Streaming Weak Submodularity: Interpreting Neural Networks on the Fly
Authors: Ethan Elenberg, Alexandros G. Dimakis, Moran Feldman, Amin Karbasi
NeurIPS 2017 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | An experimental evaluation of our algorithm in two applications: nonlinear sparse regression using pairwise products of features and interpretability of black-box neural network classifiers. |
| Researcher Affiliation | Academia | Ethan R. Elenberg Department of Electrical and Computer Engineering The University of Texas at Austin EMAIL Alexandros G. Dimakis Department of Electrical and Computer Engineering The University of Texas at Austin EMAIL Moran Feldman Department of Mathematics and Computer Science Open University of Israel EMAIL Amin Karbasi Department of Electrical Engineering Department of Computer Science Yale University EMAIL |
| Pseudocode | Yes | Algorithm 1 THRESHOLD GREEDY(f, k, ) |
| Open Source Code | Yes | Code for these experiments is available at https://github.com/eelenberg/streak. |
| Open Datasets | Yes | In this experiment, a sparse logistic regression is fit on 2000 training and 2000 test observations from the Phishing dataset [Lichman, 2013]. |
| Dataset Splits | No | The paper mentions '2000 training and 2000 test observations' for the Phishing dataset but does not specify validation splits or other detailed splitting methodologies. |
| Hardware Specification | No | The paper does not provide specific details about the hardware used to run experiments, such as exact GPU/CPU models or memory amounts. |
| Software Dependencies | No | The paper mentions Inception V3 and LIME but does not specify versions for these or any other software components or libraries. |
| Experiment Setup | Yes | Figure 1(a) shows both the final log likelihood and the generalization accuracy for RANDOMSUBSET, LOCALSEARCH, and our STREAK algorithm for " = {0.75, 0.1} and k = {20, 40, 80}. |