pwc-archive/papers-with-abstracts huggingface dataset:
Varying-coefficient models with isotropic Gaussian process priors
Authors: Matthias Bussas, Christoph Sawade, Tobias Scheffer, Niels Landwehr
Date: 2015-08-28 00:00:00
Tasks: Bayesian Inference

We study learning problems in which the conditional distribution of the
output given the input varies as a function of additional task variables. In
varying-coefficient models with Gaussian process priors, a Gaussian process
generates the functional relationship between the task variables and the
parameters of this conditional. Varying-coefficient models subsume hierarchical
Bayesian multitask models, but also generalizations in which the conditional
varies continuously, for instance, in time or space. However, Bayesian
inference in varying-coefficient models is generally intractable. We show that
inference for varying-coefficient models with isotropic Gaussian process priors
resolves to standard inference for a Gaussian process that can be solved
efficiently. MAP inference in this model resolves to multitask learning using
task and instance kernels, and inference for hierarchical Bayesian multitask
models can be carried out efficiently using graph-Laplacian kernels. We report
on experiments for geospatial prediction.
Better Exploiting OS-CNNs for Better Event Recognition in Images
Authors: Limin Wang, Zhe Wang, Sheng Guo, Yu Qiao
Date: 2015-10-14 00:00:00
Tasks: Object, Object Recognition, Scene Recognition

Event recognition from still images is one of the most important problems for
image understanding. However, compared with object recognition and scene
recognition, event recognition has received much less research attention in
computer vision community. This paper addresses the problem of cultural event
recognition in still images and focuses on applying deep learning methods on
this problem. In particular, we utilize the successful architecture of
Object-Scene Convolutional Neural Networks (OS-CNNs) to perform event
recognition. OS-CNNs are composed of object nets and scene nets, which transfer
the learned representations from the pre-trained models on large-scale object
and scene recognition datasets, respectively. We propose four types of
scenarios to explore OS-CNNs for event recognition by treating them as either
"end-to-end event predictors" or "generic feature extractors". Our experimental
results demonstrate that the global and local representations of OS-CNNs are
complementary to each other. Finally, based on our investigation of OS-CNNs, we
come up with a solution for the cultural event recognition track at the ICCV
ChaLearn Looking at People (LAP) challenge 2015. Our team secures the third
place at this challenge and our result is very close to the best performance.
On Equivalence of Martingale Tail Bounds and Deterministic Regret Inequalities
Authors: Alexander Rakhlin, Karthik Sridharan
Date: 2015-10-13 00:00:00

We study an equivalence of (i) deterministic pathwise statements appearing in
the online learning literature (termed \emph{regret bounds}), (ii)
high-probability tail bounds for the supremum of a collection of martingales
(of a specific form arising from uniform laws of large numbers for
martingales), and (iii) in-expectation bounds for the supremum. By virtue of
the equivalence, we prove exponential tail bounds for norms of Banach space
valued martingales via deterministic regret bounds for the online mirror
descent algorithm with an adaptive step size. We extend these results beyond
the linear structure of the Banach space: we define a notion of
\emph{martingale type} for general classes of real-valued functions and show
its equivalence (up to a logarithmic factor) to various sequential complexities
of the class (in particular, the sequential Rademacher complexity and its
offset version). For classes with the general martingale type 2, we exhibit a
finer notion of variation that allows partial adaptation to the function
indexing the martingale. Our proof technique rests on sequential symmetrization
and on certifying the \emph{existence} of regret minimization strategies for
certain online prediction problems.
Variable-state Latent Conditional Random Fields for Facial Expression Recognition and Action Unit Detection
Authors: Robert Walecki, Ognjen Rudovic, Vladimir Pavlovic, Maja Pantic
Date: 2015-10-13 00:00:00
Tasks: Action Unit Detection, Facial Expression Recognition, Facial Expression Recognition (FER)

