pwc-archive/papers-with-abstracts huggingface dataset:
Extraction of digital wavefront sets using applied harmonic analysis and deep neural networks
Authors: Héctor Andrade-Loarca, Gitta Kutyniok, Ozan Öktem, Philipp Petersen
Date: 2019-01-05 00:00:00

Microlocal analysis provides deep insight into singularity structures and is often crucial for solving inverse problems, predominately, in imaging sciences. Of particular importance is the analysis of wavefront sets and the correct extraction of those. In this paper, we introduce the first algorithmic approach to extract the wavefront set of images, which combines data-based and model-based methods. Based on a celebrated property of the shearlet transform to unravel information on the wavefront set, we extract the wavefront set of an image by first applying a discrete shearlet transform and then feeding local patches of this transform to a deep convolutional neural network trained on labeled data. The resulting algorithm outperforms all competing algorithms in edge-orientation and ramp-orientation detection.
Brain segmentation based on multi-atlas guided 3D fully convolutional network ensembles
Authors: Jiong Wu, Xiaoying Tang
Date: 2019-01-05 00:00:00
Tasks: Brain Segmentation, Segmentation

In this study, we proposed and validated a multi-atlas guided 3D fully convolutional network (FCN) ensemble model (M-FCN) for segmenting brain regions of interest (ROIs) from structural magnetic resonance images (MRIs). One major limitation of existing state-of-the-art 3D FCN segmentation models is that they often apply image patches of fixed size throughout training and testing, which may miss some complex tissue appearance patterns of different brain ROIs. To address this limitation, we trained a 3D FCN model for each ROI using patches of adaptive size and embedded outputs of the convolutional layers in the deconvolutional layers to further capture the local and global context patterns. In addition, with an introduction of multi-atlas based guidance in M-FCN, our segmentation was generated by combining the information of images and labels, which is highly robust. To reduce over-fitting of the FCN model on the training data, we adopted an ensemble strategy in the learning procedure. Evaluation was performed on two brain MRI datasets, aiming respectively at segmenting 14 subcortical and ventricular structures and 54 brain ROIs. The segmentation results of the proposed method were compared with those of a state-of-the-art multi-atlas based segmentation method and an existing 3D FCN segmentation model. Our results suggested that the proposed method had a superior segmentation performance.
Deep Reinforcement Learning for Imbalanced Classification
Authors: Enlu Lin, Qiong Chen, Xiaoming Qi
Date: 2019-01-05 00:00:00
Tasks: Classification, Decision Making, Deep Reinforcement Learning, General Classification, imbalanced classification, Q-Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL), Sequential Decision Making

Data in real-world application often exhibit skewed class distribution which
poses an intense challenge for machine learning. Conventional classification
algorithms are not effective in the case of imbalanced data distribution, and
may fail when the data distribution is highly imbalanced. To address this
issue, we propose a general imbalanced classification model based on deep
reinforcement learning. We formulate the classification problem as a sequential
decision-making process and solve it by deep Q-learning network. The agent
performs a classification action on one sample at each time step, and the
environment evaluates the classification action and returns a reward to the
agent. The reward from minority class sample is larger so the agent is more
sensitive to the minority class. The agent finally finds an optimal
classification policy in imbalanced data under the guidance of specific reward
function and beneficial learning environment. Experiments show that our
proposed model outperforms the other imbalanced classification algorithms, and
it can identify more minority samples and has great classification performance.
Population-Guided Large Margin Classifier for High-Dimension Low -Sample-Size Problems
Authors: Qingbo Yin, Ehsan Adeli, Liran Shen, Dinggang Shen
Date: 2019-01-05 00:00:00
Tasks: Face Recognition, General Classification, Medical Image Analysis

