AAAI
Collection
Accepted papers for AAAI (AAAI Conference on Artificial Intelligence), one dataset per year. • 10 items • Updated
paper_id string | title string | paper_url string | pdf_url string | authors list | abstract large_string | track string | primary_area string | doi string | volume string | issue string | pages string | abstract_source string | arxiv_id string | arxiv_id_source string |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
10.1609/aaai.v33i01.3301459 | Robust Online Matching with User Arrival Distribution Drift | https://ojs.aaai.org/index.php/AAAI/article/view/3818 | https://ojs.aaai.org/index.php/AAAI/article/download/3818/3696 | [
"Yu-Hang Zhou",
"Chen Liang",
"Nan Li",
"Cheng Yang",
"Shenghuo Zhu",
"Rong Jin"
] | Recently, online matching problems have attracted much attention due to its emerging applications in internet advertising. Most existing online matching methods have adopted either adversarial or stochastic user arrival assumption, while on both of them significant limitation exists. The adversarial model does not expl... | main | AI and the Web | 10.1609/aaai.v33i01.3301459 | 33 | 01 | 459-466 | official | null | null |
10.1609/aaai.v33i01.3301305 | VistaNet: Visual Aspect Attention Network for Multimodal Sentiment Analysis | https://ojs.aaai.org/index.php/AAAI/article/view/3799 | https://ojs.aaai.org/index.php/AAAI/article/download/3799/3677 | [
"Quoc-Tuan Truong",
"Hady W. Lauw"
] | Detecting the sentiment expressed by a document is a key task for many applications, e.g., modeling user preferences, monitoring consumer behaviors, assessing product quality. Traditionally, the sentiment analysis task primarily relies on textual content. Fueled by the rise of mobile phones that are often the only came... | main | AI and the Web | 10.1609/aaai.v33i01.3301305 | 33 | 01 | 305-312 | official | null | null |
10.1609/aaai.v33i01.3301313 | UGSD: User Generated Sentiment Dictionaries from Online Customer Reviews | https://ojs.aaai.org/index.php/AAAI/article/view/3800 | https://ojs.aaai.org/index.php/AAAI/article/download/3800/3678 | [
"Chun-Hsiang Wang",
"Kang-Chun Fan",
"Chuan-Ju Wang",
"Ming-Feng Tsai"
] | Customer reviews on platforms such as TripAdvisor and Amazon provide rich information about the ways that people convey sentiment on certain domains. Given these kinds of user reviews, this paper proposes UGSD, a representation learning framework for constructing domain-specific sentiment dictionaries from online custo... | main | AI and the Web | 10.1609/aaai.v33i01.3301313 | 33 | 01 | 313-320 | official | null | null |
10.1609/aaai.v33i01.3301321 | Community Detection in Social Networks Considering Topic Correlations | https://ojs.aaai.org/index.php/AAAI/article/view/3801 | https://ojs.aaai.org/index.php/AAAI/article/download/3801/3679 | [
"Yingkui Wang",
"Di Jin",
"Katarzyna Musial",
"Jianwu Dang"
] | Network contents including node contents and edge contents can be utilized for community detection in social networks. Thus, the topic of each community can be extracted as its semantic information. A plethora of models integrating topic model and network topologies have been proposed. However, a key problem has not be... | main | AI and the Web | 10.1609/aaai.v33i01.3301321 | 33 | 01 | 321-328 | official | null | null |
10.1609/aaai.v33i01.3301329 | Community Focusing: Yet Another Query-Dependent Community Detection | https://ojs.aaai.org/index.php/AAAI/article/view/3802 | https://ojs.aaai.org/index.php/AAAI/article/download/3802/3680 | [
"Zhuo Wang",
"Weiping Wang",
"Chaokun Wang",
"Xiaoyan Gu",
"Bo Li",
"Dan Meng"
] | As a major kind of query-dependent community detection, community search finds a densely connected subgraph containing a set of query nodes. As density is the major consideration of community search, most methods of community search often find a dense subgraph with many vertices far from the query nodes, which are not ... | main | AI and the Web | 10.1609/aaai.v33i01.3301329 | 33 | 01 | 329-337 | official | null | null |
10.1609/aaai.v33i01.3301338 | Multi-Level Deep Cascade Trees for Conversion Rate Prediction in Recommendation System | https://ojs.aaai.org/index.php/AAAI/article/view/3803 | https://ojs.aaai.org/index.php/AAAI/article/download/3803/3681 | [
"Hong Wen",
"Jing Zhang",
"Quan Lin",
"Keping Yang",
"Pipei Huang"
] | Developing effective and efficient recommendation methods is very challenging for modern e-commerce platforms. Generally speaking, two essential modules named “ClickThrough Rate Prediction” (CTR) and “Conversion Rate Prediction” (CVR) are included, where CVR module is a crucial factor that affects the final purchasing ... | main | AI and the Web | 10.1609/aaai.v33i01.3301338 | 33 | 01 | 338-345 | official | 1805.09484 | title_snapshot |
10.1609/aaai.v33i01.3301346 | Session-Based Recommendation with Graph Neural Networks | https://ojs.aaai.org/index.php/AAAI/article/view/3804 | https://ojs.aaai.org/index.php/AAAI/article/download/3804/3682 | [
"Shu Wu",
"Yuyuan Tang",
"Yanqiao Zhu",
"Liang Wang",
"Xing Xie",
"Tieniu Tan"
] | The problem of session-based recommendation aims to predict user actions based on anonymous sessions. Previous methods model a session as a sequence and estimate user representations besides item representations to make recommendations. Though achieved promising results, they are insufficient to obtain accurate user ve... | main | AI and the Web | 10.1609/aaai.v33i01.3301346 | 33 | 01 | 346-353 | official | 1811.00855 | title_snapshot |
10.1609/aaai.v33i01.3301354 | CISI-net: Explicit Latent Content Inference and Imitated Style Rendering for Image Inpainting | https://ojs.aaai.org/index.php/AAAI/article/view/3805 | https://ojs.aaai.org/index.php/AAAI/article/download/3805/3683 | [
"Jing Xiao",
"Liang Liao",
"Qiegen Liu",
"Ruimin Hu"
] | Convolutional neural networks (CNNs) have presented their potential in filling large missing areas with plausible contents. To address the blurriness issue commonly existing in the CNN-based inpainting, a typical approach is to conduct texture refinement on the initially completed images by replacing the neural patch i... | main | AI and the Web | 10.1609/aaai.v33i01.3301354 | 33 | 01 | 354-362 | official | null | null |
10.1609/aaai.v33i01.3301363 | Structured and Sparse Annotations for Image Emotion Distribution Learning | https://ojs.aaai.org/index.php/AAAI/article/view/3806 | https://ojs.aaai.org/index.php/AAAI/article/download/3806/3684 | [
"Haitao Xiong",
"Hongfu Liu",
"Bineng Zhong",
"Yun Fu"
] | Label distribution learning methods effectively address the label ambiguity problem and have achieved great success in image emotion analysis. However, these methods ignore structured and sparse information naturally contained in the annotations of emotions. For example, emotions can be grouped and ordered due to their... | main | AI and the Web | 10.1609/aaai.v33i01.3301363 | 33 | 01 | 363-370 | official | null | null |
10.1609/aaai.v33i01.3301371 | Multi-Interactive Memory Network for Aspect Based Multimodal Sentiment Analysis | https://ojs.aaai.org/index.php/AAAI/article/view/3807 | https://ojs.aaai.org/index.php/AAAI/article/download/3807/3685 | [
"Nan Xu",
"Wenji Mao",
"Guandan Chen"
] | As a fundamental task of sentiment analysis, aspect-level sentiment analysis aims to identify the sentiment polarity of a specific aspect in the context. Previous work on aspect-level sentiment analysis is text-based. With the prevalence of multimodal user-generated content (e.g. text and image) on the Internet, multim... | main | AI and the Web | 10.1609/aaai.v33i01.3301371 | 33 | 01 | 371-378 | official | null | null |
10.1609/aaai.v33i01.3301379 | Multi-View Information-Theoretic Co-Clustering for Co-Occurrence Data | https://ojs.aaai.org/index.php/AAAI/article/view/3808 | https://ojs.aaai.org/index.php/AAAI/article/download/3808/3686 | [
"Peng Xu",
"Zhaohong Deng",
"Kup-Sze Choi",
"Longbing Cao",
"Shitong Wang"
] | Multi-view clustering has received much attention recently. Most of the existing multi-view clustering methods only focus on one-sided clustering. As the co-occurring data elements involve the counts of sample-feature co-occurrences, it is more efficient to conduct two-sided clustering along the samples and features si... | main | AI and the Web | 10.1609/aaai.v33i01.3301379 | 33 | 01 | 379-386 | official | 1905.10594 | title_snapshot |
10.1609/aaai.v33i01.3301395 | Adversarial Training for Community Question Answer Selection Based on Multi-Scale Matching | https://ojs.aaai.org/index.php/AAAI/article/view/3810 | https://ojs.aaai.org/index.php/AAAI/article/download/3810/3688 | [
"Xiao Yang",
"Madian Khabsa",
"Miaosen Wang",
"Wei Wang",
"Ahmed Hassan Awadallah",
"Daniel Kifer",
"C. Lee Giles"
] | Community-based question answering (CQA) websites represent an important source of information. As a result, the problem of matching the most valuable answers to their corresponding questions has become an increasingly popular research topic. We frame this task as a binary (relevant/irrelevant) classification problem, ... | main | AI and the Web | 10.1609/aaai.v33i01.3301395 | 33 | 01 | 395-402 | official | 1804.08058 | title_snapshot |
