Suchergebnisse - ML: Deep Generative Models & Autoencoders

ProT-VAE: Protein Transformer Variational AutoEncoder for functional protein design.

Autoren: Sevgen, Emre Moller, Joshua Lange, Adrian et al.

Quelle: Proceedings of the National Academy of Sciences of the United States of America; 10/14/2025, Vol. 122 Issue 41, p1-10, 21p

Schlagwörter: PROTEIN engineering, SYNTHETIC proteins, TRANSFORMER models, ENZYME biotechnology, FUNCTIONAL assessment, LATENT variables, MACHINE learning, PROBABILISTIC generative models

Interpretable generative machine learning model based in-situ process monitoring in robotic wire arc based directed energy deposition of aluminum alloys.

Autoren: Kumar, Deepak Jha, Sunil

Quelle: CIRP: Journal of Manufacturing Science & Technology. Dec2025, Vol. 63, p185-204. 20p.

Schlagwörter: Acoustic emission, Fault diagnosis, Process control systems, Metallurgical analysis, Aluminum alloys, Robotic welding, Generative artificial intelligence

Bridging the gap between machine learning and psychometrics.

Autoren: Templin, Jonathan

Quelle: Behaviormetrika; Jul2025, Vol. 52 Issue 2, p217-219, 3p

Schlagwörter: MACHINE learning, PSYCHOMETRICS, ARTIFICIAL neural networks, FACTOR analysis, ITEM response theory, PROBABILISTIC generative models, SAT (Educational test)

Deep Generative Models for the Discovery of Antiviral Peptides Targeting Dengue Virus: A Systematic Review.

Autoren: Duy, Huynh Anh Srisongkram, Tarapong

Quelle: International Journal of Molecular Sciences; Jul2025, Vol. 26 Issue 13, p6159, 25p

Schlagwörter: DENGUE viruses, ANTIVIRAL agents, ARTIFICIAL intelligence, DRUG discovery, PROBABILISTIC generative models, PEPTIDE synthesis, TREATMENT effectiveness, VIRAL replication

Deep Generative Models for Materials Discovery and Machine Learning-Accelerated Innovation

Autoren: Addis S. Fuhr Bobby G. Sumpter

Quelle: Frontiers in Materials, Vol 9 (2022)

Schlagwörter: Technology, machine learning, generative models, inverse design, 02 engineering and technology, generative adversarial networks, 0210 nano-technology, 01 natural sciences, materials discovery, variational autoencoders, 0104 chemical sciences

Zugangs-URL: https://doaj.org/article/e61de32b93654efb969a59a7f996ce24

Optimizing Industry 5.0 Machine Learning-Based Applications via Synthetic Data Generation

Autoren: Colombi, Lorenzo Brina, Matteo Vespa, Michela et al.

Quelle: 2024 IEEE 29th International Workshop on Computer Aided Modeling and Design of Communication Links and Networks (CAMAD). :1-6

Schlagwörter: Industry 5.0 Machine Learning Deep Generative Model Generative Adversarial Network Diffusion Model Variational Auto encoder, Machine Learning, Variational Autoencoder, Deep Generative Model, Diffusion Model, Generative Adversarial Network, Industry 5.0

Dateibeschreibung: application/pdf

Zugangs-URL: https://ieeexplore.ieee.org/document/10942898
https://doi.org/10.1109/CAMAD62243.2024.10942898
https://hdl.handle.net/11583/3000680
https://hdl.handle.net/11392/2587991
https://doi.org/10.1109/camad62243.2024.10942898
https://hdl.handle.net/11585/1014122
https://doi.org/10.1109/camad62243.2024.10942898

Cross-modal deep generative models reveal the cortical representation of dancing.

Autoren: Takagi, Yu Shimizu, Daichi Wakabayashi, Mina et al.

Quelle: Nature Communications; 11/18/2025, Vol. 16 Issue 1, p1-12, 12p

Schlagwörter: DANCE, FUNCTIONAL magnetic resonance imaging, COGNITION, PROBABILISTIC generative models, EXPERTISE, EMOTIONAL experience

Inverse design of catalytic active sites via interpretable topology-based deep generative models.

Autoren: Wang, Bingxu Zheng, Shisheng Wu, Jie et al.

