This study presents a novel protocol to comprehensively analyse various sources such as pharmaceutical patents and biomedical databases, and identify drug repositioning candidates with both technological potential and scientific evidence.
Finally, we develop an GPT-enabled extractive QA model, which provides improved performance and shows the possibility of automatically correcting annotations.
Second, we investigate how the geometric structure changes based on text conditioning in Stable Diffusion.
In this paper, we propose a novel generative model based on the semi-dual formulation of Unbalanced Optimal Transport (UOT).
This semantic basis represents sample-independent meaningful perturbations that change the same semantic attribute of an image on the entire latent space.
Recent extensively competitive business environment makes companies to keep their eyes on social media, as there is a growing recognition over customer languages (e. g., needs, interests, and complaints) as source of future opportunities.
In this paper, we approach this problem through a geometric analysis of latent spaces as a manifold.
The global warpage implies that the latent space is not well-aligned globally and therefore the global traversal directions are bound to show limited success on it.
In this study, we address the challenges in developing a deep learning-based automatic patent citation recommendation system.
To represent these generative factors of data, we introduce two sets of continuous latent variables, private variable and public variable.
We replace the dynamic routing and squash activation function of the capsule network with dynamic routing (CapsuleNet) with the attention routing and capsule activation.