10.1609/aaai.v33i01.3301403 | TransNFCM: Translation-Based Neural Fashion Compatibility Modeling | https://ojs.aaai.org/index.php/AAAI/article/view/3811 | https://ojs.aaai.org/index.php/AAAI/article/download/3811/3689 | [
"Xun Yang",
"Yunshan Ma",
"Lizi Liao",
"Meng Wang",
"Tat-Seng Chua"
] | Identifying mix-and-match relationships between fashion items is an urgent task in a fashion e-commerce recommender system. It will significantly enhance user experience and satisfaction. However, due to the challenges of inferring the rich yet complicated set of compatibility patterns in a large e-commerce corpus of f... | main | AI and the Web | 10.1609/aaai.v33i01.3301403 | 33 | 01 | 403-410 | official | 1812.10021 | title_snapshot |
10.1609/aaai.v33i01.3301411 | Data Augmentation Based on Adversarial Autoencoder Handling Imbalance for Learning to Rank | https://ojs.aaai.org/index.php/AAAI/article/view/3812 | https://ojs.aaai.org/index.php/AAAI/article/download/3812/3690 | [
"Qian Yu",
"Wai Lam"
] | Data imbalance is a key limiting factor for Learning to Rank (LTR) models in information retrieval. Resampling methods and ensemble methods cannot handle the imbalance problem well since none of them incorporate more informative data into the training procedure of LTR models. We propose a data generation model based on... | main | AI and the Web | 10.1609/aaai.v33i01.3301411 | 33 | 01 | 411-418 | official | null | null |
10.1609/aaai.v33i01.3301419 | Cross-Relation Cross-Bag Attention for Distantly-Supervised Relation Extraction | https://ojs.aaai.org/index.php/AAAI/article/view/3813 | https://ojs.aaai.org/index.php/AAAI/article/download/3813/3691 | [
"Yujin Yuan",
"Liyuan Liu",
"Siliang Tang",
"Zhongfei Zhang",
"Yueting Zhuang",
"Shiliang Pu",
"Fei Wu",
"Xiang Ren"
] | Distant supervision leverages knowledge bases to automatically label instances, thus allowing us to train relation extractor without human annotations. However, the generated training data typically contain massive noise, and may result in poor performances with the vanilla supervised learning. In this paper, we propos... | main | AI and the Web | 10.1609/aaai.v33i01.3301419 | 33 | 01 | 419-426 | official | 1812.10604 | title_snapshot |
10.1609/aaai.v33i01.3301427 | Text Assisted Insight Ranking Using Context-Aware Memory Network | https://ojs.aaai.org/index.php/AAAI/article/view/3814 | https://ojs.aaai.org/index.php/AAAI/article/download/3814/3692 | [
"Qi Zeng",
"Liangchen Luo",
"Wenhao Huang",
"Yang Tang"
] | Extracting valuable facts or informative summaries from multi-dimensional tables, i.e. insight mining, is an important task in data analysis and business intelligence. However, ranking the importance of insights remains a challenging and unexplored task. The main challenge is that explicitly scoring an insight or givin... | main | AI and the Web | 10.1609/aaai.v33i01.3301427 | 33 | 01 | 427-434 | official | 1811.05563 | title_snapshot |
10.1609/aaai.v33i01.3301435 | Hierarchical Reinforcement Learning for Course Recommendation in MOOCs | https://ojs.aaai.org/index.php/AAAI/article/view/3815 | https://ojs.aaai.org/index.php/AAAI/article/download/3815/3693 | [
"Jing Zhang",
"Bowen Hao",
"Bo Chen",
"Cuiping Li",
"Hong Chen",
"Jimeng Sun"
] | The proliferation of massive open online courses (MOOCs) demands an effective way of personalized course recommendation. The recent attention-based recommendation models can distinguish the effects of different historical courses when recommending different target courses. However, when a user has interests in many dif... | main | AI and the Web | 10.1609/aaai.v33i01.3301435 | 33 | 01 | 435-442 | official | null | null |
10.1609/aaai.v33i01.3301443 | Regularizing Neural Machine Translation by Target-Bidirectional Agreement | https://ojs.aaai.org/index.php/AAAI/article/view/3816 | https://ojs.aaai.org/index.php/AAAI/article/download/3816/3694 | [
"Zhirui Zhang",
"Shuangzhi Wu",
"Shujie Liu",
"Mu Li",
"Ming Zhou",
"Tong Xu"
] | Although Neural Machine Translation (NMT) has achieved remarkable progress in the past several years, most NMT systems still suffer from a fundamental shortcoming as in other sequence generation tasks: errors made early in generation process are fed as inputs to the model and can be quickly amplified, harming subsequen... | main | AI and the Web | 10.1609/aaai.v33i01.3301443 | 33 | 01 | 443-450 | official | 1808.04064 | title_snapshot |
10.1609/aaai.v33i01.3301451 | Addressing the Under-Translation Problem from the Entropy Perspective | https://ojs.aaai.org/index.php/AAAI/article/view/3817 | https://ojs.aaai.org/index.php/AAAI/article/download/3817/3695 | [
"Yang Zhao",
"Jiajun Zhang",
"Chengqing Zong",
"Zhongjun He",
"Hua Wu"
] | Neural Machine Translation (NMT) has drawn much attention due to its promising translation performance in recent years. However, the under-translation problem still remains a big challenge. In this paper, we focus on the under-translation problem and attempt to find out what kinds of source words are more likely to be ... | main | AI and the Web | 10.1609/aaai.v33i01.3301451 | 33 | 01 | 451-458 | official | null | null |
10.1609/aaai.v33i01.3301387 | Context-Aware Self-Attention Networks | https://ojs.aaai.org/index.php/AAAI/article/view/3809 | https://ojs.aaai.org/index.php/AAAI/article/download/3809/3687 | [
"Baosong Yang",
"Jian Li",
"Derek F. Wong",
"Lidia S. Chao",
"Xing Wang",
"Zhaopeng Tu"
] | Self-attention model has shown its flexibility in parallel computation and the effectiveness on modeling both long- and short-term dependencies. However, it calculates the dependencies between representations without considering the contextual information, which has proven useful for modeling dependencies among neural ... | main | AI and the Web | 10.1609/aaai.v33i01.3301387 | 33 | 01 | 387-394 | official | 1902.05766 | title_snapshot |
10.1609/aaai.v33i01.3301241 | Unsupervised Neural Machine Translation with SMT as Posterior Regularization | https://ojs.aaai.org/index.php/AAAI/article/view/3791 | https://ojs.aaai.org/index.php/AAAI/article/download/3791/3669 | [
"Shuo Ren",
"Zhirui Zhang",
"Shujie Liu",
"Ming Zhou",
"Shuai Ma"
] | Without real bilingual corpus available, unsupervised Neural Machine Translation (NMT) typically requires pseudo parallel data generated with the back-translation method for the model training. However, due to weak supervision, the pseudo data inevitably contain noises and errors that will be accumulated and reinforced... | main | AI and the Web | 10.1609/aaai.v33i01.3301241 | 33 | 01 | 241-248 | official | 1901.04112 | title_snapshot |
10.1609/aaai.v33i01.3301249 | Mining Entity Synonyms with Efficient Neural Set Generation | https://ojs.aaai.org/index.php/AAAI/article/view/3792 | https://ojs.aaai.org/index.php/AAAI/article/download/3792/3670 | [
"Jiaming Shen",
"Ruiliang Lyu",
"Xiang Ren",
"Michelle Vanni",
"Brian Sadler",
"Jiawei Han"
] | Mining entity synonym sets (i.e., sets of terms referring to the same entity) is an important task for many entity-leveraging applications. Previous work either rank terms based on their similarity to a given query term, or treats the problem as a two-phase task (i.e., detecting synonymy pairs, followed by organizing t... | main | AI and the Web | 10.1609/aaai.v33i01.3301249 | 33 | 01 | 249-256 | official | 1811.07032 | title_snapshot |
10.1609/aaai.v33i01.3301257 | Surveys without Questions: A Reinforcement Learning Approach | https://ojs.aaai.org/index.php/AAAI/article/view/3793 | https://ojs.aaai.org/index.php/AAAI/article/download/3793/3671 | [
"Atanu R Sinha",
"Deepali Jain",
"Nikhil Sheoran",
"Sopan Khosla",
"Reshmi Sasidharan"
] | The ‘old world’ instrument, survey, remains a tool of choice for firms to obtain ratings of satisfaction and experience that customers realize while interacting online with firms. While avenues for survey have evolved from emails and links to pop-ups while browsing, the deficiencies persist. These include - reliance on... | main | AI and the Web | 10.1609/aaai.v33i01.3301257 | 33 | 01 | 257-264 | official | 2006.06323 | title_snapshot |
10.1609/aaai.v33i01.3301265 | ATP: Directed Graph Embedding with Asymmetric Transitivity Preservation | https://ojs.aaai.org/index.php/AAAI/article/view/3794 | https://ojs.aaai.org/index.php/AAAI/article/download/3794/3672 | [
"Jiankai Sun",
"Bortik Bandyopadhyay",
"Armin Bashizade",
"Jiongqian Liang",
"P. Sadayappan",
"Srinivasan Parthasarathy"
] | Directed graphs have been widely used in Community Question Answering services (CQAs) to model asymmetric relationships among different types of nodes in CQA graphs, e.g., question, answer, user. Asymmetric transitivity is an essential property of directed graphs, since it can play an important role in downstream graph... | main | AI and the Web | 10.1609/aaai.v33i01.3301265 | 33 | 01 | 265-272 | official | 1811.00839 | title_snapshot |
10.1609/aaai.v33i01.3301273 | Learning from Web Data Using Adversarial Discriminative Neural Networks for Fine-Grained Classification | https://ojs.aaai.org/index.php/AAAI/article/view/3795 | https://ojs.aaai.org/index.php/AAAI/article/download/3795/3673 | [
"Xiaoxiao Sun",
"Liyi Chen",
"Jufeng Yang"
