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..
Understanding and Leveraging the Learning Phases of Neural Networks
Authors: Johannes Schneider, Mohit Prabhushankar
AAAI 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We empirically show the existence of three phases using common datasets and architectures such as Res Net and VGG: (i) near constant reconstruction loss, (ii) decrease, and (iii) increase. We also derive an empirically grounded data model and prove the existence of phases for single-layer networks. |
| Researcher Affiliation | Academia | Johannes Schneider1 , Mohit Prabhushankar 2 1University of Liechtenstein, Vaduz, Liechtenstein 2Georgia Institute of Technology, Atlanta, USA |
| Pseudocode | No | The paper does not contain any structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | *Code/Proofs: https://github.com/JohnTailor/LearnPhase |
| Open Datasets | Yes | We used CIFAR-10/100 (Krizhevsky and Hinton 2009), Fashion MNIST (Xiao, Rasul, and Vollgraf 2017), and MNIST (Deng 2012), all scaled to 32x32, available under the MIT (first 3 datasets) and GNU 3.0 license. |
| Dataset Splits | No | The paper mentions using CIFAR-10/100, Fashion MNIST, and MNIST, but does not explicitly provide specific train/validation/test dataset split percentages, sample counts, or direct references to predefined splits used for their experiments. |
| Hardware Specification | No | No specific hardware details such as GPU models, CPU types, or memory specifications used for running experiments are provided in the paper. |
| Software Dependencies | No | The paper mentions optimizers like 'Adam optimizer' and activation functions like 'Re LU' but does not specify software dependencies with version numbers (e.g., Python, PyTorch, TensorFlow, or specific library versions). |
| Experiment Setup | Yes | We used a fixed learning rate of 0.002 and stochastic gradient descent with batches of size 128 training for 256 epochs. [...] For each computation of the metrics, we trained the decoder for 30 epochs using the Adam optimizer with a learning rate of 0.0003. |