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..
Minimal Random Code Learning: Getting Bits Back from Compressed Model Parameters
Authors: Marton Havasi, Robert Peharz, José Miguel Hernández-Lobato
ICLR 2019 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our method sets new state-of-the-art in neural network compression, as it strictly dominates previous approaches in a Pareto sense: On the benchmarks Le Net-5/MNIST and VGG-16/CIFAR-10, our approach yields the best test performance for a fixed memory budget, and vice versa, it achieves the highest compression rates for a fixed test performance. |
| Researcher Affiliation | Collaboration | Marton Havasi Department of Engineering University of Cambridge EMAIL Robert Peharz Department of Engineering University of Cambridge EMAIL Jos e Miguel Hern andez-Lobato Department of Engineering University of Cambridge, Microsoft Research, Alan Turing Institute EMAIL |
| Pseudocode | Yes | Algorithm 1 Minimal Random Coding; Algorithm 2 Minimal Random Code Learning (MIRACLE) |
| Open Source Code | Yes | The code is publicly available at https://github.com/cambridge-mlg/miracle |
| Open Datasets | Yes | The experiments were conducted on two common benchmarks: Le Net-5 on MNIST and VGG-16 on CIFAR-10. |
| Dataset Splits | No | The paper mentions 'optimizing the expected loss on the training set' and evaluating on the 'test set', and references 'validation' within Algorithm 2 (VARIATIONAL UPDATES(I)) for internal variational updates. However, it does not explicitly provide specific percentages, sample counts, or clear references to predefined splits for a dedicated 'validation' dataset split for its experiments, as distinct from training or testing. |
| Hardware Specification | Yes | 1 day on a single NVIDIA P100 for VGG |
| Software Dependencies | No | The paper mentions using 'Adam (Kingma & Ba, 2014)' but does not provide specific version numbers for any software dependencies like programming languages, libraries, or frameworks (e.g., Python, PyTorch, TensorFlow, CUDA). |
| Experiment Setup | Yes | For training MIRACLE, we used Adam (Kingma & Ba, 2014) with the default learning rate (10-3) and we set ϵβ0 = 10-8 and ϵβ = 5 · 10-5. The local coding goal Cloc was fixed at 20 bits for Le Net-5 and it was varied between 15 and 5 bits for VGG (B was kept constant). For the number of intermediate variational updates I, we used I = 50 for Le Net-5 and I = 1 for VGG, in order to keep training time reasonable ( 1 day on a single NVIDIA P100 for VGG). |