Generic and scalable framework for automated time-series anomaly detection N Laptev, S Amizadeh, I Flint Proceedings of the 21th ACM SIGKDD international conference on knowledge …, 2015 | 559 | 2015 |
Learning to solve circuit-sat: An unsupervised differentiable approach S Amizadeh, S Matusevych, M Weimer International Conference on Learning Representations, 2018 | 97 | 2018 |
Neuro-symbolic visual reasoning: Disentangling S Amizadeh, H Palangi, A Polozov, Y Huang, K Koishida International Conference on Machine Learning, 279-290, 2020 | 89 | 2020 |
Machine learning at Microsoft with ML. NET Z Ahmed, S Amizadeh, M Bilenko, R Carr, WS Chin, Y Dekel, X Dupre, ... Proceedings of the 25th ACM SIGKDD international conference on knowledge …, 2019 | 73 | 2019 |
Inertial hidden markov models: Modeling change in multivariate time series G Montanez, S Amizadeh, N Laptev Proceedings of the AAAI Conference on Artificial Intelligence 29 (1), 2015 | 61 | 2015 |
Towards a learning optimizer for shared clouds C Wu, A Jindal, S Amizadeh, H Patel, W Le, S Qiao, S Rao Proceedings of the VLDB Endowment 12 (3), 210-222, 2018 | 59 | 2018 |
Wangchao Le, Shi Qiao, and Sriram Rao. 2018. Towards a learning optimizer for shared clouds C Wu, A Jindal, S Amizadeh, H Patel Proceedings of the VLDB Endowment 12 (3), 210-222, 2018 | 57 | 2018 |
Coded elastic computing Y Yang, M Interlandi, P Grover, S Kar, S Amizadeh, M Weimer 2019 IEEE International Symposium on Information Theory (ISIT), 2654-2658, 2019 | 35 | 2019 |
PDP: A general neural framework for learning constraint satisfaction solvers S Amizadeh, S Matusevych, M Weimer arXiv preprint arXiv:1903.01969, 2019 | 27 | 2019 |
Yahoo anomaly detection dataset s5 N Laptev, S Amizadeh URL http://webscope. sandbox. yahoo. com/catalog. php, 2015 | 20 | 2015 |
Interactive learning in continuous multimodal space: a bayesian approach to action-based soft partitioning and learning H Firouzi, MN Ahmadabadi, BN Araabi, S Amizadeh, MS Mirian, ... IEEE Transactions on Autonomous Mental Development 4 (2), 124-138, 2011 | 12 | 2011 |
S5-A labeled anomaly detection dataset, version 1.0 (16M) N Laptev, S Amizadeh, Y Billawala Mar, 2015 | 10 | 2015 |
WindTunnel: towards differentiable ML pipelines beyond a single model GI Yu, S Amizadeh, S Kim, A Pagnoni, C Zhang, BG Chun, M Weimer, ... Proceedings of the VLDB Endowment 15 (1), 11-20, 2021 | 8 | 2021 |
Yahoo anomaly detection dataset S5 (2015) N Laptev, S Amizadeh URL http://webscope. sandbox. yahoo. com. Retrieved on 15, 2019 | 8 | 2019 |
A neural framework for learning DAG to DAG translation M Kaluza, S Amizadeh, R Yu NeurIPS’2018 Workshop, 2018 | 8 | 2018 |
A bayesian approach to conceptualization using reinforcement learning S Amizadeh, MN Ahmadabadi, BN Araabi, R Siegwart 2007 IEEE/ASME international conference on advanced intelligent mechatronics …, 2007 | 8 | 2007 |
Accelerating inference of traditional ml pipelines with neural network frameworks M Interlandi, M Weimer, S Amizadeh, K Karanasos, SC Nakandala, ... US Patent App. 16/993,900, 2022 | 6 | 2022 |
Selecting a neural network architecture for a supervised machine learning problem S Amizadeh, G Yang, N Fusi, FP Casale US Patent App. 15/976,514, 2019 | 6 | 2019 |
Making classical machine learning pipelines differentiable: A neural translation approach GI Yu, S Amizadeh, S Kim, A Pagnoni, BG Chun, M Weimer, M Interlandi arXiv preprint arXiv:1906.03822, 2019 | 6 | 2019 |
Machine learning at microsoft with ml. net M Interlandi, S Matusevych, S Amizadeh, S Zahirazami, M Weimer | 6 | 2018 |