STLnet: Signal Temporal Logic Enforced Multivariate Recurrent Neural Networks
Authors: Meiyi Ma, Ji Gao, Lu Feng, John Stankovic
NeurIPS 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We evaluate the performance of STLnet using large-scale real-world city data. The experimental results show STLnet not only improves the accuracy of predictions, but importantly also guarantees the satisfaction of model properties and increases the robustness of RNNs. |
| Researcher Affiliation | Academia | Meiyi Ma, Ji Gao, Lu Feng, John Stankovic University of Virginia {meiyi,jg6yd,lu.feng,stankovic}@virginia.edu |
| Pseudocode | Yes | Algorithm 1 Converting STL to DNF with Calculation of Satisfaction Range |
| Open Source Code | No | No explicit statement about providing open-source code or a direct link to a code repository for the described methodology was found. |
| Open Datasets | Yes | The dataset includes 1.3 million instances of 6 pollutants (i.e., PM2.5, PM10, CO, SO2, NO2, O3) collected from 130 locations in Beijing every hour between 5/1/2014 and 4/30/2015 [15]. |
| Dataset Splits | No | The paper discusses "training phase" and "testing phase" but does not provide specific details on training, validation, or test dataset splits (e.g., percentages, sample counts, or explicit cross-validation setup). |
| Hardware Specification | Yes | The experiments are evaluated on a server machine with 20 CPUs, each core is 2.2GHz, and 4 Nvidia Ge Force RTX 2080Ti GPUs. The operating system is Centos 7. |
| Software Dependencies | No | The paper refers to the use of LSTM and Transformer networks, but does not provide specific version numbers for any software libraries, frameworks, or programming languages used (e.g., Python, PyTorch, TensorFlow versions). |
| Experiment Setup | No | The paper states that it applies STLnet to LSTM and Transformer networks for multivariate sequential prediction, and mentions some general setup like concatenating variables. However, it does not provide specific experimental setup details such as hyperparameter values (e.g., learning rate, batch size, number of epochs), optimizer settings, or other training configurations. |