pwc-archive/papers-with-abstracts huggingface dataset:
Development of formal models, algorithms, procedures, engineering and functioning of the software system "Instrumental complex for ontological engineering purpose"
Authors: A. V. Palagin, N. G. Petrenko, V. Yu. Velychko, K. S. Malakhov
Date: 2018-03-24 00:00:00

The given paper considered a generalized model representation of the software system "Instrumental complex for ontological engineering purpose". Represented complete software system development process. Developed relevant formal models of the software system "Instrumental complex for ontological engineering purpose", represented as mathematical expressions, UML diagrams, and also described the three-tier architecture of the software system "Instrumental complex for ontological engineering purpose" in a client-server environment.
Global optimality conditions for deep neural networks
Authors: Chulhee Yun, Suvrit Sra, Ali Jadbabaie
Date: 2017-07-08 00:00:00
Proceeding: ICLR 2018 1

We study the error landscape of deep linear and nonlinear neural networks
with the squared error loss. Minimizing the loss of a deep linear neural
network is a nonconvex problem, and despite recent progress, our understanding
of this loss surface is still incomplete. For deep linear networks, we present
necessary and sufficient conditions for a critical point of the risk function
to be a global minimum. Surprisingly, our conditions provide an efficiently
checkable test for global optimality, while such tests are typically
intractable in nonconvex optimization. We further extend these results to deep
nonlinear neural networks and prove similar sufficient conditions for global
optimality, albeit in a more limited function space setting.
A Single-shot-per-pose Camera-Projector Calibration System For Imperfect Planar Targets
Authors: Bingyao Huang, Samed Ozdemir, Ying Tang, Chunyuan Liao, Haibin Ling
Date: 2018-03-24 00:00:00

Existing camera-projector calibration methods typically warp feature points
from a camera image to a projector image using estimated homographies, and
often suffer from errors in camera parameters and noise due to imperfect
planarity of the calibration target. In this paper we propose a simple yet
robust solution that explicitly deals with these challenges. Following the
structured light (SL) camera-project calibration framework, a carefully
designed correspondence algorithm is built on top of the De Bruijn patterns.
Such correspondence is then used for initial camera-projector calibration.
Then, to gain more robustness against noises, especially those from an
imperfect planar calibration board, a bundle adjustment algorithm is developed
to jointly optimize the estimated camera and projector models. Aside from the
robustness, our solution requires only one shot of SL pattern for each
calibration board pose, which is much more convenient than multi-shot solutions
in practice. Data validations are conducted on both synthetic and real
datasets, and our method shows clear advantages over existing methods in all
experiments.
Discovering Political Topics in Facebook Discussion threads with Graph Contextualization
Authors: Yilin Zhang, Marie Poux-Berthe, Chris Wells, Karolina Koc-Michalska, Karl Rohe
Date: 2017-08-23 00:00:00
Tasks: Clustering

We propose a graph contextualization method, pairGraphText, to study
political engagement on Facebook during the 2012 French presidential election.
It is a spectral algorithm that contextualizes graph data with text data for
online discussion thread. In particular, we examine the Facebook posts of the
eight leading candidates and the comments beneath these posts. We find evidence
of both (i) candidate-centered structure, where citizens primarily comment on
the wall of one candidate and (ii) issue-centered structure (i.e. on political
topics), where citizens' attention and expression is primarily directed towards
a specific set of issues (e.g. economics, immigration, etc). To identify
issue-centered structure, we develop pairGraphText, to analyze a network with
high-dimensional features on the interactions (i.e. text). This technique
scales to hundreds of thousands of nodes and thousands of unique words. In the
Facebook data, spectral clustering without the contextualizing text information
finds a mixture of (i) candidate and (ii) issue clusters. The contextualized
information with text data helps to separate these two structures. We conclude
by showing that the novel methodology is consistent under a statistical model.
SeqFace: Make full use of sequence information for face recognition
Authors: Wei Hu, Yangyu Huang, Fan Zhang, Ruirui Li, Wei Li, Guodong Yuan
Date: 2018-03-17 00:00:00
Tasks: Face Recognition, Face Verification

