pwc-archive/papers-with-abstracts huggingface dataset:
Densely Semantically Aligned Person Re-Identification
Authors: Zhizheng Zhang, Cuiling Lan, Wen-Jun Zeng, Zhibo Chen
Date: 2018-12-21 00:00:00
Proceeding: CVPR 2019 6
Tasks: Human Detection, Person Re-Identification

We propose a densely semantically aligned person re-identification framework.
It fundamentally addresses the body misalignment problem caused by
pose/viewpoint variations, imperfect person detection, occlusion, etc. By
leveraging the estimation of the dense semantics of a person image, we
construct a set of densely semantically aligned part images (DSAP-images),
where the same spatial positions have the same semantics across different
images. We design a two-stream network that consists of a main full image
stream (MF-Stream) and a densely semantically-aligned guiding stream
(DSAG-Stream). The DSAG-Stream, with the DSAP-images as input, acts as a
regulator to guide the MF-Stream to learn densely semantically aligned features
from the original image. In the inference, the DSAG-Stream is discarded and
only the MF-Stream is needed, which makes the inference system computationally
efficient and robust. To the best of our knowledge, we are the first to make
use of fine grained semantics to address the misalignment problems for re-ID.
Our method achieves rank-1 accuracy of 78.9% (new protocol) on the CUHK03
dataset, 90.4% on the CUHK01 dataset, and 95.7% on the Market1501 dataset,
outperforming state-of-the-art methods.
Lifelong Testing of Smart Autonomous Systems by Shepherding a Swarm of Watchdog Artificial Intelligence Agents
Authors: Hussein Abbass, John Harvey, Kate Yaxley
Date: 2018-12-21 00:00:00
Tasks: Decision Making, Lifelong learning

Artificial Intelligence (AI) technologies could be broadly categorised into
Analytics and Autonomy. Analytics focuses on algorithms offering perception,
comprehension, and projection of knowledge gleaned from sensorial data.
Autonomy revolves around decision making, and influencing and shaping the
environment through action production. A smart autonomous system (SAS) combines
analytics and autonomy to understand, learn, decide and act autonomously. To be
useful, SAS must be trusted and that requires testing. Lifelong learning of a
SAS compounds the testing process. In the remote chance that it is possible to
fully test and certify the system pre-release, which is theoretically an
undecidable problem, it is near impossible to predict the future behaviours
that these systems, alone or collectively, will exhibit. While it may be
feasible to severely restrict such systems\textquoteright \ learning abilities
to limit the potential unpredictability of their behaviours, an undesirable
consequence may be severely limiting their utility. In this paper, we propose
the architecture for a watchdog AI (WAI) agent dedicated to lifelong functional
testing of SAS. We further propose system specifications including a level of
abstraction whereby humans shepherd a swarm of WAI agents to oversee an
ecosystem made of humans and SAS. The discussion extends to the challenges,
pros, and cons of the proposed concept.
On the Relative Expressiveness of Bayesian and Neural Networks
Authors: Arthur Choi, Ruocheng Wang, Adnan Darwiche
Date: 2018-12-21 00:00:00

