pwc-archive/papers-with-abstracts huggingface dataset:
Low-Cost Scene Modeling using a Density Function Improves Segmentation Performance
Authors: Vivek Sharma, Sule Yildirim-Yayilgan, Luc van Gool
Date: 2016-05-26 00:00:00
Tasks: Human-Object Interaction Detection, Object, Segmentation, Semantic Segmentation

We propose a low cost and effective way to combine a free simulation software
and free CAD models for modeling human-object interaction in order to improve
human & object segmentation. It is intended for research scenarios related to
safe human-robot collaboration (SHRC) and interaction (SHRI) in the industrial
domain. The task of human and object modeling has been used for detecting
activity, and for inferring and predicting actions, different from those works,
we do human and object modeling in order to learn interactions in RGB-D data
for improving segmentation. For this purpose, we define a novel density
function to model a three dimensional (3D) scene in a virtual environment
(VREP). This density function takes into account various possible
configurations of human-object and object-object relationships and interactions
governed by their affordances. Using this function, we synthesize a large,
realistic and highly varied synthetic RGB-D dataset that we use for training.
We train a random forest classifier, and the pixelwise predictions obtained is
integrated as a unary term in a pairwise conditional random fields (CRF). Our
evaluation shows that modeling these interactions improves segmentation
performance by ~7\% in mean average precision and recall over state-of-the-art
methods that ignore these interactions in real-world data. Our approach is
computationally efficient, robust and can run real-time on consumer hardware.
Suppressing Background Radiation Using Poisson Principal Component Analysis
Authors: P. Tandon, P. Huggins, A. Dubrawski, S. Labov, K. Nelson
Date: 2016-05-26 00:00:00

Performance of nuclear threat detection systems based on gamma-ray
spectrometry often strongly depends on the ability to identify the part of
measured signal that can be attributed to background radiation. We have
successfully applied a method based on Principal Component Analysis (PCA) to
obtain a compact null-space model of background spectra using PCA projection
residuals to derive a source detection score. We have shown the method's
utility in a threat detection system using mobile spectrometers in urban scenes
(Tandon et al 2012). While it is commonly assumed that measured photon counts
follow a Poisson process, standard PCA makes a Gaussian assumption about the
data distribution, which may be a poor approximation when photon counts are
low. This paper studies whether and in what conditions PCA with a Poisson-based
loss function (Poisson PCA) can outperform standard Gaussian PCA in modeling
background radiation to enable more sensitive and specific nuclear threat
detection.
ProtVec: A Continuous Distributed Representation of Biological Sequences
Authors: Ehsaneddin Asgari, Mohammad R. K. Mofrad
Date: 2015-03-17 00:00:00
Tasks: Classification, General Classification

We introduce a new representation and feature extraction method for
biological sequences. Named bio-vectors (BioVec) to refer to biological
sequences in general with protein-vectors (ProtVec) for proteins (amino-acid
sequences) and gene-vectors (GeneVec) for gene sequences, this representation
can be widely used in applications of deep learning in proteomics and genomics.
In the present paper, we focus on protein-vectors that can be utilized in a
wide array of bioinformatics investigations such as family classification,
protein visualization, structure prediction, disordered protein identification,
and protein-protein interaction prediction. In this method, we adopt artificial
neural network approaches and represent a protein sequence with a single dense
n-dimensional vector. To evaluate this method, we apply it in classification of
324,018 protein sequences obtained from Swiss-Prot belonging to 7,027 protein
families, where an average family classification accuracy of 93%+-0.06% is
obtained, outperforming existing family classification methods. In addition, we
use ProtVec representation to predict disordered proteins from structured
proteins. Two databases of disordered sequences are used: the DisProt database
as well as a database featuring the disordered regions of nucleoporins rich
with phenylalanine-glycine repeats (FG-Nups). Using support vector machine
classifiers, FG-Nup sequences are distinguished from structured protein
sequences found in Protein Data Bank (PDB) with a 99.8% accuracy, and
unstructured DisProt sequences are differentiated from structured DisProt
sequences with 100.0% accuracy. These results indicate that by only providing
sequence data for various proteins into this model, accurate information about
protein structure can be determined.
Domain-Adversarial Training of Neural Networks
Authors: Yaroslav Ganin, Evgeniya Ustinova, Hana Ajakan, Pascal Germain, Hugo Larochelle, François Laviolette, Mario Marchand, Victor Lempitsky
Date: 2015-05-28 00:00:00
Tasks: Domain Adaptation, Domain Generalization, General Classification, image-classification, Image Classification, Person Re-Identification, Representation Learning, Sentiment Analysis, Unsupervised Domain Adaptation

