pwc-archive/papers-with-abstracts huggingface dataset:
3D Surface-to-Structure Translation using Deep Convolutional Networks
Authors: Takumi Moriya, Kazuyuki Saito, Hiroya Tanaka
Date: 2017-12-08 00:00:00
Tasks: Translation

Our demonstration shows a system that estimates internal body structures from
3D surface models using deep convolutional neural networks trained on CT
(computed tomography) images of the human body. To take pictures of structures
inside the body, we need to use a CT scanner or an MRI (Magnetic Resonance
Imaging) scanner. However, assuming that the mutual information between outer
shape of the body and its inner structure is not zero, we can obtain an
approximate internal structure from a 3D surface model based on MRI and CT
image database. This suggests that we could know where and what kind of disease
a person is likely to have in his/her body simply by 3D scanning surface of the
body. As a first prototype, we developed a system for estimating internal body
structures from surface models based on Visible Human Project DICOM CT Datasets
from the University of Iowa Magnetic Resonance Research Facility.
Recruitment Market Trend Analysis with Sequential Latent Variable Models
Authors: Chen Zhu, HengShu Zhu, Hui Xiong, Pengliang Ding, Fang Xie
Date: 2017-12-08 00:00:00

Recruitment market analysis provides valuable understanding of
industry-specific economic growth and plays an important role for both
employers and job seekers. With the rapid development of online recruitment
services, massive recruitment data have been accumulated and enable a new
paradigm for recruitment market analysis. However, traditional methods for
recruitment market analysis largely rely on the knowledge of domain experts and
classic statistical models, which are usually too general to model large-scale
dynamic recruitment data, and have difficulties to capture the fine-grained
market trends. To this end, in this paper, we propose a new research paradigm
for recruitment market analysis by leveraging unsupervised learning techniques
for automatically discovering recruitment market trends based on large-scale
recruitment data. Specifically, we develop a novel sequential latent variable
model, named MTLVM, which is designed for capturing the sequential dependencies
of corporate recruitment states and is able to automatically learn the latent
recruitment topics within a Bayesian generative framework. In particular, to
capture the variability of recruitment topics over time, we design hierarchical
dirichlet processes for MTLVM. These processes allow to dynamically generate
the evolving recruitment topics. Finally, we implement a prototype system to
empirically evaluate our approach based on real-world recruitment data in
China. Indeed, by visualizing the results from MTLVM, we can successfully
reveal many interesting findings, such as the popularity of LBS related jobs
reached the peak in the 2nd half of 2014, and decreased in 2015.
Learning 2D Gabor Filters by Infinite Kernel Learning Regression
Authors: Kamaledin Ghiasi-Shirazi
Date: 2017-12-08 00:00:00
Tasks: Face Recognition, regression, Translation

Gabor functions have wide-spread applications in image processing and
computer vision. In this paper, we prove that 2D Gabor functions are
translation-invariant positive-definite kernels and propose a novel formulation
for the problem of image representation with Gabor functions based on infinite
kernel learning regression. Using this formulation, we obtain a support vector
expansion of an image based on a mixture of Gabor functions. The problem with
this representation is that all Gabor functions are present at all support
vector pixels. Applying LASSO to this support vector expansion, we obtain a
sparse representation in which each Gabor function is positioned at a very
small set of pixels. As an application, we introduce a method for learning a
dataset-specific set of Gabor filters that can be used subsequently for feature
extraction. Our experiments show that use of the learned Gabor filters improves
the recognition accuracy of a recently introduced face recognition algorithm.
Shape from Shading through Shape Evolution
Authors: Dawei Yang, Jia Deng
Date: 2017-12-08 00:00:00
Proceeding: CVPR 2018 6

