
Deep Amortized Clustering
We propose a deep amortized clustering (DAC), a neural architecture whic...
read it

Set Transformer
Many machine learning tasks such as multiple instance learning, 3D shape...
read it

Bayesian inference on random simple graphs with power law degree distributions
We present a model for random simple graphs with a degree distribution t...
read it

TreeGuided MCMC Inference for Normalized Random Measure Mixture Models
Normalized random measures (NRMs) provide a broad class of discrete rand...
read it

Bayesian Hierarchical Clustering with Exponential Family: SmallVariance Asymptotics and Reducibility
Bayesian hierarchical clustering (BHC) is an agglomerative clustering me...
read it

DropMax: Adaptive Stochastic Softmax
We propose DropMax, a stochastic version of softmax classifier which at ...
read it

Adaptive Network Sparsification via Dependent Variational BetaBernoulli Dropout
While variational dropout approaches have been shown to be effective for...
read it

UncertaintyAware Attention for Reliable Interpretation and Prediction
Attention mechanism is effective in both focusing the deep learning mode...
read it

Mixed Effect Composite RNNGP: A Personalized and Reliable Prediction Model for Healthcare
We present a personalized and reliable prediction model for healthcare, ...
read it

Transductive Propagation Network for Fewshot Learning
Fewshot learning aims to build a learner that quickly generalizes to no...
read it

A Bayesian model for sparse graphs with flexible degree distribution and overlapping community structure
We consider a nonprojective class of inhomogeneous random graph models ...
read it

Beyond the Chinese Restaurant and PitmanYor processes: Statistical Models with Double Powerlaw Behavior
Bayesian nonparametric approaches, in particular the PitmanYor process ...
read it

A unified construction for series representations and finite approximations of completely random measures
Infiniteactivity completely random measures (CRMs) have become importan...
read it

Graph Embedding VAE: A Permutation Invariant Model of Graph Structure
Generative models of graph structure have applications in biology and so...
read it
Juho Lee
is this you? claim profile