pwc-archive/papers-with-abstracts huggingface dataset:
Combining multiple resolutions into hierarchical representations for kernel-based image classification
Authors: Yanwei Cui, Sébastien Lefevre, Laetitia Chapel, Anne Puissant
Date: 2016-07-09 00:00:00
Tasks: Classification, General Classification, image-classification, Image Classification

Geographic object-based image analysis (GEOBIA) framework has gained
increasing interest recently. Following this popular paradigm, we propose a
novel multiscale classification approach operating on a hierarchical image
representation built from two images at different resolutions. They capture the
same scene with different sensors and are naturally fused together through the
hierarchical representation, where coarser levels are built from a Low Spatial
Resolution (LSR) or Medium Spatial Resolution (MSR) image while finer levels
are generated from a High Spatial Resolution (HSR) or Very High Spatial
Resolution (VHSR) image. Such a representation allows one to benefit from the
context information thanks to the coarser levels, and subregions spatial
arrangement information thanks to the finer levels. Two dedicated structured
kernels are then used to perform machine learning directly on the constructed
hierarchical representation. This strategy overcomes the limits of conventional
GEOBIA classification procedures that can handle only one or very few
pre-selected scales. Experiments run on an urban classification task show that
the proposed approach can highly improve the classification accuracy w.r.t.
conventional approaches working on a single scale.
City-Identification of Flickr Videos Using Semantic Acoustic Features
Authors: Benjamin Elizalde, Guan-Lin Chao, Ming Zeng, Ian Lane
Date: 2016-07-12 00:00:00

City-identification of videos aims to determine the likelihood of a video
belonging to a set of cities. In this paper, we present an approach using only
audio, thus we do not use any additional modality such as images, user-tags or
geo-tags. In this manner, we show to what extent the city-location of videos
correlates to their acoustic information. Success in this task suggests
improvements can be made to complement the other modalities. In particular, we
present a method to compute and use semantic acoustic features to perform
city-identification and the features show semantic evidence of the
identification. The semantic evidence is given by a taxonomy of urban sounds
and expresses the potential presence of these sounds in the city- soundtracks.
We used the MediaEval Placing Task set, which contains Flickr videos labeled by
city. In addition, we used the UrbanSound8K set containing audio clips labeled
by sound- type. Our method improved the state-of-the-art performance and
provides a novel semantic approach to this task
A Variational Model for Joint Motion Estimation and Image Reconstruction
Authors: Martin Burger, Hendrik Dirks, Carola-Bibiane Schönlieb
Date: 2016-07-12 00:00:00
Tasks: Image Reconstruction, Motion Estimation

The aim of this paper is to derive and analyze a variational model for the
joint estimation of motion and reconstruction of image sequences, which is
based on a time-continuous Eulerian motion model. The model can be set up in
terms of the continuity equation or the brightness constancy equation. The
analysis in this paper focuses on the latter for robust motion estimation on
sequences of two-dimensional images. We rigorously prove the existence of a
minimizer in a suitable function space setting. Moreover, we discuss the
numerical solution of the model based on primal-dual algorithms and investigate
several examples. Finally, the benefits of our model compared to existing
techniques, such as sequential image reconstruction and motion estimation, are
shown.
Network Trimming: A Data-Driven Neuron Pruning Approach towards Efficient Deep Architectures
Authors: Hengyuan Hu, Rui Peng, Yu-Wing Tai, Chi-Keung Tang
Date: 2016-07-12 00:00:00
Tasks: Efficient Neural Network

State-of-the-art neural networks are getting deeper and wider. While their
performance increases with the increasing number of layers and neurons, it is
crucial to design an efficient deep architecture in order to reduce
computational and memory costs. Designing an efficient neural network, however,
is labor intensive requiring many experiments, and fine-tunings. In this paper,
we introduce network trimming which iteratively optimizes the network by
pruning unimportant neurons based on analysis of their outputs on a large
dataset. Our algorithm is inspired by an observation that the outputs of a
significant portion of neurons in a large network are mostly zero, regardless
of what inputs the network received. These zero activation neurons are
redundant, and can be removed without affecting the overall accuracy of the
network. After pruning the zero activation neurons, we retrain the network
using the weights before pruning as initialization. We alternate the pruning
and retraining to further reduce zero activations in a network. Our experiments
on the LeNet and VGG-16 show that we can achieve high compression ratio of
parameters without losing or even achieving higher accuracy than the original
network.
Deep CNNs along the Time Axis with Intermap Pooling for Robustness to Spectral Variations
Authors: Hwaran Lee, Geonmin Kim, Ho-Gyeong Kim, Sang-Hoon Oh, Soo-Young Lee
Date: 2016-06-10 00:00:00

