pwc-archive/papers-with-abstracts huggingface dataset:
Saliency Prediction on Omnidirectional Images with Generative Adversarial Imitation Learning
Authors: Mai Xu, Li Yang, Xiaoming Tao, Yiping Duan, Zulin Wang
Date: 2019-04-15 00:00:00
Tasks: Deep Reinforcement Learning, Imitation Learning, Reinforcement Learning, Saliency Prediction

When watching omnidirectional images (ODIs), subjects can access different
viewports by moving their heads. Therefore, it is necessary to predict
subjects' head fixations on ODIs. Inspired by generative adversarial imitation
learning (GAIL), this paper proposes a novel approach to predict saliency of
head fixations on ODIs, named SalGAIL. First, we establish a dataset for
attention on ODIs (AOI). In contrast to traditional datasets, our AOI dataset
is large-scale, which contains the head fixations of 30 subjects viewing 600
ODIs. Next, we mine our AOI dataset and determine three findings: (1) The
consistency of head fixations are consistent among subjects, and it grows
alongside the increased subject number; (2) The head fixations exist with a
front center bias (FCB); and (3) The magnitude of head movement is similar
across subjects. According to these findings, our SalGAIL approach applies deep
reinforcement learning (DRL) to predict the head fixations of one subject, in
which GAIL learns the reward of DRL, rather than the traditional human-designed
reward. Then, multi-stream DRL is developed to yield the head fixations of
different subjects, and the saliency map of an ODI is generated via convoluting
predicted head fixations. Finally, experiments validate the effectiveness of
our approach in predicting saliency maps of ODIs, significantly better than 10
state-of-the-art approaches.
Semantic query-by-example speech search using visual grounding
Authors: Herman Kamper, Aristotelis Anastassiou, Karen Livescu
Date: 2019-04-15 00:00:00
Tasks: Retrieval, Semantic Retrieval, Visual Grounding

A number of recent studies have started to investigate how speech systems can
be trained on untranscribed speech by leveraging accompanying images at
training time. Examples of tasks include keyword prediction and within- and
across-mode retrieval. Here we consider how such models can be used for
query-by-example (QbE) search, the task of retrieving utterances relevant to a
given spoken query. We are particularly interested in semantic QbE, where the
task is not only to retrieve utterances containing exact instances of the
query, but also utterances whose meaning is relevant to the query. We follow a
segmental QbE approach where variable-duration speech segments (queries, search
utterances) are mapped to fixed-dimensional embedding vectors. We show that a
QbE system using an embedding function trained on visually grounded speech data
outperforms a purely acoustic QbE system in terms of both exact and semantic
retrieval performance.
Painting on Placement: Forecasting Routing Congestion using Conditional Generative Adversarial Nets
Authors: Cunxi Yu, Zhiru Zhang
Date: 2019-04-15 00:00:00
Tasks: Colorization, Translation

Physical design process commonly consumes hours to days for large designs,
and routing is known as the most critical step. Demands for accurate routing
quality prediction raise to a new level to accelerate hardware innovation with
advanced technology nodes. This work presents an approach that forecasts the
density of all routing channels over the entire floorplan, with features
collected up to placement, using conditional GANs. Specifically, forecasting
the routing congestion is constructed as an image translation (colorization)
problem. The proposed approach is applied to a) placement exploration for
minimum congestion, b) constrained placement exploration and c) forecasting
congestion in real-time during incremental placement, using eight designs
targeting a fixed FPGA architecture.
A deep learning framework for quality assessment and restoration in video endoscopy
Authors: Sharib Ali, Felix Zhou, Adam Bailey, Barbara Braden, James East, Xin Lu, Jens Rittscher
Date: 2019-04-15 00:00:00
Tasks: Deblurring, Image Restoration

