Global Feature Extractor As mentioned in the paper, the global feature extractor GFE()is composed of 3 modules: GFE(Mn) = GAP(Conv(Sample(Mn))). The sampling module Sample()is implemented by the built-in interpolation interface in Firedrake[Rathgeber et al., 2016], with sampling density 32x32. The convolutional block contains 4 convolutional layers. The SELU [Klambauer et al., 2017] activation function is used to increase the representation capability of the model. The output tensor from the convolutional block is then fed into a Global Average Pooling (GAP) layer to get a mesh resolution invariant global feature embedding En.

The results in this sections are well-known and can be found in different forms in standard textbooks [1, 5-7]. For completeness, we summarize the key results used in our analysis.

When you purchase through links in our articles, we may earn a small commission. Why choose one AI model when this $79.97 Lock in lifetime access to 1minAI's Advanced Business Plan for $79.97 (MSRP $540) through April 26 -- and tap into multiple top AI models from one dashboard. There's a point where testing prompts across ChatGPT, Gemini, and everything else stops feeling productive and starts feeling like busywork. It's built to be approachable but still powerful, giving you flexibility without the usual friction.

De novo drug design is a pivotal issue in pharmacology and a new area of focus in AI for science research. A central challenge in this field is to generate molecules with specific properties while also producing a wide range of diverse candidates. Although advanced technologies such as transformer models and reinforcement learning have been applied in drug design, their potential has not been fully realized. Therefore, we propose MolRL-MGPT, a reinforcement learning algorithm with multiple GPT agents for drug molecular generation. To promote molecular diversity, we encourage the agents to collaborate in searching for desirable molecules in diverse directions. Our algorithm has shown promising results on the GuacaMol benchmark and exhibits efficacy in designing inhibitors against SARS-CoV-2 protein targets. The codes are available at: https://github.com/HXYfighter/

B.3 Derivations of Eq. (19) Similar to derivation above, we give the gradient with respect to weight vector w RM+, which is given by wDKL = w log Z(U,w) wEU,w (log pθ(X |z))T1N + wEU,w (log pθ(U |z))Tw . The learning rate of each stochastic gradient descent step is γt t 1, where t {1,,T}denotes the iteration for optimization. We already report the t-SNE visualization of ByPE-VAE and standard VAE in Figure. Here we give more t-SNE visualization results. First, we randomly sample from ByPE-VAEs trained on different datasets, namely, MNIST, Fashion MNIST, and Celeba, as shown in Fig.7.

Since out-of-distribution generalization is a generally ill-posed problem, various proxy targets (e.g., calibration, adversarial robustness, algorithmic corruptions, invariance across shifts) were studied across different research programs resulting in different recommendations. While sharing the same aspirational goal, these approaches have never been tested under the same experimental conditions on real data. In this paper, we take a unified view of previous work, highlighting message discrepancies that we address empirically, and providing recommendations on how to measure the robustness of a model and how to improve it. To this end, we collect 172 publicly available dataset pairs for training and out-of-distribution evaluation of accuracy, calibration error, adversarial attacks, environment invariance, and synthetic corruptions.