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 Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Building Concise Logical Patterns by Constraining Tsetlin Machine Clause Size
Authors: K. Darshana Abeyrathna, Ahmed A. O. Abouzeid, Bimal Bhattarai, Charul Giri, Sondre Glimsdal, Ole-Christoffer Granmo, Lei Jiao, Rupsa Saha, Jivitesh Sharma, Svein A. Tunheim, Xuan Zhang
IJCAI 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | To evaluate CSC-TM, we conduct classification, clustering, and regression experiments on tabular data, natural language text, images, and board games. Our results show that CSC-TM maintains accuracy with up to 80 times fewer literals. |
| Researcher Affiliation | Collaboration | 1Centre for Artificial Intelligence Research (CAIR), University of Agder, Grimstad, Norway 2Norwegian Research Centre (NORCE), Grimstad, Norway 3DNV, Oslo, Norway |
| Pseudocode | No | No structured pseudocode or algorithm blocks were found. Table 1 presents feedback rules, but it is not pseudocode. |
| Open Source Code | No | The paper refers to a general TM learning resource (https://cair.github.io/ijcai2023_clause_rationing.html) but does not explicitly state that the source code for the CSC-TM methodology described in this paper is released or provided. |
| Open Datasets | Yes | We evaluate CSC-TM on five NLP datasets: BBC sports [Greene and Cunningham, 2006], R8 [Debole and Sebastiani, 2005], TREC-6 [Chang et al., 2002], Sem Eval 2010 Semantic Relations [Hendrickx et al., 2009], and ACL Internet Movie Database (IMDb) [Maas et al., 2011]. ... We evaluate our approach on two image datasets: MNIST and CIFAR-2... We use the Energy Performance dataset to evaluate regression performance based on [Abeyrathna et al., 2020c]. |
| Dataset Splits | No | The paper mentions using well-known datasets like MNIST but does not explicitly provide the training/test/validation dataset splits (e.g., percentages or sample counts) needed for reproduction. |
| Hardware Specification | Yes | The experiments use a CUDA implementation of CSC-TM and runs on Intel Xeon Platinum 8168 CPU at 2.70 GHz and a Nvidia DGX-2 with Tesla V100 GPU. |
| Software Dependencies | No | The paper mentions 'CUDA implementation' but does not provide specific version numbers for software dependencies or libraries. |
| Experiment Setup | Yes | For TM hyperparameters in BBC Sports, TREC, and R8, we use 8000 clauses, a voting margin T of 100, and specificity s of 10.0. For hyperparameters, we adopt 8000 clauses per class, a voting margin T as 10000, and specificity s as 5.0 in the MNIST experiments. For CIFAR-2, the number of clauses is 8000, T is 6000, and s is 10.0. |