Endoscopy is a routine imaging technique used for both diagnosis and
minimally invasive surgical treatment. Artifacts such as motion blur, bubbles,
specular reflections, floating objects and pixel saturation impede the visual
interpretation and the automated analysis of endoscopy videos. Given the
widespread use of endoscopy in different clinical applications, we contend that
the robust and reliable identification of such artifacts and the automated
restoration of corrupted video frames is a fundamental medical imaging problem.
Existing state-of-the-art methods only deal with the detection and restoration
of selected artifacts. However, typically endoscopy videos contain numerous
artifacts which motivates to establish a comprehensive solution.
  We propose a fully automatic framework that can: 1) detect and classify six
different primary artifacts, 2) provide a quality score for each frame and 3)
restore mildly corrupted frames. To detect different artifacts our framework
exploits fast multi-scale, single stage convolutional neural network detector.
We introduce a quality metric to assess frame quality and predict image
restoration success. Generative adversarial networks with carefully chosen
regularization are finally used to restore corrupted frames.
  Our detector yields the highest mean average precision (mAP at 5% threshold)
of 49.0 and the lowest computational time of 88 ms allowing for accurate
real-time processing. Our restoration models for blind deblurring, saturation
correction and inpainting demonstrate significant improvements over previous
methods. On a set of 10 test videos we show that our approach preserves an
average of 68.7% which is 25% more frames than that retained from the raw
videos.
A Hierarchical Attention Model for Social Contextual Image Recommendation
Authors: Le Wu, Lei Chen, Richang Hong, Yanjie Fu, Xing Xie, Meng Wang
Date: 2018-06-03 00:00:00

Image based social networks are among the most popular social networking
services in recent years. With tremendous images uploaded everyday,
understanding users' preferences on user-generated images and making
recommendations have become an urgent need. In fact, many hybrid models have
been proposed to fuse various kinds of side information~(e.g., image visual
representation, social network) and user-item historical behavior for enhancing
recommendation performance. However, due to the unique characteristics of the
user generated images in social image platforms, the previous studies failed to
capture the complex aspects that influence users' preferences in a unified
framework. Moreover, most of these hybrid models relied on predefined weights
in combining different kinds of information, which usually resulted in
sub-optimal recommendation performance. To this end, in this paper, we develop
a hierarchical attention model for social contextual image recommendation. In
addition to basic latent user interest modeling in the popular matrix
factorization based recommendation, we identify three key aspects (i.e., upload
history, social influence, and owner admiration) that affect each user's latent
preferences, where each aspect summarizes a contextual factor from the complex
relationships between users and images. After that, we design a hierarchical
attention network that naturally mirrors the hierarchical relationship
(elements in each aspects level, and the aspect level) of users' latent
interests with the identified key aspects. Specifically, by taking embeddings
from state-of-the-art deep learning models that are tailored for each kind of
data, the hierarchical attention network could learn to attend differently to
more or less content. Finally, extensive experimental results on real-world
datasets clearly show the superiority of our proposed model.
A Hybrid Evolutionary Algorithm Framework for Optimising Power Take Off and Placements of Wave Energy Converters
Authors: Mehdi Neshat, Bradley Alexander, Nataliia Sergiienko, Markus Wagner
Date: 2019-04-15 00:00:00
Tasks: Heuristic Search

Ocean wave energy is a source of renewable energy that has gained much
attention for its potential to contribute significantly to meeting the global
energy demand. In this research, we investigate the problem of maximising the
energy delivered by farms of wave energy converters (WEC's). We consider
state-of-the-art fully submerged three-tether converters deployed in arrays.
The goal of this work is to use heuristic search to optimise the power output
of arrays in a size-constrained environment by configuring WEC locations and
the power-take-off (PTO) settings for each WEC. Modelling the complex
hydrodynamic interactions in wave farms is expensive, which constrains search
to only a few thousand model evaluations. We explore a variety of heuristic
approaches including cooperative and hybrid methods. The effectiveness of these
approaches is assessed in two real wave scenarios (Sydney and Perth) with farms
of two different scales. We find that a combination of symmetric local search
with Nelder-Mead Simplex direct search combined with a back-tracking
optimization strategy is able to outperform previously defined search
techniques by up to 3\%.
Deep neural networks can predict mortality from 12-lead electrocardiogram voltage data
Authors: Sushravya Raghunath, Alvaro E. Ulloa Cerna, Linyuan Jing, David P. vanMaanen, Joshua Stough, Dustin N. Hartzel, Joseph B. Leader, H. Lester Kirchner, Christopher W. Good, Aalpen A. Patel, Brian P. Delisle, Amro Alsaid, Dominik Beer, Christopher M. Haggerty, Brandon K. Fornwalt
Date: 2019-04-15 00:00:00
Tasks: Electrocardiography (ECG), Mortality Prediction

