pwc-archive/papers-with-abstracts huggingface dataset:
An Adaptive Learning Method of Deep Belief Network by Layer Generation Algorithm
Authors: Shin Kamada, Takumi Ichimura
Date: 2018-07-10 00:00:00

Deep Belief Network (DBN) has a deep architecture that represents multiple
features of input patterns hierarchically with the pre-trained Restricted
Boltzmann Machines (RBM). A traditional RBM or DBN model cannot change its
network structure during the learning phase. Our proposed adaptive learning
method can discover the optimal number of hidden neurons and weights and/or
layers according to the input space. The model is an important method to take
account of the computational cost and the model stability. The regularities to
hold the sparse structure of network is considerable problem, since the
extraction of explicit knowledge from the trained network should be required.
In our previous research, we have developed the hybrid method of adaptive
structural learning method of RBM and Learning Forgetting method to the trained
RBM. In this paper, we propose the adaptive learning method of DBN that can
determine the optimal number of layers during the learning. We evaluated our
proposed model on some benchmark data sets.
Neural Task Graphs: Generalizing to Unseen Tasks from a Single Video Demonstration
Authors: De-An Huang, Suraj Nair, Danfei Xu, Yuke Zhu, Animesh Garg, Li Fei-Fei, Silvio Savarese, Juan Carlos Niebles
Date: 2018-07-10 00:00:00
Proceeding: CVPR 2019 6

Our goal is to generate a policy to complete an unseen task given just a
single video demonstration of the task in a given domain. We hypothesize that
to successfully generalize to unseen complex tasks from a single video
demonstration, it is necessary to explicitly incorporate the compositional
structure of the tasks into the model. To this end, we propose Neural Task
Graph (NTG) Networks, which use conjugate task graph as the intermediate
representation to modularize both the video demonstration and the derived
policy. We empirically show NTG achieves inter-task generalization on two
complex tasks: Block Stacking in BulletPhysics and Object Collection in
AI2-THOR. NTG improves data efficiency with visual input as well as achieve
strong generalization without the need for dense hierarchical supervision. We
further show that similar performance trends hold when applied to real-world
data. We show that NTG can effectively predict task structure on the JIGSAWS
surgical dataset and generalize to unseen tasks.
An Adaptive Learning Method of Restricted Boltzmann Machine by Neuron Generation and Annihilation Algorithm
Authors: Shin Kamada, Takumi Ichimura
Date: 2018-07-10 00:00:00

Restricted Boltzmann Machine (RBM) is a generative stochastic energy-based
model of artificial neural network for unsupervised learning. Recently, RBM is
well known to be a pre-training method of Deep Learning. In addition to visible
and hidden neurons, the structure of RBM has a number of parameters such as the
weights between neurons and the coefficients for them. Therefore, we may meet
some difficulties to determine an optimal network structure to analyze big
data. In order to evade the problem, we investigated the variance of parameters
to find an optimal structure during learning. For the reason, we should check
the variance of parameters to cause the fluctuation for energy function in RBM
model. In this paper, we propose the adaptive learning method of RBM that can
discover an optimal number of hidden neurons according to the training
situation by applying the neuron generation and annihilation algorithm. In this
method, a new hidden neuron is generated if the energy function is not still
converged and the variance of the parameters is large. Moreover, the
inactivated hidden neuron will be annihilated if the neuron does not affect the
learning situation. The experimental results for some benchmark data sets were
discussed in this paper.
Shape analysis of framed space curves
Authors: Tom Needham
Date: 2018-07-10 00:00:00

