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. I
The proposed method is a combination of data augmentation techniques and a regularization method known as “adversarial training” to improve the robustness of deep learning-based malware detectors. The authors of the paper evaluated the effectiveness of their proposed method using a dataset of malware...
Simple deep learning API for implementing neural nets written in Rust with Dense Layers, CSV and MNIST dataset types, L2 regularization and Adam Optimizer and common activation functions like Relu, Sigmoid, Softmax, Tanh. Only uses ndarray for linear algebra functionality Resources Readme License...
Transcriptional regulation, which involves a complex interplay between regulatory sequences and proteins, directs all biological processes. Computational models of transcription lack generalizability to accurately extrapolate to unseen cell types and conditions. Here we introduce GET (general expression transform...
To choose the adequate regularization strength, the classifier accuracy and the loss value were inspected against epoch numbers. The classifier accuracy was estimated by a k-fold cross-validation, of which the dataset was randomly split (k = 3). The learning rate, epoch number, and ...
VAEs optimize two objectives: reconstruction loss and regularization loss. They can generate new data samples by sampling from the learned latent space distribution. VAEs find applications in image and text generation, as well as data compression. They are a powerful framework for unsupervised ...
Thus, regardless of the choice between regression and classification, it is essential to implement robust techniques such as regularization, cross-validation, and feature engineering to mitigate overfitting and preserve critical information. This ensures that the model remains predictive and practical for ...
In the fourth round of adjustment, lambda_l1 and lambda_l2 were tuned, representing the L1 and L2 regularization terms, respectively, which were used to filter the features and control their influence in the model to prevent some features from greatly affecting the whole model. Finally, we ...
A deeper understanding can, in turn, help us adjust our business model to suit each of the categories better. Classification Techniques Method Advantage Drawback Generalized linear models (Logistic regression) Good probabilistic interpretationAvoids overfitting with regularizationEasily updated with new data...
grid search or randomized search, which involve systematically testing different combinations of parameter values and evaluating their performance. Once you’ve found the optimal set of parameters, you can fine-tune the model by adjusting the learning rate or regularization to improve its accuracy ...