: Prognosis, Topic Models

Multiple adverse health conditions co-occurring in a patient are typically
associated with poor prognosis and increased office or hospital visits.
Developing methods to identify patterns of co-occurring conditions can assist
in diagnosis. Thus identifying patterns of associations among co-occurring
conditions is of growing interest. In this paper, we report preliminary results
from a data-driven study, in which we apply a machine learning method, namely,
topic modeling, to electronic medical records, aiming to identify patterns of
associated conditions. Specifically, we use the well established latent
dirichlet allocation, a method based on the idea that documents can be modeled
as a mixture of latent topics, where each topic is a distribution over words.
In our study, we adapt the LDA model to identify latent topics in patients'
EMRs. We evaluate the performance of our method both qualitatively, and show
that the obtained topics indeed align well with distinct medical phenomena
characterized by co-occurring conditions.
Phonological (un)certainty weights lexical activation
Authors: Laura Gwilliams, David Poeppel, Alec Marantz, Tal Linzen
Date: 2017-11-17 00:00:00
Proceeding: WS 2018 1

Spoken word recognition involves at least two basic computations. First is
matching acoustic input to phonological categories (e.g. /b/, /p/, /d/). Second
is activating words consistent with those phonological categories. Here we test
the hypothesis that the listener's probability distribution over lexical items
is weighted by the outcome of both computations: uncertainty about phonological
discretisation and the frequency of the selected word(s). To test this, we
record neural responses in auditory cortex using magnetoencephalography, and
model this activity as a function of the size and relative activation of
lexical candidates. Our findings indicate that towards the beginning of a word,
the processing system indeed weights lexical candidates by both phonological
certainty and lexical frequency; however, later into the word, activation is
weighted by frequency alone.
Learning a visuomotor controller for real world robotic grasping using simulated depth images
Authors: Ulrich Viereck, Andreas ten Pas, Kate Saenko, Robert Platt
Date: 2017-06-14 00:00:00
Tasks: Robotic Grasping

We want to build robots that are useful in unstructured real world
applications, such as doing work in the household. Grasping in particular is an
important skill in this domain, yet it remains a challenge. One of the key
hurdles is handling unexpected changes or motion in the objects being grasped
and kinematic noise or other errors in the robot. This paper proposes an
approach to learning a closed-loop controller for robotic grasping that
dynamically guides the gripper to the object. We use a wrist-mounted sensor to
acquire depth images in front of the gripper and train a convolutional neural
network to learn a distance function to true grasps for grasp configurations
over an image. The training sensor data is generated in simulation, a major
advantage over previous work that uses real robot experience, which is costly
to obtain. Despite being trained in simulation, our approach works well on real
noisy sensor images. We compare our controller in simulated and real robot
experiments to a strong baseline for grasp pose detection, and find that our
approach significantly outperforms the baseline in the presence of kinematic
noise, perceptual errors and disturbances of the object during grasping.
Canonical Correlation Inference for Mapping Abstract Scenes to Text
Authors: Nikos Papasarantopoulos, Helen Jiang, Shay B. Cohen
Date: 2016-08-09 00:00:00
Tasks: Structured Prediction

We describe a technique for structured prediction, based on canonical
correlation analysis. Our learning algorithm finds two projections for the
input and the output spaces that aim at projecting a given input and its
correct output into points close to each other. We demonstrate our technique on
a language-vision problem, namely the problem of giving a textual description
to an "abstract scene".
Learning Deep Neural Network Representations for Koopman Operators of Nonlinear Dynamical Systems
Authors: Enoch Yeung, Soumya Kundu, Nathan Hodas
Date: 2017-08-22 00:00:00
Tasks: Model Discovery

