Filtering with Abstract Particles
Authors: Jacob Steinhardt, Percy Liang
ICML 2014 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Empirically, our method outperforms beam search and sequential Monte Carlo on both a text reconstruction task and a multiple object tracking task. |
| Researcher Affiliation | Academia | Jacob Steinhardt JSTEINHARDT@CS.STANFORD.EDU Percy Liang PLIANG@CS.STANFORD.EDU Stanford University, 353 Serra Street, Stanford, CA 94305 USA |
| Pseudocode | Yes | Algorithm 1 Abstract beam search algorithm. Inputs are the space X, a refinement function r, a fitting method Fit, and a beam size k. Generates a sequence of hierarchical decompositions, each of which defines a distribution over X. |
| Open Source Code | No | The paper does not provide an explicit statement about the availability of its source code or a link to a repository. |
| Open Datasets | Yes | For the text reconstruction task, we fit an n-gram model for the transitions using interpolated Kneser-Ney (Kneser & Ney, 1995) trained on The Complete Works of William Shakespeare (about 125, 000 lines in total). |
| Dataset Splits | Yes | The first 115, 000 lines were used to train the model and each of the next 5, 000 lines were used as a development and test set, respectively. |
| Hardware Specification | Yes | Runtime was computed using a single core of a 3.4GHz machine with 32GB of RAM. |
| Software Dependencies | No | The paper does not specify any software libraries, frameworks, or their version numbers used in the implementation of the experiments. |
| Experiment Setup | Yes | We fit these by minimizing perplexity on the development set, and found that n = 8, λ = 0.9 was optimal. |