pwc-archive/papers-with-abstracts huggingface dataset:
Graph Convolutions on Spectral Embeddings: Learning of Cortical Surface Data
Authors: Karthik Gopinath, Christian Desrosiers, Herve Lombaert
Date: 2018-03-27 00:00:00
Tasks: Graph Matching

Neuronal cell bodies mostly reside in the cerebral cortex. The study of this
thin and highly convoluted surface is essential for understanding how the brain
works. The analysis of surface data is, however, challenging due to the high
variability of the cortical geometry. This paper presents a novel approach for
learning and exploiting surface data directly across surface domains. Current
approaches rely on geometrical simplifications, such as spherical inflations, a
popular but costly process. For instance, the widely used FreeSurfer takes
about 3 hours to parcellate brain surfaces on a standard machine. Direct
learning of surface data via graph convolutions would provide a new family of
fast algorithms for processing brain surfaces. However, the current limitation
of existing state-of-the-art approaches is their inability to compare surface
data across different surface domains. Surface bases are indeed incompatible
between brain geometries. This paper leverages recent advances in spectral
graph matching to transfer surface data across aligned spectral domains. This
novel approach enables a direct learning of surface data across compatible
surface bases. It exploits spectral filters over intrinsic representations of
surface neighborhoods. We illustrate the benefits of this approach with an
application to brain parcellation. We validate the algorithm over 101 manually
labeled brain surfaces. The results show a significant improvement in labeling
accuracy over recent Euclidean approaches, while gaining a drastic speed
improvement over conventional methods.
Adaptive Affinity Fields for Semantic Segmentation
Authors: Tsung-Wei Ke, Jyh-Jing Hwang, Ziwei Liu, Stella X. Yu
Date: 2018-03-27 00:00:00
Proceeding: ECCV 2018 9
Tasks: Segmentation, Semantic Segmentation

Semantic segmentation has made much progress with increasingly powerful
pixel-wise classifiers and incorporating structural priors via Conditional
Random Fields (CRF) or Generative Adversarial Networks (GAN). We propose a
simpler alternative that learns to verify the spatial structure of segmentation
during training only. Unlike existing approaches that enforce semantic labels
on individual pixels and match labels between neighbouring pixels, we propose
the concept of Adaptive Affinity Fields (AAF) to capture and match the semantic
relations between neighbouring pixels in the label space. We use adversarial
learning to select the optimal affinity field size for each semantic category.
It is formulated as a minimax problem, optimizing our segmentation neural
network in a best worst-case learning scenario. AAF is versatile for
representing structures as a collection of pixel-centric relations, easier to
train than GAN and more efficient than CRF without run-time inference. Our
extensive evaluations on PASCAL VOC 2012, Cityscapes, and GTA5 datasets
demonstrate its above-par segmentation performance and robust generalization
across domains.
Deep Regionlets for Object Detection
Authors: Hongyu Xu, Xutao Lv, Xiaoyu Wang, Zhou Ren, Navaneeth Bodla, Rama Chellappa
Date: 2017-12-06 00:00:00
Proceeding: ECCV 2018 9
Tasks: feature selection, Novel Object Detection, Object, object-detection, Object Detection

In this paper, we propose a novel object detection framework named "Deep
Regionlets" by establishing a bridge between deep neural networks and
conventional detection schema for accurate generic object detection. Motivated
by the abilities of regionlets for modeling object deformation and multiple
aspect ratios, we incorporate regionlets into an end-to-end trainable deep
learning framework. The deep regionlets framework consists of a region
selection network and a deep regionlet learning module. Specifically, given a
detection bounding box proposal, the region selection network provides guidance
on where to select regions to learn the features from. The regionlet learning
module focuses on local feature selection and transformation to alleviate local
variations. To this end, we first realize non-rectangular region selection
within the detection framework to accommodate variations in object appearance.
Moreover, we design a "gating network" within the regionlet leaning module to
enable soft regionlet selection and pooling. The Deep Regionlets framework is
trained end-to-end without additional efforts. We perform ablation studies and
conduct extensive experiments on the PASCAL VOC and Microsoft COCO datasets.
The proposed framework outperforms state-of-the-art algorithms, such as
RetinaNet and Mask R-CNN, even without additional segmentation labels.
ReBNet: Residual Binarized Neural Network
Authors: Mohammad Ghasemzadeh, Mohammad Samragh, Farinaz Koushanfar
Date: 2017-11-03 00:00:00
Tasks: Binarization, General Classification

