pwc-archive/papers-with-abstracts huggingface dataset:
Multi-View Fuzzy Logic System with the Cooperation between Visible and Hidden Views
Authors: Te Zhang, Zhaohong Deng, Dongrui Wu, Shitong Wang
Date: 2018-07-23 00:00:00
Tasks: MULTI-VIEW LEARNING

Multi-view datasets are frequently encountered in learning tasks, such as web
data mining and multimedia information analysis. Given a multi-view dataset,
traditional learning algorithms usually decompose it into several single-view
datasets, from each of which a single-view model is learned. In contrast, a
multi-view learning algorithm can achieve better performance by cooperative
learning on the multi-view data. However, existing multi-view approaches mainly
focus on the views that are visible and ignore the hidden information behind
the visible views, which usually contains some intrinsic information of the
multi-view data, or vice versa. To address this problem, this paper proposes a
multi-view fuzzy logic system, which utilizes both the hidden information
shared by the multiple visible views and the information of each visible view.
Extensive experiments were conducted to validate its effectiveness.
Broad cross-reactivity of the T-cell repertoire achieves specific and sufficiently rapid target searching
Authors: Jin Xu, Junghyo Jo
Date: 2017-12-13 00:00:00

The molecular recognition of T-cell receptors is the hallmark of the adaptive immunity. Given the finiteness of the T-cell repertoire, individual T-cell receptors are necessary to be cross-reactive to multiple antigenic peptides. In this study, we quantify the variability of the cross-reactivity by using a string model that estimates the binding affinity between two sequences of amino acids. We examine sequences of 10,000 human T-cell receptors and 10,000 antigenic peptides, and obtain a full spectrum of cross-reactivity of the receptor-peptide binding. Then, we find that the cross-reactivity spectrum is broad. Some T cells are reactive to 1,000 peptides, but some T cells are reactive to only one or two peptides. Since the degree of cross-reactivity has a correlation with the (un)binding affinity of receptors, we further investigate how the broad crossreactivity affects the target searching of T cells. High cross-reactive T cells may not require many trials for searching correct targets, but they may spend long time to unbind from incorrect targets. In contrast, low cross-reactive T cells may not spend long time to ignore incorrect targets, but they require many trials for screening correct targets. We evaluate this hypothesis, and show that the broad cross-reactivity of the natural T-cell repertoire can balance the trade-off between the rapid screening and unbinding penalty.
FasTer: Fast Tensor Completion with Nonconvex Regularization
Authors: Quanming Yao, James T. Kwok, Bo Han
Date: 2018-07-23 00:00:00

Low-rank tensor completion problem aims to recover a tensor from limited
observations, which has many real-world applications. Due to the easy
optimization, the convex overlapping nuclear norm has been popularly used for
tensor completion. However, it over-penalizes top singular values and lead to
biased estimations. In this paper, we propose to use the nonconvex regularizer,
which can less penalize large singular values, instead of the convex one for
tensor completion. However, as the new regularizer is nonconvex and overlapped
with each other, existing algorithms are either too slow or suffer from the
huge memory cost. To address these issues, we develop an efficient and scalable
algorithm, which is based on the proximal average (PA) algorithm, for
real-world problems. Compared with the direct usage of PA algorithm, the
proposed algorithm runs orders faster and needs orders less space. We further
speed up the proposed algorithm with the acceleration technique, and show the
convergence to critical points is still guaranteed. Experimental comparisons of
the proposed approach are made with various other tensor completion approaches.
Empirical results show that the proposed algorithm is very fast and can produce
much better recovery performance.
NullaNet: Training Deep Neural Networks for Reduced-Memory-Access Inference
Authors: Mahdi Nazemi, Ghasem Pasandi, Massoud Pedram
Date: 2018-07-23 00:00:00
Tasks: speech-recognition, Speech Recognition

