Non-Autoregressive Electron Redistribution Modeling for Reaction Prediction
Authors: Hangrui Bi, Hengyi Wang, Chence Shi, Connor Coley, Jian Tang, Hongyu Guo
ICML 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments on the USPTO-MIT dataset show that, our approach has established a new state-of-the-art top-1 accuracy and achieves at least 27 times inference speedup over the state-of-the-art methods. |
| Researcher Affiliation | Academia | 1Peking University 2Mila Quebec AI Institute 3University of Montr eal 4MIT 5CIFAR AI Research Chair 6HEC Montr eal 7National Research Council Canada. |
| Pseudocode | No | The paper describes the framework and components of the model but does not include any explicitly labeled pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any explicit statements about open-source code availability or links to code repositories. |
| Open Datasets | Yes | We evaluate our model using the popular benchmarking USPTO-MIT dataset. This dataset was created by Jin et al. (2017) by removing duplicate and erroneous reactions from Lowe (2012) s original data and filtering to those with contiguous reaction centers. |
| Dataset Splits | No | The paper mentions using the USPTO-MIT dataset and excluding certain reactants from training and testing, implying a split, but does not provide explicit details on the training, validation, or test set sizes or percentages. |
| Hardware Specification | Yes | We train the models for 100 epochs with a batch size of 128 using two Nvidia V100 GPUs (took about 3 days). All models are evaluated on a single Nvidia V100 GPU and a Intel(R) Xeon(R) CPU E5-2690 v4 @ 2.60GHz with 28 cores. |
| Software Dependencies | No | The paper states 'We implement NERF using Pytorch (Paszke et al., 2019)' but does not provide specific version numbers for PyTorch or any other software dependencies. |
| Experiment Setup | Yes | The node embedding dimension is 256 and the dimension of the latent representation is 64. The model is optimized with Adam W (Kingma & Ba, 2014) optimizer at learning rate 10 4 with linear warmup and linear learning rate decay. We train the models for 100 epochs with a batch size of 128 using two Nvidia V100 GPUs (took about 3 days). |