Výsledky vyhledávání - Constrained Graph Variational Autoencoder*

Conditional Constrained Graph Variational Autoencoders for Molecule Design

Autoři: Davide Rigoni Nicolo' Navarin Alessandro Sperduti

Zdroj: 2020 IEEE Symposium Series on Computational Intelligence (SSCI). :729-736

Témata: FOS: Computer and information sciences, Computer Science - Machine Learning, Artificial Intelligence (cs.AI), Computer Science - Artificial Intelligence, 0202 electrical engineering, electronic engineering, information engineering, 02 engineering and technology, 01 natural sciences, Machine Learning (cs.LG), 0104 chemical sciences

Popis souboru: application/pdf

Přístupová URL adresa: http://arxiv.org/pdf/2009.00725
http://arxiv.org/abs/2009.00725
https://arxiv.org/pdf/2009.00725
https://www.research.unipd.it/handle/11577/3389931
https://arxiv.org/abs/2009.00725
http://dblp.uni-trier.de/db/journals/corr/corr2009.html#abs-2009-00725
https://dblp.uni-trier.de/db/journals/corr/corr2009.html#abs-2009-00725
https://hdl.handle.net/11577/3389931
https://doi.org/10.1109/SSCI47803.2020.9308554

Genetic Constrained Graph Variational Autoencoder for COVID-19 Drug Discovery

Autoři: Cheng, Tianyue Fan, Tianchi Wang, Landi

Témata: Computer Science - Machine Learning, Quantitative Biology - Quantitative Methods, text

URL: http://arxiv.org/abs/2104.11674

Conditional Constrained Graph Variational Autoencoders for Molecule Design

Autoři: Rigoni, Davide Navarin, Nicolò Sperduti, Alessandro

Témata: Computer Science - Machine Learning, Computer Science - Artificial Intelligence, text

URL: http://arxiv.org/abs/2009.00725

On the Generation of Novel Ligands for SARS-CoV-2 Protease and ACE2 Receptor via Constrained Graph Variational Autoencoders

Autoři: Jasper Kyle Catapang Junie B. Billones

Témata: 0301 basic medicine, 03 medical and health sciences, 01 natural sciences, 3. Good health, 0104 chemical sciences

Přístupová URL adresa: https://chemrxiv.org/engage/api-gateway/chemrxiv/assets/orp/resource/item/60c74990842e658846db2d79/original/on-the-generation-of-novel-ligands-for-sars-co-v-2-protease-and-ace2-receptor-via-constrained-graph-variational-autoencoders.pdf
https://chemrxiv.org/engage/api-gateway/chemrxiv/assets/orp/resource/item/60c7494f4c8919e3e9ad3078/original/on-the-generation-of-novel-ligands-for-sars-co-v-2-protease-and-ace2-receptor-via-constrained-graph-variational-autoencoders.pdf
https://chemrxiv.org/engage/api-gateway/chemrxiv/assets/orp/resource/item/60c7490e337d6c09f4e27666/original/on-the-generation-of-novel-ligands-for-sars-co-v-2-protease-and-ace2-receptor-via-constrained-graph-variational-autoencoders.pdf
https://europepmc.org/article/PPR/PPR118788
https://chemrxiv.org/engage/chemrxiv/article-details/60c7490e337d6c09f4e27666
https://doi.org/10.26434%2Fchemrxiv.12011157.v3

Constrained Graph Variational Autoencoders for Molecule Design

Autoři: Liu, Qi Allamanis, Miltiadis Brockschmidt, Marc a další

Témata: Computer Science - Machine Learning, Statistics - Machine Learning, text

URL: http://arxiv.org/abs/1805.09076

Unified Embeddings of Structural and Functional Connectome via a Function-Constrained Structural Graph Variational Auto-Encoder

Autoři: Amodeo, Carlo Fortel, Igor Ajilore, Olusola a další

Témata: Quantitative Biology - Neurons and Cognition, Computer Science - Machine Learning, text

URL: http://arxiv.org/abs/2207.02328

Structured Graph Variational Autoencoders for Indoor Furniture layout Generation

Autoři: Chattopadhyay, Aditya Zhang, Xi Wipf, David Paul a další

Témata: Computer Science - Computer Vision and Pattern Recognition, text

URL: http://arxiv.org/abs/2204.04867

Constrained Generation of Semantically Valid Graphs via Regularizing Variational Autoencoders

Autoři: Ma, Tengfei Chen, Jie Xiao, Cao

Témata: Computer Science - Machine Learning, Statistics - Machine Learning, text

URL: http://arxiv.org/abs/1809.02630

Self-Organizing Maps-Assisted Variational Autoencoder for Unsupervised Network Anomaly Detection.

Autoři: Huang, Hailong Yang, Jiahong Zeng, Hang a další

Zdroj: Symmetry (20738994). Apr2025, Vol. 17 Issue 4, p520. 15p.