In the elastic shape analysis approach to shape matching and object
classification, plane curves are represented as points in an
infinite-dimensional Riemannian manifold, wherein shape dissimilarity is
measured by geodesic distance. A remarkable result of Younes, Michor, Shah and
Mumford says that the space of closed planar shapes, endowed with a natural
metric, is isometric to an infinite-dimensional Grassmann manifold via the
so-called square root transform. This result facilitates efficient shape
comparison by virtue of explicit descriptions of Grassmannian geodesics. In
this paper, we extend this shape analysis framework to treat shapes of framed
space curves. By considering framed curves, we are able to generalize the
square root transform by using quaternionic arithmetic and properties of the
Hopf fibration. Under our coordinate transformation, the space of closed framed
curves corresponds to an infinite-dimensional complex Grassmannian. This allows
us to describe geodesics in framed curve space explicitly. We are also able to
produce explicit geodesics between closed, unframed space curves by studying
the action of the loop group of the circle on the Grassmann manifold. Averages
of collections of plane and space curves are computed via a novel algorithm
utilizing flag means.
Learning a Single Tucker Decomposition Network for Lossy Image Compression with Multiple Bits-Per-Pixel Rates
Authors: Jianrui Cai, Zisheng Cao, Lei Zhang
Date: 2018-07-10 00:00:00
Tasks: Decoder, Image Compression, MS-SSIM, Quantization, SSIM

Lossy image compression (LIC), which aims to utilize inexact approximations
to represent an image more compactly, is a classical problem in image
processing. Recently, deep convolutional neural networks (CNNs) have achieved
interesting results in LIC by learning an encoder-quantizer-decoder network
from a large amount of data. However, existing CNN-based LIC methods usually
can only train a network for a specific bits-per-pixel (bpp). Such a "one
network per bpp" problem limits the generality and flexibility of CNNs to
practical LIC applications. In this paper, we propose to learn a single CNN
which can perform LIC at multiple bpp rates. A simple yet effective Tucker
Decomposition Network (TDNet) is developed, where there is a novel tucker
decomposition layer (TDL) to decompose a latent image representation into a set
of projection matrices and a core tensor. By changing the rank of the core
tensor and its quantization, we can easily adjust the bpp rate of latent image
representation within a single CNN. Furthermore, an iterative non-uniform
quantization scheme is presented to optimize the quantizer, and a
coarse-to-fine training strategy is introduced to reconstruct the decompressed
images. Extensive experiments demonstrate the state-of-the-art compression
performance of TDNet in terms of both PSNR and MS-SSIM indices.
Pairwise Covariates-adjusted Block Model for Community Detection
Authors: Sihan Huang, Jiajin Sun, Yang Feng
Date: 2018-07-10 00:00:00
Tasks: Clustering, Community Detection, feature selection, model, Model Selection, Stochastic Block Model

One of the most fundamental problems in network study is community detection. The stochastic block model (SBM) is a widely used model, for which various estimation methods have been developed with their community detection consistency results unveiled. However, the SBM is restricted by the strong assumption that all nodes in the same community are stochastically equivalent, which may not be suitable for practical applications. We introduce a pairwise covariates-adjusted stochastic block model (PCABM), a generalization of SBM that incorporates pairwise covariate information. We study the maximum likelihood estimates of the coefficients for the covariates as well as the community assignments. It is shown that both the coefficient estimates of the covariates and the community assignments are consistent under suitable sparsity conditions. Spectral clustering with adjustment (SCWA) is introduced to efficiently solve PCABM. Under certain conditions, we derive the error bound of community detection under SCWA and show that it is community detection consistent. In addition, we investigate model selection in terms of the number of communities and feature selection for the pairwise covariates, and propose two corresponding algorithms. PCABM compares favorably with the SBM or degree-corrected stochastic block model (DCBM) under a wide range of simulated and real networks when covariate information is accessible.
SceneEDNet: A Deep Learning Approach for Scene Flow Estimation
Authors: Ravi Kumar Thakur, Snehasis Mukherjee
Date: 2018-07-10 00:00:00
Tasks: Decoder, Deep Learning, Scene Flow Estimation

