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 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 ...
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 ...
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,...
Accelerating the distance-minimizing method for data-driven elasticity with adaptive hyperparameters. Computational Mechanics , 2022 , 70: 621 -638 CrossRef Google Scholar [38] Bai X, Yang J, Yan W, et al. A data-driven approach for instability analysis of thin composite structures. Com...
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,...
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,...
DNA interference states of the hypercompact CRISPR–CasΦ effector Article 11 August 2021 DNA interference is controlled by R-loop length in a type I-F1 CRISPR-Cas system Article Open access 15 June 2020 The compact Casπ (Cas12l) ‘bracelet’ provides a unique structural platform for ...
In recent years, there has been an increasing interest in utilizing deep learning-based techniques to predict solutions to various partial differential equations. In this study, we investigate the identification of an unknown flux function and diffusion coefficient in a one-dimensional convection-diffusio...
Hyperparameter sensitivity studies To investigate effects of hyperparameters of DSP, we report results from ablation studies on the HELA-2 mixed, ACR = 8 scenario in Ta- ble 1. In the first part, we see that adding LDSP to the simple semi-weak basel...