Automated recognition of facial expressions of emotions, and detection of
facial action units (AUs), from videos depends critically on modeling of their
dynamics. These dynamics are characterized by changes in temporal phases
(onset-apex-offset) and intensity of emotion expressions and AUs, the
appearance of which may vary considerably among target subjects, making the
recognition/detection task very challenging. The state-of-the-art Latent
Conditional Random Fields (L-CRF) framework allows one to efficiently encode
these dynamics through the latent states accounting for the temporal
consistency in emotion expression and ordinal relationships between its
intensity levels, these latent states are typically assumed to be either
unordered (nominal) or fully ordered (ordinal). Yet, such an approach is often
too restrictive. For instance, in the case of AU detection, the goal is to
discriminate between the segments of an image sequence in which this AU is
active or inactive. While the sequence segments containing activation of the
target AU may better be described using ordinal latent states, the inactive
segments better be described using unordered (nominal) latent states, as no
assumption can be made about their underlying structure (since they can contain
either neutral faces or activations of non-target AUs). To address this, we
propose the variable-state L-CRF (VSL-CRF) model that automatically selects the
optimal latent states for the target image sequence. To reduce the model
overfitting either the nominal or ordinal latent states, we propose a novel
graph-Laplacian regularization of the latent states. Our experiments on three
public expression databases show that the proposed model achieves better
generalization performance compared to traditional L-CRFs and other related
state-of-the-art models.
Nonlinear memory capacity of parallel time-delay reservoir computers in the processing of multidimensional signals
Authors: Lyudmila Grigoryeva, Julie Henriques, Laurent Larger, Juan-Pablo Ortega
Date: 2015-10-13 00:00:00

This paper addresses the reservoir design problem in the context of
delay-based reservoir computers for multidimensional input signals, parallel
architectures, and real-time multitasking. First, an approximating reservoir
model is presented in those frameworks that provides an explicit functional
link between the reservoir parameters and architecture and its performance in
the execution of a specific task. Second, the inference properties of the ridge
regression estimator in the multivariate context is used to assess the impact
of finite sample training on the decrease of the reservoir capacity. Finally,
an empirical study is conducted that shows the adequacy of the theoretical
results with the empirical performances exhibited by various reservoir
architectures in the execution of several nonlinear tasks with multidimensional
inputs. Our results confirm the robustness properties of the parallel reservoir
architecture with respect to task misspecification and parameter choice that
had already been documented in the literature.
Complex Politics: A Quantitative Semantic and Topological Analysis of UK House of Commons Debates
Authors: Stefano Gurciullo, Michael Smallegan, María Pereda, Federico Battiston, Alice Patania, Sebastian Poledna, Daniel Hedblom, Bahattin Tolga Oztan, Alexander Herzog, Peter John, Slava Mikhaylov
Date: 2015-10-13 00:00:00
Tasks: Dynamic Topic Modeling, Topological Data Analysis

This study is a first, exploratory attempt to use quantitative semantics
techniques and topological analysis to analyze systemic patterns arising in a
complex political system. In particular, we use a rich data set covering all
speeches and debates in the UK House of Commons between 1975 and 2014. By the
use of dynamic topic modeling (DTM) and topological data analysis (TDA) we show
that both members and parties feature specific roles within the system,
consistent over time, and extract global patterns indicating levels of
political cohesion. Our results provide a wide array of novel hypotheses about
the complex dynamics of political systems, with valuable policy applications.
Accelerating Optimization via Adaptive Prediction
Authors: Mehryar Mohri, Scott Yang
Date: 2015-09-18 00:00:00
Tasks: Prediction

We present a powerful general framework for designing data-dependent
optimization algorithms, building upon and unifying recent techniques in
adaptive regularization, optimistic gradient predictions, and problem-dependent
randomization. We first present a series of new regret guarantees that hold at
any time and under very minimal assumptions, and then show how different
relaxations recover existing algorithms, both basic as well as more recent
sophisticated ones. Finally, we show how combining adaptivity, optimism, and
problem-dependent randomization can guide the design of algorithms that benefit
from more favorable guarantees than recent state-of-the-art methods.
Wide-Area Image Geolocalization with Aerial Reference Imagery
Authors: Scott Workman, Richard Souvenir, Nathan Jacobs
Date: 2015-10-13 00:00:00
Proceeding: ICCV 2015 12

