reproducibilityindex.ai

Ex Uno Pluria: Insights on Ensembling in Low Precision Number Systems

Authors: Giung Nam, Juho Lee

Reproducibility Variable	Result	LLM Response
Research Type	Experimental	Our empirical analysis demonstrates the effectiveness of our proposed low precision ensembling method compared to existing ensemble approaches.
Researcher Affiliation	Academia	Giung Nam Kim Jaechul Graduate School of AI KAIST, Daejeon, South Korea giung@kaist.ac.kr Juho Lee Kim Jaechul Graduate School of AI KAIST, Daejeon, South Korea juholee@kaist.ac.kr
Pseudocode	No	The paper does not contain explicitly labeled pseudocode or algorithm blocks.
Open Source Code	Yes	The code is available at https://github.com/cs-giung/lpe-bsr.
Open Datasets	Yes	We employed two datasets for our experiments: Image Net (Russakovsky et al., 2015) for Vi T and CLIP-Vi T models, and MMLU (Hendrycks et al., 2021) for LLa Ma.
Dataset Splits	Yes	Table 1 summarizes the evaluation results on a subset of the Image Net validation split, along with the parameter count for each model.
Hardware Specification	Yes	We conducted experiments using TPUv2/v3/v4 cores, with flexibility in selecting the cores based on the memory requirements of each experiment.
Software Dependencies	No	We built our experimental code using JAX (Bradbury et al., 2018) and Transformers (Wolf et al., 2020), both licensed under Apache-2.0.
Experiment Setup	Yes	The optimization process for CLIP-Vi T-L/14 models concludes after 100,000 iterations with a minibatch size of 64, employing a cosine decaying learning rate schedule.