7 citations · 8 across the 3 of their papers we have counts for
3 papers
Distributional Depth-Based Estimation of Object Articulation Models
Ajinkya Jain, Stephen Giguere, Rudolf Lioutikov +1
We propose a method that efficiently learns distributions over articulation model parameters directly from depth images without the need to know articulation model categories a pri…
Fairkit, Fairkit, on the Wall, Who's the Fairest of Them All? Supporting Data Scientists in Training Fair Models
Brittany Johnson, Jesse Bartola, Rico Angell +4
Modern software relies heavily on data and machine learning, and affects decisions that shape our world. Unfortunately, recent studies have shown that because of biases in data, so…
A Manifold Approach to Learning Mutually Orthogonal Subspaces
Stephen Giguere, Francisco Garcia, Sridhar Mahadevan
Although many machine learning algorithms involve learning subspaces with particular characteristics, optimizing a parameter matrix that is constrained to represent a subspace can…