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..
The Entropy Enigma: Success and Failure of Entropy Minimization
Authors: Ori Press, Ravid Shwartz-Ziv, Yann Lecun, Matthias Bethge
ICML 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments on 23 challenging datasets show that our method sets the So TA with a mean absolute error of 5.75%, an improvement of 29.62% over the previous So TA on this task. |
| Researcher Affiliation | Collaboration | 1University of T ubingen, T ubingen AI Center, Germany 2New York University 3Meta AI, FAIR. |
| Pseudocode | No | No structured pseudocode or algorithm blocks are present in the paper. |
| Open Source Code | Yes | Our code is available at: https://github. com/oripress/Entropy Enigma |
| Open Datasets | Yes | Experiments on 23 challenging datasets show that our method sets the So TA with a mean absolute error of 5.75%, an improvement of 29.62% over the previous So TA on this task. Our chosen datasets encompass a wide spectrum, from various types of noise (IN-C, IN-C, IN-3DCC, CCC) and domain shifts (IN-R, IN-V2, IN-D), to adversarial noises (Patch-IN, BG Challenge, IN-Obfuscations), and even images featuring classes not present in Image Net (NINCO). |
| Dataset Splits | Yes | The training set was replicated seven times, systematically omitting images for which the ground truth label lay somewhere in the pre-trained model’s top-k predictions... The model’s accuracy was then evaluated on the holdout set, with evaluations every ten iterations, spanning a total of 1,000 iterations. |
| Hardware Specification | No | No specific details about the hardware (e.g., GPU/CPU models, memory, or cloud instances) used for running experiments are provided in the paper. |
| Software Dependencies | No | The paper does not provide specific software dependencies (e.g., library or solver names with version numbers like Python 3.8, PyTorch 1.9) needed to replicate the experiment. |
| Experiment Setup | Yes | We used a Res Net-50 (He et al., 2016)." and "RDumb uses a SGD with a learning rate of 2.5 10 4, and a batch size of 64, and is reset to its pre-trained state every 1,000 iterations." and "E0 = 0.4 ln103." and "α = 0.9." |