Suchergebnisse - "Conditioned Variational Autoencoder"

Out-of-Clinical-Distribution Detection with a Softmax-Conditioned Variational Autoencoder Regulariser: Application to Fetal Ultrasound

Autoren: Kangning Zhang Jianbo Jiao J. Alison Noble

Quelle: Lecture Notes in Electrical Engineering ISBN: 9789819638628

RGCVAE: relational graph conditioned variational autoencoder for molecule design

Autoren: Davide Rigoni Nicolò Navarin Alessandro Sperduti

Quelle: Machine Learning. 114

Schlagwörter: FOS: Computer and information sciences, Computer Science - Machine Learning, Artificial Intelligence (cs.AI), Quantitative Biology - Biomolecules, Computer Science - Artificial Intelligence, FOS: Biological sciences, Biomolecules (q-bio.BM), Machine Learning (cs.LG)

Zugangs-URL: http://arxiv.org/abs/2305.11699

Forward/Inverse Kinematics Modeling for Tensegrity Manipulator Based on Goal-Conditioned Variational Autoencoder

Autoren: Yuhei Yoshimitsu Takayuki Osa Shuhei Ikemoto

Quelle: 2023 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). :6668-6673

Adaptive Transformer-Based Conditioned Variational Autoencoder for Incomplete Social Event Classification

Autoren: Zhangming Li Shengsheng Qian Jie Cao et al.

Quelle: Proceedings of the 30th ACM International Conference on Multimedia. :1698-1707

Schlagwörter: 0202 electrical engineering, electronic engineering, information engineering, 02 engineering and technology

A Demographic-Conditioned Variational Autoencoder for Removal of Confounds from fMRI Data

Autoren: Anton Orlichenko Gang Qu Ziyu Zhou et al.

Schlagwörter: 3. Good health

A Demographic-Conditioned Variational Autoencoder for fMRI Distribution Sampling and Removal of Confounds

Autoren: Anton Orlichenko Gang Qu Ziyu Zhou et al.

Schlagwörter: FOS: Computer and information sciences, Computer Science - Machine Learning, Quantitative Biology - Neurons and Cognition, FOS: Biological sciences, Neurons and Cognition (q-bio.NC), Quantitative Biology - Quantitative Methods, Quantitative Methods (q-bio.QM), Machine Learning (cs.LG), 3. Good health

Zugangs-URL: https://pubmed.ncbi.nlm.nih.gov/38800653
https://pubmed.ncbi.nlm.nih.gov/38798580
http://arxiv.org/abs/2405.07977

A Look Inside the Black-Box: Towards the Interpretability of Conditioned Variational Autoencoder for Collaborative Filtering

Autoren: Carraro T. Polato M. Aiolli F.

Quelle: Adjunct Publication of the 28th ACM Conference on User Modeling, Adaptation and Personalization. :233-236

Schlagwörter: collaborative filtering, explainability, interpretability, recommender systems, variational autoencoder, 0202 electrical engineering, electronic engineering, information engineering, 02 engineering and technology

Dateibeschreibung: application/pdf

Zugangs-URL: https://dblp.uni-trier.de/db/conf/um/umap2020a.html#CarraroPA20
https://dl.acm.org/doi/pdf/10.1145/3386392.3399305
https://hdl.handle.net/2318/1870203
https://doi.org/10.1145/3386392.3399305

A Demographic-Conditioned Variational Autoencoder for fMRI Distribution Sampling and Removal of Confounds

Autoren: Orlichenko, Anton Qu, Gang Zhou, Ziyu et al.

Index Begriffe: Quantitative Biology - Quantitative Methods, Computer Science - Machine Learning, Quantitative Biology - Neurons and Cognition, text

URL: http://arxiv.org/abs/2405.07977

Forward/Inverse Kinematics Modeling for Tensegrity Manipulator Based on Goal-Conditioned Variational Autoencoder.

Additional Titles: Forward/Inverse Kinematics Modeling for Tensegrity Manipulator based on Goal-conditioned Variational Autoencoder

Autoren: Osa, Takayuki 00588353 Ikemoto, Shuhei et al.

Index Begriffe: Training, Pneumatic actuators, Musculoskeletal system, Loading, Null space, Estimation, Kinematics, Journal Article., AM.

URL: http://hdl.handle.net/10228/0002000329
https://kyutech.repo.nii.ac.jp/records/2000329
https://doi.org/10.1109/IROS55552.2023.10341525
https://kyutech.repo.nii.ac.jp/record/2000329/files/10422459.pdf
978-1-6654-9190-7
978-1-6654-9191-4

Conditioned Variational Autoencoder for Top-N Item Recommendation

Autoren: Tommaso Carraro Mirko Polato Luca Bergamin et al.