Convolutional neural networks (CNNs) with convolutional and pooling
operations along the frequency axis have been proposed to attain invariance to
frequency shifts of features. However, this is inappropriate with regard to the
fact that acoustic features vary in frequency. In this paper, we contend that
convolution along the time axis is more effective. We also propose the addition
of an intermap pooling (IMP) layer to deep CNNs. In this layer, filters in each
group extract common but spectrally variant features, then the layer pools the
feature maps of each group. As a result, the proposed IMP CNN can achieve
insensitivity to spectral variations characteristic of different speakers and
utterances. The effectiveness of the IMP CNN architecture is demonstrated on
several LVCSR tasks. Even without speaker adaptation techniques, the
architecture achieved a WER of 12.7% on the SWB part of the Hub5'2000
evaluation test set, which is competitive with other state-of-the-art methods.
Weakly Supervised Learning of Heterogeneous Concepts in Videos
Authors: Sohil Shah, Kuldeep Kulkarni, Arijit Biswas, Ankit Gandhi, Om Deshmukh, Larry Davis
Date: 2016-07-12 00:00:00
Tasks: General Classification, Weakly-supervised Learning

Typical textual descriptions that accompany online videos are 'weak': i.e.,
they mention the main concepts in the video but not their corresponding
spatio-temporal locations. The concepts in the description are typically
heterogeneous (e.g., objects, persons, actions). Certain location constraints
on these concepts can also be inferred from the description. The goal of this
paper is to present a generalization of the Indian Buffet Process (IBP) that
can (a) systematically incorporate heterogeneous concepts in an integrated
framework, and (b) enforce location constraints, for efficient classification
and localization of the concepts in the videos. Finally, we develop posterior
inference for the proposed formulation using mean-field variational
approximation. Comparative evaluations on the Casablanca and the A2D datasets
show that the proposed approach significantly outperforms other
state-of-the-art techniques: 24% relative improvement for pairwise concept
classification in the Casablanca dataset and 9% relative improvement for
localization in the A2D dataset as compared to the most competitive baseline.
Local identifiability of $l_1$-minimization dictionary learning: a sufficient and almost necessary condition
Authors: Siqi Wu, Bin Yu
Date: 2015-05-17 00:00:00
Tasks: Dictionary Learning

We study the theoretical properties of learning a dictionary from $N$ signals
$\mathbf x_i\in \mathbb R^K$ for $i=1,...,N$ via $l_1$-minimization. We assume
that $\mathbf x_i$'s are $i.i.d.$ random linear combinations of the $K$ columns
from a complete (i.e., square and invertible) reference dictionary $\mathbf D_0
\in \mathbb R^{K\times K}$. Here, the random linear coefficients are generated
from either the $s$-sparse Gaussian model or the Bernoulli-Gaussian model.
First, for the population case, we establish a sufficient and almost necessary
condition for the reference dictionary $\mathbf D_0$ to be locally
identifiable, i.e., a local minimum of the expected $l_1$-norm objective
function. Our condition covers both sparse and dense cases of the random linear
coefficients and significantly improves the sufficient condition by Gribonval
and Schnass (2010). In addition, we show that for a complete $\mu$-coherent
reference dictionary, i.e., a dictionary with absolute pairwise column
inner-product at most $\mu\in[0,1)$, local identifiability holds even when the
random linear coefficient vector has up to $O(\mu^{-2})$ nonzeros on average.
Moreover, our local identifiability results also translate to the finite sample
case with high probability provided that the number of signals $N$ scales as
$O(K\log K)$.
Local feature hierarchy for face recognition across pose and illumination
Authors: Xiaoyue Jiang, Dong Zhang, Xiaoyi Feng
Date: 2016-07-12 00:00:00
Tasks: Face Recognition