] | Fine-grained classification is absorbed in recognizing the subordinate categories of one field, which need a large number of labeled images, while it is expensive to label these images. Utilizing web data has been an attractive option to meet the demands of training data for convolutional neural networks (CNNs), especi... | main | AI and the Web | 10.1609/aaai.v33i01.3301273 | 33 | 01 | 273-280 | official | null | null |
10.1609/aaai.v33i01.3301281 | Meimei: An Efficient Probabilistic Approach for Semantically Annotating Tables | https://ojs.aaai.org/index.php/AAAI/article/view/3796 | https://ojs.aaai.org/index.php/AAAI/article/download/3796/3674 | [
"Kunihiro Takeoka",
"Masafumi Oyamada",
"Shinji Nakadai",
"Takeshi Okadome"
] | Given a large amount of table data, how can we find the tables that contain the contents we want? A naive search fails when the column names are ambiguous, such as if columns containing stock price information are named “Close” in one table and named “P” in another table.One way of dealing with this problem that has be... | main | AI and the Web | 10.1609/aaai.v33i01.3301281 | 33 | 01 | 281-288 | official | null | null |
10.1609/aaai.v33i01.3301289 | DeepTileBars: Visualizing Term Distribution for Neural Information Retrieval | https://ojs.aaai.org/index.php/AAAI/article/view/3797 | https://ojs.aaai.org/index.php/AAAI/article/download/3797/3675 | [
"Zhiwen Tang",
"Grace Hui Yang"
] | Most neural Information Retrieval (Neu-IR) models derive query-to-document ranking scores based on term-level matching. Inspired by TileBars, a classical term distribution visualization method, in this paper, we propose a novel Neu-IR model that handles query-to-document matching at the subtopic and higher levels. Our ... | main | AI and the Web | 10.1609/aaai.v33i01.3301289 | 33 | 01 | 289-296 | official | 1811.00606 | title_snapshot |
10.1609/aaai.v33i01.3301297 | Entity Alignment between Knowledge Graphs Using Attribute Embeddings | https://ojs.aaai.org/index.php/AAAI/article/view/3798 | https://ojs.aaai.org/index.php/AAAI/article/download/3798/3676 | [
"Bayu Distiawan Trisedya",
"Jianzhong Qi",
"Rui Zhang"
] | The task of entity alignment between knowledge graphs aims to find entities in two knowledge graphs that represent the same real-world entity. Recently, embedding-based models are proposed for this task. Such models are built on top of a knowledge graph embedding model that learns entity embeddings to capture the seman... | main | AI and the Web | 10.1609/aaai.v33i01.3301297 | 33 | 01 | 297-304 | official | null | null |
10.1609/aaai.v33i01.3301232 | Multi-Perspective Relevance Matching with Hierarchical ConvNets for Social Media Search | https://ojs.aaai.org/index.php/AAAI/article/view/3790 | https://ojs.aaai.org/index.php/AAAI/article/download/3790/3668 | [
"Jinfeng Rao",
"Wei Yang",
"Yuhao Zhang",
"Ferhan Ture",
"Jimmy Lin"
] | Despite substantial interest in applications of neural networks to information retrieval, neural ranking models have mostly been applied to “standard” ad hoc retrieval tasks over web pages and newswire articles. This paper proposes MP-HCNN (Multi-Perspective Hierarchical Convolutional Neural Network), a novel neural ra... | main | AI and the Web | 10.1609/aaai.v33i01.3301232 | 33 | 01 | 232-240 | official | 1805.08159 | title_snapshot |
10.1609/aaai.v33i01.3301224 | DTMT: A Novel Deep Transition Architecture for Neural Machine Translation | https://ojs.aaai.org/index.php/AAAI/article/view/3789 | https://ojs.aaai.org/index.php/AAAI/article/download/3789/3667 | [
"Fandong Meng",
"Jinchao Zhang"
] | Past years have witnessed rapid developments in Neural Machine Translation (NMT). Most recently, with advanced modeling and training techniques, the RNN-based NMT (RNMT) has shown its potential strength, even compared with the well-known Transformer (self-attentional) model. Although the RNMT model can possess very dee... | main | AI and the Web | 10.1609/aaai.v33i01.3301224 | 33 | 01 | 224-231 | official | 1812.07807 | title_snapshot |
10.1609/aaai.v33i01.3301142 | Exploiting Background Knowledge in Compact Answer Generation for Why-Questions | https://ojs.aaai.org/index.php/AAAI/article/view/3779 | https://ojs.aaai.org/index.php/AAAI/article/download/3779/3657 | [
"Ryu Iida",
"Canasai Kruengkrai",
"Ryo Ishida",
"Kentaro Torisawa",
"Jong-Hoon Oh",
"Julien Kloetzer"
] | This paper proposes a novel method for generating compact answers to open-domain why-questions, such as the following answer, “Because deep learning technologies were introduced,” to the question, “Why did Google’s machine translation service improve so drastically?” Although many works have dealt with why-question ans... | main | AI and the Web | 10.1609/aaai.v33i01.3301142 | 33 | 01 | 142-151 | official | null | null |
10.1609/aaai.v33i01.3301152 | Graph Convolutional Networks Meet Markov Random Fields: Semi-Supervised Community Detection in Attribute Networks | https://ojs.aaai.org/index.php/AAAI/article/view/3780 | https://ojs.aaai.org/index.php/AAAI/article/download/3780/3658 | [
"Di Jin",
"Ziyang Liu",
"Weihao Li",
"Dongxiao He",
"Weixiong Zhang"
] | Community detection is a fundamental problem in network science with various applications. The problem has attracted much attention and many approaches have been proposed. Among the existing approaches are the latest methods based on Graph Convolutional Networks (GCN) and on statistical modeling of Markov Random Fields... | main | AI and the Web | 10.1609/aaai.v33i01.3301152 | 33 | 01 | 152-159 | official | null | null |
10.1609/aaai.v33i01.3301160 | Incorporating Network Embedding into Markov Random Field for Better Community Detection | https://ojs.aaai.org/index.php/AAAI/article/view/3781 | https://ojs.aaai.org/index.php/AAAI/article/download/3781/3659 | [
"Di Jin",
"Xinxin You",
"Weihao Li",
"Dongxiao He",
"Peng Cui",
"Françoise Fogelman-Soulié",
"Tanmoy Chakraborty"
] | Recent research on community detection focuses on learning representations of nodes using different network embedding methods, and then feeding them as normal features to clustering algorithms. However, we find that though one may have good results by direct clustering based on such network embedding features, there is... | main | AI and the Web | 10.1609/aaai.v33i01.3301160 | 33 | 01 | 160-167 | official | null | null |
10.1609/aaai.v33i01.3301168 | Crawling the Community Structure of Multiplex Networks | https://ojs.aaai.org/index.php/AAAI/article/view/3782 | https://ojs.aaai.org/index.php/AAAI/article/download/3782/3660 | [
"Ricky Laishram",
"Jeremy D. Wendt",
"Sucheta Soundarajan"
] | We examine the problem of crawling the community structure of a multiplex network containing multiple layers of edge relationships. While there has been a great deal of work examining community structure in general, and some work on the problem of sampling a network to preserve its community structure, to the best of o... | main | AI and the Web | 10.1609/aaai.v33i01.3301168 | 33 | 01 | 168-175 | official | null | null |
10.1609/aaai.v33i01.3301176 | Coupled CycleGAN: Unsupervised Hashing Network for Cross-Modal Retrieval | https://ojs.aaai.org/index.php/AAAI/article/view/3783 | https://ojs.aaai.org/index.php/AAAI/article/download/3783/3661 | [
"Chao Li",
"Cheng Deng",
"Lei Wang",
"De Xie",
"Xianglong Liu"
] | 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 cr... | main | AI and the Web | 10.1609/aaai.v33i01.3301176 | 33 | 01 | 176-183 | official | 1903.02149 | title_snapshot |
10.1609/aaai.v33i01.3301184 | Supervised User Ranking in Signed Social Networks | https://ojs.aaai.org/index.php/AAAI/article/view/3784 | https://ojs.aaai.org/index.php/AAAI/article/download/3784/3662 | [
"Xiaoming Li",
"Hui Fang",
"Jie Zhang"
] | The task of user ranking in signed networks, aiming to predict potential friends and enemies for each user, has attracted increasing attention in numerous applications. Existing approaches are mainly extended from heuristics of the traditional models in unsigned networks. They suffer from two limitations: (1) mainly fo... | main | AI and the Web | 10.1609/aaai.v33i01.3301184 | 33 | 01 | 184-191 | official | null | null |
10.1609/aaai.v33i01.3301192 | Personalized Question Routing via Heterogeneous Network Embedding | https://ojs.aaai.org/index.php/AAAI/article/view/3785 | https://ojs.aaai.org/index.php/AAAI/article/download/3785/3663 | [
"Zeyu Li",
"Jyun-Yu Jiang",
"Yizhou Sun",
"Wei Wang"
] | Question Routing (QR) on Community-based Question Answering (CQA) websites aims at recommending answerers that have high probabilities of providing the “accepted answers” to new questions. The existing question routing algorithms simply predict the ranking of users based on query content. As a consequence, the question... | main | AI and the Web | 10.1609/aaai.v33i01.3301192 | 33 | 01 | 192-199 | official | null | null |
10.1609/aaai.v33i01.3301200 | Popularity Prediction on Online Articles with Deep Fusion of Temporal Process and Content Features | https://ojs.aaai.org/index.php/AAAI/article/view/3786 | https://ojs.aaai.org/index.php/AAAI/article/download/3786/3664 | [
"Dongliang Liao",
"Jin Xu",
"Gongfu Li",
"Weijie Huang",
"Weiqing Liu",
"Jing Li"
