Ruiqi Gao

According to our database¹, Ruiqi Gao authored at least 46 papers between 2017 and 2023.

Collaborative distances:

Dijkstra number² of four.
Erdős number³ of four.

Timeline

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In proceedings

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PhD thesis

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Links

On csauthors.net:

Bibliography

2023

ReconFusion: 3D Reconstruction with Diffusion Priors.

[BibT_eX]

[DOI]

CoRR, 2023

Conformal Normalization in Recurrent Neural Network of Grid Cells.

[BibT_eX]

[DOI]

CoRR, 2023

Learning Energy-Based Models by Cooperative Diffusion Recovery Likelihood.

[BibT_eX]

[DOI]

CoRR, 2023

Understanding the Diffusion Objective as a Weighted Integral of ELBOs.

[BibT_eX]

[DOI]

Diederik P. Kingma

Ruiqi Gao

CoRR, 2023

Learning Energy-Based Prior Model with Diffusion-Amortized MCMC.

[BibT_eX]

[DOI]

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Understanding Diffusion Objectives as the ELBO with Simple Data Augmentation.

[BibT_eX]

[DOI]

Diederik P. Kingma

Ruiqi Gao

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Compiling Parallel Symbolic Execution with Continuations.

[BibT_eX]

[DOI]

Proceedings of the 45th IEEE/ACM International Conference on Software Engineering, 2023

On Distillation of Guided Diffusion Models.

[BibT_eX]

[DOI]

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2023

Weightings in Incremental ADCs: A Tutorial Review.

[BibT_eX]

[DOI]

Proceedings of the IEEE Custom Integrated Circuits Conference, 2023

2022

Deformable Generator Networks: Unsupervised Disentanglement of Appearance and Geometry.

[BibT_eX]

[DOI]

IEEE Trans. Pattern Anal. Mach. Intell., 2022

Generative VoxelNet: Learning Energy-Based Models for 3D Shape Synthesis and Analysis.

[BibT_eX]

[DOI]

IEEE Trans. Pattern Anal. Mach. Intell., 2022

On Distillation of Guided Diffusion Models.

[BibT_eX]

[DOI]

CoRR, 2022

Imagen Video: High Definition Video Generation with Diffusion Models.

[BibT_eX]

[DOI]

CoRR, 2022

Latent Diffusion Energy-Based Model for Interpretable Text Modeling.

[BibT_eX]

[DOI]

CoRR, 2022

Conformal Isometry of Lie Group Representation in Recurrent Network of Grid Cells.

[BibT_eX]

[DOI]

Proceedings of the NeurIPS Workshop on Symmetry and Geometry in Neural Representations, 2022

Latent Diffusion Energy-Based Model for Interpretable Text Modelling.

[BibT_eX]

[DOI]

Proceedings of the International Conference on Machine Learning, 2022

MCMC Should Mix: Learning Energy-Based Model with Neural Transport Latent Space MCMC.

[BibT_eX]

[DOI]

Proceedings of the Tenth International Conference on Learning Representations, 2022

Learning V1 Simple Cells with Vector Representation of Local Content and Matrix Representation of Local Motion.

[BibT_eX]

[DOI]

Proceedings of the Thirty-Sixth AAAI Conference on Artificial Intelligence, 2022

2021

Effective Learning of Descriptive and Generator Models and Learning Representations for Grid Cells and V1 Cells.

[BibT_eX]

[DOI]

Ruiqi Gao

PhD thesis, 2021

On Path Integration of Grid Cells: Group Representation and Isotropic Scaling.

[BibT_eX]

[DOI]

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

A Theory of Label Propagation for Subpopulation Shift.

[BibT_eX]

[DOI]

Proceedings of the 38th International Conference on Machine Learning, 2021

Learning Energy-Based Models by Diffusion Recovery Likelihood.

[BibT_eX]

[DOI]

Proceedings of the 9th International Conference on Learning Representations, 2021

Learning Neural Representation of Camera Pose with Matrix Representation of Pose Shift via View Synthesis.