In this paper, we address the shape-from-shading problem by training deep
networks with synthetic images. Unlike conventional approaches that combine
deep learning and synthetic imagery, we propose an approach that does not need
any external shape dataset to render synthetic images. Our approach consists of
two synergistic processes: the evolution of complex shapes from simple
primitives, and the training of a deep network for shape-from-shading. The
evolution generates better shapes guided by the network training, while the
training improves by using the evolved shapes. We show that our approach
achieves state-of-the-art performance on a shape-from-shading benchmark.
Sequence to Sequence Networks for Roman-Urdu to Urdu Transliteration
Authors: Mehreen Alam, Sibt Ul Hussain
Date: 2017-12-08 00:00:00
Tasks: Decoder, Machine Translation, Sentence, Translation, Transliteration

Neural Machine Translation models have replaced the conventional phrase based
statistical translation methods since the former takes a generic, scalable,
data-driven approach rather than relying on manual, hand-crafted features. The
neural machine translation system is based on one neural network that is
composed of two parts, one that is responsible for input language sentence and
other part that handles the desired output language sentence. This model based
on encoder-decoder architecture also takes as input the distributed
representations of the source language which enriches the learnt dependencies
and gives a warm start to the network. In this work, we transform Roman-Urdu to
Urdu transliteration into sequence to sequence learning problem. To this end,
we make the following contributions. We create the first ever parallel corpora
of Roman-Urdu to Urdu, create the first ever distributed representation of
Roman-Urdu and present the first neural machine translation model that
transliterates text from Roman-Urdu to Urdu language. Our model has achieved
the state-of-the-art results using BLEU as the evaluation metric. Precisely,
our model is able to correctly predict sentences up to length 10 while
achieving BLEU score of 48.6 on the test set. We are hopeful that our model and
our results shall serve as the baseline for further work in the domain of
neural machine translation for Roman-Urdu to Urdu using distributed
representation.
Dense Optical Flow based Change Detection Network Robust to Difference of Camera Viewpoints
Authors: Ken Sakurada, Weimin WANG, Nobuo Kawaguchi, Ryosuke Nakamura
Date: 2017-12-08 00:00:00
Tasks: Change Detection, Optical Flow Estimation

This paper presents a novel method for detecting scene changes from a pair of
images with a difference of camera viewpoints using a dense optical flow based
change detection network. In the case that camera poses of input images are
fixed or known, such as with surveillance and satellite cameras, the pixel
correspondence between the images captured at different times can be known.
Hence, it is possible to comparatively accurately detect scene changes between
the images by modeling the appearance of the scene. On the other hand, in case
of cameras mounted on a moving object, such as ground and aerial vehicles, we
must consider the spatial correspondence between the images captured at
different times. However, it can be difficult to accurately estimate the camera
pose or 3D model of a scene, owing to the scene changes or lack of imagery. To
solve this problem, we propose a change detection convolutional neural network
utilizing dense optical flow between input images to improve the robustness to
the difference between camera viewpoints. Our evaluation based on the panoramic
change detection dataset shows that the proposed method outperforms
state-of-the-art change detection algorithms.
Chaining Identity Mapping Modules for Image Denoising
Authors: Saeed Anwar, Cong Phouc Huynh, Fatih Porikli
Date: 2017-12-08 00:00:00
Tasks: Denoising, Image Denoising

We propose to learn a fully-convolutional network model that consists of a Chain of Identity Mapping Modules (CIMM) for image denoising. The CIMM structure possesses two distinctive features that are important for the noise removal task. Firstly, each residual unit employs identity mappings as the skip connections and receives pre-activated input in order to preserve the gradient magnitude propagated in both the forward and backward directions. Secondly, by utilizing dilated kernels for the convolution layers in the residual branch, in other words within an identity mapping module, each neuron in the last convolution layer can observe the full receptive field of the first layer. After being trained on the BSD400 dataset, the proposed network produces remarkably higher numerical accuracy and better visual image quality than the state-of-the-art when being evaluated on conventional benchmark images and the BSD68 dataset.
Artificial Intelligence and Statistics
Authors: Bin Yu, Karl Kumbier
Date: 2017-12-08 00:00:00
Tasks: Experimental Design, Self-Driving Cars

