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..
Harmonic Exponential Families on Manifolds
Authors: Taco Cohen, Max Welling
ICML 2015 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our experimental results show that harmonic densities yield a significantly higher likelihood than the best competing method, while being orders of magnitude faster to train. |
| Researcher Affiliation | Academia | Taco S. Cohen EMAIL University of Amsterdam Max Welling EMAIL University of Amsterdam University of California Irvine Canadian Institute for Advanced Research |
| Pseudocode | Yes | So we have an efficient algorithm for computing moments: 1. Compute ϕ = exp (F 1η). 2. Compute M = F ϕ 3. Compute Ep(g|η) [T(g)] = M/M 0 00. [...] To find the optimal transformation, first perform posterior inference (steps 1 and 2) and then maximize (step 3): 1. Compute ˆx = F x and ˆy = F y. 2. Compute ηλ = ηλ + 1 σ2 dim λ ˆxλˆy T λ 3. Compute g = arg maxi[F 1 η](gi) |
| Open Source Code | No | The paper does not provide concrete access to its own source code. |
| Open Datasets | Yes | We obtained the Significant Earthquake Dataset (NGDC, 2015) from the National Geophysical Datacenter of the National Oceanographic and Athmospheric Administration. |
| Dataset Splits | Yes | Figure 2 shows the average train and test log-likelihood over 5 cross-validation folds, for the spherical harmonic density and the mixture of Kent distribution. |
| Hardware Specification | No | The paper does not provide specific details about the hardware used for running its experiments. |
| Software Dependencies | No | The paper mentions software like Python, NFFT library, SciPy routines, and L-BFGS algorithm, but does not provide specific version numbers for these dependencies. |
| Experiment Setup | No | The paper describes regularization methods and optimization algorithms, but does not provide specific concrete hyperparameter values (e.g., learning rate, batch size) or detailed training configurations for its experiments. |