AI WORLD 2019 世界人工智慧峰會精彩重放！
10 月 18 日，2019 中關村論壇平行論壇 ——AI WORLD 2019 世界人工智慧峰會在北京啟幕。新智元楊靜、科大訊飛胡鬱、微軟王永東、華為王成錄、英特爾宋繼強、曠視及智源學者孫劍、滴滴葉傑平、AWS 張崢、依圖顏水成、地平線黃暢、autowise.ai 黃超等重磅嘉賓中關村論劍，重啟充滿創新活力的 AI 未來。峰會現場，新智元揭曉AI Era 創新大獎，並重磅發布AI 開放創新平臺和獻禮新書《智周萬物：人工智慧改變中國》。回放連結：
【騰訊科技】
客戶端：https://view.inews.qq.com/a/TEC2019101600718500
PC端：http://v.qq.com/live/p/topic/74606/preview.html
【海澱融媒】
https://m.toutiaoimg.cn/i6748195040323062540
【新浪科技】
http://video.sina.com.cn/l/p/1728577.html
新智元報導
來源：GitHub
編輯：元子
【新智元導讀】NeurIPS圖表示學習研討會錄取論文揭曉，共有92篇論文入選。近年來，圖表示學習的研究激增，包括用於深圖嵌入的技術，卷積神經網絡對圖結構數據的泛化以及受信念傳播啟發的神經信息傳遞方法。圖可以看作是對更簡單類型的結構化數據（例如圖像）的自然概括，因此，它們代表了機器學習的下一個突破口。歡迎來新智元 AI 朋友圈與大咖一起討論~
從電信網絡到量子化學，圖形結構化數據在自然科學和社會科學中無處不在。如果我們希望系統可以從此類數據中進行學習，推理和生成，則在深度學習體系結構中建立關係歸納偏差至關重要。
此外，圖可以看作是對更簡單類型的結構化數據（例如圖像）的自然概括，因此，它們代表了機器學習的下一個突破口。
近年來，圖表示學習的研究激增，包括用於深圖嵌入的技術，卷積神經網絡對圖結構數據的泛化以及受信念傳播啟發的神經信息傳遞方法。
圖神經網絡和相關技術的這些進步導致了許多領域的最新技術成果，包括化學合成，3D視覺，推薦系統，問題解答和社交網絡分析。對此領域日益普及的最大證明也許是最近關於該主題的四篇受歡迎的評論論文發表，每篇論文都試圖統一各個領域相似思想的不同表述。
這表明該主題已達到臨界水平，需要召開專門的研討會，召集研究人員共同確定有意義的感興趣領域，討論我們如何設計新的更好的基準，鼓勵討論並促進協作。
來自谷歌、Twitter、MIT、斯坦福等公司和高校的9位科學家共同組織了NeurIPS 2019 Workshop。
該Workshop將包括有關該領域各種方法和問題的演講，海報和邀請演講，包括但不限於：
在圖上進行監督式深度學習（例如圖神經網絡）
互動和關係網絡
無監督圖嵌入方法
圖的深層生成模型
用於化學/藥物設計的深度學習
在manifolds，點雲和計算機視覺上進行深度學習
關係歸納偏差（例如，用於強化學習）
基準數據集和評估方法
組織者歡迎原創研究論文，這些論文以前從未在機器學習會議或研討會上發表過。所有被接受的論文均將以poster的形式展示，並選擇三篇貢獻作品進行口頭演示。
除了傳統的研究論文提交之外，我們還將歡迎以一頁紙的形式提交的論文，描述圖形表示學習領域的開放性問題和挑戰。這些未解決的問題將在茶歇之前立即進行簡短演講（5-10分鐘），以促進討論。
該研討會的主要目標是促進社區建設。隨著數百名新的研究人員開始在該領域開展項目，我們希望將他們聚集在一起，以將這個快速增長的圖形表示學習領域整合為一個健康而充滿活力的子領域。
被接受論文列表
原創研究
Pre-training Graph Neural Networks.Weihua Hu, Bowen Liu, Joseph Gomes, Marinka itnik, Vijay S. Pande, Percy Liang and Jure Leskovec
Variational Graph Convolutional Networks.Louis C. Tiao, Pantelis Elinas, Harrison Tri Tue Nguyen and Edwin V. Bonilla
Probabilistic End-to-End Graph-based Semi-Supervised Learning.Mariana Vargas Vieyra, Aurélien Bellet and Pascal Denis
開放問題及挑戰
Between the Interaction of Graph Neural Networks and Semantic Web.Francisco Xavier Sumba Toral
Disentangling structure and position in graphs.Komal Teru and Will Hamilton
Approximation Power of Invariant Graph Networks.Haggai Maron, Heli Ben-Hamu and Yaron Lipman
Intrinsic evaluation of unsupervised node embedding.Chi Thang Duong, Dung Trung Hoang, Quoc Viet Hung Nguyen, Ha The Hien Dang and Karl Aberer
Leveraging Time Dependency in Graphs.Arinbj rn Kolbeinsson, Naman Shukla, Akhil Gupta and Lavanya Marla
Poster演講
Node2Motif: Hierarchical Invariant Embeddings of Structured Graphs Using the Bispectrum.Sophia Sanborn, Ram Mehta, Noah Shutty and Christopher Hillar
Learning Hierarchical Representations in Kinematic Space.Adarsh Jamadandi and Uma Mudenagudi
Applying Graph Neural Networks on Heterogeneous Nodes and Edge Features.Frederik Diehl
Natural Question Generation with Reinforcement Learning Based Graph-to-Sequence Model.Yu Chen, Lingfei Wu and Mohammed Zaki
Convolution, attention and structure embedding.Jean-Marc Andreoli
Disentangling Interpretable Generative Parameters of Random and Real-World Graphs.Niklas Stoehr, Marc Brockschmidt, Jan Stuehmer and Emine Yilmaz
Graph Few-shot Learning via Knowledge Transfer.Huaxiu Yao, Chuxu Zhang, Ying Wei, Meng Jiang, Suhang Wang, Junzhou Huang, Nitesh Chawla and Zhenhui (Jessie) Li
Group Representation Theory for Knowledge Graph Embedding.Chen Cai