Even though face recognition in frontal view and normal lighting condition
works very well, the performance degenerates sharply in extreme conditions.
Recently there are many work dealing with pose and illumination problems,
respectively. However both the lighting and pose variation will always be
encountered at the same time. Accordingly we propose an end-to-end face
recognition method to deal with pose and illumination simultaneously based on
convolutional networks where the discriminative nonlinear features that are
invariant to pose and illumination are extracted. Normally the global structure
for images taken in different views is quite diverse. Therefore we propose to
use the 1*1 convolutional kernel to extract the local features. Furthermore the
parallel multi-stream multi-layer 1*1 convolution network is developed to
extract multi-hierarchy features. In the experiments we obtained the average
face recognition rate of 96.9% on multiPIE dataset,which improves the
state-of-the-art of face recognition across poses and illumination by 7.5%.
Especially for profile-wise positions, the average recognition rate of our
proposed network is 97.8%, which increases the state-of-the-art recognition
rate by 19%.
Information Projection and Approximate Inference for Structured Sparse Variables
Authors: Rajiv Khanna, Joydeep Ghosh, Russell Poldrack, Oluwasanmi Koyejo
Date: 2016-07-12 00:00:00

Approximate inference via information projection has been recently introduced
as a general-purpose approach for efficient probabilistic inference given
sparse variables. This manuscript goes beyond classical sparsity by proposing
efficient algorithms for approximate inference via information projection that
are applicable to any structure on the set of variables that admits enumeration
using a \emph{matroid}. We show that the resulting information projection can
be reduced to combinatorial submodular optimization subject to matroid
constraints. Further, leveraging recent advances in submodular optimization, we
provide an efficient greedy algorithm with strong optimization-theoretic
guarantees. The class of probabilistic models that can be expressed in this way
is quite broad and, as we show, includes group sparse regression, group sparse
principal components analysis and sparse canonical correlation analysis, among
others. Moreover, empirical results on simulated data and high dimensional
neuroimaging data highlight the superior performance of the information
projection approach as compared to established baselines for a range of
probabilistic models.
Rapid Prediction of Player Retention in Free-to-Play Mobile Games
Authors: Anders Drachen, Eric Thurston Lundquist, Yungjen Kung, Pranav Simha Rao, Diego Klabjan, Rafet Sifa, Julian Runge
Date: 2016-07-12 00:00:00
Tasks: General Classification

Predicting and improving player retention is crucial to the success of mobile
Free-to-Play games. This paper explores the problem of rapid retention
prediction in this context. Heuristic modeling approaches are introduced as a
way of building simple rules for predicting short-term retention. Compared to
common classification algorithms, our heuristic-based approach achieves
reasonable and comparable performance using information from the first session,
day, and week of player activity.
Multi-Step Bayesian Optimization for One-Dimensional Feasibility Determination
Authors: J. Massey Cashore, Lemuel Kumarga, Peter I. Frazier
Date: 2016-07-11 00:00:00
Tasks: Bayesian Optimization

Bayesian optimization methods allocate limited sampling budgets to maximize
expensive-to-evaluate functions. One-step-lookahead policies are often used,
but computing optimal multi-step-lookahead policies remains a challenge. We
consider a specialized Bayesian optimization problem: finding the superlevel
set of an expensive one-dimensional function, with a Markov process prior. We
compute the Bayes-optimal sampling policy efficiently, and characterize the
suboptimality of one-step lookahead. Our numerical experiments demonstrate that
the one-step lookahead policy is close to optimal in this problem, performing
within 98% of optimal in the experimental settings considered.
On Deterministic Conditions for Subspace Clustering under Missing Data
Authors: Wenqi Wang, Shuchin Aeron, Vaneet Aggarwal
Date: 2016-07-11 00:00:00
Tasks: Clustering

