Understanding Deep Architecture with Reasoning Layer
Authors: Xinshi Chen, Yufei Zhang, Christoph Reisinger, Le Song
NeurIPS 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our experiments aim to validate our theoretical prediction with computational simulations, rather than obtaining state-of-the-art results. We conduct two sets of experiments, where the first set of experiments strictly follows the problem setting described in Sec 2 and the second is conducted on BSD500 dataset [56] to demonstrate the possibility of generalizing the theorem to more realistic applications. Implementations in Python are released. |
| Researcher Affiliation | Academia | Xinshi Chen Georgia Institute of Technology xinshi.chen@gatech.edu Yufei Zhang University of Oxford yufei.zhang@maths.ox.ac.uk Christoph Reisinger University of Oxford christoph.reisinger@maths.ox.ac.uk Le Song Georgia Institute of Technology lsong@cc.gatech.edu |
| Pseudocode | No | The paper presents algorithm update steps (e.g., for GD and NAG) but does not provide a formally structured pseudocode block or algorithm box. |
| Open Source Code | Yes | Implementations in Python are released1. 1https://github.com/xinshi-chen/Deep-Architecture-With-Reasoning-Layer |
| Open Datasets | Yes | We split BSD500 (400 images) into a training set (100 images) and a test set (300 images). |
| Dataset Splits | No | The paper states the training and test set sizes for the BSD500 dataset, but it does not explicitly mention a separate validation set or its split. For synthetic experiments, it states "During training, n samples are randomly drawn from these 10000 data points as the training set" without specifying a validation split. |
| Hardware Specification | No | The paper does not specify any hardware details (e.g., CPU, GPU models, memory) used for running the experiments. |
| Software Dependencies | No | The paper states "Implementations in Python are released" but does not specify the version of Python or any other software libraries used, nor their versions. |
| Experiment Setup | Yes | Each model is trained by ADAM and SGD with learning rate grid-searched from [1e-2,5e-3,1e-3,5e-4,1e-4], and only the best result is reported. |