Deep Learning meets Projective Clustering
Authors: Alaa Maalouf, Harry Lang, Daniela Rus, Dan Feldman
ICLR 2021 | Conference PDF | Archive PDF | Plain Text | 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 fine tune the resulted network for 2 epochs. |