The electrocardiogram (ECG) is a widely-used medical test, typically consisting of 12 voltage versus time traces collected from surface recordings over the heart. Here we hypothesize that a deep neural network can predict an important future clinical event (one-year all-cause mortality) from ECG voltage-time traces. We show good performance for predicting one-year mortality with an average AUC of 0.85 from a model cross-validated on 1,775,926 12-lead resting ECGs, that were collected over a 34-year period in a large regional health system. Even within the large subset of ECGs interpreted as 'normal' by a physician (n=297,548), the model performance to predict one-year mortality remained high (AUC=0.84), and Cox Proportional Hazard model revealed a hazard ratio of 6.6 (p<0.005) for the two predicted groups (dead vs alive one year after ECG) over a 30-year follow-up period. A blinded survey of three cardiologists suggested that the patterns captured by the model were generally not visually apparent to cardiologists even after being shown 240 paired examples of labeled true positives (dead) and true negatives (alive). In summary, deep learning can add significant prognostic information to the interpretation of 12-lead resting ECGs, even in cases that are interpreted as 'normal' by physicians.
Synthesising 3D Facial Motion from "In-the-Wild" Speech
Authors: Panagiotis Tzirakis, Athanasios Papaioannou, Alexander Lattas, Michail Tarasiou, Björn Schuller, Stefanos Zafeiriou
Date: 2019-04-15 00:00:00
Tasks: Lip Reading, Motion Synthesis

Synthesising 3D facial motion from speech is a crucial problem manifesting in
a multitude of applications such as computer games and movies. Recently
proposed methods tackle this problem in controlled conditions of speech. In
this paper, we introduce the first methodology for 3D facial motion synthesis
from speech captured in arbitrary recording conditions ("in-the-wild") and
independent of the speaker. For our purposes, we captured 4D sequences of
people uttering 500 words, contained in the Lip Reading Words (LRW) a publicly
available large-scale in-the-wild dataset, and built a set of 3D blendshapes
appropriate for speech. We correlate the 3D shape parameters of the speech
blendshapes to the LRW audio samples by means of a novel time-warping
technique, named Deep Canonical Attentional Warping (DCAW), that can
simultaneously learn hierarchical non-linear representations and a warping path
in an end-to-end manner. We thoroughly evaluate our proposed methods, and show
the ability of a deep learning model to synthesise 3D facial motion in handling
different speakers and continuous speech signals in uncontrolled conditions.
SR-GAN: Semantic Rectifying Generative Adversarial Network for Zero-shot Learning
Authors: Zihan Ye, Fan Lyu, Linyan Li, Qiming Fu, Jinchang Ren, Fuyuan Hu
Date: 2019-04-15 00:00:00
Tasks: Generative Adversarial Network, Zero-Shot Learning

