Neural Processes with Stability
Authors: Huafeng Liu, Liping Jing, Jian Yu
NeurIPS 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | To illustrate the superiority of the proposed model, we perform experiments on both synthetic and real-world data, and the results demonstrate that our approach not only helps to achieve more accurate performance but also improves model robustness. |
| Researcher Affiliation | Academia | Huafeng Liu, Liping Jing, Jian Yu Beijing Key Lab of Traffic Data Analysis and Mining, Beijing Jiaotong University The School of Computer and Information Technology, Beijing Jiaotong University {hfliu1, lpjing, jianyu}@bjtu.edu.cn |
| Pseudocode | Yes | Algorithm 1 Learning algorithm for stable NPs |
| Open Source Code | No | The paper does not provide any explicit statement about open-sourcing the code for the described methodology, nor does it provide a link to a code repository. |
| Open Datasets | Yes | We trained the NPs on EMNIST [4] and 32 × 32 CELEBA [23] using the standard train/test split with up to 200 context/target points at training. |
| Dataset Splits | No | The paper mentions "standard train/test split" for EMNIST and CELEBA but does not explicitly mention a validation split or its details. |
| Hardware Specification | No | The paper does not provide any specific details about the hardware (e.g., GPU models, CPU types, memory) used for running the experiments. |
| Software Dependencies | No | The paper mentions using Python and libraries like PyTorch implicitly through common practice in deep learning research, but does not specify any software names with version numbers required for reproducibility. |
| Experiment Setup | Yes | For synthetic 1D regression experiments, the neural architectures for CNP, NP, ANP, BCNP, BNP, BANP, and our SCNP/SNP/SANP refer to Appendix B. The number of hidden units is dh = 128 and latent representation dz = 128. The number of layers are le = lde = lla = lqk = lv = 2. We trained all models for 100,000 steps with each step computing updates with a batch containing 100 tasks. We used the Adam optimizer with an initial learning rate 5e-4 and decayed the learning rate using Cosine annealing scheme for baselines. For SCNP/SNP/SANP, we set K = 3. The size of the context C was drawn as |C| U(3, 200). Testings were done for 3,000 batches with each batch containing 16 tasks (48,000 tasks in total). |