pwc-archive/papers-with-abstracts huggingface dataset:
Large-Scale Markov Decision Problems via the Linear Programming Dual
Authors: Yasin Abbasi-Yadkori, Peter L. Bartlett, Xi Chen, Alan Malek
Date: 2019-01-06 00:00:00

We consider the problem of controlling a fully specified Markov decision
process (MDP), also known as the planning problem, when the state space is very
large and calculating the optimal policy is intractable. Instead, we pursue the
more modest goal of optimizing over some small family of policies.
Specifically, we show that the family of policies associated with a
low-dimensional approximation of occupancy measures yields a tractable
optimization. Moreover, we propose an efficient algorithm, scaling with the
size of the subspace but not the state space, that is able to find a policy
with low excess loss relative to the best policy in this class. To the best of
our knowledge, such results did not exist in the literature previously. We
bound excess loss in the average cost and discounted cost cases, which are
treated separately. Preliminary experiments show the effectiveness of the
proposed algorithms in a queueing application.
Towards Self-constructive Artificial Intelligence: Algorithmic basis (Part I)
Authors: Fernando J. Corbacho
Date: 2019-01-06 00:00:00

Artificial Intelligence frameworks should allow for ever more autonomous and
general systems in contrast to very narrow and restricted (human pre-defined)
domain systems, in analogy to how the brain works. Self-constructive Artificial
Intelligence ($SCAI$) is one such possible framework. We herein propose that
$SCAI$ is based on three principles of organization: self-growing,
self-experimental and self-repairing. Self-growing: the ability to autonomously
and incrementally construct structures and functionality as needed to solve
encountered (sub)problems. Self-experimental: the ability to internally
simulate, anticipate and take decisions based on these expectations.
Self-repairing: the ability to autonomously re-construct a previously
successful functionality or pattern of interaction lost from a possible
sub-component failure (damage). To implement these principles of organization,
a constructive architecture capable of evolving adaptive autonomous agents is
required. We present Schema-based learning as one such architecture capable of
incrementally constructing a myriad of internal models of three kinds:
predictive schemas, dual (inverse models) schemas and goal schemas as they are
necessary to autonomously develop increasing functionality.
  We claim that artificial systems, whether in the digital or in the physical
world, can benefit very much form this constructive architecture and should be
organized around these principles of organization. To illustrate the generality
of the proposed framework, we include several test cases in structural adaptive
navigation in artificial intelligence systems in Paper II of this series, and
resilient robot motor control in Paper III of this series. Paper IV of this
series will also include $SCAI$ for problem structural discovery in predictive
Business Intelligence.
Geometrization of deep networks for the interpretability of deep learning systems
Authors: Xiao Dong, Ling Zhou
Date: 2019-01-06 00:00:00
Tasks: Deep Learning

How to understand deep learning systems remains an open problem. In this
paper we propose that the answer may lie in the geometrization of deep
networks. Geometrization is a bridge to connect physics, geometry, deep network
and quantum computation and this may result in a new scheme to reveal the rule
of the physical world. By comparing the geometry of image matching and deep
networks, we show that geometrization of deep networks can be used to
understand existing deep learning systems and it may also help to solve the
interpretability problem of deep learning systems.
Robust and High Performance Face Detector
Authors: Yundong Zhang, Xiang Xu, Xiaotao Liu
Date: 2019-01-06 00:00:00
Tasks: Data Augmentation, Face Detection, Vocal Bursts Intensity Prediction

In recent years, face detection has experienced significant performance
improvement with the boost of deep convolutional neural networks. In this
report, we reimplement the state-of-the-art detector SRN and apply some tricks
proposed in the recent literatures to obtain an extremely strong face detector,
named VIM-FD. In specific, we exploit more powerful backbone network like
DenseNet-121, revisit the data augmentation based on data-anchor-sampling
proposed in PyramidBox, and use the max-in-out label and anchor matching
strategy in SFD. In addition, we also introduce the attention mechanism to
provide additional supervision. Over the most popular and challenging face
detection benchmark, i.e., WIDER FACE, the proposed VIM-FD achieves
state-of-the-art performance.
Efficient Convolutional Neural Network Training with Direct Feedback Alignment
Authors: Donghyeon Han, Hoi-jun Yoo
Date: 2019-01-06 00:00:00
Proceeding: ICLR 2019 5
Tasks: Computational Efficiency, Object Tracking

