Streaming Weak Submodularity: Interpreting Neural Networks on the Fly
Authors: Ethan Elenberg, Alexandros G. Dimakis, Moran Feldman, Amin Karbasi
NeurIPS 2017 | Conference 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 elenberg@utexas.edu Alexandros G. Dimakis Department of Electrical and Computer Engineering The University of Texas at Austin dimakis@austin.utexas.edu Moran Feldman Department of Mathematics and Computer Science Open University of Israel moranfe@openu.ac.il Amin Karbasi Department of Electrical Engineering Department of Computer Science Yale University amin.karbasi@yale.edu |
| 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}. |