11 citations · 13 across the 4 of their papers we have counts for
4 papers
AdaDeDup: Adaptive Hybrid Data Pruning for Efficient Large-Scale Object Detection Training
Feiyang Kang, Nadine Chang, Maying Shen +4
The computational burden and inherent redundancy of large-scale datasets challenge the training of contemporary machine learning models. Data pruning offers a solution by selecting…
Uncertainty Estimation for 3D Object Detection via Evidential Learning
Nikita Durasov, Rafid Mahmood, Jiwoong Choi +4
3D object detection is an essential task for computer vision applications in autonomous vehicles and robotics. However, models often struggle to quantify detection reliability, lea…
SpaceMesh: A Continuous Representation for Learning Manifold Surface Meshes
Tianchang Shen, Zhaoshuo Li, Marc Law +5
Meshes are ubiquitous in visual computing and simulation, yet most existing machine learning techniques represent meshes only indirectly, e.g. as the level set of a scalar field or…
Graph Metanetworks for Processing Diverse Neural Architectures
Derek Lim, Haggai Maron, Marc T. Law +2
Neural networks efficiently encode learned information within their parameters. Consequently, many tasks can be unified by treating neural networks themselves as input data. When d…