There were many algorithms to substitute the back-propagation (BP) in the
deep neural network (DNN) training. However, they could not become popular
because their training accuracy and the computational efficiency were worse
than BP. One of them was direct feedback alignment (DFA), but it showed low
training performance especially for the convolutional neural network (CNN). In
this paper, we overcome the limitation of the DFA algorithm by combining with
the conventional BP during the CNN training. To improve the training stability,
we also suggest the feedback weight initialization method by analyzing the
patterns of the fixed random matrices in the DFA. Finally, we propose the new
training algorithm, binary direct feedback alignment (BDFA) to minimize the
computational cost while maintaining the training accuracy compared with the
DFA. In our experiments, we use the CIFAR-10 and CIFAR-100 dataset to simulate
the CNN learning from the scratch and apply the BDFA to the online learning
based object tracking application to examine the training in the small dataset
environment. Our proposed algorithms show better performance than conventional
BP in both two different training tasks especially when the dataset is small.
Combining Unsupervised and Supervised Learning for Asset Class Failure Prediction in Power Systems
Authors: Ming Dong
Date: 2019-01-06 00:00:00
Tasks: Asset Management, Clustering, Management

In power systems, an asset class is a group of power equipment that has the same function and shares similar electrical or mechanical characteristics. Predicting failures for different asset classes is critical for electric utilities towards developing cost-effective asset management strategies. Previously, physical age based Weibull distribution has been widely used to failure prediction. However, this mathematical model cannot incorporate asset condition data such as inspection or testing results. As a result, the prediction cannot be very specific and accurate for individual assets. To solve this important problem, this paper proposes a novel and comprehensive data-driven approach based on asset condition data: K-means clustering as an unsupervised learning method is used to analyze the inner structure of historical asset condition data and produce the asset conditional ages; logistic regression as a supervised learning method takes in both asset physical ages and conditional ages to classify and predict asset statuses. Furthermore, an index called average aging rate is defined to quantify, track and estimate the relationship between asset physical age and conditional age. This approach was applied to an urban distribution system in West Canada to predict medium-voltage cable failures. Case studies and comparison with standard Weibull distribution are provided. The proposed approach demonstrates superior performance and practicality for predicting asset class failures in power systems.
Efforts estimation of doctors annotating medical image
Authors: Yang Deng, Yao Sun, Yongpei Zhu, Yue Xu, Qianxi Yang, Shuo Zhang, Mingwang Zhu, Jirang Sun, Weiling Zhao, Xiaobo Zhou, Kehong Yuan
Date: 2019-01-06 00:00:00
Tasks: Active Learning

Accurate annotation of medical image is the crucial step for image AI
clinical application. However, annotating medical image will incur a great deal
of annotation effort and expense due to its high complexity and needing
experienced doctors. To alleviate annotation cost, some active learning methods
are proposed. But such methods just cut the number of annotation candidates and
do not study how many efforts the doctor will exactly take, which is not enough
since even annotating a small amount of medical data will take a lot of time
for the doctor.
  In this paper, we propose a new criterion to evaluate efforts of doctors
annotating medical image. First, by coming active learning and U-shape network,
we employ a suggestive annotation strategy to choose the most effective
annotation candidates. Then we exploit a fine annotation platform to alleviate
annotating efforts on each candidate and first utilize a new criterion to
quantitatively calculate the efforts taken by doctors. In our work, we take MR
brain tissue segmentation as an example to evaluate the proposed method.
  Extensive experiments on the well-known IBSR18 dataset and MRBrainS18
Challenge dataset show that, using proposed strategy, state-of-the-art
segmentation performance can be achieved by using only 60% annotation
candidates and annotation efforts can be alleviated by at least 44%, 44%, 47%
on CSF, GM, WM separately.
Sum-of-square-of-rational-function based representations of positive semidefinite polynomial matrices
Authors: Thanh-Hieu Le, Nhat-Thien Pham
Date: 2019-01-05 00:00:00