Témata: *DETECTION algorithms, *SELF-organizing maps, *FEATURE selection, *ANOMALY detection (Computer security), *AUTOENCODERS

On the Generation of Novel Ligands for SARS-CoV-2 Protease and ACE2 Receptor via Constrained Graph Variational Autoencoders

Autoři: Catapang, Jasper Kyle Billones, Junie B.

Dostupnost: https://doi.org/10.26434/chemrxiv.12011157.v1
https://chemrxiv.org/engage/api-gateway/chemrxiv/assets/orp/resource/item/60c7490e337d6c09f4e27666/original/on-the-generation-of-novel-ligands-for-sars-co-v-2-protease-and-ace2-receptor-via-constrained-graph-variational-autoencoders.pdf

Reusability report: exploring the utility of variational graph encoders for predicting molecular toxicity in drug design.

Autoři: Li, Ruijiang Lu, Jiang Liu, Ziyi a další

Zdroj: Nature Machine Intelligence; Dec2024, Vol. 6 Issue 12, p1457-1466, 10p

A Generative Framework for Predicting Antiferromagnets.

Autoři: Gong, Jianhu Zhang, Zhengming Fan, Zhenyu a další

Zdroj: Advanced Science. 12/11/2025, Vol. 12 Issue 46, p1-9. 9p.

Aerodynamic shape optimization using graph variational autoencoders and genetic algorithms.

Autoři: Jabón, Jorge Corbera, Sergio Álvarez, Roberto a další

Zdroj: Structural & Multidisciplinary Optimization; Mar2024, Vol. 67 Issue 3, p1-18, 18p

Fragment‐based deep molecular generation using hierarchical chemical graph representation and multi‐resolution graph variational autoencoder.

Autoři: Gao, Zhenxiang Wang, Xinyu Blumenfeld Gaines, Blake a další

Zdroj: Molecular Informatics; May2023, Vol. 42 Issue 5, p1-15, 15p

Témata: REPRESENTATIONS of graphs, MOLECULAR graphs, MOIETIES (Chemistry), MOLECULAR structure, DRUG discovery

TV-CCANM: a transformer variational inference in confounding cascade additive noise model for causal effect estimation.

Autoři: Ahmad, Sohail Wang, Hong

Zdroj: Journal of Statistical Computation & Simulation. Sep2025, Vol. 95 Issue 14, p3048-3076. 29p.

Témata: *CAUSAL inference, *CAUSAL models

Nearest Neighbours Graph Variational AutoEncoder.

Autoři: Arsini, Lorenzo Caccia, Barbara Ciardiello, Andrea a další

Zdroj: Algorithms; Mar2023, Vol. 16 Issue 3, p143, 17p

Témata: DEEP learning, PHASE coding

Vergleich verschiedener Variationaler Autencoder-Konzepte zur Generierung effektiver Molekülrepräsentationen von niedermolekularen organischen Verbindungen

Autoři: Szablowski, Darius Neitzke, Michael

Přispěvatelé: Szablowski, Darius Neitzke, Michael

Témata: Variationale Autoencoder, niedermolekulare organische Verbindungen, VAE, SMILES, Chem- VAE, fragmentbasiertes Deep-Generatives-Model, Constrained Graph Variational Autoencoder, CGVAE, 004: Informatik, ddc:004

Popis souboru: application/pdf

Relation: https://hdl.handle.net/20.500.12738/16162

Dostupnost: https://hdl.handle.net/20.500.12738/16162

Building-CVGAE: Constrained Variational Graph Autoencoder For Architectural Volumetric Design Generation

Autoři: Sehaba, Mohammed El Amine Marsault, Xavier Miguet, Serge a další

Přispěvatelé: Sehaba, Mohammed El Amine Marsault, Xavier Miguet, Serge a další

Zdroj: International Conference on Advances in Artificial Intelligence ; https://hal.science/hal-05235761 ; International Conference on Advances in Artificial Intelligence, Nov 2025, Manchester, United Kingdom

Témata: [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI]

Geografické téma: Manchester, United Kingdom

Dostupnost: https://hal.science/hal-05235761

DiA-gnostic VLVAE: Disentangled Alignment-Constrained Vision Language Variational AutoEncoder for Robust Radiology Reporting with Missing Modalities

Autoři: Shaik, Nagur Shareef Cherukuri, Teja Krishna Masood, Adnan a další

Témata: Computer Vision and Pattern Recognition, Artificial Intelligence, Machine Learning

Relation: http://arxiv.org/abs/2511.05968

Dostupnost: http://arxiv.org/abs/2511.05968

Multi-head Variational Graph Autoencoder Constrained by Sum-product Networks

Autoři: Xia, Riting Zhang, Yan Zhang, Chunxu a další

Přispěvatelé: Xia, Riting Zhang, Yan Zhang, Chunxu a další

Zdroj: Proceedings of the ACM Web Conference 2023 ; page 641-650

Dostupnost: https://doi.org/10.1145/3543507.3583517
https://dl.acm.org/doi/10.1145/3543507.3583517
https://dl.acm.org/doi/pdf/10.1145/3543507.3583517