Deep convolutional neural networks (CNNs) have greatly improved the Face
Recognition (FR) performance in recent years. Almost all CNNs in FR are trained
on the carefully labeled datasets containing plenty of identities. However,
such high-quality datasets are very expensive to collect, which restricts many
researchers to achieve state-of-the-art performance. In this paper, we propose
a framework, called SeqFace, for learning discriminative face features. Besides
a traditional identity training dataset, the designed SeqFace can train CNNs by
using an additional dataset which includes a large number of face sequences
collected from videos. Moreover, the label smoothing regularization (LSR) and a
new proposed discriminative sequence agent (DSA) loss are employed to enhance
discrimination power of deep face features via making full use of the sequence
data. Our method achieves excellent performance on Labeled Faces in the Wild
(LFW), YouTube Faces (YTF), only with a single ResNet. The code and models are
publicly available on-line (https://github.com/huangyangyu/SeqFace).
Towards End-to-End Prosody Transfer for Expressive Speech Synthesis with Tacotron
Authors: RJ Skerry-Ryan, Eric Battenberg, Ying Xiao, Yuxuan Wang, Daisy Stanton, Joel Shor, Ron J. Weiss, Rob Clark, Rif A. Saurous
Date: 2018-03-24 00:00:00
Proceeding: ICML 2018 7
Tasks: Expressive Speech Synthesis, Speech Synthesis

We present an extension to the Tacotron speech synthesis architecture that
learns a latent embedding space of prosody, derived from a reference acoustic
representation containing the desired prosody. We show that conditioning
Tacotron on this learned embedding space results in synthesized audio that
matches the prosody of the reference signal with fine time detail even when the
reference and synthesis speakers are different. Additionally, we show that a
reference prosody embedding can be used to synthesize text that is different
from that of the reference utterance. We define several quantitative and
subjective metrics for evaluating prosody transfer, and report results with
accompanying audio samples from single-speaker and 44-speaker Tacotron models
on a prosody transfer task.
SILVar: Single Index Latent Variable Models
Authors: Jonathan Mei, Jose' M. F. Moura
Date: 2017-05-09 00:00:00
Tasks: Time Series, Time Series Analysis

A semi-parametric, non-linear regression model in the presence of latent
variables is introduced. These latent variables can correspond to unmodeled
phenomena or unmeasured agents in a complex networked system. This new
formulation allows joint estimation of certain non-linearities in the system,
the direct interactions between measured variables, and the effects of
unmodeled elements on the observed system. The particular form of the model
adopted is justified, and learning is posed as a regularized empirical risk
minimization. This leads to classes of structured convex optimization problems
with a "sparse plus low-rank" flavor. Relations between the proposed model and
several common model paradigms, such as those of Robust Principal Component
Analysis (PCA) and Vector Autoregression (VAR), are established. Particularly
in the VAR setting, the low-rank contributions can come from broad trends
exhibited in the time series. Details of the algorithm for learning the model
are presented. Experiments demonstrate the performance of the model and the
estimation algorithm on simulated and real data.
Realtime Time Synchronized Event-based Stereo
Authors: Alex Zihao Zhu, Yibo Chen, Kostas Daniilidis
Date: 2018-03-24 00:00:00
Proceeding: ECCV 2018 9

In this work, we propose a novel event based stereo method which addresses
the problem of motion blur for a moving event camera. Our method uses the
velocity of the camera and a range of disparities to synchronize the positions
of the events, as if they were captured at a single point in time. We represent
these events using a pair of novel time synchronized event disparity volumes,
which we show remove motion blur for pixels at the correct disparity in the
volume, while further blurring pixels at the wrong disparity. We then apply a
novel matching cost over these time synchronized event disparity volumes, which
both rewards similarity between the volumes while penalizing blurriness. We
show that our method outperforms more expensive, smoothing based event stereo
methods, by evaluating on the Multi Vehicle Stereo Event Camera dataset.
Progressive Neural Architecture Search
Authors: Chenxi Liu, Barret Zoph, Maxim Neumann, Jonathon Shlens, Wei Hua, Li-Jia Li, Li Fei-Fei, Alan Yuille, Jonathan Huang, Kevin Murphy
Date: 2017-12-02 00:00:00
Proceeding: ECCV 2018 9
Tasks: Evolutionary Algorithms, General Classification, Image Classification, Neural Architecture Search, Reinforcement Learning, Reinforcement Learning (RL)