We introduce a new representation learning approach for domain adaptation, in
which data at training and test time come from similar but different
distributions. Our approach is directly inspired by the theory on domain
adaptation suggesting that, for effective domain transfer to be achieved,
predictions must be made based on features that cannot discriminate between the
training (source) and test (target) domains. The approach implements this idea
in the context of neural network architectures that are trained on labeled data
from the source domain and unlabeled data from the target domain (no labeled
target-domain data is necessary). As the training progresses, the approach
promotes the emergence of features that are (i) discriminative for the main
learning task on the source domain and (ii) indiscriminate with respect to the
shift between the domains. We show that this adaptation behaviour can be
achieved in almost any feed-forward model by augmenting it with few standard
layers and a new gradient reversal layer. The resulting augmented architecture
can be trained using standard backpropagation and stochastic gradient descent,
and can thus be implemented with little effort using any of the deep learning
packages. We demonstrate the success of our approach for two distinct
classification problems (document sentiment analysis and image classification),
where state-of-the-art domain adaptation performance on standard benchmarks is
achieved. We also validate the approach for descriptor learning task in the
context of person re-identification application.
CITlab ARGUS for historical handwritten documents
Authors: Gundram Leifert, Tobias Strauß, Tobias Grüning, Roger Labahn
Date: 2016-05-26 00:00:00
Tasks: General Classification

We describe CITlab's recognition system for the HTRtS competition attached to
the 13. International Conference on Document Analysis and Recognition, ICDAR
2015. The task comprises the recognition of historical handwritten documents.
The core algorithms of our system are based on multi-dimensional recurrent
neural networks (MDRNN) and connectionist temporal classification (CTC). The
software modules behind that as well as the basic utility technologies are
essentially powered by PLANET's ARGUS framework for intelligent text
recognition and image processing.
Dense Volume-to-Volume Vascular Boundary Detection
Authors: Jameson Merkow, David Kriegman, Alison Marsden, Zhuowen Tu
Date: 2016-05-26 00:00:00
Tasks: Boundary Detection

In this work, we present a novel 3D-Convolutional Neural Network (CNN)
architecture called I2I-3D that predicts boundary location in volumetric data.
Our fine-to-fine, deeply supervised framework addresses three critical issues
to 3D boundary detection: (1) efficient, holistic, end-to-end volumetric label
training and prediction (2) precise voxel-level prediction to capture fine
scale structures prevalent in medical data and (3) directed multi-scale,
multi-level feature learning. We evaluate our approach on a dataset consisting
of 93 medical image volumes with a wide variety of anatomical regions and
vascular structures. In the process, we also introduce HED-3D, a 3D extension
of the state-of-the-art 2D edge detector (HED). We show that our deep learning
approach out-performs, the current state-of-the-art in 3D vascular boundary
detection (structured forests 3D), by a large margin, as well as HED applied to
slices, and HED-3D while successfully localizing fine structures. With our
approach, boundary detection takes about one minute on a typical 512x512x512
volume.
Total Variation Classes Beyond 1d: Minimax Rates, and the Limitations of Linear Smoothers
Authors: Veeranjaneyulu Sadhanala, Yu-Xiang Wang, Ryan Tibshirani
Date: 2016-05-26 00:00:00
Proceeding: NeurIPS 2016 12
Tasks: Denoising

