Model Preserving Compression for Neural Networks
Authors: Jerry Chee, Megan Flynn (née Renz), Anil Damle, Christopher M. De Sa
NeurIPS 2022 | Conference 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 jerrychee@cs.cornell.edu Megan Flynn (née Renz) Department of Physics Cornell University mr2268@cornell.edu Anil Damle Department of Computer Science Cornell University damle@cs.cornell.edu Christopher De Sa Department of Computer Science Cornell University cdesa@cs.cornell.edu |
| 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. |