Russell Tsuchida

Orcid: 0000-0002-4974-8349

According to our database1, Russell Tsuchida authored at least 16 papers between 2018 and 2024.

Collaborative distances:
  • Dijkstra number2 of five.
  • Erdős number3 of four.

Timeline

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Links

Online presence:

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Bibliography

2024
Gaussian Ensemble Belief Propagation for Efficient Inference in High-Dimensional Systems.
CoRR, 2024

Label Shift Estimation for Class-Imbalance Problem: A Bayesian Approach.
Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, 2024

Exact, Fast and Expressive Poisson Point Processes via Squared Neural Families.
Proceedings of the Thirty-Eighth AAAI Conference on Artificial Intelligence, 2024

2023
Stochastic gradient updates yield deep equilibrium kernels.
Trans. Mach. Learn. Res., 2023

Earth Movers in The Big Data Era: A Review of Optimal Transport in Machine Learning.
CoRR, 2023

Squared Neural Families: A New Class of Tractable Density Models.
Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Deep equilibrium models as estimators for continuous latent variables.
Proceedings of the International Conference on Artificial Intelligence and Statistics, 2023

2022
Efficient Gaussian Process Model on Class-Imbalanced Datasets for Generalized Zero-Shot Learning.
Proceedings of the 26th International Conference on Pattern Recognition, 2022

Declarative nets that are equilibrium models.
Proceedings of the Tenth International Conference on Learning Representations, 2022

Gaussian Process Bandits with Aggregated Feedback.
Proceedings of the Thirty-Sixth AAAI Conference on Artificial Intelligence, 2022

2021
Avoiding Kernel Fixed Points: Computing with ELU and GELU Infinite Networks.
Proceedings of the Thirty-Fifth AAAI Conference on Artificial Intelligence, 2021

2020
Results on infinitely wide multi-layer perceptrons
PhD thesis, 2020

2019
Richer priors for infinitely wide multi-layer perceptrons.
CoRR, 2019

Expressive Priors in Bayesian Neural Networks: Kernel Combinations and Periodic Functions.
Proceedings of the Thirty-Fifth Conference on Uncertainty in Artificial Intelligence, 2019

Exchangeability and Kernel Invariance in Trained MLPs.
Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence, 2019

2018
Invariance of Weight Distributions in Rectified MLPs.
Proceedings of the 35th International Conference on Machine Learning, 2018


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