Learning to Convolve: A Generalized Weight-Tying Approach
Authors: Nichita Diaconu, Daniel Worrall
ICML 2019 | Conference PDF | Archive PDF | Plain Text | 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. |