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 Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
A Wrapped Normal Distribution on Hyperbolic Space for Gradient-Based Learning
Authors: Yoshihiro Nagano, Shoichiro Yamaguchi, Yasuhiro Fujita, Masanori Koyama
ICML 2019 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We demonstrate the efficacy of our distribution on various datasets including MNIST, Atari 2600 Breakout, and Word Net. |
| Researcher Affiliation | Collaboration | 1Department of Complexity Science and Engineering, The University of Tokyo, Japan 2Preferred Networks, Inc., Japan. |
| Pseudocode | Yes | Algorithm 1 is an algorithmic description of the sampling procedure based on our construction. |
| Open Source Code | No | The paper does not contain any statement about releasing source code or a link to a repository for the described methodology. |
| Open Datasets | Yes | We applied Hyperbolic VAE to a binarized version of MNIST. [...] We trained probabilistic word embedding models with Word Net nouns dataset (Miller, 1998) |
| Dataset Splits | Yes | We amassed a set of trajectories whose total length is 100,000, of which we used 80,000 as the training set, 10,000 as the validation set, and 10,000 as the test set. |
| Hardware Specification | No | The paper does not mention any specific hardware details such as GPU or CPU models, or cloud computing specifications used for running experiments. |
| Software Dependencies | No | The paper does not list specific software dependencies with their version numbers required to reproduce the experiments. |
| Experiment Setup | Yes | We used an Multi Layer Parceptron (MLP) of depth 3 and 100 hidden variables at each layer for both encoder and decoder. For activation function we used tanh. [...] We used an MLP of depth 3 and 500 hidden units at each layer for both the encoder and the decoder. [...] In particular, we initialized each weight in the ๏ฌrst linear part of the embedding by N(0, 0.01). We treated the ๏ฌrst 50 epochs as a burn-in phase and reduced the learning rate by a factor of 40 after the burn-in phase. |