We propose to use deep convolutional neural networks to address the problem
of cross-view image geolocalization, in which the geolocation of a ground-level
query image is estimated by matching to georeferenced aerial images. We use
state-of-the-art feature representations for ground-level images and introduce
a cross-view training approach for learning a joint semantic feature
representation for aerial images. We also propose a network architecture that
fuses features extracted from aerial images at multiple spatial scales. To
support training these networks, we introduce a massive database that contains
pairs of aerial and ground-level images from across the United States. Our
methods significantly out-perform the state of the art on two benchmark
datasets. We also show, qualitatively, that the proposed feature
representations are discriminative at both local and continental spatial
scales.
Fast sequential forensic camera identification
Authors: Fernando Pérez-González, Iria González-Iglesias, Miguel Masciopinto, Pedro Comesaña
Date: 2015-10-13 00:00:00

Two sequential camera source identification methods are proposed. Sequential
tests implement a log-likelihood ratio test in an incremental way, thus
enabling a reliable decision with a minimal number of observations. One of our
methods adapts Goljan et al.'s to sequential operation. The second, which
offers better performance in terms of error probabilities and average number of
test observations, is based on treating the alternative hypothesis as a doubly
stochastic model. We also discuss how the standard sequential test can be
corrected to account for the event of weak fingerprints. Finally, we validate
the goodness of our methods with experiments.
SemanticPaint: A Framework for the Interactive Segmentation of 3D Scenes
Authors: Stuart Golodetz, Michael Sapienza, Julien P. C. Valentin, Vibhav Vineet, Ming-Ming Cheng, Anurag Arnab, Victor A. Prisacariu, Olaf Kähler, Carl Yuheng Ren, David W. Murray, Shahram Izadi, Philip H. S. Torr
Date: 2015-10-13 00:00:00
Tasks: Interactive Segmentation, Segmentation

We present an open-source, real-time implementation of SemanticPaint, a
system for geometric reconstruction, object-class segmentation and learning of
3D scenes. Using our system, a user can walk into a room wearing a depth camera
and a virtual reality headset, and both densely reconstruct the 3D scene and
interactively segment the environment into object classes such as 'chair',
'floor' and 'table'. The user interacts physically with the real-world scene,
touching objects and using voice commands to assign them appropriate labels.
These user-generated labels are leveraged by an online random forest-based
machine learning algorithm, which is used to predict labels for previously
unseen parts of the scene. The entire pipeline runs in real time, and the user
stays 'in the loop' throughout the process, receiving immediate feedback about
the progress of the labelling and interacting with the scene as necessary to
refine the predicted segmentation.
Lifted Relational Neural Networks
Authors: Gustav Sourek, Vojtech Aschenbrenner, Filip Zelezny, Ondrej Kuzelka
Date: 2015-08-20 00:00:00
Tasks: Relational Reasoning

We propose a method combining relational-logic representations with neural
network learning. A general lifted architecture, possibly reflecting some
background domain knowledge, is described through relational rules which may be
handcrafted or learned. The relational rule-set serves as a template for
unfolding possibly deep neural networks whose structures also reflect the
structures of given training or testing relational examples. Different networks
corresponding to different examples share their weights, which co-evolve during
training by stochastic gradient descent algorithm. The framework allows for
hierarchical relational modeling constructs and learning of latent relational
concepts through shared hidden layers weights corresponding to the rules.
Discovery of notable relational concepts and experiments on 78 relational
learning benchmarks demonstrate favorable performance of the method.
Convective regularization for optical flow
Authors: José A. Iglesias, Clemens Kirisits
Date: 2015-05-19 00:00:00
Tasks: Optical Flow Estimation

