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..
Convolutional and Residual Networks Provably Contain Lottery Tickets
Authors: Rebekka Burkholz
ICML 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | 4. Experiments, We verify in experiments that our theory derives realistic conditions. The first target network type is a small scale example with 3 layers and is pruned and trained on MNIST (Deng, 2012). The second target that we consider has a more realistic structure that is much deeper and includes residual blocks. Similar to before, we prune and train Res Net-22 on CIFAR10 (Krizhevsky, 2009). |
| Researcher Affiliation | Academia | Rebekka Burkholz 1, 1CISPA Helmholtz Center for Information Security, Saarbr ucken, Germany. |
| Pseudocode | Yes | Pseudocode for the algorithm that constructs a LT to approximate a given target network is provided in Appendix F. Algorithm 1 2L approximation, Algorithm 2 L + 1 approximation |
| Open Source Code | Yes | The Github repository Relational ML/LT-existence contains a corresponding Pytorch implementation and all code for the experiments. |
| Open Datasets | Yes | The first target network type is a small scale example with 3 layers and is pruned and trained on MNIST (Deng, 2012). we prune and train Res Net-22 on CIFAR10 (Krizhevsky, 2009). |
| Dataset Splits | No | No specific dataset split information (exact percentages, sample counts, citations to predefined splits, or detailed splitting methodology) needed to reproduce the data partitioning was found for training/validation/test splits. |
| Hardware Specification | Yes | on a machine with Intel(R) Core(TM) i9-10850K CPU @ 3.60GHz processor and GPU NVIDIA Ge Force RTX 3080 Ti. |
| Software Dependencies | No | The Github repository Relational ML/LT-existence contains a corresponding Pytorch implementation and all code for the experiments. (No specific versions for Pytorch or other dependencies mentioned) |
| Experiment Setup | Yes | which we apply with exponential annealing of the target sparsity with 12 steps on a machine with Intel(R) Core(TM) i9-10850K CPU @ 3.60GHz processor and GPU NVIDIA Ge Force RTX 3080 Ti. Between pruning steps, we train the pruned network for 50 epochs. |