Results 111 to 120 of about 30,189 (295)
Advanced Intelligent Discovery, EarlyView.The authors develop a deep learning model for real‐time tracking of wound progression. The deep learning framework maps the nonlinear evolution of a time series of images to a latent space, where they learn a linear representation of the dynamics. The linear model is interpretable and suitable for applications in feedback control.
Fan Lu +11 more
wiley +1 more source
Recurrent neural network (RNN) model accelerates the development of antibacterial metronidazole derivatives. [PDF]
RSC Adv, 2022 Chen N +8 more
europepmc +1 more source
When Biology Meets Medicine: A Perspective on Foundation Models
Advanced Intelligent Discovery, EarlyView.Artificial intelligence, and foundation models in particular, are transforming life sciences and medicine. This perspective reviews biological and medical foundation models across scales, highlighting key challenges in data availability, model evaluation, and architectural design.
Kunying Niu +3 more
wiley +1 more source
DA-RNN: Semantic Mapping with Data Associated Recurrent Neural Networks
Robotics: Science and Systems XIII, 2017 3D scene understanding is important for robots to interact with the 3D world in a meaningful way. Most previous works on 3D scene understanding focus on recognizing geometrical or semantic properties of the scene independently. In this work, we introduce Data Associated Recurrent Neural Networks (DA-RNNs), a novel framework for joint 3D scene mapping ...
Yu Xiang 0001, Dieter Fox
openaire +2 more sources
Hypergraph Convolutional Recurrent Neural Network
, 2020 In this study, we present a hypergraph convolutional recurrent neural network (HGC-RNN), which is a prediction model for structured time-series sensor network data.
Park, Jinkyoo +3 more
core +1 more source
Exploiting Ferroelectric and Spintronic Dynamics for Neural Network Computation
Advanced Intelligent Systems, EarlyView.Ferroelectric and spintronic devices, relying on the control of polarization and magnetization, offer intrinsically fast, durable, energy‐efficient, and low‐latency building blocks for analog in‐memory computing. The hysteretic dynamics of an order parameter are leveraged to provide nonvolatile, multistate memory and nonlinear switching. Brain‐inspired
Dashiell Harrison +4 more
wiley +1 more source
Deep Gate Recurrent Neural Network [PDF]
, 2016 This paper explores the possibility of using multiplicative gates to build two recurrent neural network structures. These two structures are called Deep Simple Gated Unit (DSGU) and Simple Gated Unit (SGU), which are structures for learning long-term ...
Gao, Yuan, Glowacka, Dorota
core +1 more source
Dynamic displacement reconstruction of bridge based on physics-informed recurrent neural network
Advances in Bridge EngineeringIt is extremely challenging to directly measure the dynamic displacement which is essential in bridge state evaluation. The indirect physical-driven displacement reconstruction methods are restricted by the deviation existing between mechanism model and ...
Yi Tao +3 more
doaj +1 more source
Deep Gate Recurrent Neural Network [PDF]
, 2020 This paper explores the possibility of using multiplicative gate to build two recurrent neural network structures. These two structures are called Deep Simple Gated Unit (DSGU) and Simple Gated Unit (SGU), which are structures for learning long-term ...
Robert J Durrant +3 more
core
Real‐Time Biomass Estimation in High‐Density Yeast Fermentations Using Soft Sensor Modeling
Biotechnology and Bioengineering, EarlyView.Accurate biomass measurements, both offline and online, are essential to improve prediction and control in yeast fermentation. This study develops regression‐based predictive models to correlate offline measurements (Dry Cell Weight and OD600 from a spectrophotometer) with online OD860 probe signals to provide accurate real‐time biomass estimations and
Ana G. Del Hierro +3 more
wiley +1 more source