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 [1].
Neural Network Branching for Neural Network Verification
Authors: Jingyue Lu, M. Pawan Kumar
ICLR 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Empirically, our framework achieves roughly 50% reduction in both the number of branches and the time required for verification on various convolutional networks when compared to the best available hand-designed branching strategy. |
| Researcher Affiliation | Academia | Jingyue Lu University of Oxford EMAIL M. Pawan Kumar University of Oxford EMAIL |
| Pseudocode | Yes | Algorithm 1 Branch and Bound |
| Open Source Code | Yes | Code for all experiments is available at https://github.com/oval-group/GNN_branching. |
| Open Datasets | Yes | We adopt a similar network structure but using a more challenging dataset, namely CIFAR-10, for an increased difficulty level. |
| Dataset Splits | Yes | We use 430 properties to generate 17958 training samples and the rest of properties to generate 5923 validation samples. |
| Hardware Specification | No | The paper states 'We ran all verification experiments in parallel on 16 CPU cores' but does not specify particular CPU models (e.g., Intel Xeon, AMD Ryzen) or GPU models. |
| Software Dependencies | No | The paper mentions using 'Gurobi' and 'Adam optimizer' but does not provide specific version numbers for these or any other software dependencies. |
| Experiment Setup | Yes | We compute intermediate bounds using linear bounds relaxations... For the output lower bound, we use Planet relaxation... Adam optimizer with weight decay rate λ = 1e 4 and learning rate 1e 4... The batch size is set to 2... The threshold is set to be 0.2... γ = 1 and t = 0.1 in the loss function lossonline. |