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..
Formation of Representations in Neural Networks
Authors: Liu Ziyin, Isaac Chuang, Tomer Galanti, Tomaso Poggio
ICLR 2025 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In this section, we present experimental evidence that supports predictions resulting from the CRH. We also perform experiments to test mechanisms that break the CRH. |
| Researcher Affiliation | Collaboration | 1Massachusetts Institute of Technology 2Texas A&M University 3NTT Research |
| Pseudocode | No | The paper describes theoretical frameworks and experimental results but does not contain any clearly labeled pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any statement regarding the availability of source code or links to a code repository for the described methodology. |
| Open Datasets | Yes | res1: Res Net-18 (11M parameters) for the image classification; res2: Res Net-18 self-supervised learning tasks with the CIFAR-10/100 datasets. llm: a six-layer eight-head transformer (100M parameters) trained on the Open Web Text (OWT) dataset (Gokaslan & Cohen, 2019); |
| Dataset Splits | Yes | res1: Res Net-18 (11M parameters) for the image classification; ... We measure the covariances matrices with data points from the test set. res2: Res Net-18 for self-supervised learning tasks with the CIFAR-10/100 datasets. |
| Hardware Specification | No | The paper does not provide specific hardware details such as GPU models, CPU types, or memory specifications used for running the experiments. |
| Software Dependencies | No | The paper mentions optimizers like SGD and Adam, and activation functions like ReLU, but does not provide specific software dependencies with version numbers (e.g., PyTorch 1.9, Python 3.8). |
| Experiment Setup | Yes | fc1: ...depth of the network (D = 4), the width of the network (d = 100), weight decay strength (γ = 2 10 5), minibatch size (B = 100). fc2: ...SGD with a learning rate of 0.1 with momentum 0.9 and γ = 10 4 for 105 steps... batch size of 100. res1: ...train with SGD with a learning rate 0.01, momentum 0.9, cosine annealing for 200 epochs, and batch size 128. |