Estimating scene flow in RGB-D videos is attracting much interest of the
computer vision researchers, due to its potential applications in robotics. The
state-of-the-art techniques for scene flow estimation, typically rely on the
knowledge of scene structure of the frame and the correspondence between
frames. However, with the increasing amount of RGB-D data captured from
sophisticated sensors like Microsoft Kinect, and the recent advances in the
area of sophisticated deep learning techniques, introduction of an efficient
deep learning technique for scene flow estimation, is becoming important. This
paper introduces a first effort to apply a deep learning method for direct
estimation of scene flow by presenting a fully convolutional neural network
with an encoder-decoder (ED) architecture. The proposed network SceneEDNet
involves estimation of three dimensional motion vectors of all the scene points
from sequence of stereo images. The training for direct estimation of scene
flow is done using consecutive pairs of stereo images and corresponding scene
flow ground truth. The proposed architecture is applied on a huge dataset and
provides meaningful results.
Predicting property damage from tornadoes with zero-inflated neural networks
Authors: Jeremy Diaz, Maxwell Joseph
Date: 2018-07-10 00:00:00

Tornadoes are the most violent of all atmospheric storms. In a typical year, the United States experiences hundreds of tornadoes with associated damages on the order of one billion dollars. Community preparation and resilience would benefit from accurate predictions of these economic losses, particularly as populations in tornado-prone areas increase in density and extent. Here, we use a zero-inflated modeling approach and artificial neural networks to predict tornado-induced property damage using publicly available data. We developed a neural network that predicts whether a tornado will cause property damage (out-of-sample accuracy = 0.821 and area under the receiver operating characteristic curve, AUROC, = 0.872). Conditional on a tornado causing damage, another neural network predicts the amount of damage (out-of-sample mean squared error = 0.0918 and R2 = 0.432). When used together, these two models function as a zero-inflated log-normal regression with hidden layers. From the best-performing models, we provide static and interactive gridded maps of monthly predicted probabilities of damage and property damages for the year 2019. Two primary weaknesses include (1) model fitting requires log-scale data which leads to large natural-scale residuals and (2) beginning tornado coordinates were utilized rather than tornado paths. Ultimately, this is the first known study to directly model tornado-induced property damages, and all data, code, and tools are publicly available. The predictive capacity of this model along with an interactive interface may provide an opportunity for science-informed tornado disaster planning.
Developing Brain Atlas through Deep Learning
Authors: Asim Iqbal, Romesa Khan, Theofanis Karayannis
Date: 2018-07-10 00:00:00
Tasks: Deep Learning

Neuroscientists have devoted significant effort into the creation of standard brain reference atlases for high-throughput registration of anatomical regions of interest. However, variability in brain size and form across individuals poses a significant challenge for such reference atlases. To overcome these limitations, we introduce a fully automated deep neural network-based method (SeBRe) for registration through Segmenting Brain Regions of interest with minimal human supervision. We demonstrate the validity of our method on brain images from different mouse developmental time points, across a range of neuronal markers and imaging modalities. We further assess the performance of our method on images from MR-scanned human brains. Our registration method can accelerate brain-wide exploration of region-specific changes in brain development and, by simply segmenting brain regions of interest for high-throughput brain-wide analysis, provides an alternative to existing complex brain registration techniques.
Unsupervised Domain Adaptation for Automatic Estimation of Cardiothoracic Ratio
Authors: Nanqing Dong, Michael Kampffmeyer, Xiaodan Liang, Zeya Wang, Wei Dai, Eric P. Xing
Date: 2018-07-10 00:00:00
Tasks: Domain Adaptation, Organ Segmentation, Segmentation, Unsupervised Domain Adaptation

The cardiothoracic ratio (CTR), a clinical metric of heart size in chest
X-rays (CXRs), is a key indicator of cardiomegaly. Manual measurement of CTR is
time-consuming and can be affected by human subjectivity, making it desirable
to design computer-aided systems that assist clinicians in the diagnosis
process. Automatic CTR estimation through chest organ segmentation, however,
requires large amounts of pixel-level annotated data, which is often
unavailable. To alleviate this problem, we propose an unsupervised domain
adaptation framework based on adversarial networks. The framework learns domain
invariant feature representations from openly available data sources to produce
accurate chest organ segmentation for unlabeled datasets. Specifically, we
propose a model that enforces our intuition that prediction masks should be
domain independent. Hence, we introduce a discriminator that distinguishes
segmentation predictions from ground truth masks. We evaluate our system's
prediction based on the assessment of radiologists and demonstrate the clinical
practicability for the diagnosis of cardiomegaly. We finally illustrate on the
JSRT dataset that the semi-supervised performance of our model is also very
promising.
A New Variational Model for Binary Classification in the Supervised Learning Context
Authors: Carlos David Brito Pacheco, Carlos Francisco Brito Loeza
Date: 2018-07-10 00:00:00
Tasks: Binary Classification, General Classification

