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 Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Enhancing Simple Models by Exploiting What They Already Know
Authors: Amit Dhurandhar, Karthikeyan Shanmugam, Ronny Luss
ICML 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | The benefit of these contributions is witnessed in the experiments where on 6 UCI datasets and CIFAR-10 we outperform competitors in a majority (16 out of 27) of the cases and tie for best performance in the remaining cases. |
| Researcher Affiliation | Industry | 1IBM Research, Yorktown Heights, NY, USA. |
| Pseudocode | Yes | Algorithm 1 Our proposed method SRatio. |
| Open Source Code | No | The paper does not provide concrete access to source code for the methodology described. |
| Open Datasets | Yes | We experiment on 6 real datasets from UCI repository (Dheeru & Karra Taniskidou, 2017): Ionosphere, Ovarian Cancer (OC), Heart Disease (HD), Waveform, Human Activity Recognition (HAR), Musk as well as CIFAR-10 (Krizhevsky, 2009). |
| Dataset Splits | Yes | Datasets are randomly split into 70% train and 30% test. Results for all methods are averaged over 10 random splits and reported in Table 2 with 95% confidence intervals. Optimal values for γ and β are found using 10-fold crossvalidation. ... 500 samples from the CIFAR-10 test set are used for validation and hyperparameter tuning (details in supplement). |
| Hardware Specification | No | The paper does not provide specific hardware details (exact GPU/CPU models, processor types with speeds, memory amounts, or detailed computer specifications) used for running its experiments. |
| Software Dependencies | Yes | Tensorflow 1.5.0 was used for CIFAR-10 experiments. |
| Experiment Setup | Yes | Datasets are randomly split into 70% train and 30% test. Optimal values for γ and β are found using 10-fold crossvalidation. The complex model is an 18 unit Res Net with 15 residual (Res) blocks/units. ... Distillation (Geoffrey Hinton, 2015) employs cross-entropy loss with soft targets to train the simple model. The soft targets are the softmax outputs of the complex model s last layer rescaled by temperature t = 0.5 which was selected based on cross-validation. |