Transfer learning is crucial in training deep neural networks on new target tasks. Current transfer learning methods always assume at least one of (i) Sour
Test MTSCI from trained model First, set the scratch is False in scripts. Then, run these scripts. bash ETT_point.sh bash ETT_block.sh bash Weather_point.sh bash Weather_block.sh bash METRLA_point.sh bash METRLA_block.sh Citation If you find this repo useful, please cite our paper....
DiffLinker: Equivariant 3D-Conditional Diffusion Model for Molecular Linker Design - igashov/DiffLinker
We combine this model with the discriminator and GAN formulation from Sec. 3.2 to form our GAN baseline. We train it for Eq. 4 using a fixed =2.56, i.e., this can be viewed as the same as our main model but using a non- conditional G that can only tar...
1. We start by defining a parametric model in 2D or 3D space (e.g. a parametric bridge model; Step 1) and compose a training dataset with a large variety of different design instances of that parametric model (Step 2) and corresponding performance attributes from closed-form formulas or a...
Compared to training diffusion models from scratch, this approach significantly reduces training costs while maintaining model stability. The principle is illustrated in the Figure 5. Figure 5. LoRA schematic diagram. Specifically, let the parameter matrix of the pre-trained model be 𝑊0∈ℝ𝑑...
The generative adversarial network (GAN) [59] is a deep generative-type model that consists of two components: a generator and a discriminator. The generator aims to synthesize real samples to fool the discriminator, while the discriminator tries to distinguish the fake samples from real ones. Th...
Using a model as a pretransform for another model (e.g. using an autoencoder model for latent diffusion) Fine-tuning a pre-trained model with a modified configuration (i.e. partial initialization) Fine-tuning Fine-tuning a model involves continuning a training run from a pre-trained checkpoi...
The generative adversarial network (GAN) [59] is a deep generative-type model that consists of two components: a generator and a discriminator. The generator aims to synthesize real samples to fool the discriminator, while the discriminator tries to distinguish the fake samples from real ones. Th...
The generative adversarial network (GAN) [59] is a deep generative-type model that consists of two components: a generator and a discriminator. The generator aims to synthesize real samples to fool the discriminator, while the discriminator tries to distinguish the fake samples from real ones. Th...