Quantum-Inspired Neural Network with Runge-Kutta Method
Authors: Zipeng Fan, Jing Zhang, Peng Zhang, Qianxi Lin, Hui Gao
AAAI 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Moreover, we present the results of experiments on conversation emotion recognition and text classification tasks to validate the effectiveness of the proposed approach. |
| Researcher Affiliation | Academia | College of Intelligence and Computing, Tianjin University, Tianjin, China |
| Pseudocode | No | The paper includes diagrams of QRK method structures in Figure 2, but these are not pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any statement or link indicating the release of open-source code for the described methodology. |
| Open Datasets | Yes | For conversational emotion recognitionn task, we chose MELD (Poria et al. 2018). (2) For text classification task, we chose four text classification datasets: MR (Hu and Liu 2004), CR (Pang and Lee 2005), SUBJ (Wiebe, Wilson, and Cardie 2005), MPQA (Pang and Lee 2004). |
| Dataset Splits | No | In training dataset, MELD contains 1039 dialogues and 9989 utterances. MR contains 11.9K training data, 20K vocabulary, and two classes. (While training data size is mentioned, explicit splits for validation are not provided.) |
| Hardware Specification | Yes | we trained the QINN with quantumlike high-order RK (QMNN-QRK) methods on one NVidia Tesla K80 GPU. |
| Software Dependencies | No | The paper mentions training on a GPU and lists hyperparameters but does not provide specific software dependencies with version numbers (e.g., Python, PyTorch, TensorFlow versions). |
| Experiment Setup | Yes | QMNN-QRK hyperparameters are searched within embedding dimensions d {50, 100, 120, 160, 200}, the size of last hidden layer in {16, 24, 32, 48, 64}. Stochastic gradient descent (SGD) is used as the optimizer with a learning rate lr {0.001, 0.002, 0.005, 0.008}. The batch size varies in {24, 32, 48}. The dropout rate for the last hidden layer varies in {0.001, 0.005, 0.01, 0.05, 0.1, 0.2, 0.3, 0.5}. The α varies in {0, 0.5, 0.05, 0.0001, 0.00001}. |