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].
A Combinatorial Perspective on the Optimization of Shallow ReLU Networks
Authors: Michael S Matena, Colin A. Raffel
NeurIPS 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We ran experiments comparing batch gradient descent to solving (3) for a randomly chosen vertex on some toy datasets. We present some of our results in fig. 2. |
| Researcher Affiliation | Academia | Michael Matena Department of Computer Science University of North Carolina at Chapel Hill Chapel Hill, NC 27599 EMAIL Colin Raffel Department of Computer Science University of North Carolina at Chapel Hill Chapel Hill, NC 27599 EMAIL |
| Pseudocode | Yes | Algorithm 1 Exact ERM (Arora et al., 2016) ... Algorithm 2 Greedy Local Search (GLS) Heuristic |
| Open Source Code | Yes | Did you include the code, data, and instructions needed to reproduce the main experimental results (either in the supplemental material or as a URL)? [Yes] We have included this in the supplemental material. |
| Open Datasets | Yes | We also created toy binary classification datasets from MNIST (Le Cun et al., 2010) and Fashion MNIST (Xiao et al., 2017) |
| Dataset Splits | No | The paper mentions 'training sets' but does not explicitly provide details about train/validation/test splits or mention a 'validation' set. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., CPU/GPU models, memory, or detailed cluster specifications) used for running experiments. The authors' ethics statement acknowledges: 'Did you include the total amount of compute and the type of resources used (e.g., type of GPUs, internal cluster, or cloud provider)? [No]' |
| Software Dependencies | No | The paper mentions software libraries like 'CVXPY', 'ECOS', 'scikit-learn', 'PyTorch', and 'NumPy', but does not specify their version numbers. |
| Experiment Setup | Yes | See appendix H for details of the training procedures and for results on more d, mgen and d, N pairs. ... We used a batch size of 128 and a learning rate of 10−3. We trained for 1000 epochs using the Adam optimizer. |