The paper proves sum-of-square-of-rational-function based representations
(shortly, sosrf-based representations) of polynomial matrices that are positive
semidefinite on some special sets: $\mathbb{R}^n;$ $\mathbb{R}$ and its
intervals $[a,b]$, $[0,\infty)$; and the strips $[a,b] \times \mathbb{R}
\subset \mathbb{R}^2.$ A method for numerically computing such representations
is also presented. The methodology is divided into two stages:
  (S1) diagonalizing the initial polynomial matrix based on the Schm\"{u}dgen's
procedure \cite{Schmudgen09};
  (S2) for each diagonal element of the resulting matrix, find its low rank
sosrf-representation satisfying the Artin's theorem solving the Hilbert's 17th
problem.
  Some numerical tests and illustrations with \textsf{OCTAVE} are also
presented for each type of polynomial matrices.
Fully-automatic segmentation of kidneys in clinical ultrasound images using a boundary distance regression network
Authors: Shi Yin, Zhengqiang Zhang, Hongming Li, Qinmu Peng, Xinge You, Susan L. Furth, Gregory E. Tasian, Yong Fan
Date: 2019-01-05 00:00:00
Tasks: Classification, Distance regression, General Classification, regression

It remains challenging to automatically segment kidneys in clinical
ultrasound images due to the kidneys' varied shapes and image intensity
distributions, although semi-automatic methods have achieved promising
performance. In this study, we developed a novel boundary distance regression
deep neural network to segment the kidneys, informed by the fact that the
kidney boundaries are relatively consistent across images in terms of their
appearance. Particularly, we first use deep neural networks pre-trained for
classification of natural images to extract high-level image features from
ultrasound images, then these feature maps are used as input to learn kidney
boundary distance maps using a boundary distance regression network, and
finally the predicted boundary distance maps are classified as kidney pixels or
non-kidney pixels using a pixel classification network in an end-to-end
learning fashion. Experimental results have demonstrated that our method could
effectively improve the performance of automatic kidney segmentation,
significantly better than deep learning based pixel classification networks.
Improving noise robustness of automatic speech recognition via parallel data and teacher-student learning
Authors: Ladislav Mošner, Minhua Wu, Anirudh Raju, Sree Hari Krishnan Parthasarathi, Kenichi Kumatani, Shiva Sundaram, Roland Maas, Björn Hoffmeister
Date: 2019-01-05 00:00:00
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), speech-recognition, Speech Recognition

For real-world speech recognition applications, noise robustness is still a
challenge. In this work, we adopt the teacher-student (T/S) learning technique
using a parallel clean and noisy corpus for improving automatic speech
recognition (ASR) performance under multimedia noise. On top of that, we apply
a logits selection method which only preserves the k highest values to prevent
wrong emphasis of knowledge from the teacher and to reduce bandwidth needed for
transferring data. We incorporate up to 8000 hours of untranscribed data for
training and present our results on sequence trained models apart from cross
entropy trained ones. The best sequence trained student model yields relative
word error rate (WER) reductions of approximately 10.1%, 28.7% and 19.6% on our
clean, simulated noisy and real test sets respectively comparing to a sequence
trained teacher.
Auto-weighted Mutli-view Sparse Reconstructive Embedding
Authors: Huibing Wang, Haohao Li, Xianping Fu
Date: 2019-01-05 00:00:00
Tasks: Dimensionality Reduction

