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..
Two Tales of Single-Phase Contrastive Hebbian Learning
Authors: Rasmus Høier, Christopher Zach
ICML 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our numerical experiments aim to verify two claims: first, in Section 6.1 we demonstrate that choosing different values for α makes a significant difference in the learned weight matrices. Second, Section 6.2 validates that the proposed DP method is efficient enough to enable successful training beyond toy-sized DNNs for arbitrary choices of α 2. |
| Researcher Affiliation | Academia | 1Department of Electrical Engineering, Chalmers University of Technology, Sweden. Correspondence to: Rasmus Høier <EMAIL>. |
| Pseudocode | No | The paper describes algorithms and derivations through equations and textual descriptions, but it does not include a formally labeled "Pseudocode" or "Algorithm" block. |
| Open Source Code | Yes | The code used in our experiments is available at: github. com/Rasmuskh/dualprop_icml_2024 |
| Open Datasets | Yes | We ran experiments on MNIST and Fashion MNIST using a 784-512( 2)-10 MLP with ReLU activation functions... We also train a 16 layer VGG network using DP with a crossentropy classification loss on the CIFAR10 and CIFAR100 datasets... We restrict the more compute intensive Image Net32x32 experiments to the setting α = 1/2 and β = 0.01. |
| Dataset Splits | Yes | 10% of the training data is hold out as validation set for model selection. The hyperparameters are listed in Section B. ... We use 5% of the training data for validation and model selection, and use the public validation dataset to evaluate the selected model. |
| Hardware Specification | No | The experiments were enabled by the supercomputing resource Berzelius provided by National Supercomputer Centre at Linköping University and the Knut and Alice Wallenberg foundation. |
| Software Dependencies | No | The paper does not provide specific version numbers for any software dependencies used in the experiments (e.g., PyTorch 1.x, TensorFlow 2.x, Python 3.x). |
| Experiment Setup | Yes | We ran experiments on MNIST and Fashion MNIST using a 784-512( 2)-10 MLP with ReLU activation functions, and Table 1 reports test accuracy and Lipschitz estimates... after 20 epochs of ADAM-based training (with learning rate 0.001 and default parameters otherwise). ... We employ standard data augmentation (random crops and horizontal flips) and carry out all experiments with 3 random seeds and report mean and std. deviation. ... The experiments of Section 6.2 were carried out with a VGG16 network and the following hyper parameters: Table 4. Hyper parameters Epochs Learning rate Momentum Weight-decay Batchsize. |