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In Proceedings of the 36th International Conference on Machine Learning, ICML 2019, 9--15 June 2019, Long Beach, California, USA (Proceedings of Machine Learning Research, Vol. 97), , Kamalika Chaudhuri and Ruslan Salakhutdinov (Eds.). PMLR, 1972--1982."},{"key":"e_1_3_2_2_12_1","volume-title":"Paraphrase Generation with Latent Bag of Words. In Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019","author":"Fu Yao","year":"2019","unstructured":"Yao Fu , Yansong Feng , and John P. Cunningham . 2019 . Paraphrase Generation with Latent Bag of Words. In Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019 , NeurIPS 2019 , December 8--14, 2019, Vancouver, BC, Canada, , Hanna M. Wallach, Hugo Larochelle, Alina Beygelzimer, Florence d'Alch\u00e9 -Buc, Emily B. Fox, and Roman Garnett (Eds.). 13623--13634. Yao Fu, Yansong Feng, and John P. Cunningham. 2019. 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In SIGIR '21: The 44th International ACM SIGIR Conference on Research and Development in Information Retrieval, Virtual Event, Canada, July 11--15, 2021, , Fernando Diaz, Chirag Shah, Torsten Suel, Pablo Castells, Rosie Jones, and Tetsuya Sakai (Eds.). ACM, 1157--1166."},{"key":"e_1_3_2_2_35_1","doi-asserted-by":"publisher","DOI":"10.1145\/1454008.1454012"},{"key":"e_1_3_2_2_36_1","volume-title":"Incremental Few-Shot Object Detection. In 2020 IEEE\/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2020","author":"Juan-Manuel P\u00e9","year":"2020","unstructured":"Juan-Manuel P\u00e9 rez-R\u00fa a, Xiatian Zhu , Timothy M. Hospedales , and Tao Xiang . 2020 . Incremental Few-Shot Object Detection. In 2020 IEEE\/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2020 , Seattle, WA, USA, June 13--19 , 2020. Computer Vision Foundation \/ IEEE, 13843--13852. Juan-Manuel P\u00e9 rez-R\u00fa a, Xiatian Zhu, Timothy M. Hospedales, and Tao Xiang. 2020. 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In UAI 2009, Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, Montreal, QC, Canada, June 18--21, 2009, Jeff A. Bilmes and Andrew Y. Ng (Eds.). AUAI Press, 452--461."},{"key":"e_1_3_2_2_38_1","volume-title":"ProtoCF: Prototypical Collaborative Filtering for Few-shot Recommendation. In RecSys '21: Fifteenth ACM Conference on Recommender Systems","author":"Sankar Aravind","year":"2021","unstructured":"Aravind Sankar , Junting Wang , Adit Krishnan , and Hari Sundaram . 2021 . ProtoCF: Prototypical Collaborative Filtering for Few-shot Recommendation. In RecSys '21: Fifteenth ACM Conference on Recommender Systems , Amsterdam, The Netherlands , 27 September 2021 - 1 October 2021, , Humberto Jes\u00fa s Corona Pamp'i n, Martha A. Larson, Martijn C. Willemsen, Joseph A. Konstan, Julian J. McAuley, Jean Garcia-Gathright, Bouke Huurnink, and Even Oldridge (Eds.). ACM, 166--175. Aravind Sankar, Junting Wang, Adit Krishnan, and Hari Sundaram. 2021. 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In Proceedings of the 33nd International Conference on Machine Learning, ICML 2016, New York City, NY, USA, June 19--24, 2016 (JMLR Workshop and Conference Proceedings , Vol. 48), , Maria-Florina Balcan and Kilian Q. Weinberger (Eds.). JMLR.org, 1842-- 1850 . Adam Santoro, Sergey Bartunov, Matthew M. Botvinick, Daan Wierstra, and Timothy P. Lillicrap. 2016. Meta-Learning with Memory-Augmented Neural Networks. In Proceedings of the 33nd International Conference on Machine Learning, ICML 2016, New York City, NY, USA, June 19--24, 2016 (JMLR Workshop and Conference Proceedings, Vol. 48), , Maria-Florina Balcan and Kilian Q. Weinberger (Eds.). JMLR.org, 1842--1850."