Set Prediction without Imposing Structure as Conditional Density Estimation
Authors: David W Zhang, Gertjan J. Burghouts, Cees G. M. Snoek
ICLR 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We empirically demonstrate on a variety of datasets the capability to learn multi-modal densities and produce different plausible predictions. Our approach is competitive with previous set prediction models on standard benchmarks. |
| Researcher Affiliation | Collaboration | David W. Zhang1, Gertjan J. Burghouts2, Cees G. M. Snoek1 1University of Amsterdam {w.d.zhang, cgmsnoek}@uva.nl 2TNO {gertjan.burghouts}@tno.nl |
| Pseudocode | No | The paper provides mathematical equations and describes steps of the proposed methods, but it does not include any clearly labeled pseudocode or algorithm blocks. |
| Open Source Code | Yes | Code is available at: https://github.com/davzha/DESP. |
| Open Datasets | Yes | Following the setup from Zhang et al. (2019), we convert MNIST (Le Cun et al., 2010) into point-clouds... We re-purpose Celeb A (Liu et al., 2015) for subset anomaly detection... |
| Dataset Splits | No | The paper mentions using training and test sets but does not provide explicit details about the exact percentages, absolute counts, or methodology for training/validation/test dataset splits, nor does it specify predefined splits for the used datasets beyond the test partition. |
| Hardware Specification | No | The paper does not provide any specific details about the hardware used to run the experiments, such as GPU models, CPU types, or memory specifications. |
| Software Dependencies | No | The paper does not list any specific software dependencies with version numbers (e.g., programming language versions, library versions, or specific solver versions). |
| Experiment Setup | Yes | We adopt the same neural network architecture, hyper-parameters and padding scheme as Zhang et al. (2019), to facilitate a fair comparison. Both g and f are instantiated as 2-layer MLPs with 256 hidden dimensions. |