With the development of multimedia era, multi-view data is generated in
various fields. Contrast with those single-view data, multi-view data brings
more useful information and should be carefully excavated. Therefore, it is
essential to fully exploit the complementary information embedded in multiple
views to enhance the performances of many tasks. Especially for those
high-dimensional data, how to develop a multi-view dimension reduction
algorithm to obtain the low-dimensional representations is of vital importance
but chanllenging. In this paper, we propose a novel multi-view dimensional
reduction algorithm named Auto-weighted Mutli-view Sparse Reconstructive
Embedding (AMSRE) to deal with this problem. AMSRE fully exploits the sparse
reconstructive correlations between features from multiple views. Furthermore,
it is equipped with an auto-weighted technique to treat multiple views
discriminatively according to their contributions. Various experiments have
verified the excellent performances of the proposed AMSRE.
Accelerating Goal-Directed Reinforcement Learning by Model Characterization
Authors: Shoubhik Debnath, Gaurav Sukhatme, Lantao Liu
Date: 2019-01-04 00:00:00
Tasks: model, Model-based Reinforcement Learning, Q-Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

We propose a hybrid approach aimed at improving the sample efficiency in
goal-directed reinforcement learning. We do this via a two-step mechanism where
firstly, we approximate a model from Model-Free reinforcement learning. Then,
we leverage this approximate model along with a notion of reachability using
Mean First Passage Times to perform Model-Based reinforcement learning. Built
on such a novel observation, we design two new algorithms - Mean First Passage
Time based Q-Learning (MFPT-Q) and Mean First Passage Time based DYNA
(MFPT-DYNA), that have been fundamentally modified from the state-of-the-art
reinforcement learning techniques. Preliminary results have shown that our
hybrid approaches converge with much fewer iterations than their corresponding
state-of-the-art counterparts and therefore requiring much fewer samples and
much fewer training trials to converge.
Dataflow-based Joint Quantization of Weights and Activations for Deep Neural Networks
Authors: Xue Geng, Jie Fu, Bin Zhao, Jie Lin, Mohamed M. Sabry Aly, Christopher Pal, Vijay Chandrasekhar
Date: 2019-01-04 00:00:00
Tasks: Quantization

This paper addresses a challenging problem - how to reduce energy consumption
without incurring performance drop when deploying deep neural networks (DNNs)
at the inference stage. In order to alleviate the computation and storage
burdens, we propose a novel dataflow-based joint quantization approach with the
hypothesis that a fewer number of quantization operations would incur less
information loss and thus improve the final performance. It first introduces a
quantization scheme with efficient bit-shifting and rounding operations to
represent network parameters and activations in low precision. Then it
restructures the network architectures to form unified modules for optimization
on the quantized model. Extensive experiments on ImageNet and KITTI validate
the effectiveness of our model, demonstrating that state-of-the-art results for
various tasks can be achieved by this quantized model. Besides, we designed and
synthesized an RTL model to measure the hardware costs among various
quantization methods. For each quantization operation, it reduces area cost by
about 15 times and energy consumption by about 9 times, compared to a strong
baseline.
SMILK, linking natural language and data from the web
Authors: Cédric Lopez, Molka Dhouib, Elena Cabrio, Catherine Faron Zucker, Fabien Gandon, Frédérique Segond
Date: 2018-12-20 00:00:00
Tasks: Information Retrieval, Retrieval

As part of the SMILK Joint Lab, we studied the use of Natural Language
Processing to: (1) enrich knowledge bases and link data on the web, and
conversely (2) use this linked data to contribute to the improvement of text
analysis and the annotation of textual content, and to support knowledge
extraction. The evaluation focused on brand-related information retrieval in
the field of cosmetics. This article describes each step of our approach: the
creation of ProVoc, an ontology to describe products and brands; the automatic
population of a knowledge base mainly based on ProVoc from heterogeneous
textual resources; and the evaluation of an application which that takes the
form of a browser plugin providing additional knowledge to users browsing the
web.
A geometric characterisation of sensitivity analysis in monomial models
Authors: Manuele Leonelli, Eva Riccomagno
Date: 2018-12-18 00:00:00
Tasks: Sensitivity