] | Predicting the popularity of online article sheds light to many applications such as recommendation, advertising and information retrieval. However, there are several technical challenges to be addressed for developing the best of predictive capability. (1) The popularity fluctuates under impacts of external factors, w... | main | AI and the Web | 10.1609/aaai.v33i01.3301200 | 33 | 01 | 200-207 | official | null | null |
10.1609/aaai.v33i01.3301208 | Discrete Social Recommendation | https://ojs.aaai.org/index.php/AAAI/article/view/3787 | https://ojs.aaai.org/index.php/AAAI/article/download/3787/3665 | [
"Chenghao Liu",
"Xin Wang",
"Tao Lu",
"Wenwu Zhu",
"Jianling Sun",
"Steven Hoi"
] | Social recommendation, which aims at improving the performance of traditional recommender systems by considering social information, has attracted broad range of interests. As one of the most widely used methods, matrix factorization typically uses continuous vectors to represent user/item latent features. However, the... | main | AI and the Web | 10.1609/aaai.v33i01.3301208 | 33 | 01 | 208-215 | official | null | null |
10.1609/aaai.v33i01.3301216 | SNR: Sub-Network Routing for Flexible Parameter Sharing in Multi-Task Learning | https://ojs.aaai.org/index.php/AAAI/article/view/3788 | https://ojs.aaai.org/index.php/AAAI/article/download/3788/3666 | [
"Jiaqi Ma",
"Zhe Zhao",
"Jilin Chen",
"Ang Li",
"Lichan Hong",
"Ed H. Chi"
] | Machine learning applications, such as object detection and content recommendation, often require training a single model to predict multiple targets at the same time. Multi-task learning through neural networks became popular recently, because it not only helps improve the accuracy of many prediction tasks when they a... | main | AI and the Web | 10.1609/aaai.v33i01.3301216 | 33 | 01 | 216-223 | official | null | null |
10.1609/aaai.v33i01.330112 | Outlier Aware Network Embedding for Attributed Networks | https://ojs.aaai.org/index.php/AAAI/article/view/3763 | https://ojs.aaai.org/index.php/AAAI/article/download/3763/3641 | [
"Sambaran Bandyopadhyay",
"N. Lokesh",
"M. N. Murty"
] | Attributed network embedding has received much interest from the research community as most of the networks come with some content in each node, which is also known as node attributes. Existing attributed network approaches work well when the network is consistent in structure and attributes, and nodes behave as expect... | main | AI and the Web | 10.1609/aaai.v33i01.330112 | 33 | 01 | 12-19 | official | 1811.07609 | title_snapshot |
10.1609/aaai.v33i01.33013 | Incorporating Behavioral Constraints in Online AI Systems | https://ojs.aaai.org/index.php/AAAI/article/view/3762 | https://ojs.aaai.org/index.php/AAAI/article/download/3762/3640 | [
"Avinash Balakrishnan",
"Djallel Bouneffouf",
"Nicholas Mattei",
"Francesca Rossi"
] | AI systems that learn through reward feedback about the actions they take are increasingly deployed in domains that have significant impact on our daily life. However, in many cases the online rewards should not be the only guiding criteria, as there are additional constraints and/or priorities imposed by regulations, ... | main | AI and the Web | 10.1609/aaai.v33i01.33013 | 33 | 01 | 3-11 | official | 1809.05720 | title_snapshot |
10.1609/aaai.v33i01.330120 | Comparative Document Summarisation via Classification | https://ojs.aaai.org/index.php/AAAI/article/view/3764 | https://ojs.aaai.org/index.php/AAAI/article/download/3764/3642 | [
"Umanga Bista",
"Alexander Mathews",
"Minjeong Shin",
"Aditya Krishna Menon",
"Lexing Xie"
] | Thispaperconsidersextractivesummarisationinacomparative setting: given two or more document groups (e.g., separated by publication time), the goal is to select a small number of documents that are representative of each group, and also maximally distinguishable from other groups. We formulate a set of new objective fun... | main | AI and the Web | 10.1609/aaai.v33i01.330120 | 33 | 01 | 20-28 | official | 1812.02171 | title_snapshot |
10.1609/aaai.v33i01.330129 | ColNet: Embedding the Semantics of Web Tables for Column Type Prediction | https://ojs.aaai.org/index.php/AAAI/article/view/3765 | https://ojs.aaai.org/index.php/AAAI/article/download/3765/3643 | [
"Jiaoyan Chen",
"Ernesto Jiménez-Ruiz",
"Ian Horrocks",
"Charles Sutton"
] | Automatically annotating column types with knowledge base (KB) concepts is a critical task to gain a basic understanding of web tables. Current methods rely on either table metadata like column name or entity correspondences of cells in the KB, and may fail to deal with growing web tables with incomplete meta informati... | main | AI and the Web | 10.1609/aaai.v33i01.330129 | 33 | 01 | 29-36 | official | 1811.01304 | title_snapshot |
10.1609/aaai.v33i01.330137 | Improving One-Class Collaborative Filtering via Ranking-Based Implicit Regularizer | https://ojs.aaai.org/index.php/AAAI/article/view/3766 | https://ojs.aaai.org/index.php/AAAI/article/download/3766/3644 | [
"Jin Chen",
"Defu Lian",
"Kai Zheng"
] | One-class collaborative filtering (OCCF) problems are vital in many applications of recommender systems, such as news and music recommendation, but suffers from sparsity issues and lacks negative examples. To address this problem, the state-of-the-arts assigned smaller weights to unobserved samples and performed low-ra... | main | AI and the Web | 10.1609/aaai.v33i01.330137 | 33 | 01 | 37-44 | official | null | null |
10.1609/aaai.v33i01.330145 | Answer Identification from Product Reviews for User Questions by Multi-Task Attentive Networks | https://ojs.aaai.org/index.php/AAAI/article/view/3767 | https://ojs.aaai.org/index.php/AAAI/article/download/3767/3645 | [
"Long Chen",
"Ziyu Guan",
"Wei Zhao",
"Wanqing Zhao",
"Xiaopeng Wang",
"Zhou Zhao",
"Huan Sun"
] | Online Shopping has become a part of our daily routine, but it still cannot offer intuitive experience as store shopping. Nowadays, most e-commerce Websites offer a Question Answering (QA) system that allows users to consult other users who have purchased the product. However, users still need to wait patiently for oth... | main | AI and the Web | 10.1609/aaai.v33i01.330145 | 33 | 01 | 45-52 | official | null | null |
10.1609/aaai.v33i01.330153 | Dynamic Explainable Recommendation Based on Neural Attentive Models | https://ojs.aaai.org/index.php/AAAI/article/view/3768 | https://ojs.aaai.org/index.php/AAAI/article/download/3768/3646 | [
"Xu Chen",
"Yongfeng Zhang",
"Zheng Qin"
] | Providing explanations in a recommender system is getting more and more attention in both industry and research communities. Most existing explainable recommender models regard user preferences as invariant to generate static explanations. However, in real scenarios, a user’s preference is always dynamic, and she may b... | main | AI and the Web | 10.1609/aaai.v33i01.330153 | 33 | 01 | 53-60 | official | null | null |
10.1609/aaai.v33i01.330161 | DeepCF: A Unified Framework of Representation Learning and Matching Function Learning in Recommender System | https://ojs.aaai.org/index.php/AAAI/article/view/3769 | https://ojs.aaai.org/index.php/AAAI/article/download/3769/3647 | [
"Zhi-Hong Deng",
"Ling Huang",
"Chang-Dong Wang",
"Jian-Huang Lai",
"Philip S. Yu"
] | In general, recommendation can be viewed as a matching problem, i.e., match proper items for proper users. However, due to the huge semantic gap between users and items, it’s almost impossible to directly match users and items in their initial representation spaces. To solve this problem, many methods have been studied... | main | AI and the Web | 10.1609/aaai.v33i01.330161 | 33 | 01 | 61-68 | official | 1901.04704 | title_snapshot |
10.1609/aaai.v33i01.330169 | TableSense: Spreadsheet Table Detection with Convolutional Neural Networks | https://ojs.aaai.org/index.php/AAAI/article/view/3770 | https://ojs.aaai.org/index.php/AAAI/article/download/3770/3648 | [
"Haoyu Dong",
"Shijie Liu",
"Shi Han",
"Zhouyu Fu",
"Dongmei Zhang"
] | Spreadsheet table detection is the task of detecting all tables on a given sheet and locating their respective ranges. Automatic table detection is a key enabling technique and an initial step in spreadsheet data intelligence. However, the detection task is challenged by the diversity of table structures and table layo... | main | AI and the Web | 10.1609/aaai.v33i01.330169 | 33 | 01 | 69-76 | official | 2106.13500 | title_snapshot |
10.1609/aaai.v33i01.330177 | Triple Classification Using Regions and Fine-Grained Entity Typing | https://ojs.aaai.org/index.php/AAAI/article/view/3771 | https://ojs.aaai.org/index.php/AAAI/article/download/3771/3649 | [
"Tiansi Dong",
"Zhigang Wang",
"Juanzi Li",
"Christian Bauckhage",
"Armin B. Cremers"
] | A Triple in knowledge-graph takes a form that consists of head, relation, tail. Triple Classification is used to determine the truth value of an unknown Triple. This is a hard task for 1-to-N relations using the vector-based embedding approach. We propose a new region-based embedding approach using fine-grained type ch... | main | AI and the Web | 10.1609/aaai.v33i01.330177 | 33 | 01 | 77-85 | official | null | null |
