Neural Architecture Search with GBDT.Renqian LuoXu TanRui WangTao QinEnhong ChenTie-Yan Liu
Neural Architecture Search (NAS) is the process of optimizing and refining the structure and design of a deep neural network (DNN) to improve its performance, reduce its size, or expedite its training. NAS algorithms aim to find the most efficient neural architecture by either downsizing the net...
Neural Architecture Search with GBDT(Luo et al. 2020) - - A Study on Encodings for Neural Architecture Search(White et al. 2020) - - NASGEM: Neural Architecture Search via Graph Embedding Method(Cheng et al. 2020) - - Neuro-evolution using Game-Driven Cultural Algorithms(Waris and Reynolds...
Auto-gbdt Cifar10-pytorch Scikit-learn EfficientNet More... Hyperparameter Tuning Exhaustive search Random Search Grid Search Batch Heuristic search Naïve Evolution Anneal Hyperband PBT Bayesian optimization BOHB TPE SMAC Metis Tuner GP Tuner RL Based PPO Tuner Neural Architecture Search ...
The second is a NAS (Neural Architecture Search) [27] dataset, containing 8,403 configurations of NAS-searched models. HPO dataset. We choose representative DL models from the TensorFlow-Slim model library [55] and adopt a random strategy based on the provided APIs to generate the model ...
In a word, this experiment indicates the superiority of using IWOA to search the optimal solution of function. Therefore, IWOA is an excellent algorithm for solving optimization problems. Figure 11 The convergence curves of benchmark functions. Full size image Figure 12 The architecture of LeNet...
The new models use two pre-trained neural networks, ProteinSolver23 and ProtBert,1 to featurise individual mutations, and they employ the gradient boosting decision tree (GBDT)24 machine learning algorithm with a ranking objective function, allowing them to integrate data obtained using different in...
light gradient boosting gbdt: gradient boosted decision trees mlp: multilayer perceptron knn: k-nearest neighbors acc: accuracy sn: sensitivity sp: specificity mcc: matthews correlation coefficient tp: true positive fp: false positive tn: true negative fn: false negative roc-auc: receiver operating ...
Auto-gbdt Cifar10-pytorch Scikit-learn EfficientNet More... Hyperparameter Tuning Exhaustive search Random Search Grid Search Batch Heuristic search Naïve Evolution Anneal Hyperband Bayesian optimization BOHB TPE SMAC Metis Tuner GP Tuner RL Based PPO Tuner Neural Architecture Search ENAS...
Auto-gbdt Cifar10-pytorch Scikit-learn EfficientNet More... Hyperparameter Tuning Exhaustive search Random Search Grid Search Batch Heuristic search Naïve Evolution Anneal Hyperband Bayesian optimization BOHB TPE SMAC Metis Tuner GP Tuner RL Based PPO Tuner Neural Architecture Search ENAS...