We examine the supervised learning problem in its continuous setting and give
a general optimality condition through techniques of functional analysis and
the calculus of variations. This enables us to solve the optimality condition
for the desired function u numerically and make several comparisons with other
widely utilized supervised learning models. We employ the accuracy and area
under the receiver operating characteristic curve as metrics of the
performance. Finally, 3 analyses are conducted based on these two mentioned
metrics where we compare the models and make conclusions to determine whether
or not our method is competitive.
A GPU-Oriented Algorithm Design for Secant-Based Dimensionality Reduction
Authors: Henry Kvinge, Elin Farnell, Michael Kirby, Chris Peterson
Date: 2018-07-10 00:00:00
Tasks: Dimensionality Reduction, GPU

Dimensionality-reduction techniques are a fundamental tool for extracting
useful information from high-dimensional data sets. Because secant sets encode
manifold geometry, they are a useful tool for designing meaningful
data-reduction algorithms. In one such approach, the goal is to construct a
projection that maximally avoids secant directions and hence ensures that
distinct data points are not mapped too close together in the reduced space.
This type of algorithm is based on a mathematical framework inspired by the
constructive proof of Whitney's embedding theorem from differential topology.
Computing all (unit) secants for a set of points is by nature computationally
expensive, thus opening the door for exploitation of GPU architecture for
achieving fast versions of these algorithms. We present a polynomial-time
data-reduction algorithm that produces a meaningful low-dimensional
representation of a data set by iteratively constructing improved projections
within the framework described above. Key to our algorithm design and
implementation is the use of GPUs which, among other things, minimizes the
computational time required for the calculation of all secant lines. One goal
of this report is to share ideas with GPU experts and to discuss a class of
mathematical algorithms that may be of interest to the broader GPU community.
AudioMNIST: Exploring Explainable Artificial Intelligence for Audio Analysis on a Simple Benchmark
Authors: Sören Becker, Johanna Vielhaben, Marcel Ackermann, Klaus-Robert Müller, Sebastian Lapuschkin, Wojciech Samek
Date: 2018-07-09 00:00:00
Tasks: Audio Classification, Decision Making, Explainable artificial intelligence, Explainable Artificial Intelligence (XAI), feature selection, General Classification

Explainable Artificial Intelligence (XAI) is targeted at understanding how models perform feature selection and derive their classification decisions. This paper explores post-hoc explanations for deep neural networks in the audio domain. Notably, we present a novel Open Source audio dataset consisting of 30,000 audio samples of English spoken digits which we use for classification tasks on spoken digits and speakers' biological sex. We use the popular XAI technique Layer-wise Relevance Propagation (LRP) to identify relevant features for two neural network architectures that process either waveform or spectrogram representations of the data. Based on the relevance scores obtained from LRP, hypotheses about the neural networks' feature selection are derived and subsequently tested through systematic manipulations of the input data. Further, we take a step beyond visual explanations and introduce audible heatmaps. We demonstrate the superior interpretability of audible explanations over visual ones in a human user study.
Who is Killed by Police: Introducing Supervised Attention for Hierarchical LSTMs
Authors: Minh Nguyen, Thien Huu Nguyen
Date: 2018-07-09 00:00:00
Proceeding: COLING 2018 8

