Publications (68)
Deep Implicit Volume Compression
Danhang Tang, Saurabh Singh, Philip A. Chou +11
We describe a novel approach for compressing truncated signed distance fields (TSDF) stored in 3D voxel grids, and their corresponding textures. To compress the TSDF, our method re…
TerpreT: A Probabilistic Programming Language for Program Induction
Alexander L. Gaunt, Marc Brockschmidt, Rishabh Singh +4
We study machine learning formulations of inductive program synthesis; given input-output examples, we try to synthesize source code that maps inputs to corresponding outputs. Our…
Selecting the number of principal components: estimation of the true rank of a noisy matrix
Yunjin Choi, Jonathan Taylor, Robert Tibshirani
Principal component analysis (PCA) is a well-known tool in multivariate statistics. One significant challenge in using PCA is the choice of the number of components. In order to ad…
A lasso for hierarchical interactions
Jacob Bien, Jonathan Taylor, Robert Tibshirani
We add a set of convex constraints to the lasso to produce sparse interaction models that honor the hierarchy restriction that an interaction only be included in a model if one or…
Volumetric Capture of Humans with a Single RGBD Camera via Semi-Parametric Learning
Rohit Pandey, Anastasia Tkach, Shuoran Yang +9
Volumetric (4D) performance capture is fundamental for AR/VR content generation. Whereas previous work in 4D performance capture has shown impressive results in studio settings, th…
Rejoinder: "A significance test for the lasso"
Richard Lockhart, Jonathan Taylor, Ryan J. Tibshirani +1
Rejoinder of "A significance test for the lasso" by Richard Lockhart, Jonathan Taylor, Ryan J. Tibshirani, Robert Tibshirani [arXiv:1301.7161].