Artificial intelligence (AI) is intrinsically data-driven. It calls for the
application of statistical concepts through human-machine collaboration during
generation of data, development of algorithms, and evaluation of results. This
paper discusses how such human-machine collaboration can be approached through
the statistical concepts of population, question of interest,
representativeness of training data, and scrutiny of results (PQRS). The PQRS
workflow provides a conceptual framework for integrating statistical ideas with
human input into AI products and research. These ideas include experimental
design principles of randomization and local control as well as the principle
of stability to gain reproducibility and interpretability of algorithms and
data results. We discuss the use of these principles in the contexts of
self-driving cars, automated medical diagnoses, and examples from the authors'
collaborative research.
Exploiting Modern Hardware for High-Dimensional Nearest Neighbor Search
Authors: Fabien André
Date: 2017-12-08 00:00:00
Tasks: BIG-bench Machine Learning, Information Retrieval, Quantization, Retrieval, Vocal Bursts Intensity Prediction

Many multimedia information retrieval or machine learning problems require
efficient high-dimensional nearest neighbor search techniques. For instance,
multimedia objects (images, music or videos) can be represented by
high-dimensional feature vectors. Finding two similar multimedia objects then
comes down to finding two objects that have similar feature vectors. In the
current context of mass use of social networks, large scale multimedia
databases or large scale machine learning applications are more and more
common, calling for efficient nearest neighbor search approaches.
  This thesis builds on product quantization, an efficient nearest neighbor
search technique that compresses high-dimensional vectors into short codes.
This makes it possible to store very large databases entirely in RAM, enabling
low response times. We propose several contributions that exploit the
capabilities of modern CPUs, especially SIMD and the cache hierarchy, to
further decrease response times offered by product quantization.
Blind Multiclass Ensemble Classification
Authors: Panagiotis A. Traganitis, Alba Pagès-Zamora, Georgios B. Giannakis
Date: 2017-12-08 00:00:00
Tasks: Classification, Ensemble Learning, General Classification

The rising interest in pattern recognition and data analytics has spurred the
development of innovative machine learning algorithms and tools. However, as
each algorithm has its strengths and limitations, one is motivated to
judiciously fuse multiple algorithms in order to find the "best" performing
one, for a given dataset. Ensemble learning aims at such high-performance
meta-algorithm, by combining the outputs from multiple algorithms. The present
work introduces a blind scheme for learning from ensembles of classifiers,
using a moment matching method that leverages joint tensor and matrix
factorization. Blind refers to the combiner who has no knowledge of the
ground-truth labels that each classifier has been trained on. A rigorous
performance analysis is derived and the proposed scheme is evaluated on
synthetic and real datasets.
Multiple Adaptive Bayesian Linear Regression for Scalable Bayesian Optimization with Warm Start
Authors: Valerio Perrone, Rodolphe Jenatton, Matthias Seeger, Cedric Archambeau
Date: 2017-12-08 00:00:00
Tasks: Bayesian Optimization, BIG-bench Machine Learning, regression

Bayesian optimization (BO) is a model-based approach for gradient-free
black-box function optimization. Typically, BO is powered by a Gaussian process
(GP), whose algorithmic complexity is cubic in the number of evaluations.
Hence, GP-based BO cannot leverage large amounts of past or related function
evaluations, for example, to warm start the BO procedure. We develop a multiple
adaptive Bayesian linear regression model as a scalable alternative whose
complexity is linear in the number of observations. The multiple Bayesian
linear regression models are coupled through a shared feedforward neural
network, which learns a joint representation and transfers knowledge across
machine learning problems.
Representations of Sound in Deep Learning of Audio Features from Music
Authors: Sergey Shuvaev, Hamza Giaffar, Alexei A. Koulakov
Date: 2017-12-08 00:00:00
Tasks: Audio Classification, General Classification, Rhythm