Finding names of people killed by police has become increasingly important as
police shootings get more and more public attention (police killing detection).
Unfortunately, there has been not much work in the literature addressing this
problem. The early work in this field \cite{keith2017identifying} proposed a
distant supervision framework based on Expectation Maximization (EM) to deal
with the multiple appearances of the names in documents. However, such EM-based
framework cannot take full advantages of deep learning models, necessitating
the use of hand-designed features to improve the detection performance. In this
work, we present a novel deep learning method to solve the problem of police
killing recognition. The proposed method relies on hierarchical LSTMs to model
the multiple sentences that contain the person names of interests, and
introduce supervised attention mechanisms based on semantical word lists and
dependency trees to upweight the important contextual words. Our experiments
demonstrate the benefits of the proposed model and yield the state-of-the-art
performance for police killing detection.
High Fidelity Semantic Shape Completion for Point Clouds using Latent Optimization
Authors: Swaminathan Gurumurthy, Shubham Agrawal
Date: 2018-07-09 00:00:00
Tasks: Decoder, Retrieval, Vocal Bursts Intensity Prediction

Semantic shape completion is a challenging problem in 3D computer vision
where the task is to generate a complete 3D shape using a partial 3D shape as
input. We propose a learning-based approach to complete incomplete 3D shapes
through generative modeling and latent manifold optimization. Our algorithm
works directly on point clouds. We use an autoencoder and a GAN to learn a
distribution of embeddings for point clouds of object classes. An input point
cloud with missing regions is first encoded to a feature vector. The
representations learnt by the GAN are then used to find the best latent vector
on the manifold using a combined optimization that finds a vector in the
manifold of plausible vectors that is close to the original input (both in the
feature space and the output space of the decoder). Experiments show that our
algorithm is capable of successfully reconstructing point clouds with large
missing regions with very high fidelity without having to rely on exemplar
based database retrieval.
Hierarchical Visualization of Materials Space with Graph Convolutional Neural Networks
Authors: Tian Xie, Jeffrey C. Grossman
Date: 2018-07-09 00:00:00

The combination of high throughput computation and machine learning has led
to a new paradigm in materials design by allowing for the direct screening of
vast portions of structural, chemical, and property space. The use of these
powerful techniques leads to the generation of enormous amounts of data, which
in turn calls for new techniques to efficiently explore and visualize the
materials space to help identify underlying patterns. In this work, we develop
a unified framework to hierarchically visualize the compositional and
structural similarities between materials in an arbitrary material space with
representations learned from different layers of graph convolutional neural
networks. We demonstrate the potential for such a visualization approach by
showing that patterns emerge automatically that reflect similarities at
different scales in three representative classes of materials: perovskites,
elemental boron, and general inorganic crystals, covering material spaces of
different compositions, structures, and both. For perovskites, elemental
similarities are learned that reflects multiple aspects of atom properties. For
elemental boron, structural motifs emerge automatically showing characteristic
boron local environments. For inorganic crystals, the similarity and stability
of local coordination environments are shown combining different center and
neighbor atoms. The method could help transition to a data-centered exploration
of materials space in automated materials design.
Optimal Parameter Choices via Precise Black-Box Analysis
Authors: Benjamin Doerr, Carola Doerr, Jing Yang
Date: 2018-07-09 00:00:00
Tasks: Evolutionary Algorithms

It has been observed that some working principles of evolutionary algorithms,
in particular, the influence of the parameters, cannot be understood from
results on the asymptotic order of the runtime, but only from more precise
results. In this work, we complement the emerging topic of precise runtime
analysis with a first precise complexity theoretic result. Our vision is that
the interplay between algorithm analysis and complexity theory becomes a
fruitful tool also for analyses more precise than asymptotic orders of
magnitude.
  As particular result, we prove that the unary unbiased black-box complexity
of the OneMax benchmark function class is $n \ln(n) - cn \pm o(n)$ for a
constant $c$ which is between $0.2539$ and $0.2665$. This runtime can be
achieved with a simple (1+1)-type algorithm using a fitness-dependent mutation
strength. When translated into the fixed-budget perspective, our algorithm
finds solutions which are roughly 13\% closer to the optimum than those of the
best previously known algorithms. To prove our results, we formulate several
new versions of the variable drift theorems, which also might be of independent
interest.