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..
Learning to Convolve: A Generalized Weight-Tying Approach
Authors: Nichita Diaconu, Daniel Worrall
ICML 2019 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We present our results on some basic benchmarks. We demonstrate competitive classification performance on the CIFAR-10 image recognition dataset, and compare the equivariance properties of activations from different models. |
| Researcher Affiliation | Collaboration | Nichita Diaconu 1 * Daniel Worrall 1 * ... 1Philips Lab, University of Amsterdam, Netherlands. |
| Pseudocode | Yes | Algorithm 1 Task-specific training using pre-trained basis; Algorithm 2 The basis is pretrained offline |
| Open Source Code | No | The paper does not provide any specific links or explicit statements about releasing source code for the described methodology. |
| Open Datasets | Yes | We present our results on some basic benchmarks... experiments on MNIST and CIFAR-10. ...Krizhevsky, A. Learning multiple layers of features from tiny images. Technical report, 2009. |
| Dataset Splits | No | The paper mentions 'Validation Set Angle' and 'Validation Set Error (%)' in figures, implying a validation set was used, but it does not specify exact split percentages, absolute sample counts, or explicit references to predefined splits for reproducibility. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU/CPU models, memory) used for running its experiments. |
| Software Dependencies | No | The paper mentions using 'AMSGrad variant of Adam' but does not specify software dependencies with version numbers (e.g., Python 3.x, PyTorch x.x). |
| Experiment Setup | Yes | We used the AMSGrad variant of Adam (Reddi et al., 2018) as the optimizer and we trained for 100 epochs at learning rate 10 3 and weight decay 10 6. We use a minibatch size of 100. For data augmentation, we use random flips, color normalization and random translations of at most 4 pixels. |