Search Machine Learning Repository: @inproceedings{icml2014c1_steinhardt14,
    Publisher = {JMLR Workshop and Conference Proceedings},
    Title = {Filtering with Abstract Particles},
    Url = {},
    Abstract = {Using particles, beam search and sequential Monte Carlo can approximate distributions in an extremely flexible manner. However, they can suffer from sparsity and inadequate coverage on large state spaces. We present a new filtering method that addresses this issue by using “abstract particles” that each represent an entire region of the state space. These abstract particles are combined into a hierarchical decomposition, yielding a representation that is both compact and flexible. Empirically, our method outperforms beam search and sequential Monte Carlo on both a text reconstruction task and a multiple object tracking task.},
    Author = {Jacob Steinhardt and Percy Liang},
    Editor = {Tony Jebara and Eric P. Xing},
    Year = {2014},
    Booktitle = {Proceedings of the 31st International Conference on Machine Learning (ICML-14)},
    Pages = {727-735}