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Found 11 papers, 3 papers with code
Exploring Compositional Visual Generation with Latent Classifier Guidance
We show that this paradigm based on latent classifier guidance is agnostic to pre-trained generative models, and present competitive results for both image generation and sequential manipulation of real and synthetic images.
Learning Transferable Reward for Query Object Localization with Policy Adaptation
We propose a reinforcement learning based approach to query object localization, for which an agent is trained to localize objects of interest specified by a small exemplary set.
Provable Adaptation across Multiway Domains via Representation Learning
This paper studies zero-shot domain adaptation where each domain is indexed on a multi-dimensional array, and we only have data from a small subset of domains.
Supervised Multiscale Dimension Reduction for Spatial Interaction Networks
We introduce a multiscale supervised dimension reduction method for SPatial Interaction Network (SPIN) data, which consist of a collection of spatially coordinated interactions.
Multiresolution Tensor Decomposition for Multiple Spatial Passing Networks
This article is motivated by soccer positional passing networks collected across multiple games.
VAE Learning via Stein Variational Gradient Descent
A new method for learning variational autoencoders (VAEs) is developed, based on Stein variational gradient descent.
Variational Gaussian Copula Inference
We utilize copulas to constitute a unified framework for constructing and optimizing variational proposals in hierarchical Bayesian models.
Alternating Minimization Algorithm with Automatic Relevance Determination for Transmission Tomography under Poisson Noise
We propose a globally convergent alternating minimization (AM) algorithm for image reconstruction in transmission tomography, which extends automatic relevance determination (ARD) to Poisson noise models with Beer's law.
Dynamic Rank Factor Model for Text Streams
We propose a semi-parametric and dynamic rank factor model for topic modeling, capable of (1) discovering topic prevalence over time, and (2) learning contemporary multi-scale dependence structures, providing topic and word correlations as a byproduct.
Multiscale Shrinkage and Lévy Processes
The method we consider in detail, and for which numerical results are presented, is based on increments of a gamma process.
Integrated Non-Factorized Variational Inference
We present a non-factorized variational method for full posterior inference in Bayesian hierarchical models, with the goal of capturing the posterior variable dependencies via efficient and possibly parallel computation.