Deep neural networks have been successfully deployed in a wide variety of
applications including computer vision and speech recognition. However,
computational and storage complexity of these models has forced the majority of
computations to be performed on high-end computing platforms or on the cloud.
To cope with computational and storage complexity of these models, this paper
presents a training method that enables a radically different approach for
realization of deep neural networks through Boolean logic minimization. The
aforementioned realization completely removes the energy-hungry step of
accessing memory for obtaining model parameters, consumes about two orders of
magnitude fewer computing resources compared to realizations that use
floatingpoint operations, and has a substantially lower latency.
Data Science with Vadalog: Bridging Machine Learning and Reasoning
Authors: Luigi Bellomarini, Ruslan R. Fayzrakhmanov, Georg Gottlob, Andrey Kravchenko, Eleonora Laurenza, Yavor Nenov, Stephane Reissfelder, Emanuel Sallinger, Evgeny Sherkhonov, Lianlong Wu
Date: 2018-07-23 00:00:00
Tasks: BIG-bench Machine Learning, Knowledge Graphs, Logical Reasoning, Management

Following the recent successful examples of large technology companies, many
modern enterprises seek to build knowledge graphs to provide a unified view of
corporate knowledge and to draw deep insights using machine learning and
logical reasoning. There is currently a perceived disconnect between the
traditional approaches for data science, typically based on machine learning
and statistical modelling, and systems for reasoning with domain knowledge. In
this paper we present a state-of-the-art Knowledge Graph Management System,
Vadalog, which delivers highly expressive and efficient logical reasoning and
provides seamless integration with modern data science toolkits, such as the
Jupyter platform. We demonstrate how to use Vadalog to perform traditional data
wrangling tasks, as well as complex logical and probabilistic reasoning. We
argue that this is a significant step forward towards combining machine
learning and reasoning in data science.
The Vadalog System: Datalog-based Reasoning for Knowledge Graphs
Authors: Luigi Bellomarini, Georg Gottlob, Emanuel Sallinger
Date: 2018-07-23 00:00:00
Tasks: Knowledge Graphs

Over the past years, there has been a resurgence of Datalog-based systems in
the database community as well as in industry. In this context, it has been
recognized that to handle the complex knowl\-edge-based scenarios encountered
today, such as reasoning over large knowledge graphs, Datalog has to be
extended with features such as existential quantification. Yet, Datalog-based
reasoning in the presence of existential quantification is in general
undecidable. Many efforts have been made to define decidable fragments. Warded
Datalog+/- is a very promising one, as it captures PTIME complexity while
allowing ontological reasoning. Yet so far, no implementation of Warded
Datalog+/- was available. In this paper we present the Vadalog system, a
Datalog-based system for performing complex logic reasoning tasks, such as
those required in advanced knowledge graphs. The Vadalog system is Oxford's
contribution to the VADA research programme, a joint effort of the universities
of Oxford, Manchester and Edinburgh and around 20 industrial partners. As the
main contribution of this paper, we illustrate the first implementation of
Warded Datalog+/-, a high-performance Datalog+/- system utilizing an aggressive
termination control strategy. We also provide a comprehensive experimental
evaluation.
Contrastive Explanations for Reinforcement Learning in terms of Expected Consequences
Authors: Jasper van der Waa, Jurriaan van Diggelen, Karel van den Bosch, Mark Neerincx
Date: 2018-07-23 00:00:00
Tasks: BIG-bench Machine Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Machine Learning models become increasingly proficient in complex tasks.
However, even for experts in the field, it can be difficult to understand what
the model learned. This hampers trust and acceptance, and it obstructs the
possibility to correct the model. There is therefore a need for transparency of
machine learning models. The development of transparent classification models
has received much attention, but there are few developments for achieving
transparent Reinforcement Learning (RL) models. In this study we propose a
method that enables a RL agent to explain its behavior in terms of the expected
consequences of state transitions and outcomes. First, we define a translation
of states and actions to a description that is easier to understand for human
users. Second, we developed a procedure that enables the agent to obtain the
consequences of a single action, as well as its entire policy. The method
calculates contrasts between the consequences of a policy derived from a user
query, and of the learned policy of the agent. Third, a format for generating
explanations was constructed. A pilot survey study was conducted to explore
preferences of users for different explanation properties. Results indicate
that human users tend to favor explanations about policy rather than about
single actions.
PS-FCN: A Flexible Learning Framework for Photometric Stereo
Authors: Guan-Ying Chen, Kai Han, Kwan-Yee K. Wong
Date: 2018-07-23 00:00:00
Proceeding: ECCV 2018 9

