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 [1].
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. |