Various applications in different fields, such as gene expression analysis or computer vision, suffer from data sets with high-dimensional low-sample-size (HDLSS), which has posed significant challenges for standard statistical and modern machine learning methods. In this paper, we propose a novel linear binary classifier, denoted by population-guided large margin classifier (PGLMC), which is applicable to any sorts of data, including HDLSS. PGLMC is conceived with a projecting direction w given by the comprehensive consideration of local structural information of the hyperplane and the statistics of the training samples. Our proposed model has several advantages compared to those widely used approaches. First, it is not sensitive to the intercept term b. Second, it operates well with imbalanced data. Third, it is relatively simple to be implemented based on Quadratic Programming. Fourth, it is robust to the model specification for various real applications. The theoretical properties of PGLMC are proven. We conduct a series of evaluations on two simulated and six real-world benchmark data sets, including DNA classification, digit recognition, medical image analysis, and face recognition. PGLMC outperforms the state-of-the-art classification methods in most cases, or at least obtains comparable results.
Analysis of a Two-Layer Neural Network via Displacement Convexity
Authors: Adel Javanmard, Marco Mondelli, Andrea Montanari
Date: 2019-01-05 00:00:00
Tasks: Vocal Bursts Valence Prediction

Fitting a function by using linear combinations of a large number $N$ of `simple' components is one of the most fruitful ideas in statistical learning. This idea lies at the core of a variety of methods, from two-layer neural networks to kernel regression, to boosting. In general, the resulting risk minimization problem is non-convex and is solved by gradient descent or its variants. Unfortunately, little is known about global convergence properties of these approaches. Here we consider the problem of learning a concave function $f$ on a compact convex domain $\Omega\subseteq {\mathbb R}^d$, using linear combinations of `bump-like' components (neurons). The parameters to be fitted are the centers of $N$ bumps, and the resulting empirical risk minimization problem is highly non-convex. We prove that, in the limit in which the number of neurons diverges, the evolution of gradient descent converges to a Wasserstein gradient flow in the space of probability distributions over $\Omega$. Further, when the bump width $\delta$ tends to $0$, this gradient flow has a limit which is a viscous porous medium equation. Remarkably, the cost function optimized by this gradient flow exhibits a special property known as displacement convexity, which implies exponential convergence rates for $N\to\infty$, $\delta\to 0$. Surprisingly, this asymptotic theory appears to capture well the behavior for moderate values of $\delta, N$. Explaining this phenomenon, and understanding the dependence on $\delta,N$ in a quantitative manner remains an outstanding challenge.
Stereoscopic Dark Flash for Low-light Photography
Authors: Jian Wang, Tianfan Xue, Jonathan T. Barron, Jiawen Chen
Date: 2019-01-05 00:00:00

In this work, we present a camera configuration for acquiring "stereoscopic
dark flash" images: a simultaneous stereo pair in which one camera is a
conventional RGB sensor, but the other camera is sensitive to near-infrared and
near-ultraviolet instead of R and B. When paired with a "dark" flash (i.e., one
having near-infrared and near-ultraviolet light, but no visible light) this
camera allows us to capture the two images in a flash/no-flash image pair at
the same time, all while not disturbing any human subjects or onlookers with a
dazzling visible flash. We present a hardware prototype of this camera that
approximates an idealized camera, and we present an imaging procedure that let
us acquire dark flash stereo pairs that closely resemble those we would get
from that idealized camera. We then present a technique for fusing these stereo
pairs, first by performing registration and warping, and then by using recent
advances in hyperspectral image fusion and deep learning to produce a final
image. Because our camera configuration and our data acquisition process allow
us to capture true low-noise long exposure RGB images alongside our dark flash
stereo pairs, our learned model can be trained end-to-end to produce a fused
image that retains the color and tone of a real RGB image while having the
low-noise properties of a flash image.
Adaptive Fusion for RGB-D Salient Object Detection
Authors: Ningning Wang, Xiaojin Gong
Date: 2019-01-05 00:00:00
Tasks: Object, object-detection, Object Detection, RGB-D Salient Object Detection, RGB Salient Object Detection, Salient Object Detection

