较大的参数数量可以提供更多的灵活性和表达能力,但也可能导致过拟合问题。 2.2 模型容量(Model Capacity) 模型容量是指模型学习和表示数据的能力。它与模型的复杂性和表达能力相关。模型容量越高,模型可以学习到更多的细节和复杂关系,从而更好地拟合训练数据。然而,过高的模型容量可能导致过拟合,即模型在训练数据上表现...
The capacity of a deep learning neural network model controls the scope of the types of mapping functions that it is able to learn. A model with too little capacity cannot learn the training dataset meaning it will underfit, whereas a model with too much capacity may memorize the training da...
Machine Learning Driven Drift Capacity Model for Reinforced Concrete WallsMuneera AladsaniHenry BurtonSaman AbdullahJohn W Wallace
MLASP: machine learning for capacity planning I wrote a research paper published in Springer's EMSE Journal (DOIinformation, direct link to thefull text), describing a process called Machine Learning Assisted System Performance and Capacity Planning (MLASP). This process has been proven in an in...
In this situation, the proper action is to re-train the ML system with a more expressive learning model (e.g., a deep neural network instead of a log-linear model) or increase the model capacity. The proposed meta-learner for estimating the different types of uncertainty in the Risk ...
The main limitation of the regression model in Equation 6 lies in its limited capacity. If the underlying relation between (Y, X) is nonlinear, then the maximum likelihood estimator in Equation 5 will be suboptimal. In our problem of model error estimation, it is a priori unclear how big...
5a. It can be seen that: (1) The results of RoBERTa-base are lower than those of RoBERTa-large, which is expected since the latter has more parameters and a larger model capacity. (2) On both datasets, RoBERTa-base (finetune) has limited performance gains over RoBERTa-base, while Br...
Anomaly Detection:Using artificial data, you may test your AI model’s capacity to recognize unexpected events or faults. Continuous Testing:As your AI models mature, you may use synthetic data for ongoing review and retraining to ensure they respond to changing conditions. ...
The third extension deals with the case when unlabeled data is not available at all, using an estimated input density. Experiments are described to study these extensions in the context of capacity control and feature subset selection. 展开 年份: 2003 ...
Another situation where overfitting is warranted is in distilling, or transferring knowledge, from a large machine learning model into a smaller one. Knowledge distillation is useful when the learning capacity of the large model is not fully utilized. If that is the case, the computational complexit...