High Dimensional Level Set Estimation with Bayesian Neural Network
Authors: Huong Ha, Sunil Gupta, Santu Rana, Svetha Venkatesh12095-12103
AAAI 2021 | Conference PDF | Archive PDF | Plain Text | 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 Artiļ¬cial Intelligence Institute (A2I2), Deakin University, Geelong, Australia *Correspondence to: huong.ha@rmit.edu.au |
| 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. |