| Dropoutnet: Addressing cold start in recommender systems M Volkovs, G Yu, T Poutanen Advances in Neural Information Processing Systems 30, 2017 | 352 | 2017 |
| X-pool: Cross-modal language-video attention for text-video retrieval SK Gorti, N Vouitsis, J Ma, K Golestan, M Volkovs, A Garg, G Yu Proceedings of the IEEE/CVF conference on computer vision and pattern …, 2022 | 173 | 2022 |
| Improving transformer optimization through better initialization XS Huang, F Perez, J Ba, M Volkovs International Conference on Machine Learning, 4475-4483, 2020 | 151 | 2020 |
| Continuous data cleaning M Volkovs, F Chiang, J Szlichta, RJ Miller 2014 IEEE 30th International Conference on Data Engineering, 244-255, 2014 | 148 | 2014 |
| HGCF: Hyperbolic graph convolution networks for collaborative filtering J Sun, Z Cheng, S Zuberi, F Pérez, M Volkovs Proceedings of the Web Conference 2021, 593-601, 2021 | 138 | 2021 |
| Boltzrank: learning to maximize expected ranking gain MN Volkovs, RS Zemel Proceedings of the 26th annual international conference on machine learning …, 2009 | 132 | 2009 |
| Content-based neighbor models for cold start in recommender systems M Volkovs, GW Yu, T Poutanen Proceedings of the Recommender Systems Challenge 2017, 1-6, 2017 | 114 | 2017 |
| Context-aware scene graph generation with seq2seq transformers Y Lu, H Rai, J Chang, B Knyazev, G Yu, S Shekhar, GW Taylor, M Volkovs Proceedings of the IEEE/CVF international conference on computer vision …, 2021 | 90 | 2021 |
| Decentralized federated learning through proxy model sharing S Kalra, J Wen, JC Cresswell, M Volkovs, HR Tizhoosh Nature communications 14 (1), 2899, 2023 | 83 | 2023 |
| A flexible generative model for preference aggregation MN Volkovs, RS Zemel Proceedings of the 21st International Conference on World Wide Web, 2012 | 83 | 2012 |
| Learning to rank with multiple objective functions KM Svore, MN Volkovs, CJC Burges Proceedings of the 20th international conference on World wide web, 367-376, 2011 | 82 | 2011 |
| Effective latent models for binary feedback in recommender systems M Volkovs, GW Yu Proceedings of the 38th international ACM SIGIR conference on research and …, 2015 | 81 | 2015 |
| Predicting adverse outcomes due to diabetes complications with machine learning using administrative health data M Ravaut, H Sadeghi, KK Leung, M Volkovs, K Kornas, V Harish, ... NPJ digital medicine 4 (1), 24, 2021 | 70 | 2021 |
| Collaborative ranking with 17 parameters M Volkovs, R Zemel Advances in Neural Information Processing Systems 25, 2012 | 65 | 2012 |
| Weakly supervised action selection learning in video J Ma, SK Gorti, M Volkovs, G Yu Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2021 | 63 | 2021 |
| Probabilistic simulation of quantum circuits using a deep-learning architecture J Carrasquilla, D Luo, F Pérez, A Milsted, BK Clark, M Volkovs, L Aolita Physical Review A 104 (3), 032610, 2021 | 59 | 2021 |
| Two-stage model for automatic playlist continuation at scale M Volkovs, H Rai, Z Cheng, G Wu, Y Lu, S Sanner Proceedings of the ACM Recommender Systems Challenge 2018, 1-6, 2018 | 56 | 2018 |
| New learning methods for supervised and unsupervised preference aggregation MN Volkovs, RS Zemel The Journal of Machine Learning Research 15 (1), 1135-1176, 2014 | 55 | 2014 |
| Guided similarity separation for image retrieval C Liu, G Yu, M Volkovs, C Chang, H Rai, J Ma, SK Gorti Advances in Neural Information Processing Systems 32, 2019 | 54 | 2019 |
| Development and validation of a machine learning model using administrative health data to predict onset of type 2 diabetes M Ravaut, V Harish, H Sadeghi, KK Leung, M Volkovs, K Kornas, ... JAMA network open 4 (5), e2111315-e2111315, 2021 | 52 | 2021 |
Guangwei Yu在 cs.toronto.edu 的电子邮件经过验证
