Results 91 to 100 of about 514,020 (257)
Advanced Healthcare Materials, EarlyView.We developed a micro‐sized, biocompatible implant for postoperative sustained delivery of anti‐fibrotic antibodies in glaucoma surgery. Machine learning‐guided optimization of polymer composition, implant geometry, and porosity enabled precise control of drug release.
Mengqi Qin +5 more
wiley +1 more source
Towards few-shot learning with triplet metric learning and Kullback-Leibler optimization
Complex & Intelligent SystemsFew-shot learning has achieved great success in recent years, thanks to its requirement of limited number of labeled data. However, most of the state-of-the-art techniques of few-shot learning employ transfer learning, which still requires massive ...
Yukun Liu +5 more
doaj +1 more source
Laplacian Regularized Few-Shot Learning
, 2020 ICML 2020 ...
Ziko, Imtiaz Masud +3 more
openaire +2 more sources
Isolation Defines Identity: Functional Consequences of Extracellular Vesicle Purification Strategies
Advanced Healthcare Materials, EarlyView.Four extracellular vesicle purification strategies are compared using ovarian‐cancer ascites and ES‐2 cell supernatants. A novel workflow links purification to function by combining particle‐normalized proteomics with matched cell‐free and cell‐based assays.
Christian Preußer +10 more
wiley +1 more source
A Synovium‐on‐Chip Platform to Study Multicellular Interactions in Arthritis
Advanced Healthcare Materials, EarlyView.The Synovium‐on‐Chip comprises a thin microporous PDMS membrane to support co‐culture of fibroblast‐like synoviocytes (FLS), THP‐1‐derived macrophages, and endothelial cells, enabling real‐time analysis of synovial‐vascular interactions. FLS migration through the pores drives endothelial remodeling, while TNF‐α stimulation induces robust inflammatory ...
Laurens R. Spoelstra +8 more
wiley +1 more source
Few-Shot Learning With a Strong Teacher
IEEE Transactions on Pattern Analysis and Machine IntelligenceFew-shot learning (FSL) aims to generate a classifier using limited labeled examples. Many existing works take the meta-learning approach, constructing a few-shot learner that can learn from few-shot examples to generate a classifier. Typically, the few-shot learner is constructed or meta-trained by sampling multiple few-shot tasks in turn and ...
Han-Jia Ye +3 more
openaire +3 more sources
Ferroelectric Quantum Dots for Retinomorphic In‐Sensor Computing
Advanced Materials, EarlyView.This work has provided a protocol for fabricating retinomorphic phototransistors by integrating ferroelectric ligands with quantum dots. The resulting device combines ferroelectricity, optical responsiveness, and low‐power operation to enable adaptive signal amplification and high recognition accuracy under low‐light conditions, while supporting ...
Tingyu Long +26 more
wiley +1 more source
Crystal Engineering of Reticular Materials for Gas‐ and Liquid‐Phase Hydrocarbon Separation
Advanced Materials, EarlyView.Crystal engineering enables systematic study of structure/function relationships as exemplified by pore engineering of reticular sorbents, including porous coordination networks and covalent organic frameworks. This review assesses such studies applied across the full scope of industrially relevant hydrocarbon separations to provide insight into how ...
Xia Li +2 more
wiley +1 more source
Benchmarking Federated Few-Shot Learning for Video-Based Action Recognition
IEEE AccessFew-shot action recognition aims to train a model to classify actions in videos using only a few examples, known as “shots,” per action class.
Nguyen Anh Tu +5 more
doaj +1 more source
Alpha MAML: Adaptive Model-Agnostic Meta-Learning
, 2019 Model-agnostic meta-learning (MAML) is a meta-learning technique to train a model on a multitude of learning tasks in a way that primes the model for few-shot learning of new tasks.
Baydin, Atılım Güneş +2 more
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