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..
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. |