A neural network computes a function. A central property of neural networks
is that they are "universal approximators:" for a given continuous function,
there exists a neural network that can approximate it arbitrarily well, given
enough neurons (and some additional assumptions). In contrast, a Bayesian
network is a model, but each of its queries can be viewed as computing a
function. In this paper, we identify some key distinctions between the
functions computed by neural networks and those by marginal Bayesian network
queries, showing that the former are more expressive than the latter. Moreover,
we propose a simple augmentation to Bayesian networks (a testing operator),
which enables their marginal queries to become "universal approximators."
Reinforcement Learning for Adaptive Caching with Dynamic Storage Pricing
Authors: Alireza Sadeghi, Fatemeh Sheikholeslami, Antonio G. Marques, Georgios B. Giannakis
Date: 2018-12-17 00:00:00
Tasks: Decision Making, Q-Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Small base stations (SBs) of fifth-generation (5G) cellular networks are
envisioned to have storage devices to locally serve requests for reusable and
popular contents by \emph{caching} them at the edge of the network, close to
the end users. The ultimate goal is to shift part of the predictable load on
the back-haul links, from on-peak to off-peak periods, contributing to a better
overall network performance and service experience. To enable the SBs with
efficient \textit{fetch-cache} decision-making schemes operating in dynamic
settings, this paper introduces simple but flexible generic time-varying
fetching and caching costs, which are then used to formulate a constrained
minimization of the aggregate cost across files and time. Since caching
decisions per time slot influence the content availability in future slots, the
novel formulation for optimal fetch-cache decisions falls into the class of
dynamic programming. Under this generic formulation, first by considering
stationary distributions for the costs and file popularities, an efficient
reinforcement learning-based solver known as value iteration algorithm can be
used to solve the emerging optimization problem. Later, it is shown that
practical limitations on cache capacity can be handled using a particular
instance of the generic dynamic pricing formulation. Under this setting, to
provide a light-weight online solver for the corresponding optimization, the
well-known reinforcement learning algorithm, $Q$-learning, is employed to find
optimal fetch-cache decisions. Numerical tests corroborating the merits of the
proposed approach wrap up the paper.
Primal path algorithm for compositional data analysis
Authors: Jong-June Jeon, Yongdai Kim, Sungho Won, Hosik Choi
Date: 2018-12-21 00:00:00
Tasks: General Classification, regression

Compositional data have two unique characteristics compared to typical
multivariate data: the observed values are nonnegative and their summand is
exactly one. To reflect these characteristics, a specific regularized
regression model with linear constraints is commonly used. However, linear
constraints incur additional computational time, which becomes severe in
high-dimensional cases. As such, we propose an efficient solution path
algorithm for a $l_1$ regularized regression with compositional data. The
algorithm is then extended to a classification model with compositional
predictors. We also compare its computational speed with that of previously
developed algorithms and apply the proposed algorithm to analyze human gut
microbiome data.
Analysis Methods in Neural Language Processing: A Survey
Authors: Yonatan Belinkov, James Glass
Date: 2018-12-21 00:00:00
Proceeding: TACL 2019 3
Tasks: Survey

The field of natural language processing has seen impressive progress in
recent years, with neural network models replacing many of the traditional
systems. A plethora of new models have been proposed, many of which are thought
to be opaque compared to their feature-rich counterparts. This has led
researchers to analyze, interpret, and evaluate neural networks in novel and
more fine-grained ways. In this survey paper, we review analysis methods in
neural language processing, categorize them according to prominent research
trends, highlight existing limitations, and point to potential directions for
future work.
Enhancing Person-Job Fit for Talent Recruitment: An Ability-aware Neural Network Approach
Authors: Chuan Qin, HengShu Zhu, Tong Xu, Chen Zhu, Liang Jiang, Enhong Chen, Hui Xiong
Date: 2018-12-21 00:00:00

The wide spread use of online recruitment services has led to information
explosion in the job market. As a result, the recruiters have to seek the
intelligent ways for Person Job Fit, which is the bridge for adapting the right
job seekers to the right positions. Existing studies on Person Job Fit have a
focus on measuring the matching degree between the talent qualification and the
job requirements mainly based on the manual inspection of human resource
experts despite of the subjective, incomplete, and inefficient nature of the
human judgement. To this end, in this paper, we propose a novel end to end
Ability aware Person Job Fit Neural Network model, which has a goal of reducing
the dependence on manual labour and can provide better interpretation about the
fitting results. The key idea is to exploit the rich information available at
abundant historical job application data. Specifically, we propose a word level
semantic representation for both job requirements and job seekers' experiences
based on Recurrent Neural Network. Along this line, four hierarchical ability
aware attention strategies are designed to measure the different importance of
job requirements for semantic representation, as well as measuring the
different contribution of each job experience to a specific ability
requirement. Finally, extensive experiments on a large scale real world data
set clearly validate the effectiveness and interpretability of the APJFNN
framework compared with several baselines.
ChamNet: Towards Efficient Network Design through Platform-Aware Model Adaptation
Authors: Xiaoliang Dai, Peizhao Zhang, Bichen Wu, Hongxu Yin, Fei Sun, Yanghan Wang, Marat Dukhan, Yunqing Hu, Yiming Wu, Yangqing Jia, Peter Vajda, Matt Uyttendaele, Niraj K. Jha
Date: 2018-12-21 00:00:00
Proceeding: CVPR 2019 6
Tasks: Bayesian Optimization, CPU, Efficient Neural Network, GPU, Neural Architecture Search, Reinforcement Learning

