pwc-archive/papers-with-abstracts huggingface dataset:
Graph Capsule Convolutional Neural Networks
Authors: Saurabh Verma, Zhi-Li Zhang
Date: 2018-05-21 00:00:00
Tasks: Deep Learning, General Classification, Graph Classification

Graph Convolutional Neural Networks (GCNNs) are the most recent exciting
advancement in deep learning field and their applications are quickly spreading
in multi-cross-domains including bioinformatics, chemoinformatics, social
networks, natural language processing and computer vision. In this paper, we
expose and tackle some of the basic weaknesses of a GCNN model with a capsule
idea presented in \cite{hinton2011transforming} and propose our Graph Capsule
Network (GCAPS-CNN) model. In addition, we design our GCAPS-CNN model to solve
especially graph classification problem which current GCNN models find
challenging. Through extensive experiments, we show that our proposed Graph
Capsule Network can significantly outperforms both the existing state-of-art
deep learning methods and graph kernels on graph classification benchmark
datasets.
Stable Geodesic Update on Hyperbolic Space and its Application to Poincare Embeddings
Authors: Yosuke Enokida, Atsushi Suzuki, Kenji Yamanishi
Date: 2018-05-26 00:00:00

A hyperbolic space has been shown to be more capable of modeling complex
networks than a Euclidean space. This paper proposes an explicit update rule
along geodesics in a hyperbolic space. The convergence of our algorithm is
theoretically guaranteed, and the convergence rate is better than the
conventional Euclidean gradient descent algorithm. Moreover, our algorithm
avoids the "bias" problem of existing methods using the Riemannian gradient.
Experimental results demonstrate the good performance of our algorithm in the
\Poincare embeddings of knowledge base data.
Vehicle Instance Segmentation from Aerial Image and Video Using a Multi-Task Learning Residual Fully Convolutional Network
Authors: Lichao Mou, Xiao Xiang Zhu
Date: 2018-05-26 00:00:00
Tasks: Instance Segmentation, Multi-Task Learning, object-detection, Object Detection, Philosophy, Retrieval, Segmentation, Semantic Segmentation, vehicle detection

Object detection and semantic segmentation are two main themes in object
retrieval from high-resolution remote sensing images, which have recently
achieved remarkable performance by surfing the wave of deep learning and, more
notably, convolutional neural networks (CNNs). In this paper, we are interested
in a novel, more challenging problem of vehicle instance segmentation, which
entails identifying, at a pixel-level, where the vehicles appear as well as
associating each pixel with a physical instance of a vehicle. In contrast,
vehicle detection and semantic segmentation each only concern one of the two.
We propose to tackle this problem with a semantic boundary-aware multi-task
learning network. More specifically, we utilize the philosophy of residual
learning (ResNet) to construct a fully convolutional network that is capable of
harnessing multi-level contextual feature representations learned from
different residual blocks. We theoretically analyze and discuss why residual
networks can produce better probability maps for pixel-wise segmentation tasks.
Then, based on this network architecture, we propose a unified multi-task
learning network that can simultaneously learn two complementary tasks, namely,
segmenting vehicle regions and detecting semantic boundaries. The latter
subproblem is helpful for differentiating closely spaced vehicles, which are
usually not correctly separated into instances. Currently, datasets with
pixel-wise annotation for vehicle extraction are ISPRS dataset and IEEE GRSS
DFC2015 dataset over Zeebrugge, which specializes in semantic segmentation.
Therefore, we built a new, more challenging dataset for vehicle instance
segmentation, called the Busy Parking Lot UAV Video dataset, and we make our
dataset available at http://www.sipeo.bgu.tum.de/download so that it can be
used to benchmark future vehicle instance segmentation algorithms.
A successive difference-of-convex approximation method for a class of nonconvex nonsmooth optimization problems
Authors: Tianxiang Liu, Ting Kei Pong, Akiko Takeda
Date: 2017-10-16 00:00:00

