research
∙
11/27/2021
On Learning Domain-Invariant Representations for Transfer Learning with Multiple Sources
Domain adaptation (DA) benefits from the rigorous theoretical works that...
share
research
∙
10/29/2021
Model Fusion of Heterogeneous Neural Networks via Cross-Layer Alignment
Layer-wise model fusion via optimal transport, named OTFusion, applies s...
research
∙
06/14/2021
Temporal Predictive Coding For Model-Based Planning In Latent Space
High-dimensional observations are a major challenge in the application o...
research
∙
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...
research
∙
02/11/2021
BoMb-OT: On Batch of Mini-batches Optimal Transport
Mini-batch optimal transport (m-OT) has been successfully used in practi...
research
∙
12/21/2020
Learning Compositional Sparse Gaussian Processes with a Shrinkage Prior
Choosing a proper set of kernel functions is an important problem in lea...
research
∙
10/05/2020
Improving Relational Regularized Autoencoders with Spherical Sliced Fused Gromov Wasserstein
Relational regularized autoencoder (RAE) is a framework to learn the dis...
research
∙
03/16/2020
Vec2Face: Unveil Human Faces from their Blackbox Features in Face Recognition
Unveiling face images of a subject given his/her high-level representati...
research
∙
02/18/2020
Distributional Sliced-Wasserstein and Applications to Generative Modeling
Sliced-Wasserstein distance (SWD) and its variation, Max Sliced-Wasserst...
research
∙
02/09/2020
On Unbalanced Optimal Transport: An Analysis of Sinkhorn Algorithm
We provide a computational complexity analysis for the Sinkhorn algorith...
research
∙
09/19/2019
On Efficient Multilevel Clustering via Wasserstein Distances
We propose a novel approach to the problem of multilevel clustering, whi...
research
∙
09/04/2019
Prediction, Consistency, Curvature: Representation Learning for Locally-Linear Control
Many real-world sequential decision-making problems can be formulated as...
research
∙
09/21/2017
SpectralFPL: Online Spectral Learning for Single Topic Models
This paper studies how to efficiently learn an optimal latent variable m...
research
∙
02/09/2016
Graphical Model Sketch
Structured high-cardinality data arises in many domains, and poses a maj...
research
∙
08/06/2014
Boosted Markov Networks for Activity Recognition
We explore a framework called boosted Markov networks to combine the lea...
research
∙
08/06/2014
Human Activity Learning and Segmentation using Partially Hidden Discriminative Models
Learning and understanding the typical patterns in the daily activities ...
research
∙
09/26/2013
