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..
Why Are Convolutional Nets More Sample-Efficient than Fully-Connected Nets?
Authors: Zhiyuan Li, Yi Zhang, Sanjeev Arora
ICLR 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Figure 1: Comparison of generalization performance of convolutional versus fully-connected models trained by SGD. The grey dotted lines indicate separation, and we can see convolutional networks consistently outperform fully-connected networks. Here the input data are 3 32 32 RGB images and the binary label indicates for each image whether the first channel has larger ℓ2 norm than the second one. The input images are drawn from entry-wise independent Gaussian (left) and CIFAR-10 (right). |
| Researcher Affiliation | Academia | Zhiyuan Li, Yi Zhang Princeton University zhiyuanli,EMAIL Sanjeev Arora Princeton University & IAS EMAIL |
| Pseudocode | Yes | Algorithm 1 Iterative algorithm A; Algorithm 2 Gradient Descent for FC-NN (FC networks) |
| Open Source Code | No | The paper does not contain any explicit statement or link indicating the availability of open-source code for the described methodology. |
| Open Datasets | Yes | Figure 1: ...The input images are drawn from entry-wise independent Gaussian (left) and CIFAR-10 (right). |
| Dataset Splits | No | The paper mentions 'training data' and 'generalization performance' implying training and test sets, but does not explicitly describe a validation split or specific data partitioning for reproducibility. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU/CPU models, memory) used for running the experiments. |
| Software Dependencies | No | The paper mentions 'SGD' and 'batch-normalization Ioffe & Szegedy (2015)' but does not list specific software dependencies with version numbers. |
| Experiment Setup | Yes | Figure 1: ...The 3-layer convolutional networks consist of two 3x3 convolutions with 10 hidden channels, and a 3x3 convolution with a single output channel followed by global average pooling. The 3-layer fully-connected networks consist of two fully-connected layers with 10000 hidden channels and another fully-connected layer with a single output. The 2-layer versions have one less intermediate layer and have only 3072 hidden channels for each layer. bn stands for batch-normalization Ioffe & Szegedy (2015). |