From Trees to Continuous Embeddings and Back: Hyperbolic Hierarchical Clustering
Authors: Ines Chami, Albert Gu, Vaggos Chatziafratis, Christopher Ré
NeurIPS 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We experimentally evaluate HYPHC on a variety of HC benchmarks and find that even approximate solutions found with gradient descent have superior clustering quality than agglomerative heuristics or other gradient based algorithms. |
| Researcher Affiliation | Collaboration | Department of Computer Science, Stanford University Institute for Computational and Mathematical Engineering, Stanford University Google Research, NY {chami, albertgu, vaggos, chrismre}@cs.stanford.edu |
| Pseudocode | Yes | Algorithm 1 Hyperbolic binary tree decoding dec(Z) |
| Open Source Code | Yes | We implemented Hyp HC in Py Torch and make our implementation publicly available.6 https://github.com/Hazy Research/Hyp HC |
| Open Datasets | Yes | We measure the clustering quality of HYPHC on six standard datasets from the UCI Machine Learning repository,3 as well as CIFAR-100 [35] |
| Dataset Splits | Yes | We consider four of the HC datasets that come with categorical labels for leaf nodes, split into training, testing and validation sets (30/60/10% splits). |
| Hardware Specification | Yes | We conducted our experiments on a single NVIDIA Tesla P100 GPU. |
| Software Dependencies | No | The paper mentions using 'Py Torch' and 'geoopt [32]' but does not provide specific version numbers for these software dependencies. |
| Experiment Setup | Yes | We train HYPHC for 50 epochs (of the sampled triples) and optimize embeddings with Riemannian Adam [7]. We set the embedding dimension to two in all experiments... We perform a hyper-parameter search over learning rate values [1e 3, 5e 4, 1e 4] and temperature values [1e 1, 5e 2, 1e 2]. |