Model Preserving Compression for Neural Networks
Authors: Jerry Chee, Megan Flynn (née Renz), Anil Damle, Christopher M. De Sa
NeurIPS 2022 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We demonstrate the efficacy of our approach with strong empirical performance on a variety of tasks, models, and datasets from simple one-hiddenlayer networks to deep networks on Image Net. |
| Researcher Affiliation | Academia | Jerry Chee Department of Computer Science Cornell University EMAIL Megan Flynn (née Renz) Department of Physics Cornell University EMAIL Anil Damle Department of Computer Science Cornell University EMAIL Christopher De Sa Department of Computer Science Cornell University EMAIL |
| Pseudocode | Yes | Algorithm 1 Pruning a multilayer network with interpolative decompositions |
| Open Source Code | Yes | Our code is available at https://github.com/jerry-chee/Model Preserve Compression NN |
| Open Datasets | Yes | To complement our algorithmic developments and theoretical contributions, in Section 7 we demonstrate the efficacy of our method on Atom3D [72], CIFAR-10 [43], and Image Net [19]. |
| Dataset Splits | Yes | We then remove a class from the pruning set to simulate an under-represented class (but leave it in the train and test sets). |
| Hardware Specification | No | The paper mentions general compute aspects and computational feasibility (e.g., "computational complexity", "computationally feasible") but does not specify the hardware (e.g., specific GPU or CPU models) used for experiments. |
| Software Dependencies | No | The paper mentions general software like LAPACK and TensorFlow in references but does not specify version numbers for any software dependencies relevant to reproducing the experiments. |
| Experiment Setup | No | Full hyper-parameter details can be found in the Appendix and code. |