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..
Neural Separation of Observed and Unobserved Distributions
Authors: Tavi Halperin, Ariel Ephrat, Yedid Hoshen
ICML 2019 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments on audio and image separation tasks show that our method outperforms current methods that use the same level of supervision, and often achieves similar performance to full supervision. |
| Researcher Affiliation | Collaboration | 1Department of Computer Science, The Hebrew University of Jerusalem, Jerusalem, Israel 2Google Research 3Facebook AI Research. |
| Pseudocode | Yes | Algorithm 1 Neural Egg Separation (NES) |
| Open Source Code | No | The paper does not explicitly state that source code for the methodology is provided or include a link to a code repository. |
| Open Datasets | Yes | We split the MNIST dataset (Le Cun & Cortes, 2010) [...] Handbags (Zhu et al., 2016) and Shoes (Yu & Grauman, 2014) datasets [...] Oxford-BBC Lip Reading in the Wild (LRW) Dataset (Chung & Zisserman, 2016) [...] ESC-50 (Piczak, 2015) [...] MUSDB18 Dataset (Raο¬i et al., 2017) |
| Dataset Splits | No | The paper describes training and testing sets, for instance, for MNIST: 'We use 12k B training images as the B training set, while for each of the other 12k B training images, we randomly sample a X image and additively combine the images to create the Y training set. We evaluate the performance of our method on 5000 Y images similarly created from the test set of X and B.' However, it does not explicitly define a validation set or a comprehensive train/validation/test split methodology for all experiments. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU or CPU models, memory, or cloud instance types) used for running the experiments. |
| Software Dependencies | No | The paper does not specify software dependencies with version numbers. |
| Experiment Setup | Yes | For optimization, we use SGD using ADAM update with a learning rate of 0.001. In total we perform N = 10 iterations, each consisting of optimization of T and estimation of xt, P = 25 epochs are used for each optimization of Eq. 3. |