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An Embedding Model for Estimating Legislative Preferences from the Frequency and Sentiment of Tweets.

, , , and . EMNLP (1), page 627-641. Association for Computational Linguistics, (2020)

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Tensor-Dictionary Learning with Deep Kruskal-Factor Analysis., , , , and . AISTATS, volume 54 of Proceedings of Machine Learning Research, page 121-129. PMLR, (2017)An Embedding Model for Estimating Legislative Preferences from the Frequency and Sentiment of Tweets., , , and . EMNLP (1), page 627-641. Association for Computational Linguistics, (2020)Deep Overcomplete Tensor Rank-Decompositions., , , , and . CoRR, (2016)Algorithms for tracking with a foveal sensor., and . ACSSC, page 1563-1565. IEEE, (2015)Deep Learning for Applications in Inverse Modeling, Legislator Analysis, and Computer Vision for Security.. Duke University, Durham, NC, USA, (2023)base-search.net (ftdukeunivdsp:oai:localhost:10161/27647).Background Adaptive Faster R-CNN for Semi-Supervised Convolutional Object Detection of Threats in X-Ray Images., , , and . CoRR, (2020)Mixture Manifold Networks: A Computationally Efficient Baseline for Inverse Modeling., , , and . CoRR, (2022)Mixture Manifold Networks: A Computationally Efficient Baseline for Inverse Modeling., , , and . AAAI, page 9874-9881. AAAI Press, (2023)Application of Compositional Neural Networks for Robust Classification of Infrared Imagery., , and . IGARSS, page 2799-2802. IEEE, (2021)FLOP: Federated Learning on Medical Datasets using Partial Networks., , , , and . KDD, page 3845-3853. ACM, (2021)