pwc-archive/papers-with-abstracts huggingface dataset:
Runtime Analysis of RLS and (1+1) EA for the Dynamic Weighted Vertex Cover Problem
Authors: Mojgan Pourhassan, Vahid Roostapour, Frank Neumann
Date: 2019-03-06 00:00:00

In this paper, we perform theoretical analyses on the behaviour of an
evolutionary algorithm and a randomised search algorithm for the dynamic vertex
cover problem based on its dual formulation. The dynamic vertex cover problem
has already been theoretically investigated to some extent and it has been
shown that using its dual formulation to represent possible solutions can lead
to a better approximation behaviour. We improve some of the existing results,
i.e. we find a linear expected re-optimization time for a (1+1) EA to
re-discover a 2-approximation when edges are dynamically deleted from the
graph. Furthermore, we investigate a different setting for applying the
dynamism to the problem, in which a dynamic change happens at each step with a
probability $P_D$. We also expand these analyses to the weighted vertex cover
problem, in which weights are assigned to vertices and the goal is to find a
cover set with minimum total weight. Similar to the classical case, the dynamic
changes that we consider on the weighted vertex cover problem are adding and
removing edges to and from the graph. We aim at finding a maximal solution for
the dual problem, which gives a 2-approximate solution for the vertex cover
problem. This is equivalent to the maximal matching problem for the classical
vertex cover problem.
Robust Lane Detection from Continuous Driving Scenes Using Deep Neural Networks
Authors: Qin Zou, Hanwen Jiang, Qiyu Dai, Yuanhao Yue, Long Chen, Qian Wang
Date: 2019-03-06 00:00:00
Tasks: Autonomous Vehicles, Lane Detection, Time Series, Time Series Analysis

Lane detection in driving scenes is an important module for autonomous vehicles and advanced driver assistance systems. In recent years, many sophisticated lane detection methods have been proposed. However, most methods focus on detecting the lane from one single image, and often lead to unsatisfactory performance in handling some extremely-bad situations such as heavy shadow, severe mark degradation, serious vehicle occlusion, and so on. In fact, lanes are continuous line structures on the road. Consequently, the lane that cannot be accurately detected in one current frame may potentially be inferred out by incorporating information of previous frames. To this end, we investigate lane detection by using multiple frames of a continuous driving scene, and propose a hybrid deep architecture by combining the convolutional neural network (CNN) and the recurrent neural network (RNN). Specifically, information of each frame is abstracted by a CNN block, and the CNN features of multiple continuous frames, holding the property of time-series, are then fed into the RNN block for feature learning and lane prediction. Extensive experiments on two large-scale datasets demonstrate that, the proposed method outperforms the competing methods in lane detection, especially in handling difficult situations.
Bidirectional Attentive Memory Networks for Question Answering over Knowledge Bases
Authors: Yu Chen, Lingfei Wu, Mohammed J. Zaki
Date: 2019-03-06 00:00:00
Proceeding: NAACL 2019 6
Tasks: Information Retrieval, Knowledge Base Question Answering, Question Answering, Retrieval, Semantic Parsing

When answering natural language questions over knowledge bases (KBs), different question components and KB aspects play different roles. However, most existing embedding-based methods for knowledge base question answering (KBQA) ignore the subtle inter-relationships between the question and the KB (e.g., entity types, relation paths and context). In this work, we propose to directly model the two-way flow of interactions between the questions and the KB via a novel Bidirectional Attentive Memory Network, called BAMnet. Requiring no external resources and only very few hand-crafted features, on the WebQuestions benchmark, our method significantly outperforms existing information-retrieval based methods, and remains competitive with (hand-crafted) semantic parsing based methods. Also, since we use attention mechanisms, our method offers better interpretability compared to other baselines.
Synthesizing Chemical Plant Operation Procedures using Knowledge, Dynamic Simulation and Deep Reinforcement Learning
Authors: Shumpei Kubosawa, Takashi Onishi, Yoshimasa Tsuruoka
Date: 2019-03-06 00:00:00
Tasks: Deep Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL), valid