This paper proposes ReBNet, an end-to-end framework for training
reconfigurable binary neural networks on software and developing efficient
accelerators for execution on FPGA. Binary neural networks offer an intriguing
opportunity for deploying large-scale deep learning models on
resource-constrained devices. Binarization reduces the memory footprint and
replaces the power-hungry matrix-multiplication with light-weight XnorPopcount
operations. However, binary networks suffer from a degraded accuracy compared
to their fixed-point counterparts. We show that the state-of-the-art methods
for optimizing binary networks accuracy, significantly increase the
implementation cost and complexity. To compensate for the degraded accuracy
while adhering to the simplicity of binary networks, we devise the first
reconfigurable scheme that can adjust the classification accuracy based on the
application. Our proposition improves the classification accuracy by
representing features with multiple levels of residual binarization. Unlike
previous methods, our approach does not exacerbate the area cost of the
hardware accelerator. Instead, it provides a tradeoff between throughput and
accuracy while the area overhead of multi-level binarization is negligible.
Evolutionary Multi-objective Optimization of Real-Time Strategy Micro
Authors: Rahul Dubey, Joseph Ghantous, Sushil Louis, Siming Liu
Date: 2018-03-27 00:00:00
Tasks: Real-Time Strategy Games

We investigate an evolutionary multi-objective approach to good micro for
real-time strategy games. Good micro helps a player win skirmishes and is one
of the keys to developing better real-time strategy game play. In prior work,
the same multi-objective approach of maximizing damage done while minimizing
damage received was used to evolve micro for a group of ranged units versus a
group of melee units. We extend this work to consider groups composed from two
types of units. Specifically, this paper uses evolutionary multi-objective
optimization to generate micro for one group composed from both ranged and
melee units versus another group of ranged and melee units. Our micro behavior
representation uses influence maps to represent enemy spatial information and
potential fields generated from distance, health, and weapons cool down to
guide unit movement. Experimental results indicate that our multi-objective
approach leads to a Pareto front of diverse high-quality micro encapsulating
multiple possible tactics. This range of micro provided by the Pareto front
enables a human or AI player to trade-off among short term tactics that better
suit the player's longer term strategy - for example, choosing to minimize
friendly unit damage at the cost of only lightly damaging the enemy versus
maximizing damage to the enemy units at the cost of increased damage to
friendly units. We believe that our results indicate the usefulness of
potential fields as a representation, and of evolutionary multi-objective
optimization as an approach, for generating good micro.
Global versus Localized Generative Adversarial Nets
Authors: Guo-Jun Qi, Liheng Zhang, Hao Hu, Marzieh Edraki, Jingdong Wang, Xian-Sheng Hua
Date: 2017-11-16 00:00:00
Proceeding: CVPR 2018 6
Tasks: General Classification

In this paper, we present a novel localized Generative Adversarial Net (GAN)
to learn on the manifold of real data. Compared with the classic GAN that {\em
globally} parameterizes a manifold, the Localized GAN (LGAN) uses local
coordinate charts to parameterize distinct local geometry of how data points
can transform at different locations on the manifold. Specifically, around each
point there exists a {\em local} generator that can produce data following
diverse patterns of transformations on the manifold. The locality nature of
LGAN enables local generators to adapt to and directly access the local
geometry without need to invert the generator in a global GAN. Furthermore, it
can prevent the manifold from being locally collapsed to a dimensionally
deficient tangent subspace by imposing an orthonormality prior between
tangents. This provides a geometric approach to alleviating mode collapse at
least locally on the manifold by imposing independence between data
transformations in different tangent directions. We will also demonstrate the
LGAN can be applied to train a robust classifier that prefers locally
consistent classification decisions on the manifold, and the resultant
regularizer is closely related with the Laplace-Beltrami operator. Our
experiments show that the proposed LGANs can not only produce diverse image
transformations, but also deliver superior classification performances.
Co-evolving Real-Time Strategy Game Micro
Authors: Navin K Adhikari, Sushil J. Louis, Siming Liu, Walker Spurgeon
Date: 2018-03-27 00:00:00
Tasks: Management, Real-Time Strategy Games

