Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in [1].
Beyond Parity Constraints: Fourier Analysis of Hash Functions for Inference
Authors: Tudor Achim, Ashish Sabharwal, Stefano Ermon
ICML 2016 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Using this framework we introduce two new classes of hash functions for probabilistic inference and model counting that show promising performance on synthetic and real-world benchmarks. |
| Researcher Affiliation | Collaboration | Tudor Achim EMAIL Stanford University, 353 Serra Mall, Stanford, CA 94305 Ashish Sabharwal EMAIL Allen Institute for Artificial Intelligence, 2157 N Northlake Way, Seattle, WA 98103 Stefano Ermon EMAIL Stanford University, 353 Serra Mall, Stanford, CA 94305 |
| Pseudocode | Yes | Algorithm 1 THF-WISH-LB(w(x), n, T, Hβ) |
| Open Source Code | No | The paper does not provide an explicit statement or link to its own open-source code for the described methodology. |
| Open Datasets | No | The paper mentions "grid Ising models" and "real-world CNF formulas that encode problems in a wide range of domains (latin squares, Langford s problem, logistic planning, and hardware verification)". While these are known problem types, the paper does not provide concrete access information (e.g., URLs, DOIs, or specific citations to dataset repositories) for the exact instances used. |
| Dataset Splits | No | The paper does not specify training, validation, or test dataset splits. |
| Hardware Specification | No | The paper mentions execution times (e.g., "terminated within three seconds") and discusses a "solver" but does not specify any particular hardware components (e.g., CPU, GPU models, or memory) used for the experiments. |
| Software Dependencies | No | The paper mentions "lib DAI inference algorithm library (Mooij, 2010)" and "modern SAT solvers", but it does not provide specific version numbers for these software components. |
| Experiment Setup | Yes | We limit each MAP query in the inner loop of THF-WISH-LB and SPARSEWISH to five minutes. Because the theoretically-motivated density of variables in the sparse parity constraints used by SPARSE-WISH was still too high for the solver to find informative solutions within five minutes, we relaxed the constraint density to 5% of the variables for all runs. |