We consider the problem of estimating a function defined over $n$ locations
on a $d$-dimensional grid (having all side lengths equal to $n^{1/d}$). When
the function is constrained to have discrete total variation bounded by $C_n$,
we derive the minimax optimal (squared) $\ell_2$ estimation error rate,
parametrized by $n$ and $C_n$. Total variation denoising, also known as the
fused lasso, is seen to be rate optimal. Several simpler estimators exist, such
as Laplacian smoothing and Laplacian eigenmaps. A natural question is: can
these simpler estimators perform just as well? We prove that these estimators,
and more broadly all estimators given by linear transformations of the input
data, are suboptimal over the class of functions with bounded variation. This
extends fundamental findings of Donoho and Johnstone [1998] on 1-dimensional
total variation spaces to higher dimensions. The implication is that the
computationally simpler methods cannot be used for such sophisticated denoising
tasks, without sacrificing statistical accuracy. We also derive minimax rates
for discrete Sobolev spaces over $d$-dimensional grids, which are, in some
sense, smaller than the total variation function spaces. Indeed, these are
small enough spaces that linear estimators can be optimal---and a few
well-known ones are, such as Laplacian smoothing and Laplacian eigenmaps, as we
show. Lastly, we investigate the problem of adaptivity of the total variation
denoiser to these smaller Sobolev function spaces.
Domain Transfer Multi-Instance Dictionary Learning
Authors: Ke Wang, Jiayong Liu, Daniel González
Date: 2016-05-26 00:00:00
Tasks: Dictionary Learning, Transfer Learning

In this paper, we invest the domain transfer learning problem with
multi-instance data. We assume we already have a well-trained multi-instance
dictionary and its corresponding classifier from the source domain, which can
be used to represent and classify the bags. But it cannot be directly used to
the target domain. Thus we propose to adapt them to the target domain by adding
an adaptive term to the source domain classifier. The adaptive function is a
linear function based a domain transfer multi-instance dictionary. Given a
target domain bag, we first map it to a bag-level feature space using the
domain transfer dictionary, and then apply a the linear adaptive function to
its bag-level feature vector. To learn the domain-transfer dictionary and the
adaptive function parameter, we simultaneously minimize the average
classification error of the target domain classifier over the target domain
training set, and the complexities of both the adaptive function parameter and
the domain transfer dictionary. The minimization problem is solved by an
iterative algorithm which update the dictionary and the function parameter
alternately. Experiments over several benchmark data sets show the advantage of
the proposed method over existing state-of-the-art domain transfer
multi-instance learning methods.
Robust Downbeat Tracking Using an Ensemble of Convolutional Networks
Authors: S. Durand, J. P. Bello, B. David, G. Richard
Date: 2016-05-26 00:00:00
Tasks: Downbeat Tracking, Rhythm

In this paper, we present a novel state of the art system for automatic
downbeat tracking from music signals. The audio signal is first segmented in
frames which are synchronized at the tatum level of the music. We then extract
different kind of features based on harmony, melody, rhythm and bass content to
feed convolutional neural networks that are adapted to take advantage of each
feature characteristics. This ensemble of neural networks is combined to obtain
one downbeat likelihood per tatum. The downbeat sequence is finally decoded
with a flexible and efficient temporal model which takes advantage of the
metrical continuity of a song. We then perform an evaluation of our system on a
large base of 9 datasets, compare its performance to 4 other published
algorithms and obtain a significant increase of 16.8 percent points compared to
the second best system, for altogether a moderate cost in test and training.
The influence of each step of the method is studied to show its strengths and
shortcomings.
Generalization Properties and Implicit Regularization for Multiple Passes SGM
Authors: Junhong Lin, Raffaello Camoriano, Lorenzo Rosasco
Date: 2016-05-26 00:00:00

We study the generalization properties of stochastic gradient methods for
learning with convex loss functions and linearly parameterized functions. We
show that, in the absence of penalizations or constraints, the stability and
approximation properties of the algorithm can be controlled by tuning either
the step-size or the number of passes over the data. In this view, these
parameters can be seen to control a form of implicit regularization. Numerical
results complement the theoretical findings.
Kronecker Determinantal Point Processes
Authors: Zelda Mariet, Suvrit Sra
Date: 2016-05-26 00:00:00
Proceeding: NeurIPS 2016 12
Tasks: Point Processes, Stochastic Optimization

