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..
Tuning Frequency Bias in Neural Network Training with Nonuniform Data
Authors: Annan Yu, Yunan Yang, Alex Townsend
ICLR 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | This section presents three experiments with synthetic and real-world datasets to investigate the frequency bias of NN training using squared L2 loss and squared Hs loss. |
| Researcher Affiliation | Academia | Annan Yu Cornell University EMAIL Yunan Yang ETH Z urich EMAIL Alex Townsend Cornell University EMAIL |
| Pseudocode | No | The paper describes methods, such as gradient descent, but does not include any explicitly labeled 'Pseudocode' or 'Algorithm' blocks. |
| Open Source Code | No | The paper does not provide an explicit statement about releasing source code for the described methodology or a link to a code repository. |
| Open Datasets | Yes | MNIST handwritten digit database. ATT Labs [Online]. Available: http://yann.lecun.com/exdb/mnist, 2, 2010. and We utilize a data set {xi}2500 i=1 in (Wright & Michaels, 2015), which comes with carefully designed positive quadrature weights {ci}2500 i=1 . |
| Dataset Splits | No | The paper uses datasets like MNIST but does not provide specific details on training, validation, and test splits (e.g., percentages, sample counts, or citations to predefined splits). |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU/CPU models, memory, or cloud instance types) used for running the experiments. |
| Software Dependencies | No | The paper mentions 'keras' in a citation but does not list any specific software dependencies with version numbers required for reproducibility. |
| Experiment Setup | Yes | We set up two 2-layer Re LU-activated NNs with 5 104 hidden neurons in each layer and train them using the same training data and gradient descent procedure, except with different loss functions Φ and eΦ. and We train the autoencoder using mini-batch gradient descent with batch size equal to 256. |