The Koopman operator has recently garnered much attention for its value in
dynamical systems analysis and data-driven model discovery. However, its
application has been hindered by the computational complexity of extended
dynamic mode decomposition; this requires a combinatorially large basis set to
adequately describe many nonlinear systems of interest, e.g. cyber-physical
infrastructure systems, biological networks, social systems, and fluid
dynamics. Often the dictionaries generated for these problems are manually
curated, requiring domain-specific knowledge and painstaking tuning. In this
paper we introduce a deep learning framework for learning Koopman operators of
nonlinear dynamical systems. We show that this novel method automatically
selects efficient deep dictionaries, outperforming state-of-the-art methods. We
benchmark this method on partially observed nonlinear systems, including the
glycolytic oscillator and show it is able to predict quantitatively 100 steps
into the future, using only a single timepoint, and qualitative oscillatory
behavior 400 steps into the future.
ADVISE: Symbolism and External Knowledge for Decoding Advertisements
Authors: Keren Ye, Adriana Kovashka
Date: 2017-11-17 00:00:00
Proceeding: ECCV 2018 9
Tasks: Clustering, Image Captioning, Object Recognition, Question Answering

In order to convey the most content in their limited space, advertisements
embed references to outside knowledge via symbolism. For example, a motorcycle
stands for adventure (a positive property the ad wants associated with the
product being sold), and a gun stands for danger (a negative property to
dissuade viewers from undesirable behaviors). We show how to use symbolic
references to better understand the meaning of an ad. We further show how
anchoring ad understanding in general-purpose object recognition and image
captioning improves results. We formulate the ad understanding task as matching
the ad image to human-generated statements that describe the action that the ad
prompts, and the rationale it provides for taking this action. Our proposed
method outperforms the state of the art on this task, and on an alternative
formulation of question-answering on ads. We show additional applications of
our learned representations for matching ads to slogans, and clustering ads
according to their topic, without extra training.
Online Learning of Optimal Bidding Strategy in Repeated Multi-Commodity Auctions
Authors: Sevi Baltaoglu, Lang Tong, Qing Zhao
Date: 2017-03-07 00:00:00
Proceeding: NeurIPS 2017 12

We study the online learning problem of a bidder who participates in repeated
auctions. With the goal of maximizing his T-period payoff, the bidder
determines the optimal allocation of his budget among his bids for $K$ goods at
each period. As a bidding strategy, we propose a polynomial-time algorithm,
inspired by the dynamic programming approach to the knapsack problem. The
proposed algorithm, referred to as dynamic programming on discrete set (DPDS),
achieves a regret order of $O(\sqrt{T\log{T}})$. By showing that the regret is
lower bounded by $\Omega(\sqrt{T})$ for any strategy, we conclude that DPDS is
order optimal up to a $\sqrt{\log{T}}$ term. We evaluate the performance of
DPDS empirically in the context of virtual trading in wholesale electricity
markets by using historical data from the New York market. Empirical results
show that DPDS consistently outperforms benchmark heuristic methods that are
derived from machine learning and online learning approaches.
Attentive Explanations: Justifying Decisions and Pointing to the Evidence (Extended Abstract)
Authors: Dong Huk Park, Lisa Anne Hendricks, Zeynep Akata, Anna Rohrbach, Bernt Schiele, Trevor Darrell, Marcus Rohrbach
Date: 2017-11-17 00:00:00
Tasks: Question Answering, Visual Question Answering (VQA)

Deep models are the defacto standard in visual decision problems due to their
impressive performance on a wide array of visual tasks. On the other hand,
their opaqueness has led to a surge of interest in explainable systems. In this
work, we emphasize the importance of model explanation in various forms such as
visual pointing and textual justification. The lack of data with justification
annotations is one of the bottlenecks of generating multimodal explanations.
Thus, we propose two large-scale datasets with annotations that visually and
textually justify a classification decision for various activities, i.e. ACT-X,
and for question answering, i.e. VQA-X. We also introduce a multimodal
methodology for generating visual and textual explanations simultaneously. We
quantitatively show that training with the textual explanations not only yields
better textual justification models, but also models that better localize the
evidence that support their decision.
Universal Style Transfer via Feature Transforms
Authors: Yijun Li, Chen Fang, Jimei Yang, Zhaowen Wang, Xin Lu, Ming-Hsuan Yang
Date: 2017-05-23 00:00:00
Proceeding: NeurIPS 2017 12
Tasks: Image Reconstruction, Style Transfer

