Results 291 to 300 of about 76,480 (326)

Real‐Time Multicolor Fluorescence Microscopy via Cross‐Channel Acquisition and Deep‐Learning‐Based Inference

Advanced Intelligent Discovery, EarlyView.
Sequential multicolor fluorescence imaging in dynamic microsystems is constrained by acquisition speed and excitation dose. This study introduces a real‐time framework to reconstruct spectrally separated channels from reduced cross‐channel acquisitions (frames containing mixed spectral contributions).
Juan J. Huaroto, Yuxin Jin, Nicholas Roy, Sarthak Misra   +3 more
wiley   +1 more source

Automating AI Discovery for Biomedicine Through Knowledge Graphs and Large Language Models Agents

Advanced Intelligent Discovery, EarlyView.
This work proposes a novel framework that automates biomedical discovery by integrating knowledge graphs with multiagent large language models. A biologically aligned graph exploration strategy identifies hidden pathways between biomedical entities, and specialized agents use this pathway to iteratively design AI predictors and wet‐lab validation ...
Naafey Aamer, Muhammad Nabeel Asim, Shan Munir, Andreas Dengel   +3 more
wiley   +1 more source

Toward Predictable Nanomedicine: Current Forecasting Frameworks for Nanoparticle–Biology Interactions

Advanced Intelligent Discovery, EarlyView.
Predictive models successfully screen nanoparticles for toxicity and cellular uptake. Yet, complex biological dynamics and sparse, nonstandardized data limit their accuracy. The field urgently needs integrated artificial intelligence/machine learning, systems biology, and open‐access data protocols to bridge the gap between materials science and safe ...
Mariya L. Ivanova, Michael Nicholls, Nicola Russo, Gueorgui Mihaylov, Konstantin Nikolic   +4 more
wiley   +1 more source

Federated weakly-supervised representation learning for privacy-preserving human activity recognition using wearable sensors

lukss, Andris
openalex   +1 more source

Weakly-Supervised Learning from Incomplete Data

openaire   +1 more source

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Towards Safe Weakly Supervised Learning

IEEE Transactions on Pattern Analysis and Machine Intelligence, 2019
In this paper, we study weakly supervised learning where a large amount of data supervision is not accessible. This includes i) incomplete supervision, where only a small subset of labels is given, such as semi-supervised learning and domain adaptation; ii) inexact supervision, where only coarse-grained labels are given, such as multi-instance learning
Yu-Feng Li, Zhi-Hua Zhou, Yu-Feng Li
exaly   +3 more sources

Weakly Supervised Learning-based Table Detection

SN Computer Science, 2020
CNN has given the state-of-the-art results in computer vision and natural language processing (NLP) domain problems. This has motivated researchers to use deep learning-based techniques for document layout analysis. Due to recent advances in communication and in information technology, methods of data storage, extraction and processing are rapidly ...
Gurav Aa, Nene, Manisha J
openaire   +1 more source