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ORCIDGeorge Karypis
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- affiliation: University of Minnesota, USA
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2020 – today
- 2022
[j81]Hongkuan Zhou, Da Zheng, Israt Nisa, Vassilis N. Ioannidis, Xiang Song, George Karypis:
TGL: A General Framework for Temporal GNN Training onBillion-Scale Graphs. Proc. VLDB Endow. 15(8): 1572-1580 (2022)- [j80]Yixin Liu, Zhao Li
, Shirui Pan, Chen Gong, Chuan Zhou, George Karypis:
Anomaly Detection on Attributed Networks via Contrastive Self-Supervised Learning. IEEE Trans. Neural Networks Learn. Syst. 33(6): 2378-2392 (2022) - [c192]Yanda Chen, Ruiqi Zhong, Sheng Zha, George Karypis, He He:
Meta-learning via Language Model In-context Tuning. ACL (1) 2022: 719-730 - [c191]Trong Nghia Hoang, Anoop Deoras, Tong Zhao, Jin Li, George Karypis:
Learning Personalized Item-to-Item Recommendation Metric via Implicit Feedback. AISTATS 2022: 1062-1077 - [c190]George Karypis:
Graph Neural Network Research at AWS AI. WSDM 2022: 4 - [i58]Ancy Sarah Tom, Nesreen K. Ahmed, George Karypis:
Joint Learning of Hierarchical Community Structure and Node Representations: An Unsupervised Approach. CoRR abs/2201.09086 (2022) - [i57]Trong Nghia Hoang, Anoop Deoras, Tong Zhao, Jin Li, George Karypis:
Learning Personalized Item-to-Item Recommendation Metric via Implicit Feedback. CoRR abs/2203.12598 (2022) - [i56]Hongkuan Zhou, Da Zheng, Israt Nisa, Vasileios Ioannidis, Xiang Song, George Karypis:
TGL: A General Framework for Temporal GNN Training on Billion-Scale Graphs. CoRR abs/2203.14883 (2022) - [i55]Jiacheng Li, Tong Zhao, Jin Li, Jim Chan, Christos Faloutsos, George Karypis, Soo-Min Pantel, Julian J. McAuley:
Coarse-to-Fine Sparse Sequential Recommendation. CoRR abs/2204.01839 (2022) - [i54]Vishakh Padmakumar, Leonard Lausen, Miguel Ballesteros, Sheng Zha, He He, George Karypis:
Exploring the Role of Task Transferability in Large-Scale Multi-Task Learning. CoRR abs/2204.11117 (2022) - [i53]Zhen Zhang, Shuai Zheng, Yida Wang, Justin Chiu, George Karypis, Trishul Chilimbi, Mu Li, Xin Jin:
MiCS: Near-linear Scaling for Training Gigantic Model on Public Cloud. CoRR abs/2205.00119 (2022) - [i52]Zhenwei Dai, Vasileios Ioannidis, Soji Adeshina, Zak Jost, Christos Faloutsos, George Karypis:
ScatterSample: Diversified Label Sampling for Data Efficient Graph Neural Network Learning. CoRR abs/2206.04255 (2022) - [i51]Zhiqi Bu, Yu-Xiang Wang, Sheng Zha, George Karypis:
Automatic Clipping: Differentially Private Deep Learning Made Easier and Stronger. CoRR abs/2206.07136 (2022) - [i50]Chunxing Yin, Da Zheng, Israt Nisa, Christos Faloutsos, George Karypis, Richard W. Vuduc:
Nimble GNN Embedding with Tensor-Train Decomposition. CoRR abs/2206.10581 (2022) - [i49]Vassilis N. Ioannidis, Xiang Song, Da Zheng, Houyu Zhang, Jun Ma, Yi Xu, Belinda Zeng, Trishul Chilimbi, George Karypis:
Efficient and effective training of language and graph neural network models. CoRR abs/2206.10781 (2022) - 2021
