Search Results for author:
Found 13 papers, 8 papers with code
Learning Neural Eigenfunctions for Unsupervised Semantic Segmentation
Unsupervised semantic segmentation is a long-standing challenge in computer vision with great significance.
Iterative Teaching by Data Hallucination
We consider the problem of iterative machine teaching, where a teacher sequentially provides examples based on the status of a learner under a discrete input space (i. e., a pool of finite samples), which greatly limits the teacher's capability.
Spectral Representation Learning for Conditional Moment Models
For nonparametric conditional moment models, efficient estimation often relies on preimposed conditions on various measures of ill-posedness of the hypothesis space, which are hard to validate when flexible models are used.
Learning Counterfactually Invariant Predictors
Notions of counterfactual invariance (CI) have proven essential for predictors that are fair, robust, and generalizable in the real world.
SUMO: Unbiased Estimation of Log Marginal Probability for Latent Variable Models
Standard variational lower bounds used to train latent variable models produce biased estimates of most quantities of interest.
Measuring Uncertainty through Bayesian Learning of Deep Neural Network Structure
Bayesian neural networks (BNNs) augment deep networks with uncertainty quantification by Bayesian treatment of the network weights.
Deep Bayesian Structure Networks
Bayesian neural networks (BNNs) introduce uncertainty estimation to deep networks by performing Bayesian inference on network weights.
A Simple yet Effective Baseline for Robust Deep Learning with Noisy Labels
Recently deep neural networks have shown their capacity to memorize training data, even with noisy labels, which hurts generalization performance.
Cluster Alignment with a Teacher for Unsupervised Domain Adaptation
Deep learning methods have shown promise in unsupervised domain adaptation, which aims to leverage a labeled source domain to learn a classifier for the unlabeled target domain with a different distribution.
Ranked #3 on
Domain Adaptation
on SVNH-to-MNIST
Semi-crowdsourced Clustering with Deep Generative Models
We propose a new approach that includes a deep generative model (DGM) to characterize low-level features of the data, and a statistical relational model for noisy pairwise annotations on its subset.
Smooth Neighbors on Teacher Graphs for Semi-supervised Learning
In SNTG, a graph is constructed based on the predictions of the teacher model, i. e., the implicit self-ensemble of models.
ZhuSuan: A Library for Bayesian Deep Learning
In this paper we introduce ZhuSuan, a python probabilistic programming library for Bayesian deep learning, which conjoins the complimentary advantages of Bayesian methods and deep learning.
Conditional Generative Moment-Matching Networks
Maximum mean discrepancy (MMD) has been successfully applied to learn deep generative models for characterizing a joint distribution of variables via kernel mean embedding.