We investigate competitive co-evolution of unit micromanagement in real-time
strategy games. Although good long-term macro-strategy and good short-term unit
micromanagement both impact real-time strategy games performance, this paper
focuses on generating quality micro. Better micro, for example, can help
players win skirmishes and battles even when outnumbered. Prior work has shown
that we can evolve micro to beat a given opponent. We remove the need for a
good opponent to evolve against by using competitive co-evolution to evolve
high-quality micro for both sides from scratch. We first co-evolve micro to
control a group of ranged units versus a group of melee units. We then move to
co-evolve micro for a group of ranged and melee units versus a group of ranged
and melee units. Results show that competitive co-evolution produces good
quality micro and when combined with the well-known techniques of fitness
sharing, shared sampling, and a hall of fame takes less time to produce better
quality micro than simple co-evolution. We believe these results indicate the
viability of co-evolutionary approaches for generating good unit
micro-management.
Canonical Correlation Analysis of Datasets with a Common Source Graph
Authors: Jia Chen, Gang Wang, Yanning Shen, Georgios B. Giannakis
Date: 2018-03-27 00:00:00
Tasks: Clustering, Dimensionality Reduction, feature selection, General Classification, image-classification, Image Classification

Canonical correlation analysis (CCA) is a powerful technique for discovering
whether or not hidden sources are commonly present in two (or more) datasets.
Its well-appreciated merits include dimensionality reduction, clustering,
classification, feature selection, and data fusion. The standard CCA however,
does not exploit the geometry of the common sources, which may be available
from the given data or can be deduced from (cross-) correlations. In this
paper, this extra information provided by the common sources generating the
data is encoded in a graph, and is invoked as a graph regularizer. This leads
to a novel graph-regularized CCA approach, that is termed graph (g) CCA. The
novel gCCA accounts for the graph-induced knowledge of common sources, while
minimizing the distance between the wanted canonical variables. Tailored for
diverse practical settings where the number of data is smaller than the data
vector dimensions, the dual formulation of gCCA is also developed. One such
setting includes kernels that are incorporated to account for nonlinear data
dependencies. The resultant graph-kernel (gk) CCA is also obtained in closed
form. Finally, corroborating image classification tests over several real
datasets are presented to showcase the merits of the novel linear, dual, and
kernel approaches relative to competing alternatives.
Non-Linear Temporal Subspace Representations for Activity Recognition
Authors: Anoop Cherian, Suvrit Sra, Stephen Gould, Richard Hartley
Date: 2018-03-27 00:00:00
Proceeding: CVPR 2018 6
Tasks: Action Recognition, Activity Recognition, Riemannian optimization, Temporal Action Localization, Time Series, Time Series Analysis

Representations that can compactly and effectively capture the temporal
evolution of semantic content are important to computer vision and machine
learning algorithms that operate on multi-variate time-series data. We
investigate such representations motivated by the task of human action
recognition. Here each data instance is encoded by a multivariate feature (such
as via a deep CNN) where action dynamics are characterized by their variations
in time. As these features are often non-linear, we propose a novel pooling
method, kernelized rank pooling, that represents a given sequence compactly as
the pre-image of the parameters of a hyperplane in a reproducing kernel Hilbert
space, projections of data onto which captures their temporal order. We develop
this idea further and show that such a pooling scheme can be cast as an
order-constrained kernelized PCA objective. We then propose to use the
parameters of a kernelized low-rank feature subspace as the representation of
the sequences. We cast our formulation as an optimization problem on
generalized Grassmann manifolds and then solve it efficiently using Riemannian
optimization techniques. We present experiments on several action recognition
datasets using diverse feature modalities and demonstrate state-of-the-art
results.
To Frontalize or Not To Frontalize: Do We Really Need Elaborate Pre-processing To Improve Face Recognition?
Authors: Sandipan Banerjee, Joel Brogan, Janez Krizaj, Aparna Bharati, Brandon RichardWebster, Vitomir Struc, Patrick Flynn, Walter Scheirer
Date: 2016-10-16 00:00:00
Tasks: Face Recognition