- [c189]Saurav Manchanda, George Karypis:
Importance Assessment in Scholarly Networks. SDU@AAAI 2021 - [c188]Saurav Manchanda, Da Zheng, George Karypis:
Schema-Aware Deep Graph Convolutional Networks for Heterogeneous Graphs. IEEE BigData 2021: 480-489 - [c187]Saurav Manchanda, Mohit Sharma, George Karypis:
Distant-Supervised Slot-Filling for E-Commerce Queries. IEEE BigData 2021: 677-686 - [c186]Maria Kalantzi, George Karypis:
Position-based Hash Embeddings For Scaling Graph Neural Networks. IEEE BigData 2021: 779-789 - [c185]Haoyu He, Xingjian Shi, Jonas Mueller, Sheng Zha, Mu Li, George Karypis:
Distiller: A Systematic Study of Model Distillation Methods in Natural Language Processing. SustaiNLP@EMNLP 2021: 119-133 - [c184]Saurav Manchanda, George Karypis:
Evaluating Scholarly Impact: Towards Content-Aware Bibliometrics. EMNLP (1) 2021: 6041-6053 - [c183]Jialin Dong, Da Zheng, Lin F. Yang, George Karypis:
Global Neighbor Sampling for Mixed CPU-GPU Training on Giant Graphs. KDD 2021: 289-299 - [c182]Jianpeng Xu, Lingfei Wu, Xiaolin Pang, Mohit Sharma, Dawei Yin, George Karypis, Justin Basilico, Philip S. Yu:
2nd International Workshop on Industrial Recommendation Systems (IRS). KDD 2021: 4173-4174 - [c181]Linfeng Liu, Hoan Nguyen, George Karypis, Srinivasan Sengamedu:
Universal Representation for Code. PAKDD (3) 2021: 16-28 - [c180]Costas Mavromatis, George Karypis:
Graph InfoClust: Maximizing Coarse-Grain Mutual Information in Graphs. PAKDD (1) 2021: 541-553 - [c179]Shalini Pandey, George Karypis, Jaideep Srivastava:
IACN: Influence-Aware and Attention-Based Co-evolutionary Network for Recommendation. PAKDD (2) 2021: 561-574 - [c178]Yikun Xian, Tong Zhao, Jin Li, Jim Chan, Andrey Kan, Jun Ma, Xin Luna Dong, Christos Faloutsos, George Karypis, S. Muthukrishnan, Yongfeng Zhang:
EX3: Explainable Attribute-aware Item-set Recommendations. RecSys 2021: 484-494 - [c177]Balasubramaniam Srinivasan, Da Zheng, George Karypis:
Learning over Families of Sets - Hypergraph Representation Learning for Higher Order Tasks. SDM 2021: 756-764 - [c176]Da Zheng, Minjie Wang, Quan Gan, Xiang Song, Zheng Zhang, George Karypis:
Scalable Graph Neural Networks with Deep Graph Library. WSDM 2021: 1141-1142 - [i48]Shalini Pandey, Andrew S. Lan, George Karypis, Jaideep Srivastava:
Learning Student Interest Trajectory for MOOCThread Recommendation. CoRR abs/2101.05625 (2021) - [i47]Shalini Pandey, George Karypis, Jaideep Srivastava:
An Empirical Comparison of Deep Learning Models for Knowledge Tracing on Large-Scale Dataset. CoRR abs/2101.06373 (2021) - [i46]Balasubramaniam Srinivasan, Da Zheng, George Karypis:
Learning over Families of Sets - Hypergraph Representation Learning for Higher Order Tasks. CoRR abs/2101.07773 (2021) - [i45]Yixin Liu, Zhao Li, Shirui Pan, Chen Gong, Chuan Zhou, George Karypis:
Anomaly Detection on Attributed Networks via Contrastive Self-Supervised Learning. CoRR abs/2103.00113 (2021) - [i44]Shalini Pandey, George Karypis, Jaideep Srivastava:
IACN: Influence-aware and Attention-based Co-evolutionary Network for Recommendation. CoRR abs/2103.02866 (2021) - [i43]Linfeng Liu, Hoan Nguyen, George Karypis, Srinivasan Sengamedu:
Universal Representation for Code. CoRR abs/2103.03116 (2021) - [i42]Saurav Manchanda, Da Zheng, George Karypis:
Schema-Aware Deep Graph Convolutional Networks for Heterogeneous Graphs. CoRR abs/2105.00644 (2021) - [i41]Jialin Dong, Da Zheng, Lin F. Yang, George Karypis:
Global Neighbor Sampling for Mixed CPU-GPU Training on Giant Graphs. CoRR abs/2106.06150 (2021) - [i40]Mufei Li, Jinjing Zhou, Jiajing Hu, Wenxuan Fan, Yangkang Zhang, Yaxin Gu, George Karypis:
DGL-LifeSci: An Open-Source Toolkit for Deep Learning on Graphs in Life Science. CoRR abs/2106.14232 (2021) - [i39]Maria Kalantzi, George Karypis:
Position-based Hash Embeddings For Scaling Graph Neural Networks. CoRR abs/2109.00101 (2021) - [i38]Zonghan Wu, Da Zheng, Shirui Pan, Quan Gan, Guodong Long, George Karypis:
TraverseNet: Unifying Space and Time in Message Passing. CoRR abs/2109.02474 (2021) - [i37]Athanasios N. Nikolakopoulos, Xia Ning, Christian Desrosiers, George Karypis:
Trust your neighbors: A comprehensive survey of neighborhood-based methods for recommender systems. CoRR abs/2109.04584 (2021) - [i36]Agoritsa Polyzou, Maria Kalantzi, George Karypis:
FaiREO: User Group Fairness for Equality of Opportunity in Course Recommendation. CoRR abs/2109.05931 (2021) - [i35]Costas Mavromatis, George Karypis:
HeMI: Multi-view Embedding in Heterogeneous Graphs. CoRR abs/2109.07008 (2021) - [i34]Haoyu He, Xingjian Shi, Jonas Mueller, Sheng Zha, Mu Li, George Karypis:
Distiller: A Systematic Study of Model Distillation Methods in Natural Language Processing. CoRR abs/2109.11105 (2021) - [i33]Cole Hawkins, Vassilis N. Ioannidis, Soji Adeshina, George Karypis:
Scalable Consistency Training for Graph Neural Networks via Self-Ensemble Self-Distillation. CoRR abs/2110.06290 (2021) - [i32]Yanda Chen, Ruiqi Zhong, Sheng Zha, George Karypis, He He:
Meta-learning via Language Model In-context Tuning. CoRR abs/2110.07814 (2021) - [i31]Fabio Broccatelli, Richard Trager, Michael Reutlinger, George Karypis, Mufei Li:
Benchmarking Accuracy and Generalizability of Four Graph Neural Networks Using Large In Vitro ADME Datasets from Different Chemical Spaces. CoRR abs/2111.13964 (2021) - [i30]Costas Mavromatis, Prasanna Lakkur Subramanyam, Vassilis N. Ioannidis, Soji Adeshina, Phillip R. Howard, Tetiana Grinberg, Nagib Hakim, George Karypis:
TempoQR: Temporal Question Reasoning over Knowledge Graphs. CoRR abs/2112.05785 (2021) - [i29]Da Zheng, Xiang Song, Chengru Yang, Dominique LaSalle, Qidong Su, Minjie Wang, Chao Ma, George Karypis:
Distributed Hybrid CPU and GPU training for Graph Neural Networks on Billion-Scale Graphs. CoRR abs/2112.15345 (2021) - 2020
- [j79]Athanasios N. Nikolakopoulos, George Karypis:
Boosting Item-based Collaborative Filtering via Nearly Uncoupled Random Walks. ACM Trans. Knowl. Discov. Data 14(6): 64:1-64:26 (2020) - [c175]Saurav Manchanda, George Karypis:
CAWA: An Attention-Network for Credit Attribution. AAAI 2020: 8472-8479 - [c174]Shalini Pandey, Andrew S. Lan, George Karypis, Jaideep Srivastava:
Learning Student Interest Trajectory for MOOC Thread Recommendation. ICDM (Workshops) 2020: 400-407 - [c173]Zeren Shui, George Karypis:
Heterogeneous Molecular Graph Neural Networks for Predicting Molecule Properties. ICDM 2020: 492-500 - [c172]George Karypis:
GrAPL 2020 Keynote Speaker Deep Graph Library: Overview, Updates, and Future Developments. IPDPS Workshops 2020: 201 - [c171]Da Zheng, Minjie Wang, Quan Gan, Zheng Zhang, George Karypis:
Scalable Graph Neural Networks with Deep Graph Library. KDD 2020: 3521-3522 - [c170]Da Zheng, Chao Ma, Minjie Wang, Jinjing Zhou, Qidong Su, Xiang Song, Quan Gan, Zheng Zhang, George Karypis:
DistDGL: Distributed Graph Neural Network Training for Billion-Scale Graphs. IA3@SC 2020: 36-44 - [c169]Da Zheng, Xiang Song, Chao Ma, Zeyuan Tan, Zihao Ye, Jin Dong, Hao Xiong, Zheng Zhang, George Karypis:
DGL-KE: Training Knowledge Graph Embeddings at Scale. SIGIR 2020: 739-748 - [c168]Da Zheng, Minjie Wang, Quan Gan, Zheng Zhang, George Karypis:
Learning Graph Neural Networks with Deep Graph Library. WWW (Companion Volume) 2020: 305-306 - [i28]Sara Morsy, George Karypis:
Context-aware Non-linear and Neural Attentive Knowledge-based Models for Grade Prediction. CoRR abs/2003.05063 (2020) - [i27]Da Zheng, Xiang Song, Chao Ma, Zeyuan Tan, Zihao Ye, Jin Dong, Hao Xiong, Zheng Zhang, George Karypis:
DGL-KE: Training Knowledge Graph Embeddings at Scale. CoRR abs/2004.08532 (2020) - [i26]Xiangxiang Zeng, Xiang Song, Tengfei Ma, Xiaoqin Pan, Yadi Zhou, Yuan Hou, Zheng Zhang, George Karypis, Feixiong Cheng:
Repurpose Open Data to Discover Therapeutics for COVID-19 using Deep Learning. CoRR abs/2005.10831 (2020) - [i25]Vassilis N. Ioannidis, Da Zheng, George Karypis:
Few-shot link prediction via graph neural networks for Covid-19 drug-repurposing. CoRR abs/2007.10261 (2020) - [i24]Vassilis N. Ioannidis, Da Zheng, George Karypis:
PanRep: Universal node embeddings for heterogeneous graphs. CoRR abs/2007.10445 (2020) - [i23]Colby Wise, Vassilis N. Ioannidis, Miguel Romero Calvo, Xiang Song, George Price, Ninad Kulkarni, Ryan Brand, Parminder Bhatia, George Karypis:
COVID-19 Knowledge Graph: Accelerating Information Retrieval and Discovery for Scientific Literature. CoRR abs/2007.12731 (2020) - [i22]Costas Mavromatis, George Karypis:
Graph InfoClust: Leveraging cluster-level node information for unsupervised graph representation learning. CoRR abs/2009.06946 (2020) - [i21]Zeren Shui, George Karypis:
Heterogeneous Molecular Graph Neural Networks for Predicting Molecule Properties. CoRR abs/2009.12710 (2020) - [i20]Da Zheng, Chao Ma, Minjie Wang, Jinjing Zhou, Qidong Su, Xiang Song, Quan Gan, Zheng Zhang, George Karypis:
DistDGL: Distributed Graph Neural Network Training for Billion-Scale Graphs. CoRR abs/2010.05337 (2020) - [i19]Maria Kalantzi, Agoritsa Polyzou, George Karypis:
FERN: Fair Team Formation for Mutually Beneficial Collaborative Learning. CoRR abs/2011.11611 (2020) - [i18]Saurav Manchanda, Mohit Sharma, George Karypis:
Distant-Supervised Slot-Filling for E-Commerce Queries. CoRR abs/2012.08134 (2020)
2010 – 2019
- 2019
- [j78]David C. Anastasiu, George Karypis:
Parallel cosine nearest neighbor graph construction. J. Parallel Distributed Comput. 129: 61-82 (2019) - [j77]Mohit Sharma, F. Maxwell Harper, George Karypis:
Learning from Sets of Items in Recommender Systems. ACM Trans. Interact. Intell. Syst. 9(4): 19:1-19:26 (2019) - [j76]Agoritsa Polyzou, George Karypis:
Feature Extraction for Next-Term Prediction of Poor Student Performance. IEEE Trans. Learn. Technol. 12(2): 237-248 (2019) - [c167]Saurav Manchanda, Mohit Sharma, George Karypis:
Intent Term Weighting in E-commerce Queries. CIKM 2019: 2345-2348 - [c166]Sara Morsy, George Karypis:
Neural Attentive Knowledge-based Model for Grade Prediction. EDM 2019 - [c165]Shalini Pandey, George Karypis:
A Self Attentive model for Knowledge Tracing. EDM 2019 - [c164]Agoritsa Polyzou, Athanasios N. Nikolakopoulos, George Karypis:
Scholars Walk: A Markov Chain Framework for Course Recommendation. EDM 2019 - [c163]Shalini Pandey, George Karypis:
Structured Dictionary Learning for Energy Disaggregation. e-Energy 2019: 24-34 - [c162]Venkata Rohit Jakkula, George Karypis:
Streaming and Batch Algorithms for Truss Decomposition. GC 2019: 51-59 - [c161]Ancy Sarah Tom, George Karypis:
A 2D Parallel Triangle Counting Algorithm for Distributed-Memory Architectures. ICPP 2019: 45:1-45:10 - [c160]Sara Morsy, George Karypis:
A Study on Curriculum Planning and Its Relationship with Graduation GPA and Time To Degree. LAK 2019: 26-35 - [c159]Asmaa Elbadrawy, George Karypis:
UPM: Discovering Course Enrollment Sequences Associated with Success. LAK 2019: 373-382 - [c158]Prableen Kaur, Agoritsa Polyzou, George Karypis:
Causal Inference in Higher Education: Building Better Curriculums. L@S 2019: 49:1-49:4 - [c157]Athanasios N. Nikolakopoulos, Dimitris Berberidis, George Karypis, Georgios B. Giannakis:
Personalized diffusions for top-n recommendation. RecSys 2019: 260-268 - [c156]Athanasios N. Nikolakopoulos, George Karypis:
RecWalk: Nearly Uncoupled Random Walks for Top-N Recommendation. WSDM 2019: 150-158 - [c155]Mohit Sharma, George Karypis:
Adaptive matrix completion for the users and the items in tail. WWW 2019: 3223-3229 - [e11]Lisa Singh, Richard D. De Veaux, George Karypis, Francesco Bonchi, Jennifer Hill:
2019 IEEE International Conference on Data Science and Advanced Analytics, DSAA 2019, Washington, DC, USA, October 5-8, 2019. IEEE 2019, ISBN 978-1-7281-4493-1 [contents] - [e10]Ankur Teredesai, Vipin Kumar, Ying Li, Rómer Rosales, Evimaria Terzi, George Karypis:
Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, KDD 2019, Anchorage, AK, USA, August 4-8, 2019. ACM 2019, ISBN 978-1-4503-6201-6 [contents] - [i17]Saurav Manchanda, George Karypis:
Distributed representation of multi-sense words: A loss-driven approach. CoRR abs/1904.06725 (2019) - [i16]Saurav Manchanda, George Karypis:
Text segmentation on multilabel documents: A distant-supervised approach. CoRR abs/1904.06730 (2019) - [i15]Sara Morsy, George Karypis:
Will this Course Increase or Decrease Your GPA? Towards Grade-aware Course Recommendation. CoRR abs/1904.11798 (2019) - [i14]Mohit Sharma, Jiayu Zhou, Junling Hu, George Karypis:
Feature-based factorized Bilinear Similarity Model for Cold-Start Top-n Item Recommendation. CoRR abs/1904.11799 (2019) - [i13]Mohit Sharma, George Karypis:
Adaptive Matrix Completion for the Users and the Items in Tail. CoRR abs/1904.11800 (2019) - [i12]Sara Morsy, George Karypis:
Sparse Neural Attentive Knowledge-based Models for Grade Prediction. CoRR abs/1904.11858 (2019) - [i11]Mohit Sharma, F. Maxwell Harper, George Karypis:
Learning from Sets of Items in Recommender Systems. CoRR abs/1904.12643 (2019) - [i10]Agoritsa Polyzou, George Karypis:
Grade prediction with course and student specific models. CoRR abs/1906.00792 (2019) - [i9]Shalini Pandey, George Karypis:
Structured Dictionary Learning for Energy Disaggregation. CoRR abs/1907.06581 (2019) - [i8]Shalini Pandey, George Karypis:
A Self-Attentive model for Knowledge Tracing. CoRR abs/1907.06837 (2019) - [i7]Ancy Sarah Tom, George Karypis:
A 2D Parallel Triangle Counting Algorithm for Distributed-Memory Architectures. CoRR abs/1907.09575 (2019) - [i6]Saurav Manchanda, Mohit Sharma, George Karypis:
Intent term selection and refinement in e-commerce queries. CoRR abs/1908.08564 (2019) - [i5]Venkata Rohit Jakkula, George Karypis:
Streaming and Batch Algorithms for Truss Decomposition. CoRR abs/1908.10550 (2019) - [i4]Athanasios N. Nikolakopoulos, George Karypis:
Boosting Item-based Collaborative Filtering via Nearly Uncoupled Random Walks. CoRR abs/1909.03579 (2019) - [i3]Saurav Manchanda, George Karypis:
CAWA: An Attention-Network for Credit Attribution. CoRR abs/1911.11358 (2019) - 2018
- [j75]Jeremy Iverson, George Karypis:
A virtual memory manager optimized for node-level cooperative multi-tasking in memory constrained systems. Int. J. High Perform. Comput. Appl. 32(5): 744-759 (2018) - [j74]Shaden Smith, Jongsoo Park, George Karypis:
HPC formulations of optimization algorithms for tensor completion. Parallel Comput. 74: 99-117 (2018) - [j73]Lisa Singh, Amol Deshpande, Wenchao Zhou, Arindam Banerjee, Alex J. Bowers, Sorelle A. Friedler, H. V. Jagadish, George Karypis, Zoran Obradovic, Anil Vullikanti, Wangda Zuo:
NSF BIGDATA PI Meeting - Domain-Specific Research Directions and Data Sets. SIGMOD Rec. 47(3): 32-35 (2018) - [c154]Agoritsa Polyzou, George Karypis:
Feature extraction for classifying students based on their academic performance. EDM 2018 - [c153]Saurav Manchanda, George Karypis:
Text Segmentation on Multilabel Documents: A Distant-Supervised Approach. ICDM 2018: 1170-1175 - [c152]Evangelia Christakopoulou, George Karypis:
Local Latent Space Models for Top-N Recommendation. KDD 2018: 1235-1243 - [c151]Saurav Manchanda, George Karypis:
Distributed Representation of Multi-sense Words: A Loss Driven Approach. PAKDD (2) 2018: 337-349 - [c150]Shaden Smith, Kejun Huang, Nicholas D. Sidiropoulos, George Karypis:
Streaming Tensor Factorization for Infinite Data Sources. SDM 2018: 81-89 - [c149]George Karypis:
Recent Advances in Recommender Systems: Sets, Local Models, Coverage, and Errors. WWW (Companion Volume) 2018: 1369 - [p9]Evangelia Christakopoulou, Shaden Smith, Mohit Sharma, Alex Richards, David C. Anastasiu, George Karypis:
Scalability and Distribution of Collaborative Recommenders. Collaborative Recommendations 2018: 369-404 - [r5]Ying Zhao, George Karypis:
Document Clustering. Encyclopedia of Database Systems (2nd ed.) 2018 - [i2]Zhuliu Li, Raphael Petegrosso, Shaden Smith, David Sterling, George Karypis, Rui Kuang:
Scalable Label Propagation for Multi-relational Learning on Tensor Product Graph. CoRR abs/1802.07379 (2018) - 2017
- [j72]David C. Anastasiu, George Karypis:
Efficient identification of Tanimoto nearest neighbors. Int. J. Data Sci. Anal. 4(3): 153-172 (2017) - [j71]Faisal M. Almutairi, Nicholas D. Sidiropoulos, George Karypis:
Context-Aware Recommendation-Based Learning Analytics Using Tensor and Coupled Matrix Factorization. IEEE J. Sel. Top. Signal Process. 11(5): 729-741 (2017) - [c148]Qian Hu, Agoritsa Polyzou, George Karypis, Huzefa Rangwala:
Enriching Course-Specific Regression Models with Content Features for Grade Prediction. DSAA 2017: 504-513 - [c147]Shaden Smith, George Karypis:
Accelerating the Tucker Decomposition with Compressed Sparse Tensors. Euro-Par 2017: 653-668 - [c146]Shaden Smith, Xing Liu, Nesreen K. Ahmed, Ancy Sarah Tom, Fabrizio Petrini, George Karypis:
Truss decomposition on shared-memory parallel systems. HPEC 2017: 1-6 - [c145]Ancy Sarah Tom, Narayanan Sundaram, Nesreen K. Ahmed, Shaden Smith, Stijn Eyerman, Midhunchandra Kodiyath, Ibrahim Hur, Fabrizio Petrini, George Karypis:
Exploring optimizations on shared-memory platforms for parallel triangle counting algorithms. HPEC 2017: 1-7 - [c144]Shaden Smith, Alec Beri, George Karypis:
Constrained Tensor Factorization with Accelerated AO-ADMM. ICPP 2017: 111-120 - [c143]Shaden Smith, Jongsoo Park, George Karypis:
Sparse Tensor Factorization on Many-Core Processors with High-Bandwidth Memory. IPDPS 2017: 1058-1067 - [c142]George Karypis:
Improving Higher Education: Learning Analytics & Recommender Systems Research. RecSys 2017: 2 - [c141]Sara Morsy, George Karypis:
Cumulative Knowledge-based Regression Models for Next-term Grade Prediction. SDM 2017: 552-560 - [e9]George Karypis, Jia Zhang:
2017 IEEE International Congress on Big Data, BigData Congress 2017, Honolulu, HI, USA, June 25-30, 2017. IEEE Computer Society 2017, ISBN 978-1-5386-1996-4 [contents] - [e8]Vijay Raghavan, Srinivas Aluru, George Karypis, Lucio Miele, Xindong Wu:
2017 IEEE International Conference on Data Mining, ICDM 2017, New Orleans, LA, USA, November 18-21, 2017. IEEE Computer Society 2017, ISBN 978-1-5386-3835-4 [contents] - [e7]Raju Gottumukkala, Xia Ning, Guozhu Dong, Vijay Raghavan, Srinivas Aluru, George Karypis, Lucio Miele, Xindong Wu:
2017 IEEE International Conference on Data Mining Workshops, ICDM Workshops 2017, New Orleans, LA, USA, November 18-21, 2017. IEEE Computer Society 2017, ISBN 978-1-5386-3800-2 [contents] - 2016
- [j70]