Quelle: NPJ Computational Materials; 5/24/2025, Vol. 11 Issue 1, p1-12, 12p

Schlagwörter: HIGH-entropy alloys, CATALYST structure, AUTOENCODERS, CATALYSTS

Deep Learning and Machine Learning, Advancing Big Data Analytics and Management: Generative Models

Autoren: Silin Chen Tianyang Wang Xinyuan Song et al.

Schlagwörter: Engineering

Causal Inference with Conditional Instruments Using Deep Generative Models

Autoren: Cheng, Debo Xu, Ziqi Li, Jiuyong et al.

Quelle: Proceedings of the AAAI Conference on Artificial Intelligence; Vol. 37 No. 6: AAAI-23 Technical Tracks 6; 7122-7130 ; 2374-3468 ; 2159-5399

Schlagwörter: ML: Deep Generative Models & Autoencoders, ML: Causal Learning, RU: Causality

Dateibeschreibung: application/pdf

Relation: https://ojs.aaai.org/index.php/AAAI/article/view/25869/25641; https://ojs.aaai.org/index.php/AAAI/article/view/25869

Verfügbarkeit: https://ojs.aaai.org/index.php/AAAI/article/view/25869
https://doi.org/10.1609/aaai.v37i6.25869

On Explicit Curvature Regularization in Deep Generative Models

Autoren: Lee, Yonghyeon Park, Frank Chongwoo

Index Begriffe: Computer Science - Artificial Intelligence, Computer Science - Machine Learning, text

URL: http://arxiv.org/abs/2309.10237

Selective Deep Autoencoder for Unsupervised Feature Selection

Autoren: Hassanieh, Wael Chehade, Abdallah

Quelle: Proceedings of the AAAI Conference on Artificial Intelligence; Vol. 38 No. 11: AAAI-24 Technical Tracks 11; 12322-12330 ; 2374-3468 ; 2159-5399

Schlagwörter: ML: Dimensionality Reduction/Feature Selection, DMKM: Data Compression, ML: Applications, ML: Deep Generative Models & Autoencoders, ML: Deep Neural Architectures and Foundation Models

Dateibeschreibung: application/pdf

Relation: https://ojs.aaai.org/index.php/AAAI/article/view/29123/30124; https://ojs.aaai.org/index.php/AAAI/article/view/29123/30125; https://ojs.aaai.org/index.php/AAAI/article/view/29123

Verfügbarkeit: https://ojs.aaai.org/index.php/AAAI/article/view/29123
https://doi.org/10.1609/aaai.v38i11.29123

Recall-Oriented Continual Learning with Generative Adversarial Meta-Model

Autoren: Kang, Haneol Choi, Dong-Wan

Quelle: Proceedings of the AAAI Conference on Artificial Intelligence; Vol. 38 No. 12: AAAI-24 Technical Tracks 12; 13040-13048 ; 2374-3468 ; 2159-5399

Schlagwörter: ML: Life-Long and Continual Learning, ML: Deep Generative Models & Autoencoders, ML: Bio-inspired Learning, ML: Deep Learning Algorithms

Dateibeschreibung: application/pdf

Relation: https://ojs.aaai.org/index.php/AAAI/article/view/29202/30269; https://ojs.aaai.org/index.php/AAAI/article/view/29202/30270; https://ojs.aaai.org/index.php/AAAI/article/view/29202

Verfügbarkeit: https://ojs.aaai.org/index.php/AAAI/article/view/29202
https://doi.org/10.1609/aaai.v38i12.29202

Unified Framework for Diffusion Generative Models in SO(3): Applications in Computer Vision and Astrophysics

Autoren: Jagvaral, Yesukhei Lanusse, Francois Mandelbaum, Rachel

Quelle: Proceedings of the AAAI Conference on Artificial Intelligence; Vol. 38 No. 11: AAAI-24 Technical Tracks 11; 12754-12762 ; 2374-3468 ; 2159-5399

Schlagwörter: ML: Learning with Manifolds, APP: Natural Sciences, APP: Other Applications, CV: Applications, CV: Vision for Robotics & Autonomous Driving, ML: Bayesian Learning, ML: Deep Generative Models & Autoencoders

Dateibeschreibung: application/pdf

Relation: https://ojs.aaai.org/index.php/AAAI/article/view/29171/30215; https://ojs.aaai.org/index.php/AAAI/article/view/29171/30216; https://ojs.aaai.org/index.php/AAAI/article/view/29171

Verfügbarkeit: https://ojs.aaai.org/index.php/AAAI/article/view/29171
https://doi.org/10.1609/aaai.v38i11.29171

Generative AI for cyber threat intelligence: applications, challenges, and analysis of real-world case studies.