},{"key":"e_1_3_2_2_40_1","volume-title":"Prototypical Networks for Few-shot Learning. In Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017","author":"Snell Jake","year":"2017","unstructured":"Jake Snell , Kevin Swersky , and Richard S. Zemel . 2017 . Prototypical Networks for Few-shot Learning. In Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017 , December 4 --9 , 2017 , Long Beach, CA, USA, , Isabelle Guyon, Ulrike von Luxburg, Samy Bengio, Hanna M. Wallach, Rob Fergus, S. V. N. Vishwanathan, and Roman Garnett (Eds.). 4077--4087. Jake Snell, Kevin Swersky, and Richard S. Zemel. 2017. Prototypical Networks for Few-shot Learning. In Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, December 4--9, 2017, Long Beach, CA, USA, , Isabelle Guyon, Ulrike von Luxburg, Samy Bengio, Hanna M. Wallach, Rob Fergus, S. V. N. Vishwanathan, and Roman Garnett (Eds.). 4077--4087."},{"key":"e_1_3_2_2_41_1","volume-title":"TAdaNet: Task-Adaptive Network for Graph-Enriched Meta-Learning. 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In Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, NeurIPS 2020, December 6--12, 2020, virtual, Hugo Larochelle, Marc'Aurelio Ranzato, Raia Hadsell, Maria-Florina Balcan, and Hsuan-Tien Lin (Eds.)."},{"key":"e_1_3_2_2_43_1","volume-title":"Investigating and Mitigating Degree-Related Biases in Graph Convoltuional Networks. In CIKM '20: The 29th ACM International Conference on Information and Knowledge Management","author":"Tang Xianfeng","year":"2020","unstructured":"Xianfeng Tang , Huaxiu Yao , Yiwei Sun , Yiqi Wang , Jiliang Tang , Charu C. Aggarwal , Prasenjit Mitra , and Suhang Wang . 2020 b. Investigating and Mitigating Degree-Related Biases in Graph Convoltuional Networks. In CIKM '20: The 29th ACM International Conference on Information and Knowledge Management , Virtual Event, Ireland, October 19--23 , 2020, , Mathieu d'Aquin, Stefan Dietze, Claudia Hauff, Edward Curry, and Philippe Cudr\u00e9 -Mauroux (Eds.). ACM, 1435--1444. Xianfeng Tang, Huaxiu Yao, Yiwei Sun, Yiqi Wang, Jiliang Tang, Charu C. Aggarwal, Prasenjit Mitra, and Suhang Wang. 2020b. Investigating and Mitigating Degree-Related Biases in Graph Convoltuional Networks. In CIKM '20: The 29th ACM International Conference on Information and Knowledge Management, Virtual Event, Ireland, October 19--23, 2020, , Mathieu d'Aquin, Stefan Dietze, Claudia Hauff, Edward Curry, and Philippe Cudr\u00e9 -Mauroux (Eds.). ACM, 1435--1444."},{"key":"e_1_3_2_2_44_1","volume-title":"Graph Convolutional Matrix Completion. CoRR","author":"van den Berg Rianne","year":"2017","unstructured":"Rianne van den Berg , Thomas N. Kipf , and Max Welling . 2017. Graph Convolutional Matrix Completion. CoRR , Vol. abs\/ 1706 .02263 ( 2017 ). Rianne van den Berg, Thomas N. Kipf, and Max Welling. 2017. Graph Convolutional Matrix Completion. CoRR , Vol. abs\/1706.02263 (2017)."},{"key":"e_1_3_2_2_45_1","doi-asserted-by":"publisher","DOI":"10.5555\/2627435.2697068"},{"key":"e_1_3_2_2_46_1","volume-title":"Graph Attention Networks. In 6th International Conference on Learning Representations, ICLR 2018, Vancouver, BC, Canada, April 30 - May 3, 2018, Conference Track Proceedings. OpenReview.net.","author":"Velickovic Petar","year":"2018","unstructured":"Petar Velickovic , Guillem Cucurull , Arantxa Casanova , Adriana Romero , Pietro Li\u00f2 , and Yoshua Bengio . 2018 . Graph Attention Networks. In 6th International Conference on Learning Representations, ICLR 2018, Vancouver, BC, Canada, April 30 - May 3, 2018, Conference Track Proceedings. OpenReview.net. Petar Velickovic, Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Li\u00f2 , and Yoshua Bengio. 2018. 