This paper addresses the problem of photometric stereo for non-Lambertian
surfaces. Existing approaches often adopt simplified reflectance models to make
the problem more tractable, but this greatly hinders their applications on
real-world objects. In this paper, we propose a deep fully convolutional
network, called PS-FCN, that takes an arbitrary number of images of a static
object captured under different light directions with a fixed camera as input,
and predicts a normal map of the object in a fast feed-forward pass. Unlike the
recently proposed learning based method, PS-FCN does not require a pre-defined
set of light directions during training and testing, and can handle multiple
images and light directions in an order-agnostic manner. Although we train
PS-FCN on synthetic data, it can generalize well on real datasets. We further
show that PS-FCN can be easily extended to handle the problem of uncalibrated
photometric stereo.Extensive experiments on public real datasets show that
PS-FCN outperforms existing approaches in calibrated photometric stereo, and
promising results are achieved in uncalibrated scenario, clearly demonstrating
its effectiveness.
Hybrid Diffusion: Spectral-Temporal Graph Filtering for Manifold Ranking
Authors: Ahmet Iscen, Yannis Avrithis, Giorgos Tolias, Teddy Furon, Ondrej Chum
Date: 2018-07-23 00:00:00
Tasks: Image Retrieval, Retrieval

State of the art image retrieval performance is achieved with CNN features
and manifold ranking using a k-NN similarity graph that is pre-computed
off-line. The two most successful existing approaches are temporal filtering,
where manifold ranking amounts to solving a sparse linear system online, and
spectral filtering, where eigen-decomposition of the adjacency matrix is
performed off-line and then manifold ranking amounts to dot-product search
online. The former suffers from expensive queries and the latter from
significant space overhead. Here we introduce a novel, theoretically
well-founded hybrid filtering approach allowing full control of the space-time
trade-off between these two extremes. Experimentally, we verify that our hybrid
method delivers results on par with the state of the art, with lower memory
demands compared to spectral filtering approaches and faster compared to
temporal filtering.
Automatic Speech Recognition for Humanitarian Applications in Somali
Authors: Raghav Menon, Astik Biswas, Armin Saeb, John Quinn, Thomas Niesler
Date: 2018-07-23 00:00:00
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), Data Augmentation, Humanitarian, Keyword Spotting, Language Modeling, Language Modelling, speech-recognition, Speech Recognition

We present our first efforts in building an automatic speech recognition
system for Somali, an under-resourced language, using 1.57 hrs of annotated
speech for acoustic model training. The system is part of an ongoing effort by
the United Nations (UN) to implement keyword spotting systems supporting
humanitarian relief programmes in parts of Africa where languages are severely
under-resourced. We evaluate several types of acoustic model, including recent
neural architectures. Language model data augmentation using a combination of
recurrent neural networks (RNN) and long short-term memory neural networks
(LSTMs) as well as the perturbation of acoustic data are also considered. We
find that both types of data augmentation are beneficial to performance, with
our best system using a combination of convolutional neural networks (CNNs),
time-delay neural networks (TDNNs) and bi-directional long short term memory
(BLSTMs) to achieve a word error rate of 53.75%.
ASR-free CNN-DTW keyword spotting using multilingual bottleneck features for almost zero-resource languages
Authors: Raghav Menon, Herman Kamper, Emre Yilmaz, John Quinn, Thomas Niesler
Date: 2018-07-23 00:00:00
Tasks: Dynamic Time Warping, Humanitarian, Keyword Spotting, Template Matching