We consider a class of nonconvex nonsmooth optimization problems whose
objective is the sum of a smooth function and a finite number of nonnegative
proper closed possibly nonsmooth functions (whose proximal mappings are easy to
compute), some of which are further composed with linear maps. This kind of
problems arises naturally in various applications when different regularizers
are introduced for inducing simultaneous structures in the solutions. Solving
these problems, however, can be challenging because of the coupled nonsmooth
functions: the corresponding proximal mapping can be hard to compute so that
standard first-order methods such as the proximal gradient algorithm cannot be
applied efficiently. In this paper, we propose a successive
difference-of-convex approximation method for solving this kind of problems. In
this algorithm, we approximate the nonsmooth functions by their Moreau
envelopes in each iteration. Making use of the simple observation that Moreau
envelopes of nonnegative proper closed functions are continuous {\em
difference-of-convex} functions, we can then approximately minimize the
approximation function by first-order methods with suitable majorization
techniques. These first-order methods can be implemented efficiently thanks to
the fact that the proximal mapping of {\em each} nonsmooth function is easy to
compute. Under suitable assumptions, we prove that the sequence generated by
our method is bounded and any accumulation point is a stationary point of the
objective. We also discuss how our method can be applied to concrete
applications such as nonconvex fused regularized optimization problems and
simultaneously structured matrix optimization problems, and illustrate the
performance numerically for these two specific applications.
Automatic Annotation of Locative and Directional Expressions in Arabic
Authors: Rita Hijazi, Amani Sabra, Moustafa Al-Hajj
Date: 2018-05-16 00:00:00
Tasks: Descriptive

In this paper, we introduce a rule-based approach to annotate Locative and
Directional Expressions in Arabic natural language text. The annotation is
based on a constructed semantic map of the spatiality domain. Challenges are
twofold: first, we need to study how locative and directional expressions are
expressed linguistically in these texts; and second, we need to automatically
annotate the relevant textual segments accordingly. The research method we will
use in this article is analytic-descriptive. We will validate this approach on
specific novel rich with these expressions and show that it has very promising
results. We will be using NOOJ as a software tool to implement finite-state
transducers to annotate linguistic elements according to Locative and
Directional Expressions. In conclusion, NOOJ allowed us to write linguistic
rules for the automatic annotation in Arabic text of Locative and Directional
Expressions.
Look at Boundary: A Boundary-Aware Face Alignment Algorithm
Authors: Wayne Wu, Chen Qian, Shuo Yang, Quan Wang, Yici Cai, Qiang Zhou
Date: 2018-05-26 00:00:00
Proceeding: CVPR 2018 6
Tasks: Face Alignment, Facial Landmark Detection

We present a novel boundary-aware face alignment algorithm by utilising
boundary lines as the geometric structure of a human face to help facial
landmark localisation. Unlike the conventional heatmap based method and
regression based method, our approach derives face landmarks from boundary
lines which remove the ambiguities in the landmark definition. Three questions
are explored and answered by this work: 1. Why using boundary? 2. How to use
boundary? 3. What is the relationship between boundary estimation and landmarks
localisation? Our boundary- aware face alignment algorithm achieves 3.49% mean
error on 300-W Fullset, which outperforms state-of-the-art methods by a large
margin. Our method can also easily integrate information from other datasets.
By utilising boundary information of 300-W dataset, our method achieves 3.92%
mean error with 0.39% failure rate on COFW dataset, and 1.25% mean error on
AFLW-Full dataset. Moreover, we propose a new dataset WFLW to unify training
and testing across different factors, including poses, expressions,
illuminations, makeups, occlusions, and blurriness. Dataset and model will be
publicly available at https://wywu.github.io/projects/LAB/LAB.html
Nonlinear Inductive Matrix Completion based on One-layer Neural Networks
Authors: Kai Zhong, Zhao Song, Prateek Jain, Inderjit S. Dhillon
Date: 2018-05-26 00:00:00
Tasks: Clustering, Matrix Completion, Recommendation Systems