Chemical plants are complex and dynamical systems consisting of many
components for manipulation and sensing, whose state transitions depend on
various factors such as time, disturbance, and operation procedures. For the
purpose of supporting human operators of chemical plants, we are developing an
AI system that can semi-automatically synthesize operation procedures for
efficient and stable operation. Our system can provide not only appropriate
operation procedures but also reasons why the procedures are considered to be
valid. This is achieved by integrating automated reasoning and deep
reinforcement learning technologies with a chemical plant simulator and
external knowledge. Our preliminary experimental results demonstrate that it
can synthesize a procedure that achieves a much faster recovery from a
malfunction compared to standard PID control.
Graph Neural Networks for User Identity Linkage
Authors: Wen Zhang, Kai Shu, Huan Liu, Yalin Wang
Date: 2019-03-06 00:00:00
Tasks: Graph Neural Network

The increasing popularity and diversity of social media sites has encouraged
more and more people to participate in multiple online social networks to enjoy
their services. Each user may create a user identity to represent his or her
unique public figure in every social network. User identity linkage across
online social networks is an emerging task and has attracted increasing
attention, which could potentially impact various domains such as
recommendations and link predictions. The majority of existing work focuses on
mining network proximity or user profile data for discovering user identity
linkages. With the recent advancements in graph neural networks (GNNs), it
provides great potential to advance user identity linkage since users are
connected in social graphs, and learning latent factors of users and items is
the key. However, predicting user identity linkages based on GNNs faces
challenges. For example, the user social graphs encode both \textit{local}
structure such as users' neighborhood signals, and \textit{global} structure
with community properties. To address these challenges simultaneously, in this
paper, we present a novel graph neural network framework ({\m}) for user
identity linkage. In particular, we provide a principled approach to jointly
capture local and global information in the user-user social graph and propose
the framework {\m}, which jointly learning user representations for user
identity linkage. Extensive experiments on real-world datasets demonstrate the
effectiveness of the proposed framework.
Representative Task Self-selection for Flexible Clustered Lifelong Learning
Authors: Gan Sun, Yang Cong, Qianqian Wang, Bineng Zhong, Yun Fu
Date: 2019-03-06 00:00:00
Tasks: Lifelong learning, Model Optimization, Multi-Task Learning

Consider the lifelong machine learning paradigm whose objective is to learn a sequence of tasks depending on previous experiences, e.g., knowledge library or deep network weights. However, the knowledge libraries or deep networks for most recent lifelong learning models are with prescribed size, and can degenerate the performance for both learned tasks and coming ones when facing with a new task environment (cluster). To address this challenge, we propose a novel incremental clustered lifelong learning framework with two knowledge libraries: feature learning library and model knowledge library, called Flexible Clustered Lifelong Learning (FCL3). Specifically, the feature learning library modeled by an autoencoder architecture maintains a set of representation common across all the observed tasks, and the model knowledge library can be self-selected by identifying and adding new representative models (clusters). When a new task arrives, our proposed FCL3model firstly transfers knowledge from these libraries to encode the new task, i.e.,effectively and selectively soft-assigning this new task to multiple representative models over feature learning library. Then, 1) the new task with a higher outlier probability will be judged as a new representative, and used to redefine both feature learning library and representative models over time; or 2) the new task with lower outlier probability will only refine the feature learning library. For model optimization, we cast this lifelong learning problem as an alternating direction minimization problem as a new task comes. Finally, we evaluate the proposed framework by analyzing several multi-task datasets, and the experimental results demonstrate that our FCL3 model can achieve better performance than most lifelong learning frameworks, even batch clustered multi-task learning models.
AAAI-2019 Workshop on Games and Simulations for Artificial Intelligence
Authors: Marwan Mattar, Roozbeh Mottaghi, Julian Togelius, Danny Lange
Date: 2019-03-06 00:00:00

This volume represents the accepted submissions from the AAAI-2019 Workshop
on Games and Simulations for Artificial Intelligence held on January 29, 2019
in Honolulu, Hawaii, USA. https://www.gamesim.ai
Efficient Multi-Objective Optimization through Population-based Parallel Surrogate Search
Authors: Taimoor Akhtar, Christine A. Shoemaker
Date: 2019-03-06 00:00:00