10.1609/aaai.v33i01.330186 | Dynamic Layer Aggregation for Neural Machine Translation with Routing-by-Agreement | https://ojs.aaai.org/index.php/AAAI/article/view/3772 | https://ojs.aaai.org/index.php/AAAI/article/download/3772/3650 | [
"Zi-Yi Dou",
"Zhaopeng Tu",
"Xing Wang",
"Longyue Wang",
"Shuming Shi",
"Tong Zhang"
] | With the promising progress of deep neural networks, layer aggregation has been used to fuse information across layers in various fields, such as computer vision and machine translation. However, most of the previous methods combine layers in a static fashion in that their aggregation strategy is independent of specifi... | main | AI and the Web | 10.1609/aaai.v33i01.330186 | 33 | 01 | 86-93 | official | 1902.05770 | title_snapshot |
10.1609/aaai.v33i01.330194 | Deeply Fusing Reviews and Contents for Cold Start Users in Cross-Domain Recommendation Systems | https://ojs.aaai.org/index.php/AAAI/article/view/3773 | https://ojs.aaai.org/index.php/AAAI/article/download/3773/3651 | [
"Wenjing Fu",
"Zhaohui Peng",
"Senzhang Wang",
"Yang Xu",
"Jin Li"
] | As one promising way to solve the challenging issues of data sparsity and cold start in recommender systems, crossdomain recommendation has gained increasing research interest recently. Cross-domain recommendation aims to improve the recommendation performance by means of transferring explicit or implicit feedback from... | main | AI and the Web | 10.1609/aaai.v33i01.330194 | 33 | 01 | 94-101 | official | null | null |
10.1609/aaai.v33i01.3301102 | Feature Sampling Based Unsupervised Semantic Clustering for Real Web Multi-View Content | https://ojs.aaai.org/index.php/AAAI/article/view/3774 | https://ojs.aaai.org/index.php/AAAI/article/download/3774/3652 | [
"Xiaolong Gong",
"Linpeng Huang",
"Fuwei Wang"
] | Real web datasets are often associated with multiple views such as long and short commentaries, users preference and so on. However, with the rapid growth of user generated texts, each view of the dataset has a large feature space and leads to the computational challenge during matrix decomposition process. In this pap... | main | AI and the Web | 10.1609/aaai.v33i01.3301102 | 33 | 01 | 102-109 | official | null | null |
10.1609/aaai.v33i01.3301110 | Cooperative Multimodal Approach to Depression Detection in Twitter | https://ojs.aaai.org/index.php/AAAI/article/view/3775 | https://ojs.aaai.org/index.php/AAAI/article/download/3775/3653 | [
"Tao Gui",
"Liang Zhu",
"Qi Zhang",
"Minlong Peng",
"Xu Zhou",
"Keyu Ding",
"Zhigang Chen"
] | The advent of social media has presented a promising new opportunity for the early detection of depression. To do so effectively, there are two challenges to overcome. The first is that textual and visual information must be jointly considered to make accurate inferences about depression. The second challenge is that d... | main | AI and the Web | 10.1609/aaai.v33i01.3301110 | 33 | 01 | 110-117 | official | null | null |
10.1609/aaai.v33i01.3301118 | Anchors Bring Ease: An Embarrassingly Simple Approach to Partial Multi-View Clustering | https://ojs.aaai.org/index.php/AAAI/article/view/3776 | https://ojs.aaai.org/index.php/AAAI/article/download/3776/3654 | [
"Jun Guo",
"Jiahui Ye"
] | Clustering on multi-view data has attracted much more attention in the past decades. Most previous studies assume that each instance appears in all views, or there is at least one view containing all instances. However, real world data often suffers from missing some instances in each view, leading to the research prob... | main | AI and the Web | 10.1609/aaai.v33i01.3301118 | 33 | 01 | 118-125 | official | null | null |
10.1609/aaai.v33i01.3301126 | Y2Seq2Seq: Cross-Modal Representation Learning for 3D Shape and Text by Joint Reconstruction and Prediction of View and Word Sequences | https://ojs.aaai.org/index.php/AAAI/article/view/3777 | https://ojs.aaai.org/index.php/AAAI/article/download/3777/3655 | [
"Zhizhong Han",
"Mingyang Shang",
"Xiyang Wang",
"Yu-Shen Liu",
"Matthias Zwicker"
] | Jointly learning representations of 3D shapes and text is crucial to support tasks such as cross-modal retrieval or shape captioning. A recent method employs 3D voxels to represent 3D shapes, but this limits the approach to low resolutions due to the computational cost caused by the cubic complexity of 3D voxels. Hence... | main | AI and the Web | 10.1609/aaai.v33i01.3301126 | 33 | 01 | 126-133 | official | 1811.02745 | title_judge |
10.1609/aaai.v33i01.3301134 | Learning to Align Question and Answer Utterances in Customer Service Conversation with Recurrent Pointer Networks | https://ojs.aaai.org/index.php/AAAI/article/view/3778 | https://ojs.aaai.org/index.php/AAAI/article/download/3778/3656 | [
"Shizhu He",
"Kang Liu",
"Weiting An"
] | Customers ask questions, and customer service staffs answer those questions. It is the basic service manner of customer service (CS). The progress of CS is a typical multi-round conversation. However, there are no explicit corresponding relations among conversational utterances. This paper focuses on obtaining explicit... | main | AI and the Web | 10.1609/aaai.v33i01.3301134 | 33 | 01 | 134-141 | official | null | null |
10.1609/aaai.v33i01.33011294 | DeepDPM: Dynamic Population Mapping via Deep Neural Network | https://ojs.aaai.org/index.php/AAAI/article/view/3925 | https://ojs.aaai.org/index.php/AAAI/article/download/3925/3803 | [
"Zefang Zong",
"Jie Feng",
"Kechun Liu",
"Hongzhi Shi",
"Yong Li"
] | Dynamic high resolution data on human population distribution is of great importance for a wide spectrum of activities and real-life applications, but is too difficult and expensive to obtain directly. Therefore, generating fine-scaled population distributions from coarse population data is of great significance. Howev... | main | Applications | 10.1609/aaai.v33i01.33011294 | 33 | 01 | 1294-1301 | official | 1811.02644 | title_snapshot |
10.1609/aaai.v33i01.33011286 | One-Class Adversarial Nets for Fraud Detection | https://ojs.aaai.org/index.php/AAAI/article/view/3924 | https://ojs.aaai.org/index.php/AAAI/article/download/3924/3802 | [
"Panpan Zheng",
"Shuhan Yuan",
"Xintao Wu",
"Jun Li",
"Aidong Lu"
] | Many online applications, such as online social networks or knowledge bases, are often attacked by malicious users who commit different types of actions such as vandalism on Wikipedia or fraudulent reviews on eBay. Currently, most of the fraud detection approaches require a training dataset that contains records of bot... | main | Applications | 10.1609/aaai.v33i01.33011286 | 33 | 01 | 1286-1293 | official | 1803.01798 | title_snapshot |
10.1609/aaai.v33i01.33011278 | SAFE: A Neural Survival Analysis Model for Fraud Early Detection | https://ojs.aaai.org/index.php/AAAI/article/view/3923 | https://ojs.aaai.org/index.php/AAAI/article/download/3923/3801 | [
"Panpan Zheng",
"Shuhan Yuan",
"Xintao Wu"
] | Many online platforms have deployed anti-fraud systems to detect and prevent fraudulent activities. However, there is usually a gap between the time that a user commits a fraudulent action and the time that the user is suspended by the platform. How to detect fraudsters in time is a challenging problem. Most of the exi... | main | Applications | 10.1609/aaai.v33i01.33011278 | 33 | 01 | 1278-1285 | official | 1809.04683 | title_snapshot |
10.1609/aaai.v33i01.33011190 | Private Model Compression via Knowledge Distillation | https://ojs.aaai.org/index.php/AAAI/article/view/3913 | https://ojs.aaai.org/index.php/AAAI/article/download/3913/3791 | [
"Ji Wang",
"Weidong Bao",
"Lichao Sun",
"Xiaomin Zhu",
"Bokai Cao",
"Philip S. Yu"
] | The soaring demand for intelligent mobile applications calls for deploying powerful deep neural networks (DNNs) on mobile devices. However, the outstanding performance of DNNs notoriously relies on increasingly complex models, which in turn is associated with an increase in computational expense far surpassing mobile d... | main | Applications | 10.1609/aaai.v33i01.33011190 | 33 | 01 | 1190-1197 | official | 1811.05072 | title_snapshot |
10.1609/aaai.v33i01.33011118 | PhoneMD: Learning to Diagnose Parkinson’s Disease from Smartphone Data | https://ojs.aaai.org/index.php/AAAI/article/view/3904 | https://ojs.aaai.org/index.php/AAAI/article/download/3904/3782 | [
"Patrick Schwab",
"Walter Karlen"
] | Parkinson’s disease is a neurodegenerative disease that can affect a person’s movement, speech, dexterity, and cognition. Clinicians primarily diagnose Parkinson’s disease by performing a clinical assessment of symptoms. However, misdiagnoses are common. One factor that contributes to misdiagnoses is that the symptoms ... | main | Applications | 10.1609/aaai.v33i01.33011118 | 33 | 01 | 1118-1125 | official | 1810.01485 | title_snapshot |
10.1609/aaai.v33i01.33011126 | GAMENet: Graph Augmented MEmory Networks for Recommending Medication Combination | https://ojs.aaai.org/index.php/AAAI/article/view/3905 | https://ojs.aaai.org/index.php/AAAI/article/download/3905/3783 | [
"Junyuan Shang",
"Cao Xiao",
"Tengfei Ma",
"Hongyan Li",
"Jimeng Sun"
