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..
High Dimensional Level Set Estimation with Bayesian Neural Network
Authors: Huong Ha, Sunil Gupta, Santu Rana, Svetha Venkatesh12095-12103
AAAI 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Numerical experiments on both synthetic and real-world datasets show that our proposed method can achieve better results compared to existing state-of-the-art approaches. |
| Researcher Affiliation | Academia | Huong Ha*, Sunil Gupta, Santu Rana, Svetha Venkatesh Applied Arti๏ฌcial Intelligence Institute (A2I2), Deakin University, Geelong, Australia *Correspondence to: EMAIL |
| Pseudocode | Yes | Algorithm 1 Exp HLSE: Explicit High Dimensional LSE via Bayesian Neural Network and Algorithm 2 Imp HLSE: Implicit High Dimensional LSE via Bayesian Neural Network |
| Open Source Code | Yes | Our source code is publicly available at https://github.com/Huong Ha12/Highdim LSE. |
| Open Datasets | Yes | We evaluate the performance of the methods on three ten dimensional benchmark test functions: Ackley10, Levy10 and Alpine10... For this task, we use the Rhodopsin-family protein dataset provided in (Karasuyama et al. 2018)... We use two benchmark datasets published in (Siegmund et al. 2015) for the software performance prediction problem: HSMGP (3456 data points) and HIPACC (13485 data points). |
| Dataset Splits | No | The paper mentions splitting data into training and validation sets for hyperparameter tuning ('we split the current observed data Dt into a training and a validation set'), but it does not provide specific percentages, sample counts, or explicit details about how these splits are defined or how to reproduce them. |
| Hardware Specification | Yes | All the experiments are running on multiple servers where each server has multiple Tesla V100 SXM2 32GB GPUs. |
| Software Dependencies | No | The paper mentions using 'Thermo-Calc software' and 'feedforward neural network (FNN)' but does not provide specific version numbers for any software dependencies. |
| Experiment Setup | Yes | For all the problems with dimension d, the optimization process is initialized with an initial 3d points (for synthetic functions), and 5d points (for real-world problems) sampled following a latin hypercube sample scheme (Jones 2001). For all the tasks, the experiments were repeated 5 times for the synthetic functions and 3 times for the real-world experiments... The major hyper-parameters for the i NAS tuning process are the number of layer and the number of neurons per layer whilst the minor hyperparameters are the learning rate and the drop-out rate. The i NAS tuning process is initialized with a FNN with 1 layer and 256 neurons/layer... the batch size is set to 10d with d being the dimension of the LSE problem. |