Behavioral of training network curve for both training (blue line) and test (black line) losses for \(\sim 10,000\) epochs, based on the Binary Cross-Entropy as cost functions, which has a low loss value of \(\sim 0.37\). Full size image The inverse network structure is composed of...
64, 32 neurons, respectively, each utilizing the LeakyReLU activation function with a negative slope of 0.2. The output layer employs the Sigmoid activation function to produce a probability score. Both networks are trained using the Binary Cross-Entropy loss function (PyTorch 2.3.0BCELossfunction)...
14. However, optimizing over a non-convex objective function in a high-dimensional latent space is difficult15. Generative adversarial networks (GANs) have also seen success in molecular generation tasks. While GANs circumvent the need for
Such methods are often optimized to reduce the mean squared error (MSE) or binary cross entropy between the output and a training dataset of optimized designs. While convenient, we show that this choice may be myopic. Specifically, we compare two methods of optimizing the hyperparameters of ...
terminal statessTand approximate the sum through sampling from the generative model. We use a stack-augmented recurrent neural network (stack-RNN) as the generative model trained with cross-entropy loss function minimization and the REINFORCE algorithm to conduct policy gradient updates during learning...
1The reconstruction loss is calculated as the weighted cross entropy loss between the ground truth and predicted polymer string given the latent representation z. Further, as will be outlined in Section 2.3, we have a combination of labelled and unlabelled data. To handle partially labelled data,...
This CVPR paper is the Open Access version, provided by the Computer Vision Foundation. Except for this watermark, it is identical to the accepted version; the final published version of the proceedings is available on IEEE Xplore. WildLight: In-the-wild Inverse Rendering...
Understanding the mechanisms of deformation of biological materials is important for improved diagnosis and therapy, fundamental investigations in mechanobiology, and applications in tissue engineering. Here we demonstrate the essential role of interstit
In order to solve the inverse kinematics (IK) of complex manipulators efficiently, a hybrid equilibrium optimizer slime mould algorithm (EOSMA) is proposed. Firstly, the concentration update operator of the equilibrium optimizer is used to guide the anis
Concurrence, negativity and linear entropy. (a) Top: Concurrence versus inter-emitter distance for dielectric structures obtained by setting the concurrence (orange) and the negativity (blue) as the optimization function (P/γ = 5 ⋅ 10−3). Bottom: Negativity versus distance for the same de...