We argue that the time derivative in a fixed coordinate frame may not be the
most appropriate measure of time regularity of an optical flow field. Instead,
for a given velocity field $v$ we consider the convective acceleration $v_t +
\nabla v v$ which describes the acceleration of objects moving according to
$v$. Consequently we investigate the suitability of the nonconvex functional
$\|v_t + \nabla v v\|^2_{L^2}$ as a regularization term for optical flow. We
demonstrate that this term acts as both a spatial and a temporal regularizer
and has an intrinsic edge-preserving property. We incorporate it into a
contrast invariant and time-regularized variant of the Horn-Schunck functional,
prove existence of minimizers and verify experimentally that it addresses some
of the problems of basic quadratic models. For the minimization we use an
iterative scheme that approximates the original nonlinear problem with a
sequence of linear ones. We believe that the convective acceleration may be
gainfully introduced in a variety of optical flow models.
Cascaded Sparse Spatial Bins for Efficient and Effective Generic Object Detection
Authors: David Novotny, Jiri Matas
Date: 2015-04-27 00:00:00
Proceeding: ICCV 2015 12
Tasks: object-detection, Object Detection

A novel efficient method for extraction of object proposals is introduced.
Its "objectness" function exploits deep spatial pyramid features, a novel
fast-to-compute HoG-based edge statistic and the EdgeBoxes score. The
efficiency is achieved by the use of spatial bins in a novel combination with
sparsity-inducing group normalized SVM. State-of-the-art recall performance is
achieved on Pascal VOC07, significantly outperforming methods with comparable
speed. Interestingly, when only 100 proposals per image are considered the
method attains 78% recall on VOC07. The method improves mAP of the RCNN
state-of-the-art class-specific detector, increasing it by 10 points when only
50 proposals are used in each image. The system trained on twenty classes
performs well on the two hundred class ILSVRC2013 set confirming generalization
capability.
A language model based approach towards large scale and lightweight language identification systems
Authors: Brij Mohan Lal Srivastava, Hari Krishna Vydana, Anil Kumar Vuppala, Manish Shrivastava
Date: 2015-10-13 00:00:00
Tasks: Language Identification, Language Modeling, Language Modelling, Spoken Dialogue Systems

Multilingual spoken dialogue systems have gained prominence in the recent
past necessitating the requirement for a front-end Language Identification
(LID) system. Most of the existing LID systems rely on modeling the language
discriminative information from low-level acoustic features. Due to the
variabilities of speech (speaker and emotional variabilities, etc.),
large-scale LID systems developed using low-level acoustic features suffer from
a degradation in the performance. In this approach, we have attempted to model
the higher level language discriminative phonotactic information for developing
an LID system. In this paper, the input speech signal is tokenized to phone
sequences by using a language independent phone recognizer. The language
discriminative phonotactic information in the obtained phone sequences are
modeled using statistical and recurrent neural network based language modeling
approaches. As this approach, relies on higher level phonotactical information
it is more robust to variabilities of speech. Proposed approach is
computationally light weight, highly scalable and it can be used in complement
with the existing LID systems.
$\ell_1$-regularized Neural Networks are Improperly Learnable in Polynomial Time
Authors: Yuchen Zhang, Jason D. Lee, Michael. I. Jordan
Date: 2015-10-13 00:00:00

We study the improper learning of multi-layer neural networks. Suppose that
the neural network to be learned has $k$ hidden layers and that the
$\ell_1$-norm of the incoming weights of any neuron is bounded by $L$. We
present a kernel-based method, such that with probability at least $1 -
\delta$, it learns a predictor whose generalization error is at most $\epsilon$
worse than that of the neural network. The sample complexity and the time
complexity of the presented method are polynomial in the input dimension and in
$(1/\epsilon,\log(1/\delta),F(k,L))$, where $F(k,L)$ is a function depending on
$(k,L)$ and on the activation function, independent of the number of neurons.
The algorithm applies to both sigmoid-like activation functions and ReLU-like
activation functions. It implies that any sufficiently sparse neural network is
learnable in polynomial time.
Multi-Region Probabilistic Dice Similarity Coefficient using the Aitchison Distance and Bipartite Graph Matching
Authors: Shawn Andrews, Ghassan Hamarneh
Date: 2015-09-24 00:00:00
Tasks: Graph Matching, Image Segmentation, Segmentation, Semantic Segmentation