Autoren: Balasubramanian, Prasasthy Liyana, Sonali Sankaran, Hamsini et al.

Quelle: Artificial Intelligence Review; Nov2025, Vol. 58 Issue 11, p1-134, 134p

Schlagwörter: GENERATIVE artificial intelligence, CYBER intelligence (Computer security), COMPUTER network traffic, DIGITAL technology, CONTEXTUAL analysis, MALWARE, DATA privacy

QCS-SGM+: Improved Quantized Compressed Sensing with Score-Based Generative Models

Autoren: Meng, Xiangming Kabashima, Yoshiyuki

Quelle: Proceedings of the AAAI Conference on Artificial Intelligence; Vol. 38 No. 13: AAAI-24 Technical Tracks 13; 14341-14349 ; 2374-3468 ; 2159-5399

Schlagwörter: ML: Deep Generative Models & Autoencoders, ML: Bayesian Learning, ML: Probabilistic Circuits and Graphical Models

Dateibeschreibung: application/pdf

Relation: https://ojs.aaai.org/index.php/AAAI/article/view/29347/30542; https://ojs.aaai.org/index.php/AAAI/article/view/29347/30543; https://ojs.aaai.org/index.php/AAAI/article/view/29347

Verfügbarkeit: https://ojs.aaai.org/index.php/AAAI/article/view/29347
https://doi.org/10.1609/aaai.v38i13.29347

Data Distribution Distilled Generative Model for Generalized Zero-Shot Recognition

Autoren: Wang, Yijie Hong, Mingjian Huangfu, Luwen et al.

Quelle: Proceedings of the AAAI Conference on Artificial Intelligence; Vol. 38 No. 6: AAAI-24 Technical Tracks 6; 5695-5703 ; 2374-3468 ; 2159-5399

Schlagwörter: CV: Learning & Optimization for CV, CV: Representation Learning for Vision, ML: Deep Generative Models & Autoencoders

Dateibeschreibung: application/pdf

Relation: https://ojs.aaai.org/index.php/AAAI/article/view/28381/28746; https://ojs.aaai.org/index.php/AAAI/article/view/28381/28747; https://ojs.aaai.org/index.php/AAAI/article/view/28381

Verfügbarkeit: https://ojs.aaai.org/index.php/AAAI/article/view/28381
https://doi.org/10.1609/aaai.v38i6.28381

Generative deep learning for predicting ultrahigh lattice thermal conductivity materials.

Autoren: Guo, Liben Liu, Yuanbin Chen, Zekun et al.

Quelle: NPJ Computational Materials; 4/11/2025, Vol. 11 Issue 1, p1-10, 10p

Schlagwörter: MACHINE learning, THERMOPHYSICAL properties, THERMAL conductivity, CARBON-based materials, THERMAL properties

Continual Variational Autoencoder via Continual Generative Knowledge Distillation

Autoren: Ye, Fei Bors, Adrian G.

Quelle: Proceedings of the AAAI Conference on Artificial Intelligence; Vol. 37 No. 9: AAAI-23 Technical Tracks 9; 10918-10926 ; 2374-3468 ; 2159-5399

Schlagwörter: ML: Deep Generative Models & Autoencoders, ML: Deep Learning Theory, ML: Ensemble Methods, ML: Lifelong and Continual Learning

Dateibeschreibung: application/pdf

Relation: https://ojs.aaai.org/index.php/AAAI/article/view/26294/26066; https://ojs.aaai.org/index.php/AAAI/article/view/26294

Verfügbarkeit: https://ojs.aaai.org/index.php/AAAI/article/view/26294
https://doi.org/10.1609/aaai.v37i9.26294

Modeling protein–ligand interactions for drug discovery in the era of deep learning.

Autoren: Wang, Yuzhe Li, Yibo Chen, Jiaxiao et al.

Quelle: Chemical Society Reviews; 12/7/2025, Vol. 54 Issue 23, p11141-11183, 43p

Schlagwörter: PROTEIN-ligand interactions, DRUG discovery, MOLECULAR dynamics, DRUG design, PREDICTION models, MOLECULAR docking, VIRTUAL high-throughput screening (Drug development), DEEP learning