Results 41 to 50 of about 16,806 (262)
Learning with Small Data: Subgraph Counting Queries
Data Science and Engineering, 2023 Deep Learning (DL) has been widely used in many applications, and its success is achieved with large training data. A key issue is how to provide a DL solution when there is no large training data to learn initially.
Kangfei Zhao +3 more
doaj +1 more source
Directed evolution of enzymes at the crossroads of tradition and innovation
FEBS Open Bio, EarlyView.An iterative cycle of data‐driven enzyme optimization comprising four stages: genetic diversification of a template enzyme, expression of protein variants, high‐throughput evaluation, and machine‐learning‐guided redesign of the next variant library.
Maria Tomkova +2 more
wiley +1 more source
Zero‐shot multi‐label learning via label factorisation
IET Computer Vision, 2019 This study considers the zero‐shot learning problem under the multi‐label setting where each test sample is associated with multiple labels that are unseen in training data.
Hang Shao +3 more
doaj +1 more source
Zero-Shot Hyperspectral Image Denoising Using Self-Completion With 3D Random Patterned Masks
IEEE Access, 2023 Hyperspectral images (HSIs) have higher spectral resolution than RGB images and are used in various tasks. However, HSIs are prone to degradation due to noise generated during imaging, making it difficult to obtain non-degraded images.
Tatsuki Itasaka, Masahiro Okuda
doaj +1 more source
New Directions for Adult and Continuing Education, EarlyView.ABSTRACT Advancing artificial intelligence (AI) has transformed learning and work, yet higher education and professional development programs have not systematically equipped learners for AI‐prevalent environments. This lack of preparation creates uncertainty regarding control, responsibility, trust, and accountability.
Moon‐Heum Cho, Jerusalem Merkebu
wiley +1 more source
Zero-Shot Transfer in Imitation Learning
CoRR, 2023 We present an algorithm that learns to imitate expert behavior and can transfer to previously unseen domains without retraining. Such an algorithm is extremely relevant in real-world applications such as robotic learning because 1) reward functions are difficult to design, 2) learned policies from one domain are difficult to deploy in another domain ...
Alvaro Cauderan +3 more
openaire +2 more sources
Screening Routine Clinical Notes for Epilepsy Surgery Candidates Using Large Language Models
Annals of Clinical and Translational Neurology, EarlyView.ABSTRACT Objective Epilepsy surgery is severely underutilized despite proven efficacy, with substantial under‐referral of eligible patients in routine clinical practice. This study evaluated the potential role of large language models (LLMs) as decision‐support tools for screening unstructured clinical notes to identify epilepsy surgery candidates and ...
Uriel Fennig +9 more
wiley +1 more source
A review on NLP zero-shot and few-shot learning: methods and applications
Discover Applied SciencesZero-shot and few-shot learning techniques in natural language processing (NLP), this comprehensive review traces their evolution from traditional methods to cutting-edge approaches like transfer learning and pre-trained language models, semantic ...
G. Ramesh +6 more
doaj +1 more source
Joint attribute chain prediction for zero‐shot learning
IET Computer Vision, 2018 Zero‐shot learning (ZSL) aims to classify the objects without any training samples. Attributes are used to transfer knowledge from the training set to testing one in ZSL.
Lingfeng Qiao +4 more
doaj +1 more source
What Do Large Language Models Know About Materials?
Advanced Engineering Materials, EarlyView.If large language models (LLMs) are to be used inside the material discovery and engineering process, they must be benchmarked for the accurateness of intrinsic material knowledge. The current work introduces 1) a reasoning process through the processing–structure–property–performance chain and 2) a tool for benchmarking knowledge of LLMs concerning ...
Adrian Ehrenhofer +2 more
wiley +1 more source