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..
Deep Sets
Authors: Manzil Zaheer, Satwik Kottur, Siamak Ravanbakhsh, Barnabas Poczos, Russ R. Salakhutdinov, Alexander J. Smola
NeurIPS 2017 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We demonstrate the applicability of our method on population statistic estimation, point cloud classification, set expansion, and outlier detection. |
| Researcher Affiliation | Collaboration | Manzil Zaheer1,2, Satwik Kottur1, Siamak Ravanbhakhsh1, Barnabás Póczos1, Ruslan Salakhutdinov1, Alexander J Smola1,2 1 Carnegie Mellon University 2 Amazon Web Services EMAIL |
| Pseudocode | No | No structured pseudocode or algorithm blocks are present in the paper. |
| Open Source Code | No | No explicit statement or link providing concrete access to the source code for the Deep Sets methodology described in this paper. |
| Open Datasets | Yes | MNIST8m [24] contains 8 million instances of 28 28 grey-scale stamps of digits in {0, . . . , 9}. |
| Dataset Splits | Yes | Each dataset is split into TRAIN (80%), VAL (10%) and TEST (10%). We learn models using TRAIN and evaluate on TEST, while VAL is used for hyperparameter selection and early stopping. |
| Hardware Specification | No | No specific hardware details (GPU models, CPU types, memory) used for running experiments are provided in the paper. |
| Software Dependencies | No | The paper does not provide specific version numbers for software dependencies or libraries used (e.g., Python, PyTorch, TensorFlow versions). |
| Experiment Setup | Yes | We train using L2 loss with a Deep Sets architecture having 3 fully connected layers with Re LU activation for both transformations φ and ρ. |