The work of a single musician, group or composer can vary widely in terms of
musical style. Indeed, different stylistic elements, from performance medium
and rhythm to harmony and texture, are typically exploited and developed across
an artist's lifetime. Yet, there is often a discernable character to the work
of, for instance, individual composers at the perceptual level - an experienced
listener can often pick up on subtle clues in the music to identify the
composer or performer. Here we suggest that a convolutional network may learn
these subtle clues or features given an appropriate representation of the
music. In this paper, we apply a deep convolutional neural network to a large
audio dataset and empirically evaluate its performance on audio classification
tasks. Our trained network demonstrates accurate performance on such
classification tasks when presented with 5 s examples of music obtained by
simple transformations of the raw audio waveform. A particularly interesting
example is the spectral representation of music obtained by application of a
logarithmically spaced filter bank, mirroring the early stages of auditory
signal transduction in mammals. The most successful representation of music to
facilitate discrimination was obtained via a random matrix transform (RMT).
Networks based on logarithmic filter banks and RMT were able to correctly guess
the one composer out of 31 possibilities in 68 and 84 percent of cases
respectively.
Audio-Visual Sentiment Analysis for Learning Emotional Arcs in Movies
Authors: Eric Chu, Deb Roy
Date: 2017-12-08 00:00:00
Tasks: Clustering, Sentiment Analysis

Stories can have tremendous power -- not only useful for entertainment, they
can activate our interests and mobilize our actions. The degree to which a
story resonates with its audience may be in part reflected in the emotional
journey it takes the audience upon. In this paper, we use machine learning
methods to construct emotional arcs in movies, calculate families of arcs, and
demonstrate the ability for certain arcs to predict audience engagement. The
system is applied to Hollywood films and high quality shorts found on the web.
We begin by using deep convolutional neural networks for audio and visual
sentiment analysis. These models are trained on both new and existing
large-scale datasets, after which they can be used to compute separate audio
and visual emotional arcs. We then crowdsource annotations for 30-second video
clips extracted from highs and lows in the arcs in order to assess the
micro-level precision of the system, with precision measured in terms of
agreement in polarity between the system's predictions and annotators' ratings.
These annotations are also used to combine the audio and visual predictions.
Next, we look at macro-level characterizations of movies by investigating
whether there exist `universal shapes' of emotional arcs. In particular, we
develop a clustering approach to discover distinct classes of emotional arcs.
Finally, we show on a sample corpus of short web videos that certain emotional
arcs are statistically significant predictors of the number of comments a video
receives. These results suggest that the emotional arcs learned by our approach
successfully represent macroscopic aspects of a video story that drive audience
engagement. Such machine understanding could be used to predict audience
reactions to video stories, ultimately improving our ability as storytellers to
communicate with each other.
Scaling Limit: Exact and Tractable Analysis of Online Learning Algorithms with Applications to Regularized Regression and PCA
Authors: Chuang Wang, Jonathan Mattingly, Yue M. Lu
Date: 2017-12-08 00:00:00

We present a framework for analyzing the exact dynamics of a class of online
learning algorithms in the high-dimensional scaling limit. Our results are
applied to two concrete examples: online regularized linear regression and
principal component analysis. As the ambient dimension tends to infinity, and
with proper time scaling, we show that the time-varying joint empirical
measures of the target feature vector and its estimates provided by the
algorithms will converge weakly to a deterministic measured-valued process that
can be characterized as the unique solution of a nonlinear PDE. Numerical
solutions of this PDE can be efficiently obtained. These solutions lead to
precise predictions of the performance of the algorithms, as many practical
performance metrics are linear functionals of the joint empirical measures. In
addition to characterizing the dynamic performance of online learning
algorithms, our asymptotic analysis also provides useful insights. In
particular, in the high-dimensional limit, and due to exchangeability, the
original coupled dynamics associated with the algorithms will be asymptotically
"decoupled", with each coordinate independently solving a 1-D effective
minimization problem via stochastic gradient descent. Exploiting this insight
for nonconvex optimization problems may prove an interesting line of future
research.
Network Analysis for Explanation
Authors: Hiroshi Kuwajima, Masayuki Tanaka
Date: 2017-12-07 00:00:00

Safety critical systems strongly require the quality aspects of artificial
intelligence including explainability. In this paper, we analyzed a trained
network to extract features which mainly contribute the inference. Based on the
analysis, we developed a simple solution to generate explanations of the
inference processes.
BLADE: Filter Learning for General Purpose Computational Photography
Authors: Pascal Getreuer, Ignacio Garcia-Dorado, John Isidoro, Sungjoon Choi, Frank Ong, Peyman Milanfar
Date: 2017-11-29 00:00:00
Tasks: Demosaicking, Denoising, Image Super-Resolution, Super-Resolution

