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..
Convergence Analysis of Two-layer Neural Networks with ReLU Activation
Authors: Yuanzhi Li, Yang Yuan
NeurIPS 2017 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | To complement our theory, we are also able to show experimentally that multi-layer networks with this mapping have better performance compared with normal vanilla networks. Our convergence theorem differs from traditional non-convex optimization techniques. We show that SGD converges to optimal in two phases : In phase I, the gradient points to the wrong direction, however, a potential function g gradually decreases. Then in phase II, SGD enters a nice one point convex region and converges. We also show that the identity mapping is necessary for convergence, as it moves the initial point to a better place for optimization. Experiment veri๏ฌes our claims. |
| Researcher Affiliation | Academia | Yuanzhi Li Computer Science Department Princeton University EMAIL Yang Yuan Computer Science Department Cornell University EMAIL |
| Pseudocode | No | No structured pseudocode or algorithm blocks were found in the paper. |
| Open Source Code | Yes | Our code can be found in the supplementary materials. |
| Open Datasets | Yes | In this experiment, we choose Cifar-10 as the dataset, and all the networks have 56-layers. |
| Dataset Splits | No | The paper mentions 'training set of size 100,000, and test set of size 10,000' in Section 5.2 but does not explicitly detail a validation split or its size. |
| Hardware Specification | No | No specific hardware details (e.g., GPU/CPU models, memory amounts, or detailed computer specifications) were mentioned for running experiments. |
| Software Dependencies | No | No specific software dependencies with version numbers (e.g., 'Python 3.8', 'PyTorch 1.9') were found in the paper. |
| Experiment Setup | Yes | We use batch size 200, step size 0.001. We run Res Link for 5 times with random initialization ( W 2 0.6 and W F 5), and plot the curves by taking the average. |