受强制性开放获取政策约束的文章 - Daniel Lowd了解详情
可在其他位置公开访问的文章：22 篇
Machine unlearning for random forests
J Brophy, D Lowd
International Conference on Machine Learning, 1092-1104, 2021
强制性开放获取政策： US Department of Defense
Unifying logical and statistical AI
P Domingos, D Lowd, S Kok, A Nath, H Poon, M Richardson, P Singla
Proceedings of the 31st Annual ACM/IEEE Symposium on Logic in Computer …, 2016
强制性开放获取政策： US National Science Foundation
A joint sentiment-target-stance model for stance classification in tweets
J Ebrahimi, D Dou, D Lowd
Proceedings of COLING 2016, the 26th international conference on …, 2016
强制性开放获取政策： US National Institutes of Health
Weakly supervised tweet stance classification by relational bootstrapping
J Ebrahimi, D Dou, D Lowd
Proceedings of the 2016 conference on empirical methods in natural language …, 2016
强制性开放获取政策： US National Institutes of Health
Unifying logical and statistical AI with Markov logic
P Domingos, D Lowd
Communications of the ACM 62 (7), 74-83, 2019
强制性开放获取政策： US National Science Foundation, US Department of Defense
Ontology matching with knowledge rules
S Jiang, D Lowd, S Kafle, D Dou
Transactions on Large-Scale Data-and Knowledge-Centered Systems XXVIII …, 2016
强制性开放获取政策： US National Science Foundation
Learning from positive and unlabeled data with arbitrary positive shift
Z Hammoudeh, D Lowd
Advances in Neural Information Processing Systems 33, 13088-13099, 2020
强制性开放获取政策： US Department of Defense
The Libra toolkit for probabilistic models.
D Lowd, A Rooshenas
J. Mach. Learn. Res. 16, 2459-2463, 2015
强制性开放获取政策： US National Institutes of Health
Identifying a training-set attack's target using renormalized influence estimation
Z Hammoudeh, D Lowd
Proceedings of the 2022 ACM SIGSAC Conference on Computer and Communications …, 2022
强制性开放获取政策： US Department of Defense
Ontology-based deep restricted boltzmann machine
H Wang, D Dou, D Lowd
Database and Expert Systems Applications: 27th International Conference …, 2016
强制性开放获取政策： US National Institutes of Health
Discriminative structure learning of arithmetic circuits
A Rooshenas, D Lowd
Artificial intelligence and statistics, 1506-1514, 2016
强制性开放获取政策： US National Science Foundation
Adapting and evaluating influence-estimation methods for gradient-boosted decision trees
J Brophy, Z Hammoudeh, D Lowd
Journal of Machine Learning Research 24 (154), 1-48, 2023
强制性开放获取政策： US Department of Defense
Exploiting causal independence in Markov logic networks: Combining undirected and directed models
S Natarajan, T Khot, D Lowd, P Tadepalli, K Kersting, J Shavlik
Machine Learning and Knowledge Discovery in Databases: European Conference …, 2010
强制性开放获取政策： Fraunhofer-Gesellschaft
Instance-based uncertainty estimation for gradient-boosted regression trees
J Brophy, D Lowd
Advances in Neural Information Processing Systems 35, 11145-11159, 2022
强制性开放获取政策： US Department of Defense
Reducing certified regression to certified classification for general poisoning attacks
Z Hammoudeh, D Lowd
2023 IEEE Conference on Secure and Trustworthy Machine Learning (SaTML), 484-523, 2023
强制性开放获取政策： US Department of Defense
Simple, attack-agnostic defense against targeted training set attacks using cosine similarity
Z Hammoudeh, D Lowd
Proceedings of the 3rd ICML workshop on uncertainty and robustness in deep …, 2021
强制性开放获取政策： US Department of Defense
Feature Partition Aggregation: A Fast Certified Defense Against a Union of Attacks
Z Hammoudeh, D Lowd
The Second Workshop on New Frontiers in Adversarial Machine Learning, 2023
强制性开放获取政策： US Department of Defense
Towards stronger adversarial baselines through human-AI collaboration
W You, D Lowd
Proceedings of NLP Power! The First Workshop on Efficient Benchmarking in …, 2022
强制性开放获取政策： US Department of Defense
On the practicality of learning models for network telemetry
J Soheil, Z Hammoudeh, R Durairajan, D Lowd, R Rejaie, W Willinger
Proceedings of Network Traffic Measurement and Analysis Conference, 2020
强制性开放获取政策： US National Science Foundation
A probabilistic approach to knowledge translation
S Jiang, D Lowd, D Dou
Proceedings of the AAAI Conference on Artificial Intelligence 30 (1), 2016
强制性开放获取政策： US National Science Foundation
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