The existing Zero-Shot learning (ZSL) methods may suffer from the vague class
attributes that are highly overlapped for different classes. Unlike these
methods that ignore the discrimination among classes, in this paper, we propose
to classify unseen image by rectifying the semantic space guided by the visual
space. First, we pre-train a Semantic Rectifying Network (SRN) to rectify
semantic space with a semantic loss and a rectifying loss. Then, a Semantic
Rectifying Generative Adversarial Network (SR-GAN) is built to generate
plausible visual feature of unseen class from both semantic feature and
rectified semantic feature. To guarantee the effectiveness of rectified
semantic features and synthetic visual features, a pre-reconstruction and a
post reconstruction networks are proposed, which keep the consistency between
visual feature and semantic feature. Experimental results demonstrate that our
approach significantly outperforms the state-of-the-arts on four benchmark
datasets.
Improved Precision and Recall Metric for Assessing Generative Models
Authors: Tuomas Kynkäänniemi, Tero Karras, Samuli Laine, Jaakko Lehtinen, Timo Aila
Date: 2019-04-15 00:00:00
Proceeding: NeurIPS 2019 12
Tasks: Image Generation

The ability to automatically estimate the quality and coverage of the samples produced by a generative model is a vital requirement for driving algorithm research. We present an evaluation metric that can separately and reliably measure both of these aspects in image generation tasks by forming explicit, non-parametric representations of the manifolds of real and generated data. We demonstrate the effectiveness of our metric in StyleGAN and BigGAN by providing several illustrative examples where existing metrics yield uninformative or contradictory results. Furthermore, we analyze multiple design variants of StyleGAN to better understand the relationships between the model architecture, training methods, and the properties of the resulting sample distribution. In the process, we identify new variants that improve the state-of-the-art. We also perform the first principled analysis of truncation methods and identify an improved method. Finally, we extend our metric to estimate the perceptual quality of individual samples, and use this to study latent space interpolations.
Depth Separations in Neural Networks: What is Actually Being Separated?
Authors: Itay Safran, Ronen Eldan, Ohad Shamir
Date: 2019-04-15 00:00:00

Existing depth separation results for constant-depth networks essentially show that certain radial functions in $\mathbb{R}^d$, which can be easily approximated with depth $3$ networks, cannot be approximated by depth $2$ networks, even up to constant accuracy, unless their size is exponential in $d$. However, the functions used to demonstrate this are rapidly oscillating, with a Lipschitz parameter scaling polynomially with the dimension $d$ (or equivalently, by scaling the function, the hardness result applies to $\mathcal{O}(1)$-Lipschitz functions only when the target accuracy $\epsilon$ is at most $\text{poly}(1/d)$). In this paper, we study whether such depth separations might still hold in the natural setting of $\mathcal{O}(1)$-Lipschitz radial functions, when $\epsilon$ does not scale with $d$. Perhaps surprisingly, we show that the answer is negative: In contrast to the intuition suggested by previous work, it \emph{is} possible to approximate $\mathcal{O}(1)$-Lipschitz radial functions with depth $2$, size $\text{poly}(d)$ networks, for every constant $\epsilon$. We complement it by showing that approximating such functions is also possible with depth $2$, size $\text{poly}(1/\epsilon)$ networks, for every constant $d$. Finally, we show that it is not possible to have polynomial dependence in both $d,1/\epsilon$ simultaneously. Overall, our results indicate that in order to show depth separations for expressing $\mathcal{O}(1)$-Lipschitz functions with constant accuracy -- if at all possible -- one would need fundamentally different techniques than existing ones in the literature.
The Efficiency Threshold for the Offspring Population Size of the ($μ$, $λ$) EA
Authors: Denis Antipov, Benjamin Doerr, Quentin Yang
Date: 2019-04-15 00:00:00
Tasks: Evolutionary Algorithms