Determinantal Point Processes (DPPs) are probabilistic models over all
subsets a ground set of $N$ items. They have recently gained prominence in
several applications that rely on "diverse" subsets. However, their
applicability to large problems is still limited due to the $\mathcal O(N^3)$
complexity of core tasks such as sampling and learning. We enable efficient
sampling and learning for DPPs by introducing KronDPP, a DPP model whose kernel
matrix decomposes as a tensor product of multiple smaller kernel matrices. This
decomposition immediately enables fast exact sampling. But contrary to what one
may expect, leveraging the Kronecker product structure for speeding up DPP
learning turns out to be more difficult. We overcome this challenge, and derive
batch and stochastic optimization algorithms for efficiently learning the
parameters of a KronDPP.
Provable Efficient Online Matrix Completion via Non-convex Stochastic Gradient Descent
Authors: Chi Jin, Sham M. Kakade, Praneeth Netrapalli
Date: 2016-05-26 00:00:00
Proceeding: NeurIPS 2016 12
Tasks: Matrix Completion

Matrix completion, where we wish to recover a low rank matrix by observing a
few entries from it, is a widely studied problem in both theory and practice
with wide applications. Most of the provable algorithms so far on this problem
have been restricted to the offline setting where they provide an estimate of
the unknown matrix using all observations simultaneously. However, in many
applications, the online version, where we observe one entry at a time and
dynamically update our estimate, is more appealing. While existing algorithms
are efficient for the offline setting, they could be highly inefficient for the
online setting.
  In this paper, we propose the first provable, efficient online algorithm for
matrix completion. Our algorithm starts from an initial estimate of the matrix
and then performs non-convex stochastic gradient descent (SGD). After every
observation, it performs a fast update involving only one row of two tall
matrices, giving near linear total runtime. Our algorithm can be naturally used
in the offline setting as well, where it gives competitive sample complexity
and runtime to state of the art algorithms. Our proofs introduce a general
framework to show that SGD updates tend to stay away from saddle surfaces and
could be of broader interests for other non-convex problems to prove tight
rates.
Pairwise Decomposition of Image Sequences for Active Multi-View Recognition
Authors: Edward Johns, Stefan Leutenegger, Andrew J. Davison
Date: 2016-05-26 00:00:00
Proceeding: CVPR 2016 6
Tasks: Object Recognition

A multi-view image sequence provides a much richer capacity for object
recognition than from a single image. However, most existing solutions to
multi-view recognition typically adopt hand-crafted, model-based geometric
methods, which do not readily embrace recent trends in deep learning. We
propose to bring Convolutional Neural Networks to generic multi-view
recognition, by decomposing an image sequence into a set of image pairs,
classifying each pair independently, and then learning an object classifier by
weighting the contribution of each pair. This allows for recognition over
arbitrary camera trajectories, without requiring explicit training over the
potentially infinite number of camera paths and lengths. Building these
pairwise relationships then naturally extends to the next-best-view problem in
an active recognition framework. To achieve this, we train a second
Convolutional Neural Network to map directly from an observed image to next
viewpoint. Finally, we incorporate this into a trajectory optimisation task,
whereby the best recognition confidence is sought for a given trajectory
length. We present state-of-the-art results in both guided and unguided
multi-view recognition on the ModelNet dataset, and show how our method can be
used with depth images, greyscale images, or both.
Local Minimax Complexity of Stochastic Convex Optimization
Authors: Yuancheng Zhu, Sabyasachi Chatterjee, John Duchi, John Lafferty
Date: 2016-05-24 00:00:00
Proceeding: NeurIPS 2016 12