In this paper we present deterministic conditions for success of sparse
subspace clustering (SSC) under missing data, when data is assumed to come from
a Union of Subspaces (UoS) model. We consider two algorithms, which are
variants of SSC with entry-wise zero-filling that differ in terms of the
optimization problems used to find affinity matrix for spectral clustering. For
both the algorithms, we provide deterministic conditions for any pattern of
missing data such that perfect clustering can be achieved. We provide extensive
sets of simulation results for clustering as well as completion of data at
missing entries, under the UoS model. Our experimental results indicate that in
contrast to the full data case, accurate clustering does not imply accurate
subspace identification and completion, indicating the natural order of
relative hardness of these problems.
A Framework for Estimating Long Term Driver Behavior
Authors: Vijay Gadepally, Ashok Krishnamurthy
Date: 2016-07-11 00:00:00
Tasks: Autonomous Vehicles

The authors present a cyber-physical systems study on the estimation of
driver behavior in autonomous vehicles and vehicle safety systems. Extending
upon previous work, the approach described is suitable for the long term
estimation and tracking of autonomous vehicle behavior. The proposed system
makes use of a previously defined Hybrid State System and Hidden Markov Model
(HSS+HMM) system which has provided good results for driver behavior
estimation. The HSS+HMM system utilizes the hybrid characteristics of
decision-behavior coupling of many systems such as the driver and the vehicle,
uses Kalman Filter estimates of observable parameters to track the
instantaneous continuous state, and estimates the most likely driver state. The
HSS+HMM system is encompassed in a HSS structure and inter-system connectivity
is determined by using Signal Processing and Pattern Recognition techniques.
The proposed method is suitable for scenarios that involve unknown decisions of
other individuals, such as lane changes or intersection precedence/access. The
long term driver behavior estimation system involves an extended HSS+HMM
structure that is capable of including external information in the estimation
process. Through the grafting and pruning of metastates, the HSS+HMM system can
be dynamically updated to best represent driver choices given external
information. Three application examples are also provided to elucidate the
theoretical system.
How to calculate partition functions using convex programming hierarchies: provable bounds for variational methods
Authors: Andrej Risteski
Date: 2016-07-11 00:00:00
Tasks: Combinatorial Optimization

We consider the problem of approximating partition functions for Ising
models. We make use of recent tools in combinatorial optimization: the
Sherali-Adams and Lasserre convex programming hierarchies, in combination with
variational methods to get algorithms for calculating partition functions in
these families. These techniques give new, non-trivial approximation guarantees
for the partition function beyond the regime of correlation decay. They also
generalize some classical results from statistical physics about the
Curie-Weiss ferromagnetic Ising model, as well as provide a partition function
counterpart of classical results about max-cut on dense graphs
\cite{arora1995polynomial}. With this, we connect techniques from two
apparently disparate research areas -- optimization and counting/partition
function approximations. (i.e. \#-P type of problems).
  Furthermore, we design to the best of our knowledge the first provable,
convex variational methods. Though in the literature there are a host of convex
versions of variational methods \cite{wainwright2003tree, wainwright2005new,
heskes2006convexity, meshi2009convexifying}, they come with no guarantees
(apart from some extremely special cases, like e.g. the graph has a single
cycle \cite{weiss2000correctness}). We consider dense and low threshold rank
graphs, and interestingly, the reason our approach works on these types of
graphs is because local correlations propagate to global correlations --
completely the opposite of algorithms based on correlation decay. In the
process we design novel entropy approximations based on the low-order moments
of a distribution.
  Our proof techniques are very simple and generic, and likely to be applicable
to many other settings other than Ising models.
Stream-based Online Active Learning in a Contextual Multi-Armed Bandit Framework
Authors: Linqi Song
Date: 2016-07-11 00:00:00
Tasks: Active Learning

We study the stream-based online active learning in a contextual multi-armed
bandit framework. In this framework, the reward depends on both the arm and the
context. In a stream-based active learning setting, obtaining the ground truth
of the reward is costly, and the conventional contextual multi-armed bandit
algorithm fails to achieve a sublinear regret due to this cost. Hence, the
algorithm needs to determine whether or not to request the ground truth of the
reward at current time slot. In our framework, we consider a stream-based
active learning setting in which a query request for the ground truth is sent
to the annotator, together with some prior information of the ground truth.
Depending on the accuracy of the prior information, the query cost varies. Our
algorithm mainly carries out two operations: the refinement of the context and
arm spaces and the selection of actions. In our algorithm, the partitions of
the context space and the arm space are maintained for a certain time slots,
and then become finer as more information about the rewards accumulates. We use
a strategic way to select the arms and to request the ground truth of the
reward, aiming to maximize the total reward. We analytically show that the
regret is sublinear and in the same order with that of the conventional
contextual multi-armed bandit algorithms, where no query cost
Algorithms for Generalized Cluster-wise Linear Regression
Authors: Young Woong Park, Yan Jiang, Diego Klabjan, Loren Williams
Date: 2016-07-05 00:00:00
Tasks: Clustering, regression