We propose a new method for learning the structure of convolutional neural
networks (CNNs) that is more efficient than recent state-of-the-art methods
based on reinforcement learning and evolutionary algorithms. Our approach uses
a sequential model-based optimization (SMBO) strategy, in which we search for
structures in order of increasing complexity, while simultaneously learning a
surrogate model to guide the search through structure space. Direct comparison
under the same search space shows that our method is up to 5 times more
efficient than the RL method of Zoph et al. (2018) in terms of number of models
evaluated, and 8 times faster in terms of total compute. The structures we
discover in this way achieve state of the art classification accuracies on
CIFAR-10 and ImageNet.
LEGO: Learning Edge with Geometry all at Once by Watching Videos
Authors: Zhenheng Yang, Peng Wang, Yang Wang, Wei Xu, Ram Nevatia
Date: 2018-03-15 00:00:00
Proceeding: CVPR 2018 6
Tasks: 3D geometry, All

Learning to estimate 3D geometry in a single image by watching unlabeled
videos via deep convolutional network is attracting significant attention. In
this paper, we introduce a "3D as-smooth-as-possible (3D-ASAP)" prior inside
the pipeline, which enables joint estimation of edges and 3D scene, yielding
results with significant improvement in accuracy for fine detailed structures.
Specifically, we define the 3D-ASAP prior by requiring that any two points
recovered in 3D from an image should lie on an existing planar surface if no
other cues provided. We design an unsupervised framework that Learns Edges and
Geometry (depth, normal) all at Once (LEGO). The predicted edges are embedded
into depth and surface normal smoothness terms, where pixels without edges
in-between are constrained to satisfy the prior. In our framework, the
predicted depths, normals and edges are forced to be consistent all the time.
We conduct experiments on KITTI to evaluate our estimated geometry and
CityScapes to perform edge evaluation. We show that in all of the tasks,
i.e.depth, normal and edge, our algorithm vastly outperforms other
state-of-the-art (SOTA) algorithms, demonstrating the benefits of our approach.
The Importance of Constraint Smoothness for Parameter Estimation in Computational Cognitive Modeling
Authors: Abraham Nunes, Alexander Rudiuk
Date: 2018-03-24 00:00:00
Tasks: Decision Making, parameter estimation, Reinforcement Learning, Reinforcement Learning (RL)

Psychiatric neuroscience is increasingly aware of the need to define
psychopathology in terms of abnormal neural computation. The central tool in
this endeavour is the fitting of computational models to behavioural data. The
most prominent example of this procedure is fitting reinforcement learning (RL)
models to decision-making data collected from mentally ill and healthy subject
populations. These models are generative models of the decision-making data
themselves, and the parameters we seek to infer can be psychologically and
neurobiologically meaningful. Currently, the gold standard approach to this
inference procedure involves Monte-Carlo sampling, which is robust but
computationally intensive---rendering additional procedures, such as
cross-validation, impractical. Searching for point estimates of model
parameters using optimization procedures remains a popular and interesting
option. On a novel testbed simulating parameter estimation from a common RL
task, we investigated the effects of smooth vs. boundary constraints on
parameter estimation using interior point and deterministic direct search
algorithms for optimization. Ultimately, we show that the use of boundary
constraints can lead to substantial truncation effects. Our results discourage
the use of boundary constraints for these applications.
Style Tokens: Unsupervised Style Modeling, Control and Transfer in End-to-End Speech Synthesis
Authors: Yuxuan Wang, Daisy Stanton, Yu Zhang, RJ Skerry-Ryan, Eric Battenberg, Joel Shor, Ying Xiao, Fei Ren, Ye Jia, Rif A. Saurous
Date: 2018-03-23 00:00:00
Proceeding: ICML 2018 7
Tasks: Speech Synthesis, Style Transfer, Text-To-Speech Synthesis

In this work, we propose "global style tokens" (GSTs), a bank of embeddings
that are jointly trained within Tacotron, a state-of-the-art end-to-end speech
synthesis system. The embeddings are trained with no explicit labels, yet learn
to model a large range of acoustic expressiveness. GSTs lead to a rich set of
significant results. The soft interpretable "labels" they generate can be used
to control synthesis in novel ways, such as varying speed and speaking style -
independently of the text content. They can also be used for style transfer,
replicating the speaking style of a single audio clip across an entire
long-form text corpus. When trained on noisy, unlabeled found data, GSTs learn
to factorize noise and speaker identity, providing a path towards highly
scalable but robust speech synthesis.
Neural Program Synthesis with Priority Queue Training
Authors: Daniel A. Abolafia, Mohammad Norouzi, Jonathan Shen, Rui Zhao, Quoc V. Le
Date: 2018-01-10 00:00:00
Tasks: Program Synthesis