Face recognition performance has improved remarkably in the last decade. Much
of this success can be attributed to the development of deep learning
techniques such as convolutional neural networks (CNNs). While CNNs have pushed
the state-of-the-art forward, their training process requires a large amount of
clean and correctly labelled training data. If a CNN is intended to tolerate
facial pose, then we face an important question: should this training data be
diverse in its pose distribution, or should face images be normalized to a
single pose in a pre-processing step? To address this question, we evaluate a
number of popular facial landmarking and pose correction algorithms to
understand their effect on facial recognition performance. Additionally, we
introduce a new, automatic, single-image frontalization scheme that exceeds the
performance of current algorithms. CNNs trained using sets of different
pre-processing methods are used to extract features from the Point and Shoot
Challenge (PaSC) and CMU Multi-PIE datasets. We assert that the subsequent
verification and recognition performance serves to quantify the effectiveness
of each pose correction scheme.
Neuroevolution for RTS Micro
Authors: Aavaas Gajurel, Sushil J. Louis, Daniel J Mendez, Siming Liu
Date: 2018-03-27 00:00:00
Tasks: Real-Time Strategy Games, Starcraft

This paper uses neuroevolution of augmenting topologies to evolve control
tactics for groups of units in real-time strategy games. In such games, players
build economies to generate armies composed of multiple types of units with
different attack and movement characteristics to combat each other. This paper
evolves neural networks to control movement and attack commands, also called
micro, for a group of ranged units skirmishing with a group of melee units. Our
results show that neuroevolution of augmenting topologies can effectively
generate neural networks capable of good micro for our ranged units against a
group of hand-coded melee units. The evolved neural networks lead to kiting
behavior for the ranged units which is a common tactic used by professional
players in ranged versus melee skirmishes in popular real-time strategy games
like Starcraft. The evolved neural networks also generalized well to other
starting positions and numbers of units. We believe these results indicate the
potential of neuroevolution for generating effective micro in real-time
strategy games.
Scalable Nonlinear AUC Maximization Methods
Authors: Majdi Khalid, Indrakshi Ray, Hamidreza Chitsaz
Date: 2017-10-02 00:00:00
Tasks: General Classification

The area under the ROC curve (AUC) is a measure of interest in various
machine learning and data mining applications. It has been widely used to
evaluate classification performance on heavily imbalanced data. The kernelized
AUC maximization machines have established a superior generalization ability
compared to linear AUC machines because of their capability in modeling the
complex nonlinear structure underlying most real-world data. However, the high
training complexity renders the kernelized AUC machines infeasible for
large-scale data. In this paper, we present two nonlinear AUC maximization
algorithms that optimize pairwise linear classifiers over a finite-dimensional
feature space constructed via the k-means Nystr\"{o}m method. Our first
algorithm maximize the AUC metric by optimizing a pairwise squared hinge loss
function using the truncated Newton method. However, the second-order batch AUC
maximization method becomes expensive to optimize for extremely massive
datasets. This motivate us to develop a first-order stochastic AUC maximization
algorithm that incorporates a scheduled regularization update and scheduled
averaging techniques to accelerate the convergence of the classifier.
Experiments on several benchmark datasets demonstrate that the proposed AUC
classifiers are more efficient than kernelized AUC machines while they are able
to surpass or at least match the AUC performance of the kernelized AUC
machines. The experiments also show that the proposed stochastic AUC classifier
outperforms the state-of-the-art online AUC maximization methods in terms of
AUC classification accuracy.
A Study of Clustering Techniques and Hierarchical Matrix Formats for Kernel Ridge Regression
Authors: Elizaveta Rebrova, Gustavo Chavez, Yang Liu, Pieter Ghysels, Xiaoye Sherry Li
Date: 2018-03-27 00:00:00
Tasks: BIG-bench Machine Learning, Clustering, regression

