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..
Rethinking the Role of Gradient-based Attribution Methods for Model Interpretability
Authors: Suraj Srinivas, Francois Fleuret
ICLR 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our experiments show that improving the alignment of the implicit density model with the data distribution enhances gradient structure and explanatory power while reducing this alignment has the opposite effect. |
| Researcher Affiliation | Academia | Suraj Srinivas Idiap Research Institute & EPFL EMAIL Franc ois Fleuret University of Geneva EMAIL |
| Pseudocode | No | The paper describes algorithms and approximations but does not include any clearly labeled 'Pseudocode' or 'Algorithm' blocks. |
| Open Source Code | No | The paper does not contain any explicit statement about releasing source code for the methodology or a link to a code repository. |
| Open Datasets | Yes | For experiments, we shall consider the CIFAR100 dataset. We present experiments with CIFAR10 in the supplementary section. |
| Dataset Splits | No | The paper refers to a 'test set' but does not specify the full train/validation/test dataset splits, percentages, or methodology used for partitioning the data. |
| Hardware Specification | No | The paper mentions running experiments but does not provide specific hardware details such as GPU models, CPU models, or memory specifications. |
| Software Dependencies | No | The paper does not list specific software dependencies with version numbers, such as Python versions, deep learning frameworks (e.g., PyTorch, TensorFlow) with their versions, or other libraries. |
| Experiment Setup | Yes | Unless stated otherwise, the network structure we use shall be a 18-layer Res Net... and the optimizer used shall be SGD with momentum. All models use the softplus non-linearity with β = 10... For this, we use a regularization constant λ = 1e 3. ... We use a threshold of τ = 1000, and regularization constant λ = 1e 4. |