Quelle: Lecture Notes in Computer Science ISBN: 9783031159305

Schlagwörter: 0209 industrial biotechnology, 0202 electrical engineering, electronic engineering, information engineering, 02 engineering and technology, Recommender systems, Collaborative filtering, Implicit feedback, Variational autoencoder, Top-N recommendation

Dateibeschreibung: application/pdf

Zugangs-URL: https://hdl.handle.net/11577/3500875
https://doi.org/10.1007/978-3-031-15931-2_64
https://hdl.handle.net/2318/1890155
https://doi.org/10.1007/978-3-031-15931-2_64

A Tool for Generating Monster Silhouettes with a Word-Conditioned Variational Autoencoder

Autoren: Adrian Gonzalez Matthew Guzdial Felix Ramos

Quelle: Proceedings of the AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment. 17:237-239

Schlagwörter: 0202 electrical engineering, electronic engineering, information engineering, 02 engineering and technology, 01 natural sciences, 0105 earth and related environmental sciences

RGCVAE: Relational Graph Conditioned Variational Autoencoder for Molecule Design

Autoren: Rigoni, Davide Navarin, Nicolò Sperduti, Alessandro

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

URL: http://arxiv.org/abs/2305.11699

T-CVAE: Transformer-Based Conditioned Variational Autoencoder for Story Completion

Autoren: Xiaojun Wan Tianming Wang

Quelle: Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence. :5233-5239

Schlagwörter: 0202 electrical engineering, electronic engineering, information engineering, 02 engineering and technology, 01 natural sciences, 0105 earth and related environmental sciences

Zugangs-URL: https://www.ijcai.org/proceedings/2019/0727.pdf
https://dblp.uni-trier.de/db/conf/ijcai/ijcai2019.html#Wang019b
https://www.ijcai.org/Proceedings/2019/727

Transformer-Based Conditioned Variational Autoencoder for Dialogue Generation

Autoren: Yang, Huihui

Schlagwörter: FOS: Computer and information sciences, Computer Science - Machine Learning, Computer Science - Computation and Language, 0202 electrical engineering, electronic engineering, information engineering, 02 engineering and technology, Computation and Language (cs.CL), Machine Learning (cs.LG)

Zugangs-URL: http://arxiv.org/abs/2210.12326

A Demographic-Conditioned Variational Autoencoder for fMRI Distribution Sampling and Removal of Confounds.

Autoren: Orlichenko A Qu G Zhou Z et al.

Quelle: ArXiv [ArXiv] 2024 May 13. Date of Electronic Publication: 2024 May 13.

Publikationsart: Preprint; Journal Article

Info zur Zeitschrift: Country of Publication: United States NLM ID: 101759493 Publication Model: Electronic Cited Medium: Internet ISSN: 2331-8422 (Electronic) Linking ISSN: 23318422 NLM ISO Abbreviation: ArXiv Subsets: PubMed not MEDLINE

Conditioned Variational Autoencoder for top-N item recommendation

Autoren: Carraro, Tommaso Polato, Mirko Aiolli, Fabio

Index Begriffe: Computer Science - Machine Learning, Computer Science - Information Retrieval, text

URL: http://arxiv.org/abs/2004.11141

Frame Rate Up-Conversion in Echocardiography Using a Conditioned Variational Autoencoder and Generative Adversarial Model

Autoren: Teresa Tsang Purang Abolmaesumi Ken Gin et al.

Quelle: Lecture Notes in Computer Science ISBN: 9783030322441

Zugangs-URL: https://rd.springer.com/chapter/10.1007/978-3-030-32245-8_78
https://link.springer.com/chapter/10.1007%2F978-3-030-32245-8_78
https://dblp.uni-trier.de/db/conf/miccai/miccai2019-2.html#DezakiGRGAT19

Learning an Optimisable Semantic Segmentation Map with Image Conditioned Variational Autoencoder

Autoren: Pengcheng Zhuang Hideo Saito Yusuke Sekikawa et al.

Quelle: Lecture Notes in Computer Science ISBN: 9783030306441

Zugangs-URL: https://link.springer.com/chapter/10.1007%2F978-3-030-30645-8_35
https://doi.org/10.1007/978-3-030-30645-8_35
http://doi.org/10.1007/978-3-030-30645-8_35
https://jglobal.jst.go.jp/en/detail?JGLOBAL_ID=201902217447895409
https://dblp.uni-trier.de/db/conf/iciap/iciap2019-2.html#ZhuangSHS19
https://rd.springer.com/chapter/10.1007/978-3-030-30645-8_35

Goal-Conditioned Variational Autoencoder Trajectory Primitives with Continuous and Discrete Latent Codes

Autoren: Takayuki Osa Shuehi Ikemoto

Quelle: SN Computer Science. 1

Schlagwörter: FOS: Computer and information sciences, Computer Science - Robotics, Computer Science - Machine Learning, 0209 industrial biotechnology, 0202 electrical engineering, electronic engineering, information engineering, 02 engineering and technology, Robotics (cs.RO), Machine Learning (cs.LG)

Zugangs-URL: https://link.springer.com/content/pdf/10.1007/s42979-020-00324-7.pdf
http://arxiv.org/abs/1912.04063
http://export.arxiv.org/pdf/1912.04063
https://arxiv.org/pdf/1912.04063v2
https://arxiv.org/abs/1912.04063v2
https://dblp.uni-trier.de/db/journals/sncs/sncs1.html#OsaI20
https://link.springer.com/article/10.1007/s42979-020-00324-7
https://link.springer.com/content/pdf/10.1007/s42979-020-00324-7.pdf
https://ci.nii.ac.jp/naid/120007147069

Goal-Conditioned Variational Autoencoder Trajectory Primitives with Continuous and Discrete Latent Codes.

Autoren: Osa, Takayuki 00588353 Ikemoto, Shuhei et al.

Index Begriffe: 548, virtual machines, large-memory VMs, VM migration, partial migration, remote paging, Journal Article., AM.

URL: http://hdl.handle.net/10228/00008467
https://kyutech.repo.nii.ac.jp/records/7263
https://doi.org/10.1007/s42979-020-00324-7
https://kyutech.repo.nii.ac.jp/record/7263/files/neuro_16.pdf