42 citations · 248 across the 35 of their papers we have counts for
9 papers · 1 filter
Monotonicity Regularization: Improved Penalties and Novel Applications to Disentangled Representation Learning and Robust Classification
Joao Monteiro, Mohamed Osama Ahmed, Hossein Hajimirsadeghi +1
We study settings where gradient penalties are used alongside risk minimization with the goal of obtaining predictors satisfying different notions of monotonicity. Specifically, we…
Continuous Latent Process Flows
Ruizhi Deng, Marcus A. Brubaker, Greg Mori +1
Partial observations of continuous time-series dynamics at arbitrary time stamps exist in many disciplines. Fitting this type of data using statistical models with continuous dynam…
Variational Selective Autoencoder: Learning from Partially-Observed Heterogeneous Data
Yu Gong, Hossein Hajimirsadeghi, Jiawei He +2
Learning from heterogeneous data poses challenges such as combining data from various sources and of different types. Meanwhile, heterogeneous data are often associated with missin…
Variational Hyper RNN for Sequence Modeling
Ruizhi Deng, Yanshuai Cao, Bo Chang +3
In this work, we propose a novel probabilistic sequence model that excels at capturing high variability in time series data, both across sequences and within an individual sequence…
Graph Generation with Variational Recurrent Neural Network
Shih-Yang Su, Hossein Hajimirsadeghi, Greg Mori
Generating graph structures is a challenging problem due to the diverse representations and complex dependencies among nodes. In this paper, we introduce Graph Variational Recurren…
Point Process Flows
Nazanin Mehrasa, Ruizhi Deng, Mohamed Osama Ahmed +5
Event sequences can be modeled by temporal point processes (TPPs) to capture their asynchronous and probabilistic nature. We propose an intensity-free framework that directly model…