Results 121 to 130 of about 37,604 (258)

HiC-GNN: A generalizable model for 3D chromosome reconstruction using graph convolutional neural networks. [PDF]

Comput Struct Biotechnol J, 2023
Hovenga V, Kalita J, Oluwadare O.
europepmc   +1 more source

ORBIT‐AMD: Ordinal Risk, Bilateral Imaging, and Trajectory Learning for Age‐Related Macular Degeneration in Multi‐Cohorts

Advanced Science, EarlyView.
Eligibility flow and real‐world AMD burden in the UKB retinal imaging cohort and TMUEH external‐validation cohort. Overview of the ORBIT‐AMD architecture, integrating retinal representation pretraining, bilateral eye‐graph modeling and concept bottleneck learning to support ordered risk, bilateral context, interpretable lesion concepts, longitudinal ...
Xuehao Cui, Dejia Wen, Patrick Yu‐Wai‐Man, Xiaorong Li   +3 more
wiley   +1 more source

Scalable training of graph convolutional neural networks for fast and accurate predictions of HOMO-LUMO gap in molecules. [PDF]

J Cheminform, 2022
Choi JY, Zhang P, Mehta K, Blanchard A, Lupo Pasini M.   +4 more
europepmc   +1 more source

Implementing the discontinuous-Galerkin finite element method using graph neural networks with application to diffusion equations

Machine learning (ML) has benefited from both software and hardware advancements, leading to increasing interest in capitalising on ML throughout academia and industry.
Chen, Boyang, Heaney, Claire E, Li, Linfeng, Xiang, Jiansheng, Dargaville, Steven, Pain, Christopher C   +5 more
core   +1 more source

People Counting and Positioning Using Low‐Resolution Infrared Images for FeFET‐Based In‐Memory Computing

Advanced Electronic Materials, EarlyView.
In this work, low‐resolution infrared imaging is combined with a 28 nm FeFET IMC architecture to enable compact, energy‐efficient edge inference. MLC FeFET devices are experimentally characterized, and controlled multi‐level current accumulation is validated at crossbar array level.
Alptekin Vardar, Li Zhang, Alexander Rehmer, Marc Lupp, Subhashree Baskaran, Osama Abdelaal, Franz Müller, Nandakishor Yadav, Bing Li, Thomas Kämpfe   +9 more
wiley   +1 more source

Machine Learning with Enormous "Synthetic" Data Sets: Predicting Glass Transition Temperature of Polyimides Using Graph Convolutional Neural Networks. [PDF]

ACS Omega, 2022
Volgin IV, Batyr PA, Matseevich AV, Dobrovskiy AY, Andreeva MV, Nazarychev VM, Larin SV, Goikhman MY, Vizilter YV, Askadskii AA, Lyulin SV.   +10 more
europepmc   +1 more source

On the Role of Preprocessing and Memristor Dynamics in Reservoir Computing for Image Classification

Advanced Electronic Materials, EarlyView.
ABSTRACT Reservoir computing (RC) is an emerging recurrent neural network architecture that has attracted growing attention for its low training cost and modest hardware requirements. Memristor‐based circuits are particularly promising for RC, as their intrinsic dynamics can reduce network size and parameter overhead in tasks such as time‐series ...
Rishona Daniels, Duna Wattad, Ronny Ronen, David Saad, Shahar Kvatinsky   +4 more
wiley   +1 more source

Simplified, interpretable graph convolutional neural networks for small molecule activity prediction. [PDF]

J Comput Aided Mol Des, 2022
Weber JK, Morrone JA, Bagchi S, Pabon JDE, Kang SG, Zhang L, Cornell WD.   +6 more
europepmc   +1 more source

Smart Exploration of Perovskite Photovoltaics: From AI Driven Discovery to Autonomous Laboratories

Advanced Energy Materials, EarlyView.
In this review, we summarize the fundamentals of AI in automated materials science, and review AI applications in perovskite solar cells. Then, we sum up recent progress in AI‐guided manufacturing optimization, and highlight AI‐driven high‐throughput and autonomous laboratories.
Wenning Chen, Zheyu Zhang, Kelvian T. Mularso, Bonghyun Jo, Hyun Suk Jung   +4 more
wiley   +1 more source

Powerset convolutional neural networks

, 2019
We present a novel class of convolutional neural networks (CNNs) for set functions,i.e., data indexed with the powerset of a finite set. The convolutions are derivedas linear, shift-equivariant functions for various notions of shifts on set functions.The
Püschel, Markus, Wendler, Chris, Alistarh, Dan-Adrian ; https://orcid.org/   +2 more
core   +1 more source