Approximation Algorithms for Cascading Prediction Models
Authors: Matthew Streeter
ICML 2018 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | 4. Experiments In this section we evaluate our cascade generation algorithm by applying it to state-of-the-art pre-trained models for the Image Net classification task. |
| Researcher Affiliation | Industry | 1Google Research. Correspondence to: Matthew Streeter <mstreeter@google.com>. |
| Pseudocode | Yes | Algorithm 1 Confident Model(x; p, ˆq, t) |
| Open Source Code | No | The paper does not provide any specific repository links or explicit statements about the release of source code for the described methodology. |
| Open Datasets | Yes | We used 25,000 images from the ILSVRC 2012 validation set (Russakovsky et al., 2015) to run the algorithm, and report results on the remaining 25,000 validation images. |
| Dataset Splits | Yes | We used 25,000 images from the ILSVRC 2012 validation set (Russakovsky et al., 2015) to run the algorithm, and report results on the remaining 25,000 validation images. |
| Hardware Specification | No | The paper does not provide specific details about the hardware (e.g., GPU/CPU models, processor types) used for running its experiments. |
| Software Dependencies | No | The paper mentions "TF-Slim library (Silberman & Guadarrama, 2016)" but does not specify its version or any other software dependencies with version numbers. |
| Experiment Setup | Yes | For our Image Net experiments, we take top-1 accuracy as the accuracy metric, and predict its value based on a vector of features derived from the model s predicted class probabilities. We use as features (1) the entropy of the vector, (2) the maximum predicted class probability, and (3) the gap between the first and second highest predictions in logit space. Our accuracy model ˆq is fit using logistic regression on a validation set of 25,000 images. |