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..
Deep Learning meets Projective Clustering
Authors: Alaa Maalouf, Harry Lang, Daniela Rus, Dan Feldman
ICLR 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experimental results on the GLUE benchmark yield networks that are both more accurate and smaller compared to the standard matrix factorization (SVD). |
| Researcher Affiliation | Academia | 1 Robotics & Big Data Labs, Department of Computer Science, University of Haifa 2 CSAIL, MIT |
| Pseudocode | No | The paper describes a pipeline with numbered steps but does not present it as formal pseudocode or an algorithm block. |
| Open Source Code | Yes | Complete open code to reproduce the resulting networks is provided. |
| Open Datasets | Yes | GLUE benchmark. We run our experiments on the General Language Understanding Evaluation (GLUE) benchmark (Wang et al., 2018). |
| Dataset Splits | No | The paper mentions fine-tuning for '2 epochs' and evaluating on the 'training set' for model selection in Appendix D, but does not specify explicit training/validation/test dataset splits with percentages or counts for the general experiments. |
| Hardware Specification | Yes | All the experiments were conducted on a AWS c5a.16xlarge machine with 64 CPUs and 128 RAM [Gi B]. |
| Software Dependencies | Yes | Transformers version 3.1.0, and Py Torch version 1.6.0 (Paszke et al., 2017). |
| Experiment Setup | Yes | Given an embedding layer from a network that is trained on a task from GLUE, an integer k 1, and an integer j 1. We build and initialize a new architecture that replaces the original embedding layer by two smaller layers as explained in Figure 3. We then ο¬ne tune the resulted network for 2 epochs. |