The Rapid and Accurate Image Super Resolution (RAISR) method of Romano,
Isidoro, and Milanfar is a computationally efficient image upscaling method
using a trained set of filters. We describe a generalization of RAISR, which we
name Best Linear Adaptive Enhancement (BLADE). This approach is a trainable
edge-adaptive filtering framework that is general, simple, computationally
efficient, and useful for a wide range of problems in computational
photography. We show applications to operations which may appear in a camera
pipeline including denoising, demosaicing, and stylization.
Columnar Database Techniques for Creating AI Features
Authors: Brad Carlile, Akiko Marti, Guy Delamarter
Date: 2017-12-07 00:00:00
Tasks: feature selection

Recent advances with in-memory columnar database techniques have increased
the performance of analytical queries on very large databases and data
warehouses. At the same time, advances in artificial intelligence (AI)
algorithms have increased the ability to analyze data. We use the term AI to
encompass both Deep Learning (DL or neural network) and Machine Learning (ML
aka Big Data analytics). Our exploration of the AI full stack has led us to a
cross-stack columnar database innovation that efficiently creates features for
AI analytics. The innovation is to create Augmented Dictionary Values (ADVs) to
add to existing columnar database dictionaries in order to increase the
efficiency of featurization by minimizing data movement and data duplication.
We show how various forms of featurization (feature selection, feature
extraction, and feature creation) can be efficiently calculated in a columnar
database. The full stack AI investigation has also led us to propose an
integrated columnar database and AI architecture. This architecture has
information flows and feedback loops to improve the whole analytics cycle
during multiple iterations of extracting data from the data sources,
featurization, and analysis.
An empirical analysis of the optimization of deep network loss surfaces
Authors: Daniel Jiwoong Im, Michael Tao, Kristin Branson
Date: 2016-12-13 00:00:00
Tasks: Stochastic Optimization

The success of deep neural networks hinges on our ability to accurately and
efficiently optimize high-dimensional, non-convex functions. In this paper, we
empirically investigate the loss functions of state-of-the-art networks, and
how commonly-used stochastic gradient descent variants optimize these loss
functions. To do this, we visualize the loss function by projecting them down
to low-dimensional spaces chosen based on the convergence points of different
optimization algorithms. Our observations suggest that optimization algorithms
encounter and choose different descent directions at many saddle points to find
different final weights. Based on consistency we observe across re-runs of the
same stochastic optimization algorithm, we hypothesize that each optimization
algorithm makes characteristic choices at these saddle points.
An End to End Deep Neural Network for Iris Segmentation in Unconstraint Scenarios
Authors: Shabab Bazrafkan, Shejin Thavalengal, Peter Corcoran
Date: 2017-12-07 00:00:00
Tasks: Iris Segmentation, Segmentation

With the increasing imaging and processing capabilities of today's mobile
devices, user authentication using iris biometrics has become feasible.
However, as the acquisition conditions become more unconstrained and as image
quality is typically lower than dedicated iris acquisition systems, the
accurate segmentation of iris regions is crucial for these devices. In this
work, an end to end Fully Convolutional Deep Neural Network (FCDNN) design is
proposed to perform the iris segmentation task for lower-quality iris images.
The network design process is explained in detail, and the resulting network is
trained and tuned using several large public iris datasets. A set of methods to
generate and augment suitable lower quality iris images from the high-quality
public databases are provided. The network is trained on Near InfraRed (NIR)
images initially and later tuned on additional datasets derived from visible
images. Comprehensive inter-database comparisons are provided together with
results from a selection of experiments detailing the effects of different
tunings of the network. Finally, the proposed model is compared with
SegNet-basic, and a near-optimal tuning of the network is compared to a
selection of other state-of-art iris segmentation algorithms. The results show
very promising performance from the optimized Deep Neural Networks design when
compared with state-of-art techniques applied to the same lower quality
datasets.