Universal style transfer aims to transfer arbitrary visual styles to content
images. Existing feed-forward based methods, while enjoying the inference
efficiency, are mainly limited by inability of generalizing to unseen styles or
compromised visual quality. In this paper, we present a simple yet effective
method that tackles these limitations without training on any pre-defined
styles. The key ingredient of our method is a pair of feature transforms,
whitening and coloring, that are embedded to an image reconstruction network.
The whitening and coloring transforms reflect a direct matching of feature
covariance of the content image to a given style image, which shares similar
spirits with the optimization of Gram matrix based cost in neural style
transfer. We demonstrate the effectiveness of our algorithm by generating
high-quality stylized images with comparisons to a number of recent methods. We
also analyze our method by visualizing the whitened features and synthesizing
textures via simple feature coloring.
A Parallelizable Acceleration Framework for Packing Linear Programs
Authors: Palma London, Shai Vardi, Adam Wierman, Hanling Yi
Date: 2017-11-17 00:00:00

This paper presents an acceleration framework for packing linear programming
problems where the amount of data available is limited, i.e., where the number
of constraints m is small compared to the variable dimension n. The framework
can be used as a black box to speed up linear programming solvers dramatically,
by two orders of magnitude in our experiments. We present worst-case guarantees
on the quality of the solution and the speedup provided by the algorithm,
showing that the framework provides an approximately optimal solution while
running the original solver on a much smaller problem. The framework can be
used to accelerate exact solvers, approximate solvers, and parallel/distributed
solvers. Further, it can be used for both linear programs and integer linear
programs.
Learning a Hierarchical Latent-Variable Model of 3D Shapes
Authors: Shikun Liu, C. Lee Giles, Alexander G. Ororbia II
Date: 2017-05-17 00:00:00
Tasks: 3D Object Classification, 3D Object Recognition, 3D Reconstruction, 3D Shape Generation, Retrieval

We propose the Variational Shape Learner (VSL), a generative model that
learns the underlying structure of voxelized 3D shapes in an unsupervised
fashion. Through the use of skip-connections, our model can successfully learn
and infer a latent, hierarchical representation of objects. Furthermore,
realistic 3D objects can be easily generated by sampling the VSL's latent
probabilistic manifold. We show that our generative model can be trained
end-to-end from 2D images to perform single image 3D model retrieval.
Experiments show, both quantitatively and qualitatively, the improved
generalization of our proposed model over a range of tasks, performing better
or comparable to various state-of-the-art alternatives.
Hardening Quantum Machine Learning Against Adversaries
Authors: Nathan Wiebe, Ram Shankar Siva Kumar
Date: 2017-11-17 00:00:00
Tasks: BIG-bench Machine Learning, Clustering, Quantum Machine Learning

Security for machine learning has begun to become a serious issue for present
day applications. An important question remaining is whether emerging quantum
technologies will help or hinder the security of machine learning. Here we
discuss a number of ways that quantum information can be used to help make
quantum classifiers more secure or private. In particular, we demonstrate a
form of robust principal component analysis that, under some circumstances, can
provide an exponential speedup relative to robust methods used at present. To
demonstrate this approach we introduce a linear combinations of unitaries
Hamiltonian simulation method that we show functions when given an imprecise
Hamiltonian oracle, which may be of independent interest. We also introduce a
new quantum approach for bagging and boosting that can use quantum
superposition over the classifiers or splits of the training set to aggregate
over many more models than would be possible classically. Finally, we provide a
private form of $k$--means clustering that can be used to prevent an all
powerful adversary from learning more than a small fraction of a bit from any
user. These examples show the role that quantum technologies can play in the
security of ML and vice versa. This illustrates that quantum computing can
provide useful advantages to machine learning apart from speedups.