We present memory-efficient and scalable algorithms for kernel methods used
in machine learning. Using hierarchical matrix approximations for the kernel
matrix the memory requirements, the number of floating point operations, and
the execution time are drastically reduced compared to standard dense linear
algebra routines. We consider both the general $\mathcal{H}$ matrix
hierarchical format as well as Hierarchically Semi-Separable (HSS) matrices.
Furthermore, we investigate the impact of several preprocessing and clustering
techniques on the hierarchical matrix compression. Effective clustering of the
input leads to a ten-fold increase in efficiency of the compression. The
algorithms are implemented using the STRUMPACK solver library. These results
confirm that --- with correct tuning of the hyperparameters --- classification
using kernel ridge regression with the compressed matrix does not lose
prediction accuracy compared to the exact --- not compressed --- kernel matrix
and that our approach can be extended to $\mathcal{O}(1M)$ datasets, for which
computation with the full kernel matrix becomes prohibitively expensive. We
present numerical experiments in a distributed memory environment up to 1,024
processors of the NERSC's Cori supercomputer using well-known datasets to the
machine learning community that range from dimension 8 up to 784.
Sparse Partially Linear Additive Models
Authors: Yin Lou, Jacob Bien, Rich Caruana, Johannes Gehrke
Date: 2014-07-17 00:00:00
Tasks: Additive models, Model Selection

The generalized partially linear additive model (GPLAM) is a flexible and
interpretable approach to building predictive models. It combines features in
an additive manner, allowing each to have either a linear or nonlinear effect
on the response. However, the choice of which features to treat as linear or
nonlinear is typically assumed known. Thus, to make a GPLAM a viable approach
in situations in which little is known $a~priori$ about the features, one must
overcome two primary model selection challenges: deciding which features to
include in the model and determining which of these features to treat
nonlinearly. We introduce the sparse partially linear additive model (SPLAM),
which combines model fitting and $both$ of these model selection challenges
into a single convex optimization problem. SPLAM provides a bridge between the
lasso and sparse additive models. Through a statistical oracle inequality and
thorough simulation, we demonstrate that SPLAM can outperform other methods
across a broad spectrum of statistical regimes, including the high-dimensional
($p\gg N$) setting. We develop efficient algorithms that are applied to real
data sets with half a million samples and over 45,000 features with excellent
predictive performance.
Rectified Gaussian Scale Mixtures and the Sparse Non-Negative Least Squares Problem
Authors: Alican Nalci, Igor Fedorov, Maher Al-Shoukairi, Thomas T. Liu, Bhaskar D. Rao
Date: 2016-01-22 00:00:00

In this paper, we develop a Bayesian evidence maximization framework to solve
the sparse non-negative least squares (S-NNLS) problem. We introduce a family
of probability densities referred to as the Rectified Gaussian Scale Mixture
(R- GSM) to model the sparsity enforcing prior distribution for the solution.
The R-GSM prior encompasses a variety of heavy-tailed densities such as the
rectified Laplacian and rectified Student- t distributions with a proper choice
of the mixing density. We utilize the hierarchical representation induced by
the R-GSM prior and develop an evidence maximization framework based on the
Expectation-Maximization (EM) algorithm. Using the EM based method, we estimate
the hyper-parameters and obtain a point estimate for the solution. We refer to
the proposed method as rectified sparse Bayesian learning (R-SBL). We provide
four R- SBL variants that offer a range of options for computational complexity
and the quality of the E-step computation. These methods include the Markov
chain Monte Carlo EM, linear minimum mean-square-error estimation, approximate
message passing and a diagonal approximation. Using numerical experiments, we
show that the proposed R-SBL method outperforms existing S-NNLS solvers in
terms of both signal and support recovery performance, and is also very robust
against the structure of the design matrix.