We consider multilingual bottleneck features (BNFs) for nearly zero-resource
keyword spotting. This forms part of a United Nations effort using keyword
spotting to support humanitarian relief programmes in parts of Africa where
languages are severely under-resourced. We use 1920 isolated keywords (40
types, 34 minutes) as exemplars for dynamic time warping (DTW) template
matching, which is performed on a much larger body of untranscribed speech.
These DTW costs are used as targets for a convolutional neural network (CNN)
keyword spotter, giving a much faster system than direct DTW. Here we consider
how available data from well-resourced languages can improve this CNN-DTW
approach. We show that multilingual BNFs trained on ten languages improve the
area under the ROC curve of a CNN-DTW system by 10.9% absolute relative to the
MFCC baseline. By combining low-resource DTW-based supervision with information
from well-resourced languages, CNN-DTW is a competitive option for low-resource
keyword spotting.
Understanding the Modeling of Computer Network Delays using Neural Networks
Authors: Albert Mestres, Eduard Alarcón, Yusheng Ji, Albert Cabellos-Aparicio
Date: 2018-07-23 00:00:00

Recent trends in networking are proposing the use of Machine Learning (ML)
techniques for the control and operation of the network. In this context, ML
can be used as a computer network modeling technique to build models that
estimate the network performance. Indeed, network modeling is a central
technique to many networking functions, for instance in the field of
optimization, in which the model is used to search a configuration that
satisfies the target policy. In this paper, we aim to provide an answer to the
following question: Can neural networks accurately model the delay of a
computer network as a function of the input traffic? For this, we assume the
network as a black-box that has as input a traffic matrix and as output delays.
Then we train different neural networks models and evaluate its accuracy under
different fundamental network characteristics: topology, size, traffic
intensity and routing. With this, we aim to have a better understanding of
computer network modeling with neural nets and ultimately provide practical
guidelines on how such models need to be trained.
Joint Anchor-Feature Refinement for Real-Time Accurate Object Detection in Images and Videos
Authors: Xingyu Chen, Junzhi Yu, Shihan Kong, Zhengxing Wu, Li Wen
Date: 2018-07-23 00:00:00
Tasks: Object, object-detection, Object Detection, Real-Time Object Detection

Object detection has been vigorously investigated for years but fast accurate detection for real-world scenes remains a very challenging problem. Overcoming drawbacks of single-stage detectors, we take aim at precisely detecting objects for static and temporal scenes in real time. Firstly, as a dual refinement mechanism, a novel anchor-offset detection is designed, which includes an anchor refinement, a feature location refinement, and a deformable detection head. This new detection mode is able to simultaneously perform two-step regression and capture accurate object features. Based on the anchor-offset detection, a dual refinement network (DRNet) is developed for high-performance static detection, where a multi-deformable head is further designed to leverage contextual information for describing objects. As for temporal detection in videos, temporal refinement networks (TRNet) and temporal dual refinement networks (TDRNet) are developed by propagating the refinement information across time. We also propose a soft refinement strategy to temporally match object motion with the previous refinement. Our proposed methods are evaluated on PASCAL VOC, COCO, and ImageNet VID datasets. Extensive comparisons on static and temporal detection verify the superiority of DRNet, TRNet, and TDRNet. Consequently, our developed approaches run in a fairly fast speed, and in the meantime achieve a significantly enhanced detection accuracy, i.e., 84.4% mAP on VOC 2007, 83.6% mAP on VOC 2012, 69.4% mAP on VID 2017, and 42.4% AP on COCO. Ultimately, producing encouraging results, our methods are applied to online underwater object detection and grasping with an autonomous system. Codes are publicly available at https://github.com/SeanChenxy/TDRN.
Auto-adaptive Resonance Equalization using Dilated Residual Networks
Authors: Maarten Grachten, Emmanuel Deruty, Alexandre Tanguy
Date: 2018-07-23 00:00:00