Multi-Objective Optimization (MOO) is very difficult for expensive functions
because most current MOO methods rely on a large number of function evaluations
to get an accurate solution. We address this problem with surrogate
approximation and parallel computation. We develop an MOO algorithm MOPLS-N for
expensive functions that combines iteratively updated surrogate approximations
of the objective functions with a structure for efficiently selecting a
population of $N$ points so that the expensive objectives for all points are
simultaneously evaluated on $N$ processors in each iteration. MOPLS
incorporates Radial Basis Function (RBF) approximation, Tabu Search and local
candidate search around multiple points to strike a balance between
exploration, exploitation and diversification during each algorithm iteration.
Eleven test problems (with 8 to 24 decision variables and two real-world
watershed problems are used to compare performance of MOPLS to ParEGO, GOMORS,
Borg, MOEA/D, and NSGA-III on a limited budget of evaluations with between 1
(serial) and 64 processors. MOPLS in serial is better than all non-RBF serial
methods tested. Parallel speedup of MOPLS is higher than all other parallel
algorithms with 16 and 64 processors. With both algorithms on 64 processors
MOPLS is at least 2 times faster than NSGA-III on the watershed problems.
Autonomy, Authenticity, Authorship and Intention in computer generated art
Authors: Jon McCormack, Toby Gifford, Patrick Hutchings
Date: 2019-03-06 00:00:00

This paper examines five key questions surrounding computer generated art.
Driven by the recent public auction of a work of `AI Art' we selectively
summarise many decades of research and commentary around topics of autonomy,
authenticity, authorship and intention in computer generated art, and use this
research to answer contemporary questions often asked about art made by
computers that concern these topics. We additionally reflect on whether current
techniques in deep learning and Generative Adversarial Networks significantly
change the answers provided by many decades of prior research.
Camera Obscurer: Generative Art for Design Inspiration
Authors: Dilpreet Singh, Nina Rajcic, Simon Colton, Jon McCormack
Date: 2019-03-06 00:00:00
Tasks: Image Retrieval, Retrieval

We investigate using generated decorative art as a source of inspiration for
design tasks. Using a visual similarity search for image retrieval, the
\emph{Camera Obscurer} app enables rapid searching of tens of thousands of
generated abstract images of various types. The seed for a visual similarity
search is a given image, and the retrieved generated images share some visual
similarity with the seed. Implemented in a hand-held device, the app empowers
users to use photos of their surroundings to search through the archive of
generated images and other image archives. Being abstract in nature, the
retrieved images supplement the seed image rather than replace it, providing
different visual stimuli including shapes, colours, textures and
juxtapositions, in addition to affording their own interpretations. This
approach can therefore be used to provide inspiration for a design task, with
the abstract images suggesting new ideas that might give direction to a graphic
design project. We describe a crowdsourcing experiment with the app to estimate
user confidence in retrieved images, and we describe a pilot study where Camera
Obscurer provided inspiration for a design task. These experiments have enabled
us to describe future improvements, and to begin to understand sources of
visual inspiration for design tasks.
Semi-Supervised Few-Shot Learning with Prototypical Random Walks
Authors: Ahmed Ayyad, Yuchen Li, Nassir Navab, Shadi Albarqouni, Mohamed Elhoseiny
Date: 2019-03-06 00:00:00
Tasks: Few-Shot Learning