Models and examples built with TensorFlow
Feature Transfer Learning for Deep Face Recognition with Under-Represented Data
Authors: Xi Yin, Xiang Yu, Kihyuk Sohn, Xiaoming Liu, Manmohan Chandraker
Date: 2018-03-23 00:00:00
Tasks: Disentanglement, Face Recognition, Transfer Learning

Despite the large volume of face recognition datasets, there is a significant portion of subjects, of which the samples are insufficient and thus under-represented. Ignoring such significant portion results in insufficient training data. Training with under-represented data leads to biased classifiers in conventionally-trained deep networks. In this paper, we propose a center-based feature transfer framework to augment the feature space of under-represented subjects from the regular subjects that have sufficiently diverse samples. A Gaussian prior of the variance is assumed across all subjects and the variance from regular ones are transferred to the under-represented ones. This encourages the under-represented distribution to be closer to the regular distribution. Further, an alternating training regimen is proposed to simultaneously achieve less biased classifiers and a more discriminative feature representation. We conduct ablative study to mimic the under-represented datasets by varying the portion of under-represented classes on the MS-Celeb-1M dataset. Advantageous results on LFW, IJB-A and MS-Celeb-1M demonstrate the effectiveness of our feature transfer and training strategy, compared to both general baselines and state-of-the-art methods. Moreover, our feature transfer successfully presents smooth visual interpolation, which conducts disentanglement to preserve identity of a class while augmenting its feature space with non-identity variations such as pose and lighting.
Face Recognition with Hybrid Efficient Convolution Algorithms on FPGAs
Authors: Chuanhao Zhuge, Xinheng Liu, Xiaofan Zhang, Sudeep Gummadi, JinJun Xiong, Deming Chen
Date: 2018-03-23 00:00:00
Tasks: Face Recognition, GPU, High-Level Synthesis

Deep Convolutional Neural Networks have become a Swiss knife in solving
critical artificial intelligence tasks. However, deploying deep CNN models for
latency-critical tasks remains to be challenging because of the complex nature
of CNNs. Recently, FPGA has become a favorable device to accelerate deep CNNs
thanks to its high parallel processing capability and energy efficiency. In
this work, we explore different fast convolution algorithms including Winograd
and Fast Fourier Transform (FFT), and find an optimal strategy to apply them
together on different types of convolutions. We also propose an optimization
scheme to exploit parallelism on novel CNN architectures such as Inception
modules in GoogLeNet. We implement a configurable IP-based face recognition
acceleration system based on FaceNet using High-Level Synthesis. Our
implementation on a Xilinx Ultrascale device achieves 3.75x latency speedup
compared to a high-end NVIDIA GPU and surpasses previous FPGA results
significantly.
Accelerating Learning in Constructive Predictive Frameworks with the Successor Representation
Authors: Craig Sherstan, Marlos C. Machado, Patrick M. Pilarski
Date: 2018-03-23 00:00:00
Tasks: Continual Learning, Reinforcement Learning, Reinforcement Learning (RL)

Here we propose using the successor representation (SR) to accelerate
learning in a constructive knowledge system based on general value functions
(GVFs). In real-world settings like robotics for unstructured and dynamic
environments, it is infeasible to model all meaningful aspects of a system and
its environment by hand due to both complexity and size. Instead, robots must
be capable of learning and adapting to changes in their environment and task,
incrementally constructing models from their own experience. GVFs, taken from
the field of reinforcement learning (RL), are a way of modeling the world as
predictive questions. One approach to such models proposes a massive network of
interconnected and interdependent GVFs, which are incrementally added over
time. It is reasonable to expect that new, incrementally added predictions can
be learned more swiftly if the learning process leverages knowledge gained from
past experience. The SR provides such a means of separating the dynamics of the
world from the prediction targets and thus capturing regularities that can be
reused across multiple GVFs. As a primary contribution of this work, we show
that using SR-based predictions can improve sample efficiency and learning
speed in a continual learning setting where new predictions are incrementally
added and learned over time. We analyze our approach in a grid-world and then
demonstrate its potential on data from a physical robot arm.
WikiRank: Improving Keyphrase Extraction Based on Background Knowledge
Authors: Yang Yu, Vincent Ng
Date: 2018-03-23 00:00:00
Tasks: Keyphrase Extraction

