Deep Message Passing on Sets
Authors: Yifeng Shi, Junier Oliva, Marc Niethammer5750-5757
AAAI 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In addition to demonstrating the interpretability of our model by learning the true underlying relational structure experimentally, we also show the effectiveness of our approach on both synthetic and real-world datasets by achieving results that are competitive with or outperform the state-of-the-art. For readers who are interested in the detailed derivations of serveral results that we present in this work, please see the supplementary material at: https://arxiv.org/abs/1909.09877. Experiments We apply DMPS and its extensions to a range of synthetic-toy and real-world datasets. For each experiment, we compare our methods against, to the best of our knowledge, the state-of-the-art results for that dataset. |
| Researcher Affiliation | Academia | Yifeng Shi, Junier Oliva, Marc Niethammer Department of Computer Science, UNC-Chapel Hill, USA {yifengs, joliva, mn}@cs.unc.edu |
| Pseudocode | Yes | Algorithm 1: Deep Message Passing on Sets with the Set-denoising Block |
| Open Source Code | No | The paper does not explicitly state that source code for the methodology is provided, nor does it provide a direct link to a code repository. The arXiv link is for supplementary material, which typically includes more text/derivations, not necessarily code. |
| Open Datasets | Yes | To test the model s ability to model set-structured data relationally, Lee et al. (2019) proposed the task of counting unique characters using the characters dataset (Lake, Salakhutdinov, and Tenenbaum 2015)... We apply DMPS and its variants to the Model Net40 dataset (Chang et al. 2015)... The breast cancer dataset introduced in Gelasca et al. (2008) consists of 58 weakly-labeled 896 768 H&E images. |
| Dataset Splits | No | The paper mentions using "test results" and "training stage" but does not provide specific details on the train/validation/test dataset splits or their percentages/counts. It defers to supplementary material for some details, but the main paper does not provide them. |
| Hardware Specification | No | The paper does not provide any specific hardware details such as GPU or CPU models, or cloud computing specifications used for running the experiments. |
| Software Dependencies | No | The paper does not specify any software dependencies (libraries, frameworks, or specialized packages) with version numbers that would be needed to replicate the experiments. |
| Experiment Setup | Yes | Unless otherwise specified, three message passing steps, set-denoising blocks, or set-residual blocks are stacked to form the final model. We emphasize that we align as much architectural choices, such as learning rate, number of training batches, batch size, etc., as we can with Lee et al. (2019) for fair comparison. |