activity
20162020
most citedGenerating Long-term Trajectories Using Deep Hierarchical Networks

72 citations · 88 across the 3 of their papers we have counts for

collaborators

8 papers

cs.CY20202 cited

The Rise of AI-Driven Simulators: Building a New Crystal Ball

Ian Foster, David Parkes, Stephan Zheng

The use of computational simulation is by now so pervasive in society that it is no exaggeration to say that continued U.S. and international prosperity, security, and health depen…

cs.SE2020

Technology Readiness Levels for AI & ML

Alexander Lavin, Gregory Renard

The development and deployment of machine learning systems can be executed easily with modern tools, but the process is typically rushed and means-to-an-end. The lack of diligence…

cs.LG2019

NAOMI: Non-Autoregressive Multiresolution Sequence Imputation

Yukai Liu, Rose Yu, Stephan Zheng +2

Missing value imputation is a fundamental problem in spatiotemporal modeling, from motion tracking to the dynamics of physical systems. Deep autoregressive models suffer from error…

cs.LG2018

Generating Multi-Agent Trajectories using Programmatic Weak Supervision

Eric Zhan, Stephan Zheng, Yisong Yue +2

We study the problem of training sequential generative models for capturing coordinated multi-agent trajectory behavior, such as offensive basketball gameplay. When modeling such s…

cs.LG2018

Multi-resolution Tensor Learning for Large-Scale Spatial Data

Stephan Zheng, Rose Yu, Yisong Yue

High-dimensional tensor models are notoriously computationally expensive to train. We present a meta-learning algorithm, MMT, that can significantly speed up the process for spatia…

cs.IR201714 cited

Fine-Grained Retrieval of Sports Plays using Tree-Based Alignment of Trajectories

Long Sha, Patrick Lucey, Stephan Zheng +3

We propose a novel method for effective retrieval of multi-agent spatiotemporal tracking data. Retrieval of spatiotemporal tracking data offers several unique challenges compared t…