] | Recent progress in deep learning is revolutionizing the healthcare domain including providing solutions to medication recommendations, especially recommending medication combination for patients with complex health conditions. Existing approaches either do not customize based on patient health history, or ignore existi... | main | Applications | 10.1609/aaai.v33i01.33011126 | 33 | 01 | 1126-1133 | official | 1809.01852 | title_snapshot |
10.1609/aaai.v33i01.33011134 | The Kelly Growth Optimal Portfolio with Ensemble Learning | https://ojs.aaai.org/index.php/AAAI/article/view/3906 | https://ojs.aaai.org/index.php/AAAI/article/download/3906/3784 | [
"Weiwei Shen",
"Bin Wang",
"Jian Pu",
"Jun Wang"
] | As a competitive alternative to the Markowitz mean-variance portfolio, the Kelly growth optimal portfolio has drawn sufficient attention in investment science. While the growth optimal portfolio is theoretically guaranteed to dominate any other portfolio with probability 1 in the long run, it practically tends to be hi... | main | Applications | 10.1609/aaai.v33i01.33011134 | 33 | 01 | 1134-1141 | official | null | null |
10.1609/aaai.v33i01.33011142 | Spatiality Preservable Factored Poisson Regression for Large-Scale Fine-Grained GPS-Based Population Analysis | https://ojs.aaai.org/index.php/AAAI/article/view/3907 | https://ojs.aaai.org/index.php/AAAI/article/download/3907/3785 | [
"Masamichi Shimosaka",
"Yuta Hayakawa",
"Kota Tsubouchi"
] | With the wide use of smartphones with Global Positioning System (GPS) sensors, the analysis of the population from GPS traces has been actively explored in the last decade. We propose herein a brand new population prediction model to capture the population trends in a fine-grained point of interest (POI) densely distri... | main | Applications | 10.1609/aaai.v33i01.33011142 | 33 | 01 | 1142-1149 | official | null | null |
10.1609/aaai.v33i01.33011150 | Subtask Gated Networks for Non-Intrusive Load Monitoring | https://ojs.aaai.org/index.php/AAAI/article/view/3908 | https://ojs.aaai.org/index.php/AAAI/article/download/3908/3786 | [
"Changho Shin",
"Sunghwan Joo",
"Jaeryun Yim",
"Hyoseop Lee",
"Taesup Moon",
"Wonjong Rhee"
] | Non-intrusive load monitoring (NILM), also known as energy disaggregation, is a blind source separation problem where a household’s aggregate electricity consumption is broken down into electricity usages of individual appliances. In this way, the cost and trouble of installing many measurement devices over numerous ho... | main | Applications | 10.1609/aaai.v33i01.33011150 | 33 | 01 | 1150-1157 | official | 1811.06692 | title_snapshot |
10.1609/aaai.v33i01.33011158 | Improving Search with Supervised Learning in Trick-Based Card Games | https://ojs.aaai.org/index.php/AAAI/article/view/3909 | https://ojs.aaai.org/index.php/AAAI/article/download/3909/3787 | [
"Christopher Solinas",
"Douglas Rebstock",
"Michael Buro"
] | In trick-taking card games, a two-step process of state sampling and evaluation is widely used to approximate move values. While the evaluation component is vital, the accuracy of move value estimates is also fundamentally linked to how well the sampling distribution corresponds the true distribution. Despite this, rec... | main | Applications | 10.1609/aaai.v33i01.33011158 | 33 | 01 | 1158-1165 | official | 1903.09604 | title_snapshot |
10.1609/aaai.v33i01.33011166 | Exploiting the Contagious Effect for Employee Turnover Prediction | https://ojs.aaai.org/index.php/AAAI/article/view/3910 | https://ojs.aaai.org/index.php/AAAI/article/download/3910/3788 | [
"Mingfei Teng",
"Hengshu Zhu",
"Chuanren Liu",
"Chen Zhu",
"Hui Xiong"
] | Talent turnover often costs a large amount of business time, money and performance. Therefore, employee turnover prediction is critical for proactive talent management. Existing approaches on turnover prediction are mainly based on profiling of employees and their working environments, while the important contagious ef... | main | Applications | 10.1609/aaai.v33i01.33011166 | 33 | 01 | 1166-1173 | official | null | null |
10.1609/aaai.v33i01.33011174 | PerformanceNet: Score-to-Audio Music Generation with Multi-Band Convolutional Residual Network | https://ojs.aaai.org/index.php/AAAI/article/view/3911 | https://ojs.aaai.org/index.php/AAAI/article/download/3911/3789 | [
"Bryan Wang",
"Yi-Hsuan Yang"
] | Music creation is typically composed of two parts: composing the musical score, and then performing the score with instruments to make sounds. While recent work has made much progress in automatic music generation in the symbolic domain, few attempts have been made to build an AI model that can render realistic music a... | main | Applications | 10.1609/aaai.v33i01.33011174 | 33 | 01 | 1174-1181 | official | 1811.04357 | title_snapshot |
10.1609/aaai.v33i01.33011182 | Differentially Private Empirical Risk Minimization with Smooth Non-Convex Loss Functions: A Non-Stationary View | https://ojs.aaai.org/index.php/AAAI/article/view/3912 | https://ojs.aaai.org/index.php/AAAI/article/download/3912/3790 | [
"Di Wang",
"Jinhui Xu"
] | In this paper, we study the Differentially Private Empirical Risk Minimization (DP-ERM) problem with non-convex loss functions and give several upper bounds for the utility in different settings. We first consider the problem in low-dimensional space. For DP-ERM with non-smooth regularizer, we generalize an existing wo... | main | Applications | 10.1609/aaai.v33i01.33011182 | 33 | 01 | 1182-1189 | official | null | null |
10.1609/aaai.v33i01.33011198 | Functional Connectivity Network Analysis with Discriminative Hub Detection for Brain Disease Identification | https://ojs.aaai.org/index.php/AAAI/article/view/3914 | https://ojs.aaai.org/index.php/AAAI/article/download/3914/3792 | [
"Mingliang Wang",
"Jiashuang Huang",
"Mingxia Liu",
"Daoqiang Zhang"
] | Brain network analysis can help reveal the pathological basis of neurological disorders and facilitate automated diagnosis of brain diseases, by exploring connectivity patterns in the human brain. Effectively representing the brain network has always been the fundamental task of computeraided brain network analysis. Pr... | main | Applications | 10.1609/aaai.v33i01.33011198 | 33 | 01 | 1198-1205 | official | null | null |
10.1609/aaai.v33i01.33011206 | Hierarchical Macro Strategy Model for MOBA Game AI | https://ojs.aaai.org/index.php/AAAI/article/view/3915 | https://ojs.aaai.org/index.php/AAAI/article/download/3915/3793 | [
"Bin Wu"
] | The next challenge of game AI lies in Real Time Strategy (RTS) games. RTS games provide partially observable gaming environments, where agents interact with one another in an action space much larger than that of GO. Mastering RTS games requires both strong macro strategies and delicate micro level execution. Recently,... | main | Applications | 10.1609/aaai.v33i01.33011206 | 33 | 01 | 1206-1213 | official | 1812.07887 | title_snapshot |
10.1609/aaai.v33i01.33011214 | G2C: A Generator-to-Classifier Framework Integrating Multi-Stained Visual Cues for Pathological Glomerulus Classification | https://ojs.aaai.org/index.php/AAAI/article/view/3916 | https://ojs.aaai.org/index.php/AAAI/article/download/3916/3794 | [
"Bingzhe Wu",
"Xiaolu Zhang",
"Shiwan Zhao",
"Lingxi Xie",
"Caihong Zeng",
"Zhihong Liu",
"Guangyu Sun"
] | Pathological glomerulus classification plays a key role in the diagnosis of nephropathy. As the difference between different subcategories is subtle, doctors often refer to slides from different staining methods to make decisions. However, creating correspondence across various stains is labor-intensive, bringing major... | main | Applications | 10.1609/aaai.v33i01.33011214 | 33 | 01 | 1214-1221 | official | 1807.03136 | title_snapshot |
10.1609/aaai.v33i01.33011222 | On Strength Adjustment for MCTS-Based Programs | https://ojs.aaai.org/index.php/AAAI/article/view/3917 | https://ojs.aaai.org/index.php/AAAI/article/download/3917/3795 | [
"I-Chen Wu",
"Ti-Rong Wu",
"An-Jen Liu",
"Hung Guei",
"Tinghan Wei"
] | This paper proposes an approach to strength adjustment for MCTS-based game-playing programs. In this approach, we use a softmax policy with a strength index z to choose moves. Most importantly, we filter low quality moves by excluding those that have a lower simulation count than a pre-defined threshold ratio of the ma... | main | Applications | 10.1609/aaai.v33i01.33011222 | 33 | 01 | 1222-1229 | official | null | null |
10.1609/aaai.v33i01.33011230 | A2-Net: Molecular Structure Estimation from Cryo-EM Density Volumes | https://ojs.aaai.org/index.php/AAAI/article/view/3918 | https://ojs.aaai.org/index.php/AAAI/article/download/3918/3796 | [
"Kui Xu",
"Zhe Wang",
"Jianping Shi",
"Hongsheng Li",
"Qiangfeng Cliff Zhang"
] | Constructing of molecular structural models from CryoElectron Microscopy (Cryo-EM) density volumes is the critical last step of structure determination by Cryo-EM technologies. Methods have evolved from manual construction by structural biologists to perform 6D translation-rotation searching, which is extremely compute... | main | Applications | 10.1609/aaai.v33i01.33011230 | 33 | 01 | 1230-1237 | official | 1901.00785 | title_judge |
10.1609/aaai.v33i01.33011238 | TET-GAN: Text Effects Transfer via Stylization and Destylization | https://ojs.aaai.org/index.php/AAAI/article/view/3919 | https://ojs.aaai.org/index.php/AAAI/article/download/3919/3797 | [