Graph Generation with Variational Recurrent Neural Network.Shih-Yang Su, Hossein Hajimirsadeghi and Greg Mori
Graph Embedding VAE: A Permutation Invariant Model of Graph Structure.Tony Duan and Juho Lee
Learning Visual Dynamics Models of Rigid Objects using Relational Inductive Biases.Fabio Ferreira, Lin Shao, Tamim Asfour and Jeannette Bohg
Image-Conditioned Graph Generation for Road Network Extraction.Davide Belli and Thomas Kipf
Deep geometric matrix completion: Are we doing it right?.Amit Boyarski, Sanketh Vendula and Alex Bronstein
Curvature Graph Network.Ze Ye, Kin Sum Liu, Tengfei Ma, Jie Gao and Chao Chen
Sequential Edge Clustering in Temporal Multigraphs.Elahe Ghalebi, Hamidreza Mahyar, Radu Grosu, Graham Taylor and Sinead Williamson
Learning interpretable hierarchical node representations via Ladder Gamma VAE.Arindam Sarkar, Nikhil Mehta and Piyush Rai
Multimodal Neural Graph Memory Networks for Visual Question Answering.Mahmoud Khademi, Parmis Naddaf and Oliver Schulte
Graph Alignment Networks with Node Matching Scores.Evgeniy Faerman, Otto Voggenreiter, Felix Borutta, Tobias Emrich, Max Berrendorf and Matthias Schubert
Graph Attacks with Latent Variable Noise Modeling.Joey Bose, Andre Cianflone and Will Hamilton
Graph Representation Learning via Multi-task Knowledge Distillation.Jiaqi Ma and Qiaozhu Mei
IsoNN: Isomorphic Neural Network for Graph Representation Learning and Classification.Lin Meng and Jiawei Zhang
Diachronic Embedding for Temporal Knowledge Graph Completion.Rishab Goel, Seyed Mehran Kazemi, Marcus Brubaker and Pascal Poupart
Improving Graph Attention Networks with Large Margin-based Constraints.Guangtao Wang, Rex Ying, Jing Huang and Jure Leskovec
Representation Learning of EHR Data via Graph-Based Medical Entity Embedding.Tong Wu, Yunlong Wang, Yue Wang, Emily Zhao, Yilian Yuan and Zhi Yang
Active Learning for Graph Neural Networks via Node Feature Propagation.Yuexin Wu, Yichong Xu, Yiming Yang and Aarti Singh
On Learning Paradigms for the Travelling Salesman Problem.Chaitanya K. Joshi, Thomas Laurent and Xavier Bresson
GNN-FiLM: Graph Neural Networks with Feature-wise Linear Modulation.Marc Brockschmidt
Graph Embeddings from Random Neural Features.Daniele Zambon, Cesare Alippi and Lorenzo Livi
Graph Structured Prediction Energy Net Algorithms.Colin Graber and Alexander Schwing
Meta-Graph: Few shot Link Prediction via Meta-Learning.Joey Bose, Ankit Jain, Piero Molino and Will Hamilton
Graph Representation Learning for Fraud Prediction: A Nearest Neighbour approach.Rafa l Van Belle, Sandra Mitrovi and Jochen De Weerdt
Tensor Graph Neural Networks for Learning on Time Varying Graphs.Osman Asif Malik, Shashanka Ubaru, Lior Horesh, Misha E. Kilmer and Haim Avron
Learning representations of Logical Formulae using Graph Neural Networks.Xavier Glorot, Ankit Anand, Eser Aygün, Shibl Mourad, Pushmeet Kohli and Doina Precup
SELFIES: a robust representation of semantically constrained graphs with an example application in chemistry.Mario Krenn, Florian Haese, Akshat Nigam, Pascal Friederich and Alan Aspuru-Guzik
Predicting Propositional Satisfiability via End-to-End Learning.Chris Cameron, Rex H.-G. Chen, Jason S. Hartford and Kevin Leyton-Brown
Residual or Gate? Towards Deeper Graph Neural Networks for Inductive Graph Representation Learning.Binxuan Huang and Kathleen M. Carley