The goal of a recommendation system is to predict the interest of a user in a
given item by exploiting the existing set of ratings as well as certain
user/item features. A standard approach to modeling this problem is Inductive
Matrix Completion where the predicted rating is modeled as an inner product of
the user and the item features projected onto a latent space. In order to learn
the parameters effectively from a small number of observed ratings, the latent
space is constrained to be low-dimensional which implies that the parameter
matrix is constrained to be low-rank. However, such bilinear modeling of the
ratings can be limiting in practice and non-linear prediction functions can
lead to significant improvements. A natural approach to introducing
non-linearity in the prediction function is to apply a non-linear activation
function on top of the projected user/item features. Imposition of
non-linearities further complicates an already challenging problem that has two
sources of non-convexity: a) low-rank structure of the parameter matrix, and b)
non-linear activation function. We show that one can still solve the non-linear
Inductive Matrix Completion problem using gradient descent type methods as long
as the solution is initialized well. That is, close to the optima, the
optimization function is strongly convex and hence admits standard optimization
techniques, at least for certain activation functions, such as Sigmoid and
tanh. We also highlight the importance of the activation function and show how
ReLU can behave significantly differently than say a sigmoid function. Finally,
we apply our proposed technique to recommendation systems and semi-supervised
clustering, and show that our method can lead to much better performance than
standard linear Inductive Matrix Completion methods.
L1-(2D)2PCANet: A Deep Learning Network for Face Recognition
Authors: YunKun Li, Xiao-Jun Wu, Josef Kittler
Date: 2018-05-26 00:00:00
Tasks: Deep Learning, Face Recognition

In this paper, we propose a novel deep learning network L1-(2D)2PCANet for
face recognition, which is based on L1-norm-based two-directional
two-dimensional principal component analysis (L1-(2D)2PCA). In our network, the
role of L1-(2D)2PCA is to learn the filters of multiple convolution layers.
After the convolution layers, we deploy binary hashing and block-wise histogram
for pooling. We test our network on some benchmark facial datasets YALE, AR,
Extended Yale B, LFW-a and FERET with CNN, PCANet, 2DPCANet and L1-PCANet as
comparison. The results show that the recognition performance of L1-(2D)2PCANet
in all tests is better than baseline networks, especially when there are
outliers in the test data. Owing to the L1-norm, L1-2D2PCANet is robust to
outliers and changes of the training images.
Multi-Label Zero-Shot Learning with Structured Knowledge Graphs
Authors: Chung-Wei Lee, Wei Fang, Chih-Kuan Yeh, Yu-Chiang Frank Wang
Date: 2017-11-17 00:00:00
Proceeding: CVPR 2018 6
Tasks: General Classification, Knowledge Graphs, Multi-Label Classification, MUlTI-LABEL-ClASSIFICATION, Multi-label zero-shot learning, Visual Reasoning, Zero-Shot Learning

In this paper, we propose a novel deep learning architecture for multi-label
zero-shot learning (ML-ZSL), which is able to predict multiple unseen class
labels for each input instance. Inspired by the way humans utilize semantic
knowledge between objects of interests, we propose a framework that
incorporates knowledge graphs for describing the relationships between multiple
labels. Our model learns an information propagation mechanism from the semantic
label space, which can be applied to model the interdependencies between seen
and unseen class labels. With such investigation of structured knowledge graphs
for visual reasoning, we show that our model can be applied for solving
multi-label classification and ML-ZSL tasks. Compared to state-of-the-art
approaches, comparable or improved performances can be achieved by our method.
Accelerating CNN inference on FPGAs: A Survey
Authors: Kamel Abdelouahab, Maxime Pelcat, Jocelyn Serot, François Berry
Date: 2018-05-26 00:00:00
Tasks: Diversity, image-classification, Image Classification, speech-recognition, Speech Recognition, Survey