RGB-D salient object detection aims to identify the most visually distinctive
objects in a pair of color and depth images. Based upon an observation that
most of the salient objects may stand out at least in one modality, this paper
proposes an adaptive fusion scheme to fuse saliency predictions generated from
two modalities. Specifically, we design a two-streamed convolutional neural
network (CNN), each of which extracts features and predicts a saliency map from
either RGB or depth modality. Then, a saliency fusion module learns a switch
map that is used to adaptively fuse the predicted saliency maps. A loss
function composed of saliency supervision, switch map supervision, and
edge-preserving constraints is designed to make full supervision, and the
entire network is trained in an end-to-end manner. Benefited from the adaptive
fusion strategy and the edge-preserving constraint, our approach outperforms
state-of-the-art methods on three publicly available datasets.
Hierarchical Reinforcement Learning via Advantage-Weighted Information Maximization
Authors: Takayuki Osa, Voot Tangkaratt, Masashi Sugiyama
Date: 2019-01-05 00:00:00
Proceeding: ICLR 2019 5
Tasks: continuous-control, Continuous Control, Diversity, Hierarchical Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Real-world tasks are often highly structured. Hierarchical reinforcement
learning (HRL) has attracted research interest as an approach for leveraging
the hierarchical structure of a given task in reinforcement learning (RL).
However, identifying the hierarchical policy structure that enhances the
performance of RL is not a trivial task. In this paper, we propose an HRL
method that learns a latent variable of a hierarchical policy using mutual
information maximization. Our approach can be interpreted as a way to learn a
discrete and latent representation of the state-action space. To learn option
policies that correspond to modes of the advantage function, we introduce
advantage-weighted importance sampling. In our HRL method, the gating policy
learns to select option policies based on an option-value function, and these
option policies are optimized based on the deterministic policy gradient
method. This framework is derived by leveraging the analogy between a
monolithic policy in standard RL and a hierarchical policy in HRL by using a
deterministic option policy. Experimental results indicate that our HRL
approach can learn a diversity of options and that it can enhance the
performance of RL in continuous control tasks.
Multi-scale Geometric Summaries for Similarity-based Sensor Fusion
Authors: Christopher J. Tralie, Paul Bendich, John Harer
Date: 2018-10-13 00:00:00
Tasks: Sensor Fusion

In this work, we address fusion of heterogeneous sensor data using
wavelet-based summaries of fused self-similarity information from each sensor.
The technique we develop is quite general, does not require domain specific
knowledge or physical models, and requires no training. Nonetheless, it can
perform surprisingly well at the general task of differentiating classes of
time-ordered behavior sequences which are sensed by more than one modality. As
a demonstration of our capabilities in the audio to video context, we focus on
the differentiation of speech sequences.
  Data from two or more modalities first are represented using self-similarity
matrices(SSMs) corresponding to time-ordered point clouds in feature spaces of
each of these data sources; we note that these feature spaces can be of
entirely different scale and dimensionality.
  A fused similarity template is then derived from the modality-specific SSMs
using a technique called similarity network fusion (SNF). We investigate
pipelines using SNF as both an upstream (feature-level) and a downstream
(ranking-level) fusion technique. Multiscale geometric features of this
template are then extracted using a recently-developed technique called the
scattering transform, and these features are then used to differentiate speech
sequences. This method outperforms unsupervised techniques which operate
directly on the raw data, and it also outperforms stovepiped methods which
operate on SSMs separately derived from the distinct modalities. The benefits
of this method become even more apparent as the simulated peak signal to noise
ratio decreases.
Learning to Remember More with Less Memorization
Authors: Hung Le, Truyen Tran, Svetha Venkatesh
Date: 2019-01-05 00:00:00
Proceeding: ICLR 2019 5
Tasks: Memorization, Sentiment Analysis, Sequential Image Classification, Text Classification

