Incrementally learning new information from a non-stationary stream of data, referred to as ‘continual learning’, is a key feature of natural intelligence, but a challenging problem for deep neural networks. In recent years, numerous deep learning methods for continual learning have been proposed,...
Refty refines each type of deep learning operator with framework-independent logical formulae that describe the computational constraints on both tensors and hyperparameters. Given the neural architecture and hyperparameter domains of a model, Refty visits every operator, generates a set of ...
The most significant contribution to our suggested model is establishing a lightweight network in the spectrum of deep learning. Therefore, parallel studies on the existing state-of-the-art models have been described to show the comparison and improved features in this model. In addition, the proc...
During the experiment, we first calibrated the performance of the trained deep neural network in each impending failure type. Then, we leveraged the architecture and hyperparameters of the neural network model trained from one type of failure as the pre-trained model for knowledge transfer. The ...
The technical overview of the papers presented in our special session is organized into five ways of improving deep learning methods: (1) better optimization; (2) better types of neural activation function and better network architectures; (3) better ways to determine the myriad hyper-parameters ...
For a more in-depth look, check out our comparison guides on AI vs machine learning and machine learning vs deep learning. AI refers to the development of programs that behave intelligently and mimic human intelligence through a set of algorithms. The field focuses on three skills: learning,...
The training set is used to train the model, the validation set helps tune hyperparameters, and the testing set evaluates the final model’s performance. Step 6: Choose a Model Based on the problem type, choose a suitable machine learning algorithm (e.g., linear regression, random forests,...
The big shift happened in the 1990s when machine learning moved from being knowledge-driven to a data-driven technique due to the availability of huge volumes of data. IBM’s Deep Blue, developed in 1997 was the first machine to defeat the world champion in the game of chess. Businesses ha...
the process of cell type annotation is labor-intensive, requiring extensive literature review of genes specific to each cluster6. Secondly, any changes made to the analysis, such as incorporating additional data or adjusting parameters, require the manual reevaluation of all previous annotations. Third...
In general, we used the learning curves to guide the changing of hyperparameters134. For the analysis of coarse cell types (Fig. 5a), a model with two hidden layers of 512 nodes each and L2 regularization was used. For the analysis of the neuronal subtypes (Fig. 5b), seven models were...