Deep Sets
Authors: Manzil Zaheer, Satwik Kottur, Siamak Ravanbakhsh, Barnabas Poczos, Russ R. Salakhutdinov, Alexander J. Smola
NeurIPS 2017 | Conference PDF | Archive PDF | Plain Text | 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 {manzilz,skottur,mravanba,bapoczos,rsalakhu,smola}@cs.cmu.edu |
| 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. |