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..
A Spline Theory of Deep Learning
Authors: Randall Balestriero, baraniuk
ICML 2018 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We now empirically demonstrate that orthogonal templates lead to significantly improved classification performance. We conducted a range of experiments with three different conventional DN architectures small CNN, large CNN, and Res Net4-4 trained on three different datasets SVHN, CIFAR10, and CIFAR100. |
| Researcher Affiliation | Academia | Randall Balestriero 1 Richard G. Baraniuk 1 1ECE Department, Rice University, Houston, TX, USA. Correspondence to: Randall B. <EMAIL>. |
| Pseudocode | No | No structured pseudocode or algorithm blocks were found in the paper. |
| Open Source Code | No | The paper does not provide a direct link to open-source code for the methodology described, nor does it explicitly state that the code is released or available in supplementary materials. |
| Open Datasets | Yes | We conducted a range of experiments with three different conventional DN architectures small CNN, large CNN, and Res Net4-4 trained on three different datasets SVHN, CIFAR10, and CIFAR100. |
| Dataset Splits | No | The paper mentions using 'standard test data sets like CIFAR100' and 'average performance and standard deviation' over runs, but does not provide specific details on the train/validation/test dataset splits (e.g., percentages, sample counts, or explicit splitting methodology). |
| Hardware Specification | No | No specific hardware details (such as GPU/CPU models, memory, or cloud instance types) used for running experiments were mentioned in the paper. |
| Software Dependencies | No | The paper does not provide specific version numbers for any software dependencies used in the experiments (e.g., programming languages, libraries, or frameworks). |
| Experiment Setup | Yes | For learning, we used the Adam optimizer with an exponential learning rate decay. All inputs were centered to zero mean and scaled to a maximum value of one. ... Each DN employed bias units, Re LU activations, and max-pooling as well as batch-normalization prior each Re LU. |