Isometric Manifold Learning Using Hierarchical Flow
Authors: Ziqi Pan, Jianfu Zhang, Li Niu, Liqing Zhang
AAAI 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results justify our theoretical analysis, demonstrate the superiority of our dimensionality reduction algorithm in cost of training time, and verify the effect of the aforementioned properties in improving performances on downstream tasks such as anomaly detection. |
| Researcher Affiliation | Academia | Ziqi Pan, Jianfu Zhang*, Li Niu*, Liqing Zhang Mo E Key Lab of Artificial Intelligence, Department of Computer Science and Engineering Shanghai Jiao Tong University, Shanghai, China {panziqi ai, c.sis, ustcnewly}@sjtu.edu.cn, zhang-lq@cs.sjtu.edu.cn |
| Pseudocode | Yes | Algorithm 1: Two-stage Dimensionality Reduction |
| Open Source Code | No | The paper does not provide an explicit statement or link to open-source code for the methodology described. |
| Open Datasets | No | The paper describes using a 'synthetic manifold' for intuitive illustration: 'We use a 1-dimensional manifold M (cos θ, sin θ) |θ π 6 (i.e., a curve) residing in the 2-dimensional Euclidean space. We draw N = 1, 000 samples from a Gaussian density N π 2 , 1 that is restricted to M, obtaining a training dataset X n x(i) N π 2 , 1 x(i) M o N which we use to train different models.' This is a generated dataset for which no public access information is provided. While it mentions 'natural image datasets' in supplementary, no specific public access details for those are given in the main text. |
| Dataset Splits | No | The paper mentions obtaining a 'training dataset' but does not specify any training, validation, or test splits (e.g., percentages, counts, or a predefined split citation) in the main text. |
| Hardware Specification | No | The paper does not explicitly describe the specific hardware (e.g., GPU/CPU models, memory) used to run its experiments. |
| Software Dependencies | No | The paper does not provide specific software names with version numbers for reproducibility. |
| Experiment Setup | No | The paper states 'All the implementation details for our experiments can also be found in supplementary', but it does not provide specific experimental setup details, such as hyperparameter values or system-level training settings, within the main text. |