Convolutional Neural Networks (CNNs) are currently adopted to solve an ever
greater number of problems, ranging from speech recognition to image
classification and segmentation. The large amount of processing required by
CNNs calls for dedicated and tailored hardware support methods. Moreover, CNN
workloads have a streaming nature, well suited to reconfigurable hardware
architectures such as FPGAs. The amount and diversity of research on the
subject of CNN FPGA acceleration within the last 3 years demonstrates the
tremendous industrial and academic interest. This paper presents a
state-of-the-art of CNN inference accelerators over FPGAs. The computational
workloads, their parallelism and the involved memory accesses are analyzed. At
the level of neurons, optimizations of the convolutional and fully connected
layers are explained and the performances of the different methods compared. At
the network level, approximate computing and datapath optimization methods are
covered and state-of-the-art approaches compared. The methods and tools
investigated in this survey represent the recent trends in FPGA CNN inference
accelerators and will fuel the future advances on efficient hardware deep
learning.
Revisiting Reweighted Wake-Sleep for Models with Stochastic Control Flow
Authors: Tuan Anh Le, Adam R. Kosiorek, N. Siddharth, Yee Whye Teh, Frank Wood
Date: 2018-05-26 00:00:00
Proceeding: ICLR 2019 5

Stochastic control-flow models (SCFMs) are a class of generative models that involve branching on choices from discrete random variables. Amortized gradient-based learning of SCFMs is challenging as most approaches targeting discrete variables rely on their continuous relaxations---which can be intractable in SCFMs, as branching on relaxations requires evaluating all (exponentially many) branching paths. Tractable alternatives mainly combine REINFORCE with complex control-variate schemes to improve the variance of naive estimators. Here, we revisit the reweighted wake-sleep (RWS) (Bornschein and Bengio, 2015) algorithm, and through extensive evaluations, show that it outperforms current state-of-the-art methods in learning SCFMs. Further, in contrast to the importance weighted autoencoder, we observe that RWS learns better models and inference networks with increasing numbers of particles. Our results suggest that RWS is a competitive, often preferable, alternative for learning SCFMs.
Universal Hypothesis Testing with Kernels: Asymptotically Optimal Tests for Goodness of Fit
Authors: Shengyu Zhu, Biao Chen, Pengfei Yang, Zhitang Chen
Date: 2018-02-21 00:00:00
Tasks: Two-sample testing

We characterize the asymptotic performance of nonparametric goodness of fit
testing. The exponential decay rate of the type-II error probability is used as
the asymptotic performance metric, and a test is optimal if it achieves the
maximum rate subject to a constant level constraint on the type-I error
probability. We show that two classes of Maximum Mean Discrepancy (MMD) based
tests attain this optimality on $\mathbb R^d$, while the quadratic-time Kernel
Stein Discrepancy (KSD) based tests achieve the maximum exponential decay rate
under a relaxed level constraint. Under the same performance metric, we proceed
to show that the quadratic-time MMD based two-sample tests are also optimal for
general two-sample problems, provided that kernels are bounded continuous and
characteristic. Key to our approach are Sanov's theorem from large deviation
theory and the weak metrizable properties of the MMD and KSD.
SJTU-NLP at SemEval-2018 Task 9: Neural Hypernym Discovery with Term Embeddings
Authors: Zhuosheng Zhang, Jiangtong Li, Hai Zhao, Bingjie Tang
Date: 2018-05-26 00:00:00
Proceeding: SEMEVAL 2018 6
Tasks: Hypernym Discovery

This paper describes a hypernym discovery system for our participation in the
SemEval-2018 Task 9, which aims to discover the best (set of) candidate
hypernyms for input concepts or entities, given the search space of a
pre-defined vocabulary. We introduce a neural network architecture for the
concerned task and empirically study various neural network models to build the
representations in latent space for words and phrases. The evaluated models
include convolutional neural network, long-short term memory network, gated
recurrent unit and recurrent convolutional neural network. We also explore
different embedding methods, including word embedding and sense embedding for
better performance.
From Knowledge Graph Embedding to Ontology Embedding? An Analysis of the Compatibility between Vector Space Representations and Rules
Authors: Víctor Gutiérrez-Basulto, Steven Schockaert
Date: 2018-05-26 00:00:00
Tasks: Graph Embedding, Knowledge Graph Embedding, Knowledge Graphs, Ontology Embedding