"Shuai Yang",
"Jiaying Liu",
"Wenjing Wang",
"Zongming Guo"
] | Text effects transfer technology automatically makes the text dramatically more impressive. However, previous style transfer methods either study the model for general style, which cannot handle the highly-structured text effects along the glyph, or require manual design of subtle matching criteria for text effects. In... | main | Applications | 10.1609/aaai.v33i01.33011238 | 33 | 01 | 1238-1245 | official | 1812.06384 | title_snapshot |
10.1609/aaai.v33i01.33011246 | Learning Phenotypes and Dynamic Patient Representations via RNN Regularized Collective Non-Negative Tensor Factorization | https://ojs.aaai.org/index.php/AAAI/article/view/3920 | https://ojs.aaai.org/index.php/AAAI/article/download/3920/3798 | [
"Kejing Yin",
"Dong Qian",
"William K. Cheung",
"Benjamin C. M. Fung",
"Jonathan Poon"
] | Non-negative Tensor Factorization (NTF) has been shown effective to discover clinically relevant and interpretable phenotypes from Electronic Health Records (EHR). Existing NTF based computational phenotyping models aggregate data over the observation window, resulting in the learned phenotypes being mixtures of diseas... | main | Applications | 10.1609/aaai.v33i01.33011246 | 33 | 01 | 1246-1253 | official | null | null |
10.1609/aaai.v33i01.33011254 | MetaStyle: Three-Way Trade-off among Speed, Flexibility, and Quality in Neural Style Transfer | https://ojs.aaai.org/index.php/AAAI/article/view/3927 | https://ojs.aaai.org/index.php/AAAI/article/download/3927/3805 | [
"Chi Zhang",
"Yixin Zhu",
"Song-Chun Zhu"
] | An unprecedented booming has been witnessed in the research area of artistic style transfer ever since Gatys et al. introduced the neural method. One of the remaining challenges is to balance a trade-off among three critical aspects—speed, flexibility, and quality: (i) the vanilla optimization-based algorithm produces ... | main | Applications | 10.1609/aaai.v33i01.33011254 | 33 | 01 | 1254-1261 | official | 1812.05233 | title_snapshot |
10.1609/aaai.v33i01.33011262 | Optimal Interdiction of Urban Criminals with the Aid of Real-Time Information | https://ojs.aaai.org/index.php/AAAI/article/view/3921 | https://ojs.aaai.org/index.php/AAAI/article/download/3921/3799 | [
"Youzhi Zhang",
"Qingyu Guo",
"Bo An",
"Long Tran-Thanh",
"Nicholas R. Jennings"
] | Most violent crimes happen in urban and suburban cities. With emerging tracking techniques, law enforcement officers can have real-time location information of the escaping criminals and dynamically adjust the security resource allocation to interdict them. Unfortunately, existing work on urban network security games l... | main | Applications | 10.1609/aaai.v33i01.33011262 | 33 | 01 | 1262-1269 | official | null | null |
10.1609/aaai.v33i01.33011270 | Incorporating Semantic Similarity with Geographic Correlation for Query-POI Relevance Learning | https://ojs.aaai.org/index.php/AAAI/article/view/3922 | https://ojs.aaai.org/index.php/AAAI/article/download/3922/3800 | [
"Ji Zhao",
"Dan Peng",
"Chuhan Wu",
"Huan Chen",
"Meiyu Yu",
"Wanji Zheng",
"Li Ma",
"Hua Chai",
"Jieping Ye",
"Xiaohu Qie"
] | Point-of-interest (POI) retrieval that searches for relevant destination locations plays a significant role in on-demand ridehailing services. Existing solutions to POI retrieval mainly retrieve and rank POIs based on their semantic similarity scores. Although intuitive, quantifying the relevance of a Query-POI pair by... | main | Applications | 10.1609/aaai.v33i01.33011270 | 33 | 01 | 1270-1277 | official | null | null |
10.1609/aaai.v33i01.33011069 | AffinityNet: Semi-Supervised Few-Shot Learning for Disease Type Prediction | https://ojs.aaai.org/index.php/AAAI/article/view/3898 | https://ojs.aaai.org/index.php/AAAI/article/download/3898/3776 | [
"Tianle Ma",
"Aidong Zhang"
] | While deep learning has achieved great success in computer vision and many other fields, currently it does not work very well on patient genomic data with the “big p, small N” problem (i.e., a relatively small number of samples with highdimensional features). In order to make deep learning work with a small amount of t... | main | Applications | 10.1609/aaai.v33i01.33011069 | 33 | 01 | 1069-1076 | official | 1805.08905 | title_snapshot |
10.1609/aaai.v33i01.33011110 | NeVAE: A Deep Generative Model for Molecular Graphs | https://ojs.aaai.org/index.php/AAAI/article/view/3903 | https://ojs.aaai.org/index.php/AAAI/article/download/3903/3781 | [
"Bidisha Samanta",
"Abir DE",
"Gourhari Jana",
"Pratim Kumar Chattaraj",
"Niloy Ganguly",
"Manuel Gomez Rodriguez"
] | Deep generative models have been praised for their ability to learn smooth latent representation of images, text, and audio, which can then be used to generate new, plausible data. However, current generative models are unable to work with molecular graphs due to their unique characteristics—their underlying structure ... | main | Applications | 10.1609/aaai.v33i01.33011110 | 33 | 01 | 1110-1117 | official | 1802.05283 | title_snapshot |
10.1609/aaai.v33i01.33011102 | Building Causal Graphs from Medical Literature and Electronic Medical Records | https://ojs.aaai.org/index.php/AAAI/article/view/3902 | https://ojs.aaai.org/index.php/AAAI/article/download/3902/3780 | [
"Galia Nordon",
"Gideon Koren",
"Varda Shalev",
"Benny Kimelfeld",
"Uri Shalit",
"Kira Radinsky"
] | Large repositories of medical data, such as Electronic Medical Record (EMR) data, are recognized as promising sources for knowledge discovery. Effective analysis of such repositories often necessitate a thorough understanding of dependencies in the data. For example, if the patient age is ignored, then one might wrongl... | main | Applications | 10.1609/aaai.v33i01.33011102 | 33 | 01 | 1102-1109 | official | null | null |
10.1609/aaai.v33i01.33011093 | Pathological Evidence Exploration in Deep Retinal Image Diagnosis | https://ojs.aaai.org/index.php/AAAI/article/view/3901 | https://ojs.aaai.org/index.php/AAAI/article/download/3901/3779 | [
"Yuhao Niu",
"Lin Gu",
"Feng Lu",
"Feifan Lv",
"Zongji Wang",
"Imari Sato",
"Zijian Zhang",
"Yangyan Xiao",
"Xunzhang Dai",
"Tingting Cheng"
] | Though deep learning has shown successful performance in classifying the label and severity stage of certain disease, most of them give few evidence on how to make prediction. Here, we propose to exploit the interpretability of deep learning application in medical diagnosis. Inspired by Koch’s Postulates, a well-known ... | main | Applications | 10.1609/aaai.v33i01.33011093 | 33 | 01 | 1093-1101 | official | 1812.02640 | title_snapshot |
10.1609/aaai.v33i01.33011085 | Difficulty-Aware Attention Network with Confidence Learning for Medical Image Segmentation | https://ojs.aaai.org/index.php/AAAI/article/view/3900 | https://ojs.aaai.org/index.php/AAAI/article/download/3900/3778 | [
"Dong Nie",
"Li Wang",
"Lei Xiang",
"Sihang Zhou",
"Ehsan Adeli",
"Dinggang Shen"
] | Medical image segmentation is a key step for various applications, such as image-guided radiation therapy and diagnosis. Recently, deep neural networks provided promising solutions for automatic image segmentation; however, they often perform good on regular samples (i.e., easy-to-segment samples), since the datasets a... | main | Applications | 10.1609/aaai.v33i01.33011085 | 33 | 01 | 1085-1092 | official | null | null |
10.1609/aaai.v33i01.33011077 | Scalable Robust Kidney Exchange | https://ojs.aaai.org/index.php/AAAI/article/view/3899 | https://ojs.aaai.org/index.php/AAAI/article/download/3899/3777 | [
"Duncan C McElfresh",
"Hoda Bidkhori",
"John P Dickerson"
] | In barter exchanges, participants directly trade their endowed goods in a constrained economic setting without money. Transactions in barter exchanges are often facilitated via a central clearinghouse that must match participants even in the face of uncertainty—over participants, existence and quality of potential trad... | main | Applications | 10.1609/aaai.v33i01.33011077 | 33 | 01 | 1077-1084 | official | 1811.03532 | title_snapshot |
10.1609/aaai.v33i01.33011061 | Play as You Like: Timbre-Enhanced Multi-Modal Music Style Transfer | https://ojs.aaai.org/index.php/AAAI/article/view/3897 | https://ojs.aaai.org/index.php/AAAI/article/download/3897/3775 | [
"Chien-Yu Lu",
"Min-Xin Xue",
"Chia-Che Chang",
"Che-Rung Lee",
"Li Su"
] | Style transfer of polyphonic music recordings is a challenging task when considering the modeling of diverse, imaginative, and reasonable music pieces in the style different from their original one. To achieve this, learning stable multi-modal representations for both domain-variant (i.e., style) and domaininvariant (i... | main | Applications | 10.1609/aaai.v33i01.33011061 | 33 | 01 | 1061-1068 | official | 1811.12214 | title_snapshot |
10.1609/aaai.v33i01.3301954 | Combo-Action: Training Agent For FPS Game with Auxiliary Tasks | https://ojs.aaai.org/index.php/AAAI/article/view/3885 | https://ojs.aaai.org/index.php/AAAI/article/download/3885/3763 | [
"Shiyu Huang",
"Hang Su",
"Jun Zhu",
"Ting Chen"
