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..
A statistical perspective on distillation
Authors: Aditya K Menon, Ankit Singh Rawat, Sashank Reddi, Seungyeon Kim, Sanjiv Kumar
ICML 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our findings are verified for linear models, neural networks, and decision trees, on both controlled synthetic and real-world datasets. |
| Researcher Affiliation | Industry | 1Google Research, New York. Correspondence to: Aditya Krishna Menon <EMAIL>. |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide concrete access to source code for the methodology described. |
| Open Datasets | Yes | On CIFAR-100, we train teachers that are Res Nets of varying depths, and distill these to a student Res Net of fixed depth 8. ... multiclass retrieval, AMAZONCAT-13K and AMAZONCAT-670K (Mc Auley & Leskovec, 2013; Bhatia et al., 2015). |
| Dataset Splits | No | The paper mentions training and test sets but does not explicitly provide details about a validation set or its split. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU/CPU models, memory amounts) used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific ancillary software details with version numbers (e.g., library or solver names with versions). |
| Experiment Setup | Yes | On CIFAR-100, we train teachers that are Res Nets of varying depths, and distill these to a student Res Net of fixed depth 8. ... We use a feedforward teacher model with a single (linear) hidden layer of width 512, trained to minimise the softmax cross-entropy. For the student , we make the hidden layer width 8 for AMAZONCAT-13K and 64 for AMAZONCAT-670K. ... apply temperature scaling with T 2 {1.0, 1.5, 2.0, . . . , 5.0}. |