Understanding representation learning for deep reinforcement learning
Representation learning is essential to practical success of reinforcement learning. Through a state representation, an agent can describe its environment to efficiently explore the state space, generalize to new states and perform credit assignment from
Le Lan, Charline
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Integrating Multimodal and Longitudinal Neuroimaging Data with Multi-Source Network Representation Learning. [PDF]
Neuroinformatics, 2022 Zhang W +6 more
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Advanced Engineering Materials, EarlyView.This study presents an infrared monitoring approach for direct laser interference patterning (DLIP) combined with a convolutional neural network (CNN). Thermal emission data captured during structuring are used to predict surface topography parameters.
Lukas Olawsky +5 more
wiley +1 more source
WAFNRLTG: A Novel Model for Predicting LncRNA Target Genes Based on Weighted Average Fusion Network Representation Learning Method. [PDF]
Front Cell Dev Biol, 2021 Li J, Yang Z, Wang D, Li Z.
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Zein‐Based Adhesives: Sustainable Extraction and Application in Bioadhesive Technologies
Advanced Engineering Materials, EarlyView.Zein is extracted from corn gluten meal using a simple and scalable process with high yield (~90%). The resulting protein is applied in bioadhesives modified with Ca2+ and Fe3+ ions, exhibiting substrate‐dependent adhesion. The findings demonstrate competitive bonding performance and highlight the role of ionic interactions in tuning adhesion ...
Paula Bertolino Sanvezzo +3 more
wiley +1 more source
Network Representation Learning in Social Media [PDF]
, 2018 : The popularity of social media has generated abundant large-scale social networks, which advances research on network analytics. Good representations of nodes in a network can facilitate many network mining tasks.
core
Dynamic Network Representation Learning Method Based on Improved GRU Network
, 2023 As social networks have been rapidly growing, traditional network representation learning methods are struggling to accurately characterize their dynamic changes, and to output effective node classification and link prediction.
Pan, Jianguo +4 more
core
Additive Gaussian Process Regression for Predictive Design of High‐Performance, Printable Silicones
Advanced Engineering Materials, EarlyView.A chemistry‐aware design framework for tuning printable polydimethylsiloxane (PDMS) for vat photopolymerization (VPP) is developed using additive Gaussian process (GP) modeling. Polymer network mechanics informs variable groupings, feasible formulation constraints, and interaction variables.
Roxana Carbonell +3 more
wiley +1 more source
Advanced Engineering Materials, EarlyView.A simplified thermoplastic pultrusion model is developed to predict thermal fields in glass fiber/polyethylene terephthalate (GF/PET) composites with reduced computational cost. By combining effective material homogenization, validation against literature data, and Gaussian‐process‐based optimization, the study reveals how heating limits, pulling speed,
Elder Soares +3 more
wiley +1 more source
GeneWalk identifies relevant gene functions for a biological context using network representation learning. [PDF]
Genome Biol, 2021 Ietswaart R +4 more
europepmc +1 more source