In music and audio production, attenuation of spectral resonances is an
important step towards a technically correct result. In this paper we present a
two-component system to automate the task of resonance equalization. The first
component is a dynamic equalizer that automatically detects resonances and
offers to attenuate them by a user-specified factor. The second component is a
deep neural network that predicts the optimal attenuation factor based on the
windowed audio. The network is trained and validated on empirical data gathered
from an experiment in which sound engineers choose their preferred attenuation
factors for a set of tracks. We test two distinct network architectures for the
predictive model and find that a dilated residual network operating directly on
the audio signal is on a par with a network architecture that requires a prior
audio feature extraction stage. Both architectures predict human-preferred
resonance attenuation factors significantly better than a baseline approach.
Region Convolutional Features for Multi-Label Remote Sensing Image Retrieval
Authors: Weixun Zhou, Xueqing Deng, Zhenfeng Shao
Date: 2018-07-23 00:00:00
Tasks: Image Retrieval, Retrieval, Segmentation

Conventional remote sensing image retrieval (RSIR) systems usually perform
single-label retrieval where each image is annotated by a single label
representing the most significant semantic content of the image. This
assumption, however, ignores the complexity of remote sensing images, where an
image might have multiple classes (i.e., multiple labels), thus resulting in
worse retrieval performance. We therefore propose a novel multi-label RSIR
approach with fully convolutional networks (FCN). In our approach, we first
train a FCN model using a pixel-wise labeled dataset,and the trained FCN is
then used to predict the segmentation maps of each image in the considered
archive. We finally extract region convolutional features of each image based
on its segmentation map.The region features can be either used to perform
region-based retrieval or further post-processed to obtain a feature vector for
similarity measure. The experimental results show that our approach achieves
state-of-the-art performance in contrast to conventional single-label and
recent multi-label RSIR approaches.
From Volcano to Toyshop: Adaptive Discriminative Region Discovery for Scene Recognition
Authors: Zhengyu Zhao, Martha Larson
Date: 2018-07-23 00:00:00
Tasks: Attribute, image-classification, Image Classification, Scene Recognition

As deep learning approaches to scene recognition emerge, they have continued
to leverage discriminative regions at multiple scales, building on practices
established by conventional image classification research. However, approaches
remain largely generic, and do not carefully consider the special properties of
scenes. In this paper, inspired by the intuitive differences between scenes and
objects, we propose Adi-Red, an adaptive approach to discriminative region
discovery for scene recognition. Adi-Red uses a CNN classifier, which was
pre-trained using only image-level scene labels, to discover discriminative
image regions directly. These regions are then used as a source of features to
perform scene recognition. The use of the CNN classifier makes it possible to
adapt the number of discriminative regions per image using a simple, yet
elegant, threshold, at relatively low computational cost. Experimental results
on the scene recognition benchmark dataset SUN397 demonstrate the ability of
Adi-Red to outperform the state of the art. Additional experimental analysis on
the Places dataset reveals the advantages of Adi-Red, and highlight how they
are specific to scenes. We attribute the effectiveness of Adi-Red to the
ability of adaptive region discovery to avoid introducing noise, while also not
missing out on important information.
Deep Learning from Label Proportions for Emphysema Quantification
Authors: Gerda Bortsova, Florian Dubost, Silas Ørting, Ioannis Katramados, Laurens Hogeweg, Laura Thomsen, Mathilde Wille, Marleen de Bruijne
Date: 2018-07-23 00:00:00
Tasks: Deep Learning

We propose an end-to-end deep learning method that learns to estimate
emphysema extent from proportions of the diseased tissue. These proportions
were visually estimated by experts using a standard grading system, in which
grades correspond to intervals (label example: 1-5% of diseased tissue). The
proposed architecture encodes the knowledge that the labels represent a
volumetric proportion. A custom loss is designed to learn with intervals. Thus,
during training, our network learns to segment the diseased tissue such that
its proportions fit the ground truth intervals. Our architecture and loss
combined improve the performance substantially (8% ICC) compared to a more
conventional regression network. We outperform traditional lung densitometry
and two recently published methods for emphysema quantification by a large
margin (at least 7% AUC and 15% ICC), and achieve near-human-level performance.
Moreover, our method generates emphysema segmentations that predict the spatial
distribution of emphysema at human level.