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..
FlexConv: Continuous Kernel Convolutions With Differentiable Kernel Sizes
Authors: David W. Romero, Robert-Jan Bruintjes, Jakub Mikolaj Tomczak, Erik J Bekkers, Mark Hoogendoorn, Jan van Gemert
ICLR 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We evaluate Flex Conv across classification tasks on sequential and image benchmark datasets, and validate the ability of MAGNets to approximate complex functions. A complete description of the datasets used is given in Appx. B. Appx. D.2 reports the parameters used in all our experiments.1 |
| Researcher Affiliation | Academia | 1 Vrije Universiteit Amsterdam 2 Delft University of Technology 3 University of Amsterdam The Netherlands |
| Pseudocode | No | No structured pseudocode or algorithm blocks were found. |
| Open Source Code | Yes | Our code is publicly available at https://github.com/rjbruin/flexconv. |
| Open Datasets | Yes | All datasets used in our experiments are publicly available. |
| Dataset Splits | Yes | The Image Net-k (Chrabaszcz et al., 2017) dataset... contains 1000 classes with 1,281,167 training samples and 50,000 validation samples. |
| Hardware Specification | No | No specific hardware details (exact GPU/CPU models, processor types with speeds, memory amounts, or detailed computer specifications) were explicitly mentioned for running the experiments. |
| Software Dependencies | No | The paper mentions using 'Weights & Biases (Biewald, 2020)' and 'torch.backends.cudnn.benchmark' but does not specify version numbers for PyTorch, CUDA, or other key software components required for replication. |
| Experiment Setup | Yes | Unless otherwise specified, we use a learning rate of 0.01 with a cosine annealing scheme (Loshchilov & Hutter, 2016) with five warmup epochs. We use a different learning rate of 0.1 the regular learning rate for the Flex Conv Gaussian mask parameters. We do not use weight decay, unless otherwise specified. |