Memory-augmented neural networks consisting of a neural controller and an
external memory have shown potentials in long-term sequential learning. Current
RAM-like memory models maintain memory accessing every timesteps, thus they do
not effectively leverage the short-term memory held in the controller. We
hypothesize that this scheme of writing is suboptimal in memory utilization and
introduces redundant computation. To validate our hypothesis, we derive a
theoretical bound on the amount of information stored in a RAM-like system and
formulate an optimization problem that maximizes the bound. The proposed
solution dubbed Uniform Writing is proved to be optimal under the assumption of
equal timestep contributions. To relax this assumption, we introduce
modifications to the original solution, resulting in a solution termed Cached
Uniform Writing. This method aims to balance between maximizing memorization
and forgetting via overwriting mechanisms. Through an extensive set of
experiments, we empirically demonstrate the advantages of our solutions over
other recurrent architectures, claiming the state-of-the-arts in various
sequential modeling tasks.
Efficient Representation Learning Using Random Walks for Dynamic Graphs
Authors: Hooman Peiro Sajjad, Andrew Docherty, Yuriy Tyshetskiy
Date: 2019-01-05 00:00:00
Tasks: BIG-bench Machine Learning, General Classification, Representation Learning

An important part of many machine learning workflows on graphs is vertex
representation learning, i.e., learning a low-dimensional vector representation
for each vertex in the graph. Recently, several powerful techniques for
unsupervised representation learning have been demonstrated to give the
state-of-the-art performance in downstream tasks such as vertex classification
and edge prediction. These techniques rely on random walks performed on the
graph in order to capture its structural properties. These structural
properties are then encoded in the vector representation space.
  However, most contemporary representation learning methods only apply to
static graphs while real-world graphs are often dynamic and change over time.
Static representation learning methods are not able to update the vector
representations when the graph changes; therefore, they must re-generate the
vector representations on an updated static snapshot of the graph regardless of
the extent of the change in the graph. In this work, we propose computationally
efficient algorithms for vertex representation learning that extend random walk
based methods to dynamic graphs. The computation complexity of our algorithms
depends upon the extent and rate of changes (the number of edges changed per
update) and on the density of the graph. We empirically evaluate our algorithms
on real world datasets for downstream machine learning tasks of multi-class and
multi-label vertex classification. The results show that our algorithms can
achieve competitive results to the state-of-the-art methods while being
computationally efficient.
Graph Neural Networks with convolutional ARMA filters
Authors: Filippo Maria Bianchi, Daniele Grattarola, Lorenzo Livi, Cesare Alippi
Date: 2019-01-05 00:00:00
Tasks: Classification, General Classification, Graph Classification, Graph Neural Network, Graph Regression, Node Classification, Skeleton Based Action Recognition

Popular graph neural networks implement convolution operations on graphs based on polynomial spectral filters. In this paper, we propose a novel graph convolutional layer inspired by the auto-regressive moving average (ARMA) filter that, compared to polynomial ones, provides a more flexible frequency response, is more robust to noise, and better captures the global graph structure. We propose a graph neural network implementation of the ARMA filter with a recursive and distributed formulation, obtaining a convolutional layer that is efficient to train, localized in the node space, and can be transferred to new graphs at test time. We perform a spectral analysis to study the filtering effect of the proposed ARMA layer and report experiments on four downstream tasks: semi-supervised node classification, graph signal classification, graph classification, and graph regression. Results show that the proposed ARMA layer brings significant improvements over graph neural networks based on polynomial filters.
AVA-ActiveSpeaker: An Audio-Visual Dataset for Active Speaker Detection
Authors: Joseph Roth, Sourish Chaudhuri, Ondrej Klejch, Radhika Marvin, Andrew Gallagher, Liat Kaver, Sharadh Ramaswamy, Arkadiusz Stopczynski, Cordelia Schmid, Zhonghua Xi, Caroline Pantofaru
Date: 2019-01-05 00:00:00
Tasks: Active Speaker Detection, Audio-Visual Active Speaker Detection, Diversity, speaker-diarization, Speaker Diarization, Speech Enhancement