Keyphrase is an efficient representation of the main idea of documents. While
background knowledge can provide valuable information about documents, they are
rarely incorporated in keyphrase extraction methods. In this paper, we propose
WikiRank, an unsupervised method for keyphrase extraction based on the
background knowledge from Wikipedia. Firstly, we construct a semantic graph for
the document. Then we transform the keyphrase extraction problem into an
optimization problem on the graph. Finally, we get the optimal keyphrase set to
be the output. Our method obtains improvements over other state-of-art models
by more than 2% in F1-score.
LayoutNet: Reconstructing the 3D Room Layout from a Single RGB Image
Authors: Chuhang Zou, Alex Colburn, Qi Shan, Derek Hoiem
Date: 2018-03-23 00:00:00
Proceeding: CVPR 2018 6
Tasks: 3D Room Layouts From A Single RGB Panorama, Translation

We propose an algorithm to predict room layout from a single image that
generalizes across panoramas and perspective images, cuboid layouts and more
general layouts (e.g. L-shape room). Our method operates directly on the
panoramic image, rather than decomposing into perspective images as do recent
works. Our network architecture is similar to that of RoomNet, but we show
improvements due to aligning the image based on vanishing points, predicting
multiple layout elements (corners, boundaries, size and translation), and
fitting a constrained Manhattan layout to the resulting predictions. Our method
compares well in speed and accuracy to other existing work on panoramas,
achieves among the best accuracy for perspective images, and can handle both
cuboid-shaped and more general Manhattan layouts.
Scalable Dense Non-rigid Structure-from-Motion: A Grassmannian Perspective
Authors: Suryansh Kumar, Anoop Cherian, Yuchao Dai, Hongdong Li
Date: 2018-03-01 00:00:00
Proceeding: CVPR 2018 6

This paper addresses the task of dense non-rigid structure-from-motion
(NRSfM) using multiple images. State-of-the-art methods to this problem are
often hurdled by scalability, expensive computations, and noisy measurements.
Further, recent methods to NRSfM usually either assume a small number of sparse
feature points or ignore local non-linearities of shape deformations, and thus
cannot reliably model complex non-rigid deformations. To address these issues,
in this paper, we propose a new approach for dense NRSfM by modeling the
problem on a Grassmann manifold. Specifically, we assume the complex non-rigid
deformations lie on a union of local linear subspaces both spatially and
temporally. This naturally allows for a compact representation of the complex
non-rigid deformation over frames. We provide experimental results on several
synthetic and real benchmark datasets. The procured results clearly demonstrate
that our method, apart from being scalable and more accurate than
state-of-the-art methods, is also more robust to noise and generalizes to
highly non-linear deformations.
Identification of release sources in advection-diffusion system by machine learning combined with Green function inverse method
Authors: Valentin G. Stanev, Filip L. Iliev, Scott Hansen, Velimir V. Vesselinov, Boian S. Alexandrov
Date: 2016-12-12 00:00:00

The identification of sources of advection-diffusion transport is based
usually on solving complex ill-posed inverse models against the available
state- variable data records. However, if there are several sources with
different locations and strengths, the data records represent mixtures rather
than the separate influences of the original sources. Importantly, the number
of these original release sources is typically unknown, which hinders
reliability of the classical inverse-model analyses. To address this challenge,
we present here a novel hybrid method for identification of the unknown number
of release sources. Our hybrid method, called HNMF, couples unsupervised
learning based on Nonnegative Matrix Factorization (NMF) and inverse-analysis
Green functions method. HNMF synergistically performs decomposition of the
recorded mixtures, finds the number of the unknown sources and uses the Green
function of advection-diffusion equation to identify their characteristics. In
the paper, we introduce the method and demonstrate that it is capable of
identifying the advection velocity and dispersivity of the medium as well as
the unknown number, locations, and properties of various sets of synthetic
release sources with different space and time dependencies, based only on the
recorded data. HNMF can be applied directly to any problem controlled by a
partial-differential parabolic equation where mixtures of an unknown number of
sources are measured at multiple locations.