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DropoutNet: Addressing Cold Start in Recommender Systems. In Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, December 4--9, 2017, Long Beach, CA, USA, , Isabelle Guyon, Ulrike von Luxburg, Samy Bengio, Hanna M. Wallach, Rob Fergus, S. V. N. Vishwanathan, and Roman Garnett (Eds.). 4957--4966."},{"key":"e_1_3_2_2_48_1","doi-asserted-by":"publisher","DOI":"10.1145\/3331184.3331267"},{"key":"e_1_3_2_2_49_1","doi-asserted-by":"publisher","DOI":"10.1145\/3292500.3330950"},{"key":"e_1_3_2_2_50_1","volume-title":"7th International Conference on Learning Representations, ICLR 2019","author":"Xu Keyulu","year":"2019","unstructured":"Keyulu Xu , Weihua Hu , Jure Leskovec , and Stefanie Jegelka . 2019 . How Powerful are Graph Neural Networks? . In 7th International Conference on Learning Representations, ICLR 2019 , New Orleans, LA, USA, May 6--9 , 2019. OpenReview.net. Keyulu Xu, Weihua Hu, Jure Leskovec, and Stefanie Jegelka. 2019. How Powerful are Graph Neural Networks?. In 7th International Conference on Learning Representations, ICLR 2019, New Orleans, LA, USA, May 6--9, 2019. OpenReview.net."},{"key":"e_1_3_2_2_51_1","doi-asserted-by":"publisher","DOI":"10.1145\/3404835.3462928"},{"key":"e_1_3_2_2_52_1","volume-title":"Proceedings of the 13th ACM Conference on Recommender Systems, RecSys 2019","author":"Yi Xinyang","year":"2019","unstructured":"Xinyang Yi , Ji Yang , Lichan Hong , Derek Zhiyuan Cheng , Lukasz Heldt , Aditee Kumthekar , Zhe Zhao , Li Wei , and Ed H. Chi . 2019. Sampling-bias-corrected neural modeling for large corpus item recommendations . In Proceedings of the 13th ACM Conference on Recommender Systems, RecSys 2019 , Copenhagen, Denmark, September 16--20 , 2019 , Toine Bogers, Alan Said, Peter Brusilovsky, and Domonkos Tikk (Eds.). ACM, 269--277. Xinyang Yi, Ji Yang, Lichan Hong, Derek Zhiyuan Cheng, Lukasz Heldt, Aditee Kumthekar, Zhe Zhao, Li Wei, and Ed H. Chi. 2019. Sampling-bias-corrected neural modeling for large corpus item recommendations. In Proceedings of the 13th ACM Conference on Recommender Systems, RecSys 2019, Copenhagen, Denmark, September 16--20, 2019, Toine Bogers, Alan Said, Peter Brusilovsky, and Domonkos Tikk (Eds.). ACM, 269--277."},{"key":"e_1_3_2_2_53_1","doi-asserted-by":"publisher","DOI":"10.14778\/2311906.2311916"},{"key":"e_1_3_2_2_54_1","volume-title":"Learning Transferrable Parameters for Long-tailed Sequential User Behavior Modeling. In KDD '20: The 26th ACM SIGKDD Conference on Knowledge Discovery and Data Mining","author":"Yin Jianwen","year":"2020","unstructured":"Jianwen Yin , Chenghao Liu , Weiqing Wang , Jianling Sun , and Steven C. H. Hoi . 2020 . Learning Transferrable Parameters for Long-tailed Sequential User Behavior Modeling. In KDD '20: The 26th ACM SIGKDD Conference on Knowledge Discovery and Data Mining , Virtual Event, CA, USA, August 23--27 , 2020 , , Rajesh Gupta, Yan Liu, Jiliang Tang, and B. Aditya Prakash (Eds.). ACM, 359--367. Jianwen Yin, Chenghao Liu, Weiqing Wang, Jianling Sun, and Steven C. H. Hoi. 2020. Learning Transferrable Parameters for Long-tailed Sequential User Behavior Modeling. In KDD '20: The 26th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, Virtual Event, CA, USA, August 23--27, 2020, , Rajesh Gupta, Yan Liu, Jiliang Tang, and B. Aditya Prakash (Eds.). ACM, 359--367."},{"key":"e_1_3_2_2_55_1","volume-title":"Machine Learning and Knowledge Discovery in Databases - European Conference, ECML PKDD","author":"Yu Donghan","year":"2020","unstructured":"Donghan Yu , Ruohong Zhang , Zhengbao Jiang , Yuexin Wu , and Yiming Yang . 2020. Graph-Revised Convolutional Network . 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