Sensitivity analysis in probabilistic discrete graphical models is usually conducted by varying one probability value at a time and observing how this affects output probabilities of interest. When one probability is varied then others are proportionally covaried to respect the sum-to-one condition of probability laws. The choice of proportional covariation is justified by a variety of optimality conditions, under which the original and the varied distributions are as close as possible under different measures of closeness. For variations of more than one parameter at a time proportional covariation is justified in some special cases only. In this work, for the large class of discrete statistical models entertaining a regular monomial parametrisation, we demonstrate the optimality of newly defined proportional multi-way schemes with respect to an optimality criterion based on the notion of I-divergence. We demonstrate that there are varying parameters choices for which proportional covariation is not optimal and identify the sub-family of model distributions where the distance between the original distribution and the one where probabilities are covaried proportionally is minimum. This is shown by adopting a new formal, geometric characterization of sensitivity analysis in monomial models, which include a wide array of probabilistic graphical models. We also demonstrate the optimality of proportional covariation for multi-way analyses in Naive Bayes classifiers.
Reliable Agglomerative Clustering
Authors: Morteza Haghir Chehreghani
Date: 2018-12-20 00:00:00
Tasks: Clustering

Standard agglomerative clustering suggests establishing a new reliable linkage at every step. However, in order to provide adaptive, density-consistent and flexible solutions, we study extracting all the reliable linkages at each step, instead of the smallest one. Such a strategy can be applied with all common criteria for agglomerative hierarchical clustering. We also study that this strategy with the single linkage criterion yields a minimum spanning tree algorithm. We perform experiments on several real-world datasets to demonstrate the performance of this strategy compared to the standard alternative.
Using deceased-donor kidneys to initiate chains of living donor kidney paired donations: algorithms and experimentation
Authors: Cristina Cornelio, Lucrezia Furian, Antonio Nicolo', Francesca Rossi
Date: 2018-12-17 00:00:00

We design a flexible algorithm that exploits deceased donor kidneys to
initiate chains of living donor kidney paired donations, combining deceased and
living donor allocation mechanisms to improve the quantity and quality of
kidney transplants. The advantages of this approach have been measured using
retrospective data on the pool of donor/recipient incompatible and desensitized
pairs at the Padua University Hospital, the largest center for living donor
kidney transplants in Italy. The experiments show a remarkable improvement on
the number of patients with incompatible donor who could be transplanted, a
decrease in the number of desensitization procedures, and an increase in the
number of UT patients (that is, patients unlikely to be transplanted for
immunological reasons) in the waiting list who could receive an organ.
Prediction of Success or Failure for Final Examination using Nearest Neighbor Method to the Trend of Weekly Online Testing
Authors: Hideo Hirose
Date: 2018-12-12 00:00:00
Tasks: Prediction

Using the trends of estimated abilities in terms of item response theory for
online testing, we can predict the success/failure status for the final
examination to each student at early stages in courses. In prediction, we
applied the newly developed nearest neighbor method for determining the
similarity of learning skill in the trends of estimated abilities, resulting a
better prediction accuracy for success or failure. This paper shows that the
use of the learning analytics incorporating the trends for abilities is
effective. ROC curve and recall precision curve are informative to assist the
proposed method.
A General End-to-end Diagnosis Framework for Manufacturing Systems
Authors: Ye Yuan, Guijun Ma, Cheng Cheng, Beitong Zhou, Huan Zhao, Hai-Tao Zhang, Han Ding
Date: 2018-12-17 00:00:00
Tasks: Management

The manufacturing sector is envisioned to be heavily influenced by artificial intelligence-based technologies with the extraordinary increases in computational power and data volumes. A central challenge in manufacturing sector lies in the requirement of a general framework to ensure satisfied diagnosis and monitoring performances in different manufacturing applications. Here we propose a general data-driven, end-to-end framework for the monitoring of manufacturing systems. This framework, derived from deep learning techniques, evaluates fused sensory measurements to detect and even predict faults and wearing conditions. This work exploits the predictive power of deep learning to automatically extract hidden degradation features from noisy, time-course data. We have experimented the proposed framework on ten representative datasets drawn from a wide variety of manufacturing applications. Results reveal that the framework performs well in examined benchmark applications and can be applied in diverse contexts, indicating its potential use as a critical corner stone in smart manufacturing.