Hierarchical interpretations for neural network predictions
Authors: Chandan Singh, W. James Murdoch, Bin Yu
ICLR 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We demonstrate the utility of ACD on both long short term memory networks (LSTMs) (Hochreiter & Schmidhuber, 1997) trained on the Stanford Sentiment Treebank (SST) (Socher et al., 2013) and CNNs trained on MNIST (Le Cun, 1998) and Image Net (Russakovsky et al., 2015). Through human experiments, we show that ACD produces intuitive visualizations that enable users to better reason about and trust DNNs. We also demonstrate that ACD s hierarchy is robust to adversarial perturbations (Szegedy et al., 2013) in CNNs. |
| Researcher Affiliation | Academia | Chandan Singh Department of EECS UC Berkeley c singh@berkeley.edu W. James Murdoch Department of Statistics UC Berkeley jmurdoch@berkeley.edu Bin Yu Department of Statistics, EECS UC Berkeley binyu@berkeley.edu |
| Pseudocode | Yes | Algorithm 1 Agglomeration algorithm. ACD(Example x, model, hyperparameter k, function CD(x, blob; model)) |
| Open Source Code | Yes | Code and scripts for running ACD and experiments available at https://github.com/csinva/ acd |
| Open Datasets | Yes | We demonstrate the utility of ACD on both long short term memory networks (LSTMs) (Hochreiter & Schmidhuber, 1997) trained on the Stanford Sentiment Treebank (SST) (Socher et al., 2013) and CNNs trained on MNIST (Le Cun, 1998) and Image Net (Russakovsky et al., 2015). |
| Dataset Splits | No | The paper mentions 'validation set' in Table 1 but does not provide explicit training/validation/test dataset splits (e.g., percentages or sample counts). |
| Hardware Specification | No | The paper states 'All models are implemented using Py Torch' but does not provide any specific hardware details such as GPU models, CPU types, or memory specifications used for running the experiments. |
| Software Dependencies | No | The paper states 'All models are implemented using Py Torch' but does not provide specific version numbers for PyTorch or any other software dependencies. |
| Experiment Setup | No | The paper mentions training models using 'standard best practices' and 'standard Py Torch example', but it does not provide specific hyperparameters like learning rate, batch size, number of epochs, or other detailed training settings. |