We extend the traditional worst-case, minimax analysis of stochastic convex
optimization by introducing a localized form of minimax complexity for
individual functions. Our main result gives function-specific lower and upper
bounds on the number of stochastic subgradient evaluations needed to optimize
either the function or its "hardest local alternative" to a given numerical
precision. The bounds are expressed in terms of a localized and computational
analogue of the modulus of continuity that is central to statistical minimax
analysis. We show how the computational modulus of continuity can be explicitly
calculated in concrete cases, and relates to the curvature of the function at
the optimum. We also prove a superefficiency result that demonstrates it is a
meaningful benchmark, acting as a computational analogue of the Fisher
information in statistical estimation. The nature and practical implications of
the results are demonstrated in simulations.
Benign-Malignant Lung Nodule Classification with Geometric and Appearance Histogram Features
Authors: Tizita Nesibu Shewaye, Alhayat Ali Mekonnen
Date: 2016-05-26 00:00:00
Tasks: Computed Tomography (CT), Diagnostic, General Classification, Lung Nodule Classification

Lung cancer accounts for the highest number of cancer deaths globally. Early
diagnosis of lung nodules is very important to reduce the mortality rate of
patients by improving the diagnosis and treatment of lung cancer. This work
proposes an automated system to classify lung nodules as malignant and benign
in CT images. It presents extensive experimental results using a combination of
geometric and histogram lung nodule image features and different linear and
non-linear discriminant classifiers. The proposed approach is experimentally
validated on the LIDC-IDRI public lung cancer screening thoracic computed
tomography (CT) dataset containing nodule level diagnostic data. The obtained
results are very encouraging correctly classifying 82% of malignant and 93% of
benign nodules on unseen test data at best.
Adaptive Neural Compilation
Authors: Rudy Bunel, Alban Desmaison, Pushmeet Kohli, Philip H. S. Torr, M. Pawan Kumar
Date: 2016-05-25 00:00:00
Proceeding: NeurIPS 2016 12

This paper proposes an adaptive neural-compilation framework to address the
problem of efficient program learning. Traditional code optimisation strategies
used in compilers are based on applying pre-specified set of transformations
that make the code faster to execute without changing its semantics. In
contrast, our work involves adapting programs to make them more efficient while
considering correctness only on a target input distribution. Our approach is
inspired by the recent works on differentiable representations of programs. We
show that it is possible to compile programs written in a low-level language to
a differentiable representation. We also show how programs in this
representation can be optimised to make them efficient on a target distribution
of inputs. Experimental results demonstrate that our approach enables learning
specifically-tuned algorithms for given data distributions with a high success
rate.
Doubly Robust Off-policy Value Evaluation for Reinforcement Learning
Authors: Nan Jiang, Lihong Li
Date: 2015-11-11 00:00:00
Tasks: Decision Making, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL), Sequential Decision Making

We study the problem of off-policy value evaluation in reinforcement learning
(RL), where one aims to estimate the value of a new policy based on data
collected by a different policy. This problem is often a critical step when
applying RL in real-world problems. Despite its importance, existing general
methods either have uncontrolled bias or suffer high variance. In this work, we
extend the doubly robust estimator for bandits to sequential decision-making
problems, which gets the best of both worlds: it is guaranteed to be unbiased
and can have a much lower variance than the popular importance sampling
estimators. We demonstrate the estimator's accuracy in several benchmark
problems, and illustrate its use as a subroutine in safe policy improvement. We
also provide theoretical results on the hardness of the problem, and show that
our estimator can match the lower bound in certain scenarios.
Sequential ranking under random semi-bandit feedback
Authors: Hossein Vahabi, Paul Lagrée, Claire Vernade, Olivier Cappé
Date: 2016-03-04 00:00:00

In many web applications, a recommendation is not a single item suggested to
a user but a list of possibly interesting contents that may be ranked in some
contexts. The combinatorial bandit problem has been studied quite extensively
these last two years and many theoretical results now exist : lower bounds on
the regret or asymptotically optimal algorithms. However, because of the
variety of situations that can be considered, results are designed to solve the
problem for a specific reward structure such as the Cascade Model. The present
work focuses on the problem of ranking items when the user is allowed to click
on several items while scanning the list from top to bottom.