Active speaker detection is an important component in video analysis algorithms for applications such as speaker diarization, video re-targeting for meetings, speech enhancement, and human-robot interaction. The absence of a large, carefully labeled audio-visual dataset for this task has constrained algorithm evaluations with respect to data diversity, environments, and accuracy. This has made comparisons and improvements difficult. In this paper, we present the AVA Active Speaker detection dataset (AVA-ActiveSpeaker) that will be released publicly to facilitate algorithm development and enable comparisons. The dataset contains temporally labeled face tracks in video, where each face instance is labeled as speaking or not, and whether the speech is audible. This dataset contains about 3.65 million human labeled frames or about 38.5 hours of face tracks, and the corresponding audio. We also present a new audio-visual approach for active speaker detection, and analyze its performance, demonstrating both its strength and the contributions of the dataset.
Sheaves: A Topological Approach to Big Data
Authors: Linas Vepstas
Date: 2019-01-04 00:00:00

This document develops general concepts useful for extracting knowledge
embedded in large graphs or datasets that have pair-wise relationships, such as
cause-effect-type relations. Almost no underlying assumptions are made, other
than that the data can be presented in terms of pair-wise relationships between
objects/events. This assumption is used to mine for patterns in the dataset,
defining a reduced graph or dataset that boils-down or concentrates information
into a more compact form. The resulting extracted structure or set of patterns
are manifestly symbolic in nature, as they capture and encode the graph
structure of the dataset in terms of a (generative) grammar. This structure is
identified as having the formal mathematical structure of a sheaf. In essence,
this paper introduces the basic concepts of sheaf theory into the domain of
graphical datasets.
Projective Decomposition and Matrix Equivalence up to Scale
Authors: Max Robinson
Date: 2019-01-04 00:00:00

A data matrix may be seen simply as a means of organizing observations into
rows ( e.g., by measured object) and into columns ( e.g., by measured variable)
so that the observations can be analyzed with mathematical tools. As a
mathematical object, a matrix defines a linear mapping between points
representing weighted combinations of its rows (the row vector space) and
points representing weighted combinations of its columns (the column vector
space). From this perspective, a data matrix defines a relationship between the
information that labels its rows and the information that labels its columns,
and numerical methods are used to analyze this relationship. A first step is to
normalize the data, transforming each observation from scales convenient for
measurement to a common scale, on which addition and multiplication can
meaningfully combine the different observations. For example, z-transformation
rescales every variable to the same scale, standardized variation from an
expected value, but ignores scale differences between measured objects. Here we
develop the concepts and properties of projective decomposition, which applies
the same normalization strategy to both rows and columns by separating the
matrix into row- and column-scaling factors and a scale-normalized matrix. We
show that different scalings of the same scale-normalized matrix form an
equivalence class, and call the scale-normalized, canonical member of the class
its scale-invariant form that preserves all pairwise relative ratios.
Projective decomposition therefore provides a means of normalizing the broad
class of ratio-scale data, in which relative ratios are of primary interest,
onto a common scale without altering the ratios of interest, and simultaneously
accounting for scale effects for both organizations of the matrix values. Both
of these properties distinguish it from z-transformation.
An Adaptive Weighted Deep Forest Classifier
Authors: Lev V. Utkin, Andrei V. Konstantinov, Viacheslav S. Chukanov, Mikhail V. Kots, Anna A. Meldo
Date: 2019-01-04 00:00:00
Tasks: General Classification

A modification of the confidence screening mechanism based on adaptive
weighing of every training instance at each cascade level of the Deep Forest is
proposed. The idea underlying the modification is very simple and stems from
the confidence screening mechanism idea proposed by Pang et al. to simplify the
Deep Forest classifier by means of updating the training set at each level in
accordance with the classification accuracy of every training instance.
However, if the confidence screening mechanism just removes instances from
training and testing processes, then the proposed modification is more flexible
and assigns weights by taking into account the classification accuracy. The
modification is similar to the AdaBoost to some extent. Numerical experiments
illustrate good performance of the proposed modification in comparison with the
original Deep Forest proposed by Zhou and Feng.