Search Results for author:
Found 7 papers, 6 papers with code
Paved2Paradise: Cost-Effective and Scalable LiDAR Simulation by Factoring the Real World
Our key insight is that, by deliberately collecting separate "background" and "object" datasets (i. e., "factoring the real world"), we can intelligently combine them to produce a combinatorially large and diverse training set.
AQuaMaM: An Autoregressive, Quaternion Manifold Model for Rapidly Estimating Complex SO(3) Distributions
Accurately modeling complex, multimodal distributions is necessary for optimal decision-making, but doing so for rotations in three-dimensions, i. e., the SO(3) group, is challenging due to the curvature of the rotation manifold.
The DEformer: An Order-Agnostic Distribution Estimating Transformer
In this paper, we propose an alternative approach for encoding feature identities, where each feature's identity is included alongside its value in the input.
baller2vec++: A Look-Ahead Multi-Entity Transformer For Modeling Coordinated Agents
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).
Ranked #1 on
Trajectory Modeling
on NBA SportVU
baller2vec: A Multi-Entity Transformer For Multi-Agent Spatiotemporal Modeling
Multi-agent spatiotemporal modeling is a challenging task from both an algorithmic design and computational complexity perspective.
A cost-effective method for improving and re-purposing large, pre-trained GANs by fine-tuning their class-embeddings
Large, pre-trained generative models have been increasingly popular and useful to both the research and wider communities.
Strike (with) a Pose: Neural Networks Are Easily Fooled by Strange Poses of Familiar Objects
Using our framework and a self-assembled dataset of 3D objects, we investigate the vulnerability of DNNs to OoD poses of well-known objects in ImageNet.