This paper proposes an efficient neural network (NN) architecture design
methodology called Chameleon that honors given resource constraints. Instead of
developing new building blocks or using computationally-intensive reinforcement
learning algorithms, our approach leverages existing efficient network building
blocks and focuses on exploiting hardware traits and adapting computation
resources to fit target latency and/or energy constraints. We formulate
platform-aware NN architecture search in an optimization framework and propose
a novel algorithm to search for optimal architectures aided by efficient
accuracy and resource (latency and/or energy) predictors. At the core of our
algorithm lies an accuracy predictor built atop Gaussian Process with Bayesian
optimization for iterative sampling. With a one-time building cost for the
predictors, our algorithm produces state-of-the-art model architectures on
different platforms under given constraints in just minutes. Our results show
that adapting computation resources to building blocks is critical to model
performance. Without the addition of any bells and whistles, our models achieve
significant accuracy improvements against state-of-the-art hand-crafted and
automatically designed architectures. We achieve 73.8% and 75.3% top-1 accuracy
on ImageNet at 20ms latency on a mobile CPU and DSP. At reduced latency, our
models achieve up to 8.5% (4.8%) and 6.6% (9.3%) absolute top-1 accuracy
improvements compared to MobileNetV2 and MnasNet, respectively, on a mobile CPU
(DSP), and 2.7% (4.6%) and 5.6% (2.6%) accuracy gains over ResNet-101 and
ResNet-152, respectively, on an Nvidia GPU (Intel CPU).
Slimmable Neural Networks
Authors: Jiahui Yu, Linjie Yang, Ning Xu, Jianchao Yang, Thomas Huang
Date: 2018-12-21 00:00:00
Proceeding: ICLR 2019 5
Tasks: Instance Segmentation, Keypoint Detection, object-detection, Object Detection, Semantic Segmentation

We present a simple and general method to train a single neural network
executable at different widths (number of channels in a layer), permitting
instant and adaptive accuracy-efficiency trade-offs at runtime. Instead of
training individual networks with different width configurations, we train a
shared network with switchable batch normalization. At runtime, the network can
adjust its width on the fly according to on-device benchmarks and resource
constraints, rather than downloading and offloading different models. Our
trained networks, named slimmable neural networks, achieve similar (and in many
cases better) ImageNet classification accuracy than individually trained models
of MobileNet v1, MobileNet v2, ShuffleNet and ResNet-50 at different widths
respectively. We also demonstrate better performance of slimmable models
compared with individual ones across a wide range of applications including
COCO bounding-box object detection, instance segmentation and person keypoint
detection without tuning hyper-parameters. Lastly we visualize and discuss the
learned features of slimmable networks. Code and models are available at:
https://github.com/JiahuiYu/slimmable_networks
Efficient Misalignment-Robust Multi-Focus Microscopical Images Fusion
Authors: Yixiong Liang, Yuan Mao, Zhihong Tang, Meng Yan, Yuqian Zhao, Jianfeng Liu
Date: 2018-12-21 00:00:00
Tasks: 4k, Multi Focus Image Fusion, Multi-Focus Microscopical Images Fusion

In this paper we propose a very efficient method to fuse the unregistered
multi-focus microscopical images based on the speed-up robust features (SURF).
Our method follows the pipeline of first registration and then fusion. However,
instead of treating the registration and fusion as two completely independent
stage, we propose to reuse the determinant of the approximate Hessian generated
in SURF detection stage as the corresponding salient response for the final
image fusion, thus it enables nearly cost-free saliency map generation. In
addition, due to the adoption of SURF scale space representation, our method
can generate scale-invariant saliency map which is desired for scale-invariant
image fusion. We present an extensive evaluation on the dataset consisting of
several groups of unregistered multi-focus 4K ultra HD microscopic images with
size of 4112 x 3008. Compared with the state-of-the-art multi-focus image
fusion methods, our method is much faster and achieve better results in the
visual performance. Our method provides a flexible and efficient way to
integrate complementary and redundant information from multiple multi-focus
ultra HD unregistered images into a fused image that contains better
description than any of the individual input images. Code is available at
https://github.com/yiqingmy/JointRF.
Deep Learning and Glaucoma Specialists: The Relative Importance of Optic Disc Features to Predict Glaucoma Referral in Fundus Photos
Authors: Sonia Phene, R. Carter Dunn, Naama Hammel, Yun Liu, Jonathan Krause, Naho Kitade, Mike Schaekermann, Rory Sayres, Derek J. Wu, Ashish Bora, Christopher Semturs, Anita Misra, Abigail E. Huang, Arielle Spitze, Felipe A. Medeiros, April Y. Maa, Monica Gandhi, Greg S. Corrado, Lily Peng, Dale R. Webster
Date: 2018-12-21 00:00:00
Tasks: Specificity

Glaucoma is the leading cause of preventable, irreversible blindness world-wide. The disease can remain asymptomatic until severe, and an estimated 50%-90% of people with glaucoma remain undiagnosed. Glaucoma screening is recommended for early detection and treatment. A cost-effective tool to detect glaucoma could expand screening access to a much larger patient population, but such a tool is currently unavailable. We trained a deep learning algorithm using a retrospective dataset of 86,618 images, assessed for glaucomatous optic nerve head features and referable glaucomatous optic neuropathy (GON). The algorithm was validated using 3 datasets. For referable GON, the algorithm had an AUC of 0.945 (95% CI, 0.929-0.960) in dataset A (1205 images, 1 image/patient; 18.1% referable), images adjudicated by panels of Glaucoma Specialists (GSs); 0.855 (95% CI, 0.841-0.870) in dataset B (9642 images, 1 image/patient; 9.2% referable), images from Atlanta Veterans Affairs Eye Clinic diabetic teleretinal screening program; and 0.881 (95% CI, 0.838-0.918) in dataset C (346 images, 1 image/patient; 81.7% referable), images from Dr. Shroff's Charity Eye Hospital's glaucoma clinic. The algorithm showed significantly higher sensitivity than 7 of 10 graders not involved in determining the reference standard, including 2 of 3 GSs, and showed higher specificity than 3 graders, while remaining comparable to others. For both GSs and the algorithm, the most crucial features related to referable GON were: presence of vertical cup-to-disc ratio of 0.7 or more, neuroretinal rim notching, retinal nerve fiber layer defect, and bared circumlinear vessels. An algorithm trained on fundus images alone can detect referable GON with higher sensitivity than and comparable specificity to eye care providers. The algorithm maintained good performance on an independent dataset with diagnoses based on a full glaucoma workup.
Pre-training with Non-expert Human Demonstration for Deep Reinforcement Learning
Authors: Gabriel V. de la Cruz, Yunshu Du, Matthew E. Taylor
Date: 2018-12-21 00:00:00
Tasks: Deep Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Deep reinforcement learning (deep RL) has achieved superior performance in
complex sequential tasks by using deep neural networks as function
approximators to learn directly from raw input images. However, learning
directly from raw images is data inefficient. The agent must learn feature
representation of complex states in addition to learning a policy. As a result,
deep RL typically suffers from slow learning speeds and often requires a
prohibitively large amount of training time and data to reach reasonable
performance, making it inapplicable to real-world settings where data is
expensive. In this work, we improve data efficiency in deep RL by addressing
one of the two learning goals, feature learning. We leverage supervised
learning to pre-train on a small set of non-expert human demonstrations and
empirically evaluate our approach using the asynchronous advantage actor-critic
algorithms (A3C) in the Atari domain. Our results show significant improvements
in learning speed, even when the provided demonstration is noisy and of low
quality.
Cluster validity index based on Jeffrey divergence
Authors: Ahmed Ben Said, Rachid Hadjidj, Sebti Foufou
Date: 2018-12-20 00:00:00
Tasks: Clustering

