| Unsupervised real-time anomaly detection for streaming data S Ahmad, A Lavin, S Purdy, Z Agha Neurocomputing 262, 134-147, 2017 | 1024 | 2017 |
| Evaluating real-time anomaly detection algorithms--the Numenta anomaly benchmark A Lavin, S Ahmad 2015 IEEE 14th international conference on machine learning and applications …, 2015 | 568 | 2015 |
| A generative vision model that trains with high data efficiency and breaks text-based CAPTCHAs D George, W Lehrach, K Kansky, M Lázaro-Gredilla, C Laan, B Marthi, ... Science 358 (6368), eaag2612, 2017 | 308 | 2017 |
| Towards accountability for machine learning datasets: Practices from software engineering and infrastructure B Hutchinson, A Smart, A Hanna, E Denton, C Greer, O Kjartansson, ... Proceedings of the 2021 ACM Conference on Fairness, Accountability, and …, 2021 | 296 | 2021 |
| Properties of sparse distributed representations and their application to hierarchical temporal memory S Ahmad, J Hawkins arXiv preprint arXiv:1503.07469, 2015 | 215 | 2015 |
| Real-time anomaly detection for streaming analytics S Ahmad, S Purdy arXiv preprint arXiv:1607.02480, 2016 | 158 | 2016 |
| Simulation intelligence: Towards a new generation of scientific methods A Lavin, D Krakauer, H Zenil, J Gottschlich, T Mattson, J Brehmer, ... arXiv preprint arXiv:2112.03235, 2021 | 100 | 2021 |
| Technology readiness levels for machine learning systems A Lavin, CM Gilligan-Lee, A Visnjic, S Ganju, D Newman, S Ganguly, ... Nature Communications 13 (1), 6039, 2022 | 93 | 2022 |
| Clustering time-series energy data from smart meters A Lavin, D Klabjan Energy efficiency 8, 681-689, 2015 | 86 | 2015 |
| Biological and machine intelligence (bami) J Hawkins, S Ahmad, S Purdy, A Lavin Initial online release 0.4, 2016 | 61 | 2016 |
| Digital Twin Earth--Coasts: Developing a fast and physics-informed surrogate model for coastal floods via neural operators P Jiang, N Meinert, H Jordão, C Weisser, S Holgate, A Lavin, B Lütjens, ... arXiv preprint arXiv:2110.07100, 2021 | 33 | 2021 |
| A pareto front-based multiobjective path planning algorithm A Lavin arXiv preprint arXiv:1505.05947, 2015 | 32 | 2015 |
| The unreasonable effectiveness of deep evidential regression N Meinert, J Gawlikowski, A Lavin Proceedings of the AAAI Conference on Artificial Intelligence 37 (8), 9134-9142, 2023 | 24 | 2023 |
| Multivariate deep evidential regression N Meinert, A Lavin arXiv preprint arXiv:2104.06135, 2021 | 19 | 2021 |
| Physically-consistent generative adversarial networks for coastal flood visualization B Lütjens, B Leshchinskiy, C Requena-Mesa, F Chishtie, ... arXiv preprint arXiv:2104.04785, 2021 | 18 | 2021 |
| Physics-informed GANs for coastal flood visualization B Lütjens, B Leshchinskiy, C Requena-Mesa, F Chishtie, ... arXiv preprint arXiv:2010.08103, 2020 | 15 | 2020 |
| Neuro-symbolic neurodegenerative disease modeling as probabilistic programmed deep kernels A Lavin International Workshop on Health Intelligence, 49-64, 2021 | 13 | 2021 |
| A pareto optimal d* search algorithm for multiobjective path planning A Lavin arXiv preprint arXiv:1511.00787, 2015 | 12 | 2015 |
| Synthesizing images from 3d models CA Bunkasem, AD Lavin US Patent App. 17/110,211, 2021 | 11 | 2021 |
| The future of fundamental science led by generative closed-loop artificial intelligence H Zenil, J Tegnér, FS Abrahão, A Lavin, V Kumar, JG Frey, A Weller, ... arXiv preprint arXiv:2307.07522, 2023 | 9 | 2023 |
Haruko WainwrightMassachusetts Institute of Technology在 mit.edu 的电子邮件经过验证