Understanding when evolutionary algorithms are efficient or not, and how they
efficiently solve problems, is one of the central research tasks in
evolutionary computation. In this work, we make progress in understanding the
interplay between parent and offspring population size of the $(\mu,\lambda)$
EA. Previous works, roughly speaking, indicate that for $\lambda \ge
(1+\varepsilon) e \mu$, this EA easily optimizes the OneMax function, whereas
an offspring population size $\lambda \le (1 -\varepsilon) e \mu$ leads to an
exponential runtime.
  Motivated also by the observation that in the efficient regime the
$(\mu,\lambda)$ EA loses its ability to escape local optima, we take a closer
look into this phase transition. Among other results, we show that when $\mu
\le n^{1/2 - c}$ for any constant $c > 0$, then for any $\lambda \le e \mu$ we
have a super-polynomial runtime. However, if $\mu \ge n^{2/3 + c}$, then for
any $\lambda \ge e \mu$, the runtime is polynomial. For the latter result we
observe that the $(\mu,\lambda)$ EA profits from better individuals also
because these, by creating slightly worse offspring, stabilize slightly
sub-optimal sub-populations. While these first results close to the phase
transition do not yet give a complete picture, they indicate that the boundary
between efficient and super-polynomial is not just the line $\lambda = e \mu$,
and that the reasons for efficiency or not are more complex than what was known
so far.
A Hitchhiker's Guide to Statistical Comparisons of Reinforcement Learning Algorithms
Authors: Cédric Colas, Olivier Sigaud, Pierre-Yves Oudeyer
Date: 2019-04-15 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Consistently checking the statistical significance of experimental results is the first mandatory step towards reproducible science. This paper presents a hitchhiker's guide to rigorous comparisons of reinforcement learning algorithms. After introducing the concepts of statistical testing, we review the relevant statistical tests and compare them empirically in terms of false positive rate and statistical power as a function of the sample size (number of seeds) and effect size. We further investigate the robustness of these tests to violations of the most common hypotheses (normal distributions, same distributions, equal variances). Beside simulations, we compare empirical distributions obtained by running Soft-Actor Critic and Twin-Delayed Deep Deterministic Policy Gradient on Half-Cheetah. We conclude by providing guidelines and code to perform rigorous comparisons of RL algorithm performances.
Disease gene prioritization using network topological analysis from a sequence based human functional linkage network
Authors: Ali Jalilvand, Behzad Akbari, Fatemeh Zare Mirakabad, Foad Ghaderi
Date: 2019-04-15 00:00:00

Sequencing large number of candidate disease genes which cause diseases in
order to identify the relationship between them is an expensive and
time-consuming task. To handle these challenges, different computational
approaches have been developed. Based on the observation that genes associated
with similar diseases have a higher likelihood of interaction, a large class of
these approaches relay on analyzing the topological properties of biological
networks. However, the incomplete and noisy nature of biological networks is
known as an important challenge in these approaches. In this paper, we propose
a two-step framework for disease gene prioritization: (1) construction of a
reliable human FLN using sequence information and machine learning techniques,
(2) prioritizing the disease gene relations based on the constructed FLN. On
our framework, unlike other FLN based frameworks that using FLNs based on
integration of various low quality biological data, the sequence of proteins is
used as the comprehensive data to construct a reliable initial network. In
addition, the physicochemical properties of amino-acids are employed to
describe the functionality of proteins. All in all, the proposed approach is
evaluated and the results indicate the high efficiency and validity of the FLN
in disease gene prioritization.
Efficient Feature Selection of Power Quality Events using Two Dimensional (2D) Particle Swarms
Authors: Faizal Hafiz, Akshya Swain, Chirag Naik, Nitish Patel
Date: 2019-04-15 00:00:00
Tasks: feature selection

A novel two-dimensional (2D) learning framework has been proposed to address
the feature selection problem in Power Quality (PQ) events. Unlike the existing
feature selection approaches, the proposed 2D learning explicitly incorporates
the information about the subset cardinality (i.e., the number of features) as
an additional learning dimension to effectively guide the search process. The
efficacy of this approach has been demonstrated considering fourteen distinct
classes of PQ events which conform to the IEEE Standard 1159. The search
performance of the 2D learning approach has been compared to the other six
well-known feature selection wrappers by considering two induction algorithms:
Naive Bayes (NB) and k-Nearest Neighbors (k-NN). Further, the robustness of the
selected/reduced feature subsets has been investigated considering seven
different levels of noise. The results of this investigation convincingly
demonstrate that the proposed 2D learning can identify significantly better and
robust feature subsets for PQ events.