Recent progress has shown that few-shot learning can be improved with access to unlabelled data, known as semi-supervised few-shot learning(SS-FSL). We introduce an SS-FSL approach, dubbed as Prototypical Random Walk Networks(PRWN), built on top of Prototypical Networks (PN). We develop a random walk semi-supervised loss that enables the network to learn representations that are compact and well-separated. Our work is related to the very recent development of graph-based approaches for few-shot learning. However, we show that compact and well-separated class representations can be achieved by modeling our prototypical random walk notion without needing additional graph-NN parameters or requiring a transductive setting where a collective test set is provided. Our model outperforms baselines in most benchmarks with significant improvements in some cases. Our model, trained with 40$\%$ of the data as labeled, compares competitively against fully supervised prototypical networks, trained on 100$\%$ of the labels, even outperforming it in the 1-shot mini-Imagenet case with 50.89$\%$ to 49.4$\%$ accuracy. We also show that our loss is resistant to distractors, unlabeled data that does not belong to any of the training classes, and hence reflecting robustness to labeled/unlabeled class distribution mismatch. Associated GitHub page can be found at https://prototypical-random-walk.github.io.
SNU_IDS at SemEval-2019 Task 3: Addressing Training-Test Class Distribution Mismatch in Conversational Classification
Authors: Sanghwan Bae, Jihun Choi, Sang-goo Lee
Date: 2019-03-06 00:00:00
Tasks: General Classification

We present several techniques to tackle the mismatch in class distributions
between training and test data in the Contextual Emotion Detection task of
SemEval 2019, by extending the existing methods for class imbalance problem.
Reducing the distance between the distribution of prediction and ground truth,
they consistently show positive effects on the performance. Also we propose a
novel neural architecture which utilizes representation of overall context as
well as of each utterance. The combination of the methods and the models
achieved micro F1 score of about 0.766 on the final evaluation.
Persona-Aware Tips Generation
Authors: Piji Li, ZiHao Wang, Lidong Bing, Wai Lam
Date: 2019-03-06 00:00:00

Tips, as a compacted and concise form of reviews, were paid less attention by
researchers. In this paper, we investigate the task of tips generation by
considering the `persona' information which captures the intrinsic language
style of the users or the different characteristics of the product items. In
order to exploit the persona information, we propose a framework based on
adversarial variational auto-encoders (aVAE) for persona modeling from the
historical tips and reviews of users and items. The latent variables from aVAE
are regarded as persona embeddings. Besides representing persona using the
latent embeddings, we design a persona memory for storing the persona related
words for users and items. Pointer Network is used to retrieve persona wordings
from the memory when generating tips. Moreover, the persona embeddings are used
as latent factors by a rating prediction component to predict the sentiment of
a user over an item. Finally, the persona embeddings and the sentiment
information are incorporated into a recurrent neural networks based tips
generation component. Extensive experimental results are reported and discussed
to elaborate the peculiarities of our framework.
Semantic Adversarial Network with Multi-scale Pyramid Attention for Video Classification
Authors: De Xie, Cheng Deng, Hao Wang, Chao Li, Dapeng Tao
Date: 2019-03-06 00:00:00
Tasks: Classification, General Classification, Optical Flow Estimation, Video Classification

Two-stream architecture have shown strong performance in video classification
task. The key idea is to learn spatio-temporal features by fusing convolutional
networks spatially and temporally. However, there are some problems within such
architecture. First, it relies on optical flow to model temporal information,
which are often expensive to compute and store. Second, it has limited ability
to capture details and local context information for video data. Third, it
lacks explicit semantic guidance that greatly decrease the classification
performance. In this paper, we proposed a new two-stream based deep framework
for video classification to discover spatial and temporal information only from
RGB frames, moreover, the multi-scale pyramid attention (MPA) layer and the
semantic adversarial learning (SAL) module is introduced and integrated in our
framework. The MPA enables the network capturing global and local feature to
generate a comprehensive representation for video, and the SAL can make this
representation gradually approximate to the real video semantics in an
adversarial manner. Experimental results on two public benchmarks demonstrate
our proposed methods achieves state-of-the-art results on standard video
datasets.
A Priori Estimates of the Population Risk for Residual Networks
Authors: Weinan E, Chao Ma, Qingcan Wang
Date: 2019-03-06 00:00:00

