Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in [1].
Representing Hyperbolic Space Accurately using Multi-Component Floats
Authors: Tao Yu, Christopher M. De Sa
NeurIPS 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Theoretically and experimentally we show our model has small numerical error, and on embedding tasks across various datasets, models represented by multi-component floating-points gain more capacity and run significantly faster on GPUs than prior work. |
| Researcher Affiliation | Academia | Tao Yu Department of Computer Science Cornell University EMAIL Christopher De Sa Department of Computer Science Cornell University EMAIL |
| Pseudocode | Yes | Algorithm 1: Two-Sum Input: p-bit floats a, b, where p 3 x fl(a + b) bvirtual fl(x a) avirtual fl(x bvirtual) broundoff fl(b bvirtual) aroundoff fl(a avirtual) y fl(aroundoff + broundoff) Return: (x, y) |
| Open Source Code | No | We will release our code in Py Torch publicly for reproducibility, and in hopes that practitioners can use MCF to reliably compute in hyperbolic space. |
| Open Datasets | Yes | We conduct embedding experiments on datasets including the Gr-Qc and commonly used Word Net Nouns, Verbs and Mammals dataset whose statistics are shown in Table 2. ... Table 2: Datasets Information Datasets Nodes Edges Word Net[12] 74374 75834 ... Gr-QC[22] 4158 13422 |
| Dataset Splits | No | The paper mentions using datasets from WordNet and Gr-QC but does not specify the train/validation/test splits (e.g., percentages, counts, or specific pre-defined split references). |
| Hardware Specification | Yes | For all timing results within this section, we use GPU: Ge Force RTX 2080 Ti and CPU: Intel(R) Xeon(R) Gold 6240 @2.60GHZ. |
| Software Dependencies | No | The paper mentions deep learning frameworks like PyTorch and TensorFlow, and Julia, but does not specify their version numbers or the versions of other software dependencies required to replicate the experiments. |
| Experiment Setup | Yes | Herein, we fix the negative sampling size to be 50 and only vary the batchsize in 32, 64, 128. |