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09/21/2023
Quasi-Monte Carlo for 3D Sliced Wasserstein
Monte Carlo (MC) approximation has been used as the standard computation...
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04/30/2023
Control Variate Sliced Wasserstein Estimators
The sliced Wasserstein (SW) distances between two probability measures a...
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04/26/2023
Energy-Based Sliced Wasserstein Distance
The sliced Wasserstein (SW) distance has been widely recognized as a sta...
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01/12/2023
Self-Attention Amortized Distributional Projection Optimization for Sliced Wasserstein Point-Cloud Reconstruction
Max sliced Wasserstein (Max-SW) distance has been widely known as a solu...
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01/10/2023
Markovian Sliced Wasserstein Distances: Beyond Independent Projections
Sliced Wasserstein (SW) distance suffers from redundant projections due ...
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10/11/2022
Robustify Transformers with Robust Kernel Density Estimation
Recent advances in Transformer architecture have empowered its empirical...
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06/01/2022
Transformer with Fourier Integral Attentions
Multi-head attention empowers the recent success of transformers, the st...
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04/04/2022
Revisiting Sliced Wasserstein on Images: From Vectorization to Convolution
The conventional sliced Wasserstein is defined between two probability m...
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03/25/2022
Amortized Projection Optimization for Sliced Wasserstein Generative Models
Seeking informative projecting directions has been an important task in ...
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10/29/2021
Model Fusion of Heterogeneous Neural Networks via Cross-Layer Alignment
Layer-wise model fusion via optimal transport, named OTFusion, applies s...
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08/22/2021
An Efficient Mini-batch Method via Partial Transportation
Mini-batch optimal transport (m-OT) has been widely used recently to dea...
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02/16/2021
Improving Bayesian Inference in Deep Neural Networks with Variational Structured Dropout
Approximate inference in deep Bayesian networks exhibits a dilemma of ho...
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02/11/2021
BoMb-OT: On Batch of Mini-batches Optimal Transport
Mini-batch optimal transport (m-OT) has been successfully used in practi...
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10/05/2020
Improving Relational Regularized Autoencoders with Spherical Sliced Fused Gromov Wasserstein
Relational regularized autoencoder (RAE) is a framework to learn the dis...
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02/18/2020
Distributional Sliced-Wasserstein and Applications to Generative Modeling
Sliced-Wasserstein distance (SWD) and its variation, Max Sliced-Wasserst...
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06/26/2018