Validation of image segmentation methods is of critical importance.
Probabilistic image segmentation is increasingly popular as it captures
uncertainty in the results. Image segmentation methods that support
multi-region (as opposed to binary) delineation are more favourable as they
capture interactions between the different objects in the image. The Dice
similarity coefficient (DSC) has been a popular metric for evaluating the
accuracy of automated or semi-automated segmentation methods by comparing their
results to the ground truth. In this work, we develop an extension of the DSC
to multi-region probabilistic segmentations (with unordered labels). We use
bipartite graph matching to establish label correspondences and propose two
functions that extend the DSC, one based on absolute probability differences
and one based on the Aitchison distance. These provide a robust and accurate
measure of multi-region probabilistic segmentation accuracy.
The intrinsic value of HFO features as a biomarker of epileptic activity
Authors: Stephen V. Gliske, Kevin R. Moon, William C. Stacey, Alfred O. Hero III
Date: 2015-10-13 00:00:00
Tasks: Dimensionality Reduction, EEG, Electroencephalogram (EEG), General Classification

High frequency oscillations (HFOs) are a promising biomarker of epileptic
brain tissue and activity. HFOs additionally serve as a prototypical example of
challenges in the analysis of discrete events in high-temporal resolution,
intracranial EEG data. Two primary challenges are 1) dimensionality reduction,
and 2) assessing feasibility of classification. Dimensionality reduction
assumes that the data lie on a manifold with dimension less than that of the
feature space. However, previous HFO analyses have assumed a linear manifold,
global across time, space (i.e. recording electrode/channel), and individual
patients. Instead, we assess both a) whether linear methods are appropriate and
b) the consistency of the manifold across time, space, and patients. We also
estimate bounds on the Bayes classification error to quantify the distinction
between two classes of HFOs (those occurring during seizures and those
occurring due to other processes). This analysis provides the foundation for
future clinical use of HFO features and buides the analysis for other discrete
events, such as individual action potentials or multi-unit activity.
On Wasserstein Two Sample Testing and Related Families of Nonparametric Tests
Authors: Aaditya Ramdas, Nicolas Garcia, Marco Cuturi
Date: 2015-09-08 00:00:00
Tasks: Two-sample testing, Vocal Bursts Valence Prediction

Nonparametric two sample or homogeneity testing is a decision theoretic
problem that involves identifying differences between two random variables
without making parametric assumptions about their underlying distributions. The
literature is old and rich, with a wide variety of statistics having being
intelligently designed and analyzed, both for the unidimensional and the
multivariate setting. Our contribution is to tie together many of these tests,
drawing connections between seemingly very different statistics. In this work,
our central object is the Wasserstein distance, as we form a chain of
connections from univariate methods like the Kolmogorov-Smirnov test, PP/QQ
plots and ROC/ODC curves, to multivariate tests involving energy statistics and
kernel based maximum mean discrepancy. Some connections proceed through the
construction of a \textit{smoothed} Wasserstein distance, and others through
the pursuit of a "distribution-free" Wasserstein test. Some observations in
this chain are implicit in the literature, while others seem to have not been
noticed thus far. Given nonparametric two sample testing's classical and
continued importance, we aim to provide useful connections for theorists and
practitioners familiar with one subset of methods but not others.
Consistent Estimation of Low-Dimensional Latent Structure in High-Dimensional Data
Authors: Xiongzhi Chen, John D. Storey
Date: 2015-10-13 00:00:00
Tasks: Dimensionality Reduction, Vocal Bursts Intensity Prediction

We consider the problem of extracting a low-dimensional, linear latent
variable structure from high-dimensional random variables. Specifically, we
show that under mild conditions and when this structure manifests itself as a
linear space that spans the conditional means, it is possible to consistently
recover the structure using only information up to the second moments of these
random variables. This finding, specialized to one-parameter exponential
families whose variance function is quadratic in their means, allows for the
derivation of an explicit estimator of such latent structure. This approach
serves as a latent variable model estimator and as a tool for dimension
reduction for a high-dimensional matrix of data composed of many related
variables. Our theoretical results are verified by simulation studies and an
application to genomic data.