8 citations · 17 across the 6 of their papers we have counts for
6 papers
Paved2Paradise: Cost-Effective and Scalable LiDAR Simulation by Factoring the Real World
Michael A. Alcorn, Noah Schwartz
To achieve strong real world performance, neural networks must be trained on large, diverse datasets; however, obtaining and annotating such datasets is costly and time-consuming,…
AQuaMaM: An Autoregressive, Quaternion Manifold Model for Rapidly Estimating Complex SO(3) Distributions
Michael A. Alcorn
Accurately modeling complex, multimodal distributions for rotations in three-dimensions, i.e., the SO(3) group, is challenging due to the curvature of the rotation manifold. The re…
The DEformer: An Order-Agnostic Distribution Estimating Transformer
Michael A. Alcorn, Anh Nguyen
Order-agnostic autoregressive distribution (density) estimation (OADE), i.e., autoregressive distribution estimation where the features can occur in an arbitrary order, is a challe…
baller2vec++: A Look-Ahead Multi-Entity Transformer For Modeling Coordinated Agents
Michael A. Alcorn, Anh Nguyen
In many multi-agent spatiotemporal systems, agents operate under the influence of shared, unobserved variables (e.g., the play a team is executing in a game of basketball). As a re…
baller2vec: A Multi-Entity Transformer For Multi-Agent Spatiotemporal Modeling
Michael A. Alcorn, Anh Nguyen
Multi-agent spatiotemporal modeling is a challenging task from both an algorithmic design and computational complexity perspective. Recent work has explored the efficacy of traditi…
Strike (with) a Pose: Neural Networks Are Easily Fooled by Strange Poses of Familiar Objects
Michael A. Alcorn, Qi Li, Zhitao Gong +4
Despite excellent performance on stationary test sets, deep neural networks (DNNs) can fail to generalize to out-of-distribution (OoD) inputs, including natural, non-adversarial on…