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..
Structured and Deep Similarity Matching via Structured and Deep Hebbian Networks
Authors: Dina Obeid, Hugo Ramambason, Cengiz Pehlevan
NeurIPS 2019 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Simulations show that our networks learn meaningful features. 6 Simulations Next, we illustrate the performance of the structured and deep similarity matching networks in various datasets. |
| Researcher Affiliation | Academia | Dina Obeid Hugo Ramambason Cengiz Pehlevan John A. Paulson School of Engineering and Applied Sciences Harvard University Cambridge, MA, USA {dinaobeid@seas,hugo_ramambason@g,cpehlevan@seas}.harvard.edu |
| Pseudocode | No | The paper describes algorithms and equations (e.g., equations 1, 2, 17, 19) but does not present them in a structured pseudocode or algorithm block. |
| Open Source Code | No | The paper does not provide any statement or link to open-source code for the methodology described. |
| Open Datasets | Yes | We trained a 3-layer, locally connected Hebbian/anti-Hebbian neural network with examples from the labeled faces in the wild" dataset [33]. We trained a single-layer structured similarity matching network on the MNIST data set. |
| Dataset Splits | No | The paper does not explicitly mention training, validation, or test dataset splits with specific percentages or counts. |
| Hardware Specification | No | The paper does not specify any hardware details (e.g., GPU/CPU models, memory) used for running the experiments. |
| Software Dependencies | No | Classification was done using scikitlearn library s Linear SVC with default parameters. No specific version numbers for software dependencies are provided. |
| Experiment Setup | Yes | Neural activation functions were f(a) = max(min(a, 1), 0). We used a regularized version of the similarity matching cost [31] to enforce pattern decorrelation in the first and second layers. We trained with different γ values, shown are features for γ = 0.01. Network had a stride 2 and each neuron received input from a patch of radius ro = 4. Neurons belonging to the same site had inhibitory recurrent connections. We used hyperbolic tangent activation function (tanh(x)). |