Results 121 to 130 of about 60,311 (308)
CoRR, 2018 In this paper, we describe the "implicit autoencoder" (IAE), a generative autoencoder in which both the generative path and the recognition path are parametrized by implicit distributions. We use two generative adversarial networks to define the reconstruction and the regularization cost functions of the implicit autoencoder, and derive the learning ...
openaire +2 more sources
Autoencoder reconstruction results using an exponential distribution.
García-Donas, Jesús +4 more
core +1 more source
An Integrated Artificial Intelligence Framework for Nanomedicine Engineering
Advanced Science, EarlyView.This review outlines a closed‐loop AI ecosystem for nanomedicine engineering. By synergizing multimodal data harmonization, objective‐driven AI architectures, and automated high‐throughput validation, this “Design‐Make‐Test‐Analyze” (DMTA) workflow transforms traditional heuristic formulation into a predictive, self‐optimizing paradigm, accelerating ...
Meichen Dong +10 more
wiley +1 more source
Quantum Autoencoder Frameworks for Network Anomaly Detection
, 2023 Quantum Autoencoder Frameworks for Network Anomaly ...
Lei Pan (13101435) +2 more
core
Enhancing Specific Stiffness of Nano‐Architected Materials Through Generative AI and Active Learning
Advanced Science, EarlyView.A structure‐centric generative framework searches voxelated design space directly, coupling a variational autoencoder with multi‐objective optimization and active learning to progressively expand the validated design domain. The discovered nano‐architectures, realized as pyrolytic carbon by two‐photon lithography, deliver 18–23% higher stiffness and 26–
Jinwook Yeo +4 more
wiley +1 more source
Hybrid Collaborative Recommendation via Semi-AutoEncoder
, 2017 In this paper, we present a novel structure, Semi-AutoEncoder, based on AutoEncoder. We generalize it into a hybrid collaborative filtering model for rating prediction as well as personalized top-n recommendations.
Liming Zhu +9 more
core +1 more source
LLM‐Assisted Design of Ultralow‐κ$\kappa$ Chalcogenides
Advanced Science, EarlyView.An AI assisted, physics guided workflow accelerates the discovery of ultralow thermal conductivity materials by combining Bayesian optimization, evidence conditioned LLM reasoning, and automated physics‐based validation. The framework uncovers dynamically stable Ag based chalcogenides with ultralow thermal conductivity and promising thermoelectric ...
Yujie Liu +9 more
wiley +1 more source
Block diagram of the locally embedding autoencoder.
, 2016 Block diagram of the locally embedding autoencoder.
Hao Ren (331165) +5 more
core +1 more source