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..
CADDA: Class-wise Automatic Differentiable Data Augmentation for EEG Signals
Authors: Cédric Rommel, Thomas Moreau, Joseph Paillard, Alexandre Gramfort
ICLR 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In the class-agnostic setting, results show that our new relaxation leads to optimal performance with faster training than competing gradient-based methods, while also outperforming gradient-free methods in the class-wise setting. Finally, in Section 6, we use the EEG sleep staging task in the class-agnostic setting to evaluate our approach against previously proposed gradient-based methods. We used the public dataset MASS Session 3 (O reilly et al., 2014). |
| Researcher Affiliation | Academia | Cédric Rommel, Thomas Moreau, Joseph Paillard & Alexandre Gramfort Université Paris-Saclay, Inria, CEA, Palaiseau, 91120, France {firstname.lastname}@inria.fr |
| Pseudocode | Yes | Algorithm 1: (C)ADDA Input : ξ, ϵ > 0, Datasets Dtrain, Dvalid, Trainable policy Tα, Model θ Result: Policy parameters α while not converged do // compute the unrolled model gθ = L(θ|Tα(Dtrain)).backward(θ) θ := θ ξgθ // Estimate αL(θ |Dvalid) g θ = L(θ |Dvalid).backward(θ) g+ α = L(θ + ϵg θ|Tα(Dtrain)).backward(α) g α = L(θ ϵg θ|Tα(Dtrain)).backward(α) gα = 1 2ϵ(g+ α g α ) // Update Policy parameters α α = α ξgα // Update the model parameters θ gθ = L(θ|Tα(Dtrain)).backward(θ) θ = θ ξgθ end |
| Open Source Code | Yes | Their implementation in python is provided in the supplementary material (braindecode-wip folder). |
| Open Datasets | Yes | We used the public dataset MASS Session 3 (O reilly et al., 2014). We also used the standard sleep Physionet data (Goldberger et al., 2000) |
| Dataset Splits | Yes | Out of 83 subjects, 8 were left out for testing and the remaining ones were then split in training and validation sets, with respective proportions of 0.8 and 0.2. |
| Hardware Specification | Yes | Training was carried on single Tesla V100 GPUs. |
| Software Dependencies | Yes | It requires using Pytorch version 1.8, which now supports fft differentiation. |
| Experiment Setup | Yes | The optimizer used to train the model above was Adam with a learning rate of 10 3, β1 = 0. and β2 = 0.999. At most 300 epochs were used for training. Early stopping was implemented with a patience of 30 epochs. For automatic search experiments, the policy learning rate ξ introduced in (7) was set to 5 104 based on a grid-search carried using the validation set. Concerning the batch size, it was always set to 16, except for CADDA, for which it was doubled to 32 |