Optimal a priori estimates are derived for the population risk, also known as the generalization error, of a regularized residual network model. An important part of the regularized model is the usage of a new path norm, called the weighted path norm, as the regularization term. The weighted path norm treats the skip connections and the nonlinearities differently so that paths with more nonlinearities are regularized by larger weights. The error estimates are a priori in the sense that the estimates depend only on the target function, not on the parameters obtained in the training process. The estimates are optimal, in a high dimensional setting, in the sense that both the bound for the approximation and estimation errors are comparable to the Monte Carlo error rates. A crucial step in the proof is to establish an optimal bound for the Rademacher complexity of the residual networks. Comparisons are made with existing norm-based generalization error bounds.
Safeguarded Dynamic Label Regression for Generalized Noisy Supervision
Authors: Jiangchao Yao, Ya zhang, Ivor W. Tsang, Jun Sun
Date: 2019-03-06 00:00:00
Tasks: Image Classification, Learning with noisy labels, regression

Learning with noisy labels, which aims to reduce expensive labors on accurate annotations, has become imperative in the Big Data era. Previous noise transition based method has achieved promising results and presented a theoretical guarantee on performance in the case of class-conditional noise. However, this type of approaches critically depend on an accurate pre-estimation of the noise transition, which is usually impractical. Subsequent improvement adapts the pre-estimation along with the training progress via a Softmax layer. However, the parameters in the Softmax layer are highly tweaked for the fragile performance due to the ill-posed stochastic approximation. To address these issues, we propose a Latent Class-Conditional Noise model (LCCN) that naturally embeds the noise transition under a Bayesian framework. By projecting the noise transition into a Dirichlet-distributed space, the learning is constrained on a simplex based on the whole dataset, instead of some ad-hoc parametric space. We then deduce a dynamic label regression method for LCCN to iteratively infer the latent labels, to stochastically train the classifier and to model the noise. Our approach safeguards the bounded update of the noise transition, which avoids previous arbitrarily tuning via a batch of samples. We further generalize LCCN for open-set noisy labels and the semi-supervised setting. We perform extensive experiments with the controllable noise data sets, CIFAR-10 and CIFAR-100, and the agnostic noise data sets, Clothing1M and WebVision17. The experimental results have demonstrated that the proposed model outperforms several state-of-the-art methods.
Coupled CycleGAN: Unsupervised Hashing Network for Cross-Modal Retrieval
Authors: Chao Li, Cheng Deng, Lei Wang, De Xie, Xianglong Liu
Date: 2019-03-06 00:00:00
Tasks: Cross-Modal Retrieval, Retrieval

In recent years, hashing has attracted more and more attention owing to its
superior capacity of low storage cost and high query efficiency in large-scale
cross-modal retrieval. Benefiting from deep leaning, continuously compelling
results in cross-modal retrieval community have been achieved. However,
existing deep cross-modal hashing methods either rely on amounts of labeled
information or have no ability to learn an accuracy correlation between
different modalities. In this paper, we proposed Unsupervised coupled Cycle
generative adversarial Hashing networks (UCH), for cross-modal retrieval, where
outer-cycle network is used to learn powerful common representation, and
inner-cycle network is explained to generate reliable hash codes. Specifically,
our proposed UCH seamlessly couples these two networks with generative
adversarial mechanism, which can be optimized simultaneously to learn
representation and hash codes. Extensive experiments on three popular benchmark
datasets show that the proposed UCH outperforms the state-of-the-art
unsupervised cross-modal hashing methods.
Why Learning of Large-Scale Neural Networks Behaves Like Convex Optimization
Authors: Hui Jiang
Date: 2019-03-06 00:00:00

In this paper, we present some theoretical work to explain why simple gradient descent methods are so successful in solving non-convex optimization problems in learning large-scale neural networks (NN). After introducing a mathematical tool called canonical space, we have proved that the objective functions in learning NNs are convex in the canonical model space. We further elucidate that the gradients between the original NN model space and the canonical space are related by a pointwise linear transformation, which is represented by the so-called disparity matrix. Furthermore, we have proved that gradient descent methods surely converge to a global minimum of zero loss provided that the disparity matrices maintain full rank. If this full-rank condition holds, the learning of NNs behaves in the same way as normal convex optimization. At last, we have shown that the chance to have singular disparity matrices is extremely slim in large NNs. In particular, when over-parameterized NNs are randomly initialized, the gradient decent algorithms converge to a global minimum of zero loss in probability.