[BibT_eX]

[DOI]

Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2021

2020

Cooperative Training of Descriptor and Generator Networks.

[BibT_eX]

[DOI]

IEEE Trans. Pattern Anal. Mach. Intell., 2020

A Representational Model of Grid Cells Based on Matrix Lie Algebras.

[BibT_eX]

[DOI]

CoRR, 2020

Learning Energy-based Model with Flow-based Backbone by Neural Transport MCMC.

[BibT_eX]

[DOI]

CoRR, 2020

Sanity-Checking Pruning Methods: Random Tickets can Win the Jackpot.

[BibT_eX]

[DOI]

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Flow Contrastive Estimation of Energy-Based Models.

[BibT_eX]

[DOI]

Proceedings of the 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2020

Motion-Based Generator Model: Unsupervised Disentanglement of Appearance, Trackable and Intrackable Motions in Dynamic Patterns.

[BibT_eX]

[DOI]

Proceedings of the Thirty-Fourth AAAI Conference on Artificial Intelligence, 2020

2019

Representation Learning: A Statistical Perspective.

[BibT_eX]

[DOI]

CoRR, 2019

Convergence of Adversarial Training in Overparametrized Networks.

[BibT_eX]

[DOI]

CoRR, 2019

A Gram-Gauss-Newton Method Learning Overparameterized Deep Neural Networks for Regression Problems.

[BibT_eX]

[DOI]

CoRR, 2019

Learning Vector Representation of Content and Matrix Representation of Change: Towards a Representational Model of V1.

[BibT_eX]

[DOI]

CoRR, 2019

Convergence of Adversarial Training in Overparametrized Neural Networks.

[BibT_eX]

[DOI]

Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

Multimodal 3D Convolutional Neural Networks for Classification of Brain Disease Using Structural MR and FDG-PET Images.

[BibT_eX]

[DOI]

Proceedings of the Data Science, 2019

Learning Grid Cells as Vector Representation of Self-Position Coupled with Matrix Representation of Self-Motion.

[BibT_eX]

[DOI]

Proceedings of the 7th International Conference on Learning Representations, 2019

Unsupervised Disentangling of Appearance and Geometry by Deformable Generator Network.

[BibT_eX]

[DOI]

Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2019

Learning Dynamic Generator Model by Alternating Back-Propagation through Time.

[BibT_eX]

[DOI]

Proceedings of the Thirty-Third AAAI Conference on Artificial Intelligence, 2019

2018

Learning Grid-like Units with Vector Representation of Self-Position and Matrix Representation of Self-Motion.

[BibT_eX]

[DOI]

CoRR, 2018

A Tale of Three Probabilistic Families: Discriminative, Descriptive and Generative Models.

[BibT_eX]

[DOI]

CoRR, 2018

Deformable Generator Network: Unsupervised Disentanglement of Appearance and Geometry.

[BibT_eX]

[DOI]

CoRR, 2018

Learning Descriptor Networks for 3D Shape Synthesis and Analysis.

[BibT_eX]

[DOI]

Proceedings of the 2018 IEEE Conference on Computer Vision and Pattern Recognition, 2018

Learning Generative ConvNets via Multi-Grid Modeling and Sampling.

[BibT_eX]

[DOI]

Proceedings of the 2018 IEEE Conference on Computer Vision and Pattern Recognition, 2018

Cooperative Learning of Energy-Based Model and Latent Variable Model via MCMC Teaching.

[BibT_eX]

[DOI]

Proceedings of the Thirty-Second AAAI Conference on Artificial Intelligence, 2018

2017

Automatic schizophrenic discrimination on fNIRS by using complex brain network analysis and SVM.

[BibT_eX]

[DOI]

Hong Song

Lei Chen

Ruiqi Gao

Iordachescu Ilie Mihaita Bogdan

BMC Medical Informatics Decis. Mak., 2017

Learning Multi-grid Generative ConvNets by Minimal Contrastive Divergence.

[BibT_eX]

[DOI]

CoRR, 2017

Ruiqi Gao

Timeline

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On csauthors.net:

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