Contextual Parameter Generation for Knowledge Graph Link Prediction. George I.Stoica, Otilia Stretcu, Anthony Platanios, Tom Mitchell and Barnabas Poczos
DynGAN: Generative Adversarial Networks for Dynamic Network Embedding.Ayush Maheshwari, Ayush Goyal, Manjesh Kumar Hanawal and Ganesh Ramakrishnan
Relational Graph Representation Learning for Predicting Object Affordances.Alexia Toumpa and Anthony Cohn
Conditional Neural Style Transfer with Peer-Regularized Feature Transform.Jan Svoboda, Asha Anoosheh, Christian Osendorfer and Jonathan Masci
Learning Compositional Koopman Operators for Model-Based Control.Yunzhu Li, Hao He, Jiajun Wu, Dina Katabi and Antonio Torralba
PiNet: Attention Pooling for Graph Classification.Peter Meltzer, Marcelo Gutierrez Mallea and Peter Bentley
On Node Features for Graph Neural Networks.Chi Thang Duong, Thanh Dat Hoang, Ha The Hien Dang, Quoc Viet Hung Nguyen and Karl Aberer
Multi-Graph Convolutional Neural Networks for Representation Learning in Recommendation.Jianing Sun and Yingxue Zhang
Keep It Simple: Graph Autoencoders Without Graph Convolutional Networks.Guillaume Salha, Romain Hennequin and Michalis Vazirgiannis
Differentiation of Black-Box Combinatorial Solvers.Marin Vlastelica Pogan i , Anselm Paulus, Vit Musil, Georg Martius and Michal Rolinek
Auto-regressive Graph Generation Modeling with Improved Evaluation Methods.Chia-Cheng Liu, Harris Chan and Kevin Luk
Policy Learning for Task-driven Discovery of Incomplete Networks.Peter Morales, Rajmonda Caceres, and Tina Eliassi-Rad
Learning interpretable disease self-representations for drug repositioning.Fabrizio Frasca, Diego Galeano, Guadalupe Gonzalez, Ivan Laponogov, Kirill Veselkov, Alberto Paccanaro and Michael Bronstein
Building Dynamic Knowledge Graphs from Text-based Games.Mikulá Zelinka, Xingdi Yuan, Marc-Alexandre C té, Romain Laroche and Adam Trischler
GraphMix: Improved Training of Graph Neural Networks for Semi-Supervised Learning.Vikas Verma, Alex M. Lamb, Juho Kannala, Yoshua Bengio and Jian Tang
Learning Node Embeddings with Exponential Family Distributions.Abdulkadir Celikkanat and Fragkiskos Malliaros
Group Anomaly Detection via Graph Autoencoders.Pierluca D』Oro, Ennio Nasca, Jonathan Masci and Matteo Matteucci
Network discovery using reinforcement learning.Harshavardhan P. Kamarthi, Priyesh Vijayan, Bryan Wilder, Balaraman Ravindran and Milind Tambe
A quantum hardware-induced graph kernel based on Gaussian Boson Sampling.Maria Schuld, Kamil Bradler, Robert Israel, Daiqin Su and Brajesh Gupt
Neural Execution of Graph Algorithms.Petar Veli kovi , Rex Ying, Matilde Padovano, Raia Hadsell and Charles Blundell
Short Text Classification using Graph Convolutional Network.Kshitij Tayal, Nikhil Rao, Karthik Subbian and Saurabh Agrawal
Logical Expressiveness of Graph Neural Networks.Mika l Monet, Jorge Pérez, Juan Reutter, Egor Kostylev, Pablo Barceló and Juan Pablo Silva
Dynamic Network Representation Learning via Gaussian Embedding.Yulong Pei, Xin Du, George Fletcher and Mykola Pechenizkiy
Tri-graph Information Propagation for Polypharmacy Side Effect Prediction.Hao Xu, Shengqi Sang and Haiping Lu
Attributed Random Walk as Matrix Factorization.Lei Chen, Shunwang Gong, Joan Bruna and Michael Bronstein
Graph Sequential Networks.Ming Tu, Jing Huang, Xiaodong He and Bowen Zhou