Cluster validity indexes are very important tools designed for two purposes:
comparing the performance of clustering algorithms and determining the number
of clusters that best fits the data. These indexes are in general constructed
by combining a measure of compactness and a measure of separation. A classical
measure of compactness is the variance. As for separation, the distance between
cluster centers is used. However, such a distance does not always reflect the
quality of the partition between clusters and sometimes gives misleading
results. In this paper, we propose a new cluster validity index for which
Jeffrey divergence is used to measure separation between clusters. Experimental
results are conducted using different types of data and comparison with widely
used cluster validity indexes demonstrates the outperformance of the proposed
index.
Calibrating Multivariate Lévy Processes with Neural Networks
Authors: Kailai Xu, Eric Darve
Date: 2018-12-20 00:00:00
Tasks: Numerical Integration

Calibrating a L\'evy process usually requires characterizing its jump distribution. Traditionally this problem can be solved with nonparametric estimation using the empirical characteristic functions (ECF), assuming certain regularity, and results to date are mostly in 1D. For multivariate L\'evy processes and less smooth L\'evy densities, the problem becomes challenging as ECFs decay slowly and have large uncertainty because of limited observations. We solve this problem by approximating the L\'evy density with a parametrized functional form; the characteristic function is then estimated using numerical integration. In our benchmarks, we used deep neural networks and found that they are robust and can capture sharp transitions in the L\'evy density. They perform favorably compared to piecewise linear functions and radial basis functions. The methods and techniques developed here apply to many other problems that involve nonparametric estimation of functions embedded in a system model.
An Optical Flow-Based Approach for Minimally-Divergent Velocimetry Data Interpolation
Authors: Berkay Kanberoglu, Dhritiman Das, Priya Nair, Pavan Turaga, David Frakes
Date: 2018-12-20 00:00:00
Tasks: Optical Flow Estimation

Three-dimensional (3D) biomedical image sets are often acquired with in-plane
pixel spacings that are far less than the out-of-plane spacings between images.
The resultant anisotropy, which can be detrimental in many applications, can be
decreased using image interpolation. Optical flow and/or other
registration-based interpolators have proven useful in such interpolation roles
in the past. When acquired images are comprised of signals that describe the
flow velocity of fluids, additional information is available to guide the
interpolation process. In this paper, we present an optical-flow based
framework for image interpolation that also minimizes resultant divergence in
the interpolated data.
Variational Cross-domain Natural Language Generation for Spoken Dialogue Systems
Authors: Bo-Hsiang Tseng, Florian Kreyssig, Pawel Budzianowski, Inigo Casanueva, Yen-chen Wu, Stefan Ultes, Milica Gasic
Date: 2018-12-20 00:00:00
Proceeding: WS 2018 7
Tasks: Diversity, Sentence, Spoken Dialogue Systems, Text Generation

Cross-domain natural language generation (NLG) is still a difficult task
within spoken dialogue modelling. Given a semantic representation provided by
the dialogue manager, the language generator should generate sentences that
convey desired information. Traditional template-based generators can produce
sentences with all necessary information, but these sentences are not
sufficiently diverse. With RNN-based models, the diversity of the generated
sentences can be high, however, in the process some information is lost. In
this work, we improve an RNN-based generator by considering latent information
at the sentence level during generation using the conditional variational
autoencoder architecture. We demonstrate that our model outperforms the
original RNN-based generator, while yielding highly diverse sentences. In
addition, our model performs better when the training data is limited.
Relevant Attributes in Formal Contexts
Authors: Tom Hanika, Maren Koyda, Gerd Stumme
Date: 2018-12-20 00:00:00
Tasks: Attribute, Attribute Extraction

Computing conceptual structures, like formal concept lattices, is in the age
of massive data sets a challenging task. There are various approaches to deal
with this, e.g., random sampling, parallelization, or attribute extraction. A
so far not investigated method in the realm of formal concept analysis is
attribute selection, as done in machine learning. Building up on this we
introduce a method for attribute selection in formal contexts. To this end, we
propose the notion of relevant attributes which enables us to define a relative
relevance function, reflecting both the order structure of the concept lattice
as well as distribution of objects on it. Finally, we overcome computational
challenges for computing the relative relevance through an approximation
approach based on information entropy.