Cluster-wise linear regression (CLR), a clustering problem intertwined with
regression, is to find clusters of entities such that the overall sum of
squared errors from regressions performed over these clusters is minimized,
where each cluster may have different variances. We generalize the CLR problem
by allowing each entity to have more than one observation, and refer to it as
generalized CLR. We propose an exact mathematical programming based approach
relying on column generation, a column generation based heuristic algorithm
that clusters predefined groups of entities, a metaheuristic genetic algorithm
with adapted Lloyd's algorithm for K-means clustering, a two-stage approach,
and a modified algorithm of Sp{\"a}th \cite{Spath1979} for solving generalized
CLR. We examine the performance of our algorithms on a stock keeping unit (SKU)
clustering problem employed in forecasting halo and cannibalization effects in
promotions using real-world retail data from a large supermarket chain. In the
SKU clustering problem, the retailer needs to cluster SKUs based on their
seasonal effects in response to promotions. The seasonal effects are the
results of regressions with predictors being promotion mechanisms and seasonal
dummies performed over clusters generated. We compare the performance of all
proposed algorithms for the SKU problem with real-world and synthetic data.
Fast Cosine Transform to increase speed-up and efficiency of Karhunen-Loeve Transform for lossy image compression
Authors: Mario Mastriani, Juliana Gambini
Date: 2016-07-11 00:00:00
Tasks: Image Compression

In this work, we present a comparison between two techniques of image
compression. In the first case, the image is divided in blocks which are
collected according to zig-zag scan. In the second one, we apply the Fast
Cosine Transform to the image, and then the transformed image is divided in
blocks which are collected according to zig-zag scan too. Later, in both cases,
the Karhunen-Loeve transform is applied to mentioned blocks. On the other hand,
we present three new metrics based on eigenvalues for a better comparative
evaluation of the techniques. Simulations show that the combined version is the
best, with minor Mean Absolute Error (MAE) and Mean Squared Error (MSE), higher
Peak Signal to Noise Ratio (PSNR) and better image quality. Finally, new
technique was far superior to JPEG and JPEG2000.
Kernel-based methods for bandit convex optimization
Authors: Sébastien Bubeck, Ronen Eldan, Yin Tat Lee
Date: 2016-07-11 00:00:00

We consider the adversarial convex bandit problem and we build the first
$\mathrm{poly}(T)$-time algorithm with $\mathrm{poly}(n) \sqrt{T}$-regret for
this problem. To do so we introduce three new ideas in the derivative-free
optimization literature: (i) kernel methods, (ii) a generalization of Bernoulli
convolutions, and (iii) a new annealing schedule for exponential weights (with
increasing learning rate). The basic version of our algorithm achieves
$\tilde{O}(n^{9.5} \sqrt{T})$-regret, and we show that a simple variant of this
algorithm can be run in $\mathrm{poly}(n \log(T))$-time per step at the cost of
an additional $\mathrm{poly}(n) T^{o(1)}$ factor in the regret. These results
improve upon the $\tilde{O}(n^{11} \sqrt{T})$-regret and
$\exp(\mathrm{poly}(T))$-time result of the first two authors, and the
$\log(T)^{\mathrm{poly}(n)} \sqrt{T}$-regret and
$\log(T)^{\mathrm{poly}(n)}$-time result of Hazan and Li. Furthermore we
conjecture that another variant of the algorithm could achieve
$\tilde{O}(n^{1.5} \sqrt{T})$-regret, and moreover that this regret is
unimprovable (the current best lower bound being $\Omega(n \sqrt{T})$ and it is
achieved with linear functions). For the simpler situation of zeroth order
stochastic convex optimization this corresponds to the conjecture that the
optimal query complexity is of order $n^3 / \epsilon^2$.