Recent years have witnessed the successful application of low-dimensional
vector space representations of knowledge graphs to predict missing facts or
find erroneous ones. However, it is not yet well-understood to what extent
ontological knowledge, e.g. given as a set of (existential) rules, can be
embedded in a principled way. To address this shortcoming, in this paper we
introduce a general framework based on a view of relations as regions, which
allows us to study the compatibility between ontological knowledge and
different types of vector space embeddings. Our technical contribution is
two-fold. First, we show that some of the most popular existing embedding
methods are not capable of modelling even very simple types of rules, which in
particular also means that they are not able to learn the type of dependencies
captured by such rules. Second, we study a model in which relations are
modelled as convex regions. We show particular that ontologies which are
expressed using so-called quasi-chained existential rules can be exactly
represented using convex regions, such that any set of facts which is induced
using that vector space embedding is logically consistent and deductively
closed with respect to the input ontology.
LoST? Appearance-Invariant Place Recognition for Opposite Viewpoints using Visual Semantics
Authors: Sourav Garg, Niko Suenderhauf, Michael Milford
Date: 2018-04-16 00:00:00
Tasks: Navigate, Scene Understanding, Semantic Segmentation, Visual Place Recognition

Human visual scene understanding is so remarkable that we are able to
recognize a revisited place when entering it from the opposite direction it was
first visited, even in the presence of extreme variations in appearance. This
capability is especially apparent during driving: a human driver can recognize
where they are when travelling in the reverse direction along a route for the
first time, without having to turn back and look. The difficulty of this
problem exceeds any addressed in past appearance- and viewpoint-invariant
visual place recognition (VPR) research, in part because large parts of the
scene are not commonly observable from opposite directions. Consequently, as
shown in this paper, the precision-recall performance of current
state-of-the-art viewpoint- and appearance-invariant VPR techniques is orders
of magnitude below what would be usable in a closed-loop system. Current
engineered solutions predominantly rely on panoramic camera or LIDAR sensing
setups; an eminently suitable engineering solution but one that is clearly very
different to how humans navigate, which also has implications for how naturally
humans could interact and communicate with the navigation system. In this paper
we develop a suite of novel semantic- and appearance-based techniques to enable
for the first time high performance place recognition in this challenging
scenario. We first propose a novel Local Semantic Tensor (LoST) descriptor of
images using the convolutional feature maps from a state-of-the-art dense
semantic segmentation network. Then, to verify the spatial semantic arrangement
of the top matching candidates, we develop a novel approach for mining
semantically-salient keypoint correspondences.
Intensive Preprocessing of KDD Cup 99 for Network Intrusion Classification Using Machine Learning Techniques
Authors: Ibrahim Obeidat, Nabhan Hamadneh, Mouhammd Al-kasassbeh, Mohammad Almseidin
Date: 2018-05-26 00:00:00
Tasks: BIG-bench Machine Learning, General Classification, Intrusion Detection

Network security engineers work to keep services available all the time by
handling intruder attacks. Intrusion Detection System (IDS) is one of the
obtainable mechanism that used to sense and classify any abnormal actions.
Therefore, the IDS must be always up to date with the latest intruder attacks
signatures to preserve confidentiality, integrity and availability of the
services. The speed of the IDS is very important issue as well learning the new
attacks. This research work illustrates how the Knowledge Discovery and Data
Mining (or Knowledge Discovery in Databases) KDD dataset is very handy for
testing and evaluating different Machine Learning Techniques. It mainly focuses
on the KDD preprocess part in order to prepare a decent and fair experimental
data set. The techniques J48, Random Forest, Random Tree, MLP, Na\"ive Bayes
and Bayes Network classifiers have been chosen for this study. It has been
proven that the Random forest classifier has achieved the highest accuracy rate
for detecting and classifying all KDD dataset attacks, which are of type (DOS,
R2L, U2R, and PROBE)