] | Deep reinforcement learning (DRL) has achieved surpassing human performance on Atari games, using raw pixels and rewards to learn everything. However, first-person-shooter (FPS) games in 3D environments contain higher levels of human concepts (enemy, weapon, spatial structure, etc.) and a large action space. In this pa... | main | Applications | 10.1609/aaai.v33i01.3301954 | 33 | 01 | 954-961 | official | null | null |
10.1609/aaai.v33i01.3301962 | Connecting the Digital and Physical World: Improving the Robustness of Adversarial Attacks | https://ojs.aaai.org/index.php/AAAI/article/view/3926 | https://ojs.aaai.org/index.php/AAAI/article/download/3926/3804 | [
"Steve T.K. Jan",
"Joseph Messou",
"Yen-Chen Lin",
"Jia-Bin Huang",
"Gang Wang"
] | While deep learning models have achieved unprecedented success in various domains, there is also a growing concern of adversarial attacks against related applications. Recent results show that by adding a small amount of perturbations to an image (imperceptible to humans), the resulting adversarial examples can force a... | main | Applications | 10.1609/aaai.v33i01.3301962 | 33 | 01 | 962-969 | official | null | null |
10.1609/aaai.v33i01.3301970 | A Memetic Approach for Sequential Security Games on a Plane with Moving Targets | https://ojs.aaai.org/index.php/AAAI/article/view/3886 | https://ojs.aaai.org/index.php/AAAI/article/download/3886/3764 | [
"Jan Karwowski",
"Jacek Mańdziuk",
"Adam Żychowski",
"Filip Grajek",
"Bo An"
] | This paper introduces a new type of Security Games (SG) played on a plane with targets moving along predefined straight line trajectories and its respective Mixed Integer Linear Programming (MILP) formulation. Three approaches for solving the game are proposed and experimentally evaluated: application of an MILP solver... | main | Applications | 10.1609/aaai.v33i01.3301970 | 33 | 01 | 970-977 | official | null | null |
10.1609/aaai.v33i01.3301978 | Crash to Not Crash: Learn to Identify Dangerous Vehicles Using a Simulator | https://ojs.aaai.org/index.php/AAAI/article/view/3887 | https://ojs.aaai.org/index.php/AAAI/article/download/3887/3765 | [
"Hoon Kim",
"Kangwook Lee",
"Gyeongjo Hwang",
"Changho Suh"
] | Developing a computer vision-based algorithm for identifying dangerous vehicles requires a large amount of labeled accident data, which is difficult to collect in the real world. To tackle this challenge, we first develop a synthetic data generator built on top of a driving simulator. We then observe that the synthetic... | main | Applications | 10.1609/aaai.v33i01.3301978 | 33 | 01 | 978-985 | official | null | null |
10.1609/aaai.v33i01.3301986 | Traffic Updates: Saying a Lot While Revealing a Little | https://ojs.aaai.org/index.php/AAAI/article/view/3888 | https://ojs.aaai.org/index.php/AAAI/article/download/3888/3766 | [
"John Krumm",
"Eric Horvitz"
] | Taking speed reports from vehicles is a proven, inexpensive way to infer traffic conditions. However, due to concerns about privacy and bandwidth, not every vehicle occupant may want to transmit data about their location and speed in real time. We show how to drastically reduce the number of transmissions in two ways, ... | main | Applications | 10.1609/aaai.v33i01.3301986 | 33 | 01 | 986-995 | official | null | null |
10.1609/aaai.v33i01.3301996 | Adversarial Learning for Weakly-Supervised Social Network Alignment | https://ojs.aaai.org/index.php/AAAI/article/view/3889 | https://ojs.aaai.org/index.php/AAAI/article/download/3889/3767 | [
"Chaozhuo Li",
"Senzhang Wang",
"Yukun Wang",
"Philip Yu",
"Yanbo Liang",
"Yun Liu",
"Zhoujun Li"
] | Nowadays, it is common for one natural person to join multiple social networks to enjoy different kinds of services. Linking identical users across multiple social networks, also known as social network alignment, is an important problem of great research challenges. Existing methods usually link social identities on t... | main | Applications | 10.1609/aaai.v33i01.3301996 | 33 | 01 | 996-1003 | official | null | null |
10.1609/aaai.v33i01.33011004 | Learning Heterogeneous Spatial-Temporal Representation for Bike-Sharing Demand Prediction | https://ojs.aaai.org/index.php/AAAI/article/view/3890 | https://ojs.aaai.org/index.php/AAAI/article/download/3890/3768 | [
"Youru Li",
"Zhenfeng Zhu",
"Deqiang Kong",
"Meixiang Xu",
"Yao Zhao"
] | Bike-sharing systems, aiming at meeting the public’s need for ”last mile” transportation, are becoming popular in recent years. With an accurate demand prediction model, shared bikes, though with a limited amount, can be effectively utilized whenever and wherever there are travel demands. Despite that some deep learnin... | main | Applications | 10.1609/aaai.v33i01.33011004 | 33 | 01 | 1004-1011 | official | null | null |
10.1609/aaai.v33i01.33011020 | DeepSTN+: Context-Aware Spatial-Temporal Neural Network for Crowd Flow Prediction in Metropolis | https://ojs.aaai.org/index.php/AAAI/article/view/3892 | https://ojs.aaai.org/index.php/AAAI/article/download/3892/3770 | [
"Ziqian Lin",
"Jie Feng",
"Ziyang Lu",
"Yong Li",
"Depeng Jin"
] | Crowd flow prediction is of great importance in a wide range of applications from urban planning, traffic control to public safety. It aims to predict the inflow (the traffic of crowds entering a region in a given time interval) and outflow (the traffic of crowds leaving a region for other places) of each region in the... | main | Applications | 10.1609/aaai.v33i01.33011020 | 33 | 01 | 1020-1027 | official | null | null |
10.1609/aaai.v33i01.33011028 | Perceptual-Sensitive GAN for Generating Adversarial Patches | https://ojs.aaai.org/index.php/AAAI/article/view/3893 | https://ojs.aaai.org/index.php/AAAI/article/download/3893/3771 | [
"Aishan Liu",
"Xianglong Liu",
"Jiaxin Fan",
"Yuqing Ma",
"Anlan Zhang",
"Huiyuan Xie",
"Dacheng Tao"
] | Deep neural networks (DNNs) are vulnerable to adversarial examples where inputs with imperceptible perturbations mislead DNNs to incorrect results. Recently, adversarial patch, with noise confined to a small and localized patch, emerged for its easy accessibility in real-world. However, existing attack strategies are s... | main | Applications | 10.1609/aaai.v33i01.33011028 | 33 | 01 | 1028-1035 | official | null | null |
10.1609/aaai.v33i01.33011036 | Joint Representation Learning for Multi-Modal Transportation Recommendation | https://ojs.aaai.org/index.php/AAAI/article/view/3894 | https://ojs.aaai.org/index.php/AAAI/article/download/3894/3772 | [
"Hao Liu",
"Ting Li",
"Renjun Hu",
"Yanjie Fu",
"Jingjing Gu",
"Hui Xiong"
] | Multi-modal transportation recommendation has a goal of recommending a travel plan which considers various transportation modes, such as walking, cycling, automobile, and public transit, and how to connect among these modes. The successful development of multi-modal transportation recommendation systems can help to sat... | main | Applications | 10.1609/aaai.v33i01.33011036 | 33 | 01 | 1036-1043 | official | null | null |
10.1609/aaai.v33i01.33011044 | DeepFuzz: Automatic Generation of Syntax Valid C Programs for Fuzz Testing | https://ojs.aaai.org/index.php/AAAI/article/view/3895 | https://ojs.aaai.org/index.php/AAAI/article/download/3895/3773 | [
"Xiao Liu",
"Xiaoting Li",
"Rupesh Prajapati",
"Dinghao Wu"
] | Compilers are among the most fundamental programming tools for building software. However, production compilers remain buggy. Fuzz testing is often leveraged with newlygenerated, or mutated inputs in order to find new bugs or security vulnerabilities. In this paper, we propose a grammarbased fuzzing tool called DEEPFUZ... | main | Applications | 10.1609/aaai.v33i01.33011044 | 33 | 01 | 1044-1051 | official | null | null |
10.1609/aaai.v33i01.33011052 | Molecular Property Prediction: A Multilevel Quantum Interactions Modeling Perspective | https://ojs.aaai.org/index.php/AAAI/article/view/3896 | https://ojs.aaai.org/index.php/AAAI/article/download/3896/3774 | [
"Chengqiang Lu",
"Qi Liu",
"Chao Wang",
"Zhenya Huang",
"Peize Lin",
"Lixin He"
] | Predicting molecular properties (e.g., atomization energy) is an essential issue in quantum chemistry, which could speed up much research progress, such as drug designing and substance discovery. Traditional studies based on density functional theory (DFT) in physics are proved to be time-consuming for predicting large... | main | Applications | 10.1609/aaai.v33i01.33011052 | 33 | 01 | 1052-1060 | official | 1906.11081 | title_snapshot |
10.1609/aaai.v33i01.33011012 | SEGAN: Structure-Enhanced Generative Adversarial Network for Compressed Sensing MRI Reconstruction | https://ojs.aaai.org/index.php/AAAI/article/view/3891 | https://ojs.aaai.org/index.php/AAAI/article/download/3891/3769 | [
"Zhongnian Li",
"Tao Zhang",
"Peng Wan",
"Daoqiang Zhang"
] | Generative Adversarial Networks (GANs) are powerful tools for reconstructing Compressed Sensing Magnetic Resonance Imaging (CS-MRI). However most recent works lack exploration of structure information of MRI images that is crucial for clinical diagnosis. To tackle this problem, we propose the Structure-Enhanced GAN (SE... | main | Applications | 10.1609/aaai.v33i01.33011012 | 33 | 01 | 1012-1019 | official | 1902.06455 | title_snapshot |
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