DARKIN: a zero-shot benchmark for phosphosite-dark kinase association using protein language models. [PDF]
Sunar EA +4 more
europepmc +1 more source
A Generative Neuro‐Symbolic AI for Protein Sequence Design
We introduce EffieDes, a neuro‐symbolic framework coupling deep learning‐based fitness landscape parameterization with exact automated reasoning. Unlike greedy sampling, EffieDes identifies sequences that globally optimize fitness while satisfying intricate design constraints.
Marianne Defresne +12 more
wiley +1 more source
TriSAM: Tri-Plane SAM for Zero-Shot Cortical Blood Vessel Segmentation in VEM Images. [PDF]
Wan J +8 more
europepmc +1 more source
Zero-shot image classification based on class representation learning and attribute embedding learning. [PDF]
Shen H, Sun X, Hu Y, Li C, Zhu Q, Li Q.
europepmc +1 more source
Zero-shot performance of selected large language and multimodal models on the 2023 Brazilian Portuguese medical residency exam. [PDF]
Truyts CAM +9 more
europepmc +1 more source
Assessing personality using zero-shot generative AI scoring of brief open-ended text. [PDF]
Wright AGC +6 more
europepmc +1 more source
Prodigy protein: Python package for zero-shot protein engineering using protein language models. [PDF]
Massett M, Carr A.
europepmc +1 more source
Zero-shot medical event prediction using a generative pretrained transformer on electronic health records. [PDF]
Redekop E +9 more
europepmc +1 more source
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Zero-shot Learning (ZSL) is a highly non-trivial task to generalize from seen to unseen classes. In this paper, we propose spherical zero-shot learning (SZSL) to address the major challenges in ZSL. By decoupling the similarity metric in the spherical embedding space into radius and angle, our SZSL can map classes to hyperspherical surfaces of ...
Jiayi Shen +3 more
openaire +1 more source
Zero-Shot Hyperspectral Sharpening
IEEE Transactions on Pattern Analysis and Machine Intelligence, 2023Fusing hyperspectral images (HSIs) with multispectral images (MSIs) of higher spatial resolution has become an effective way to sharpen HSIs. Recently, deep convolutional neural networks (CNNs) have achieved promising fusion performance. However, these methods often suffer from the lack of training data and limited generalization ability.
Renwei Dian, Anjing Guo, Shutao Li 0001
openaire +2 more sources