Dynamic Embedding on Textual Networks via a Gaussian Process.Pengyu Cheng, Yitong Li, Xinyuan Zhang, Liqun Chen, David Carlson and Lawrence Carin
Low-Dimensional Knowledge Graph Embeddings via Hyperbolic Rotations.Ines Chami, Adva Wolf, Frederic Sala and Christopher Ré
Supervised Graph Attention Network for Semi-Supervised Node Classification.Dongkwan Kim and Alice Oh
Community detection and collaborative filtering on zero inflated graphs using spectral clustering.Guilherme Gomes, Vinayak Rao and Jennifer Neville
Molecule-Augmented Attention Transformer. ukasz Maziarka, Tomasz Danel, Slawomir Mucha, Krzysztof Rataj, Jacek Tabor and Stanislaw Jastrzebski
Disentangling Mixtures of Epidemics on Graphs.Jessica Hoffmann, Soumya Basu, Surbhi Goel and Constantine Caramanis
Transferability of Spectral Graph Convolutional Neural Networks.Ron Levie, Wei Huang, Lorenzo Bucci, Michael Bronstein and Gitta Kutyniok
On the Interpretability and Evaluation of Graph Representation Learning.Antonia Gogoglou, C. Bayan Bruss and Keegan Hines
Graph Attentional Autoencoder for Anticancer Hyperfood Prediction.Shunwang Gong, Guadalupe Gonzalez, Ivan Laponogov, Kirill Veselkov and Michael Bronstein
Learning interaction patterns from surface representations of protein structure.Pablo Gainza Cirauqui, Freyr Sverrisson, Federico Monti, Emanuele Rodolà, Davide Boscaini, Michael Bronstein and Bruno Correia
Graph-Driven Generative Models for Heterogeneous Multi-Task Learning.Wenlin Wang, Hongteng Xu, Zhe Gan and Wenqi Wang
Observational causal inference using network information.Yan Leng, Martin Saveski, Alex 『Sandy』 Pentland and Dean Eckles
Relational Graph Representation Learning for Open-Domain Question Answering.Salvatore G. Vivona and Kaveh Hassani
Modeling Human Brain Connectomes using Structured Neural Networks.Uday Shankar Shanthamallu, Qunwei Li, Jayaraman Thiagarajan, Rushil Anirudh, Alan Kaplan and Peer-Timo Bremer
Neural Message Passing on High Order Paths.Daniel Flam-Shepherd
Multi-Task Learning on Graphs with Node and Graph Level Labels.Chester Holtz, Onur Atan, Ryan Carey and Tushit Jain
Learnable Aggregator for GCN.Li Zhang and Haiping Lu
Deep Generative Probabilistic Graph Neural Networks for Scene Graph Generation.Mahmoud Khademi and Oliver Schulte
Towards an Adaptive Skip-gram Model for Network Representation Learning.I-Chung Hsieh and Cheng-Te Li
R-SQAIR: Relational Sequential Attend, Infer, Repeat.Aleksandar Stani and Jürgen Schmidhuber
Understanding Graph Neural Networks via Trajectory Analysis.Ziqiao Meng, Jin Dong, Zengfeng Huang and Irwin King
Learning Vertex Convolutional Networks for Graph Classification.Yuhang Jiao, Lixin Cui, Lu Bai and Hancock Edwin
大會組織者
Rianne van den Berg, Google Research
https://riannevdberg.github.io/
Michael Bronstein, Imperial College London/Twitter/USI Lugano
https://www.imperial.ac.uk/people/m.bronstein
William L. Hamilton, McGill University/Mila
https://williamleif.github.io/
Stefanie Jegelka, Massachusetts Institute of Technology
https://people.csail.mit.edu/stefje/
Thomas Kipf,Universiteit van Amsterdam
Jure Leskovec, Stanford University/Pinterest
https://cs.stanford.edu/~jure/
Renjie Liao, University of Toronto
http://www.cs.toronto.edu/~rjliao/
Yizhou Sun, University of California, Los Angeles
http://web.cs.ucla.edu/~yzsun/
Petar Veli kovi ,DeepMind
https://www.cst.cam.ac.uk/~pv273/
參考連結：
https://grlearning.github.io/