Global Optimization Networks
Authors: Sen Zhao, Erez Louidor, Maya Gupta
ICML 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments show the GON maximizers are statistically significantly better predictions than those produced by convex fits, GPR, or DNNs, and form more reasonable predictions for real-world problems. |
| Researcher Affiliation | Industry | 1Google Research, Mountain View, CA 94043 USA. Correspondence to: Sen Zhao <senzhao@google.com>. |
| Pseudocode | No | The paper includes block diagrams and mathematical proofs, but no explicitly labeled "Pseudocode" or "Algorithm" blocks with structured steps. |
| Open Source Code | Yes | Code for some experiments can be found at https://github.com/google-research/googleresearch/tree/master/gon. |
| Open Datasets | Yes | The data can be downloaded at www.kaggle.com/senzhaogoogle/kingsreign. / publicly available at www.kaggle.com/senzhaogoogle/puzzlesales. / Using Kaggle data from Wine Enthusiast Magazine3...3www.kaggle.com/dbahri/wine-ratings / CIFAR10/100 (Krizhevsky, 2009), Fashion MNIST (Xiao et al., 2017), MNIST (Le Cun et al., 2010) and cropped SVHN (Netzer et al., 2011) datasets |
| Dataset Splits | Yes | For each experiment and for each method, we train a set of models with different hyperparameter choices, select the best model according to a validation or cross-validation metric (metric described below) / non-IID train/validation/test sets had 36/32/27 puzzles / 84,642 train samples, 12,092 validation samples, and 24,185 test samples, all IID. / default train/test splits, and use 10% of the train set as validation. |
| Hardware Specification | No | The paper mentions "our machines with 128GB of memory" but does not specify any particular GPU models, CPU models, or other detailed hardware components used for running experiments. |
| Software Dependencies | No | The paper mentions software like sklearn, TensorFlow, Keras, and ADAM, but it does not provide specific version numbers for these libraries or packages. |
| Experiment Setup | Yes | used ADAM (Kingma & Ba, 2015) with a default learning rate of .001 (preliminary experiments with learning rates of 0.0003 as suggested in Liu et al. (2020) yielded similar results). Batch size was N for N < 100, 1000 for the larger wine experiment in Sec 5.4, and 100 otherwise. / For GON and CGON, we use an ensemble of D unimodal lattices. All methods are trained for 250 epochs. / For both GON and CGON, we first use D PLFs with K keypoints to calibrate the D inputs for optimization. The unimodal function consists of an enesemble of D unimodal lattices, each fuses 3 inputs with V keypoints. |