Search Results for author: Yaniv Ovadia

Found 4 papers, 2 papers with code

SketchGraphs: A Large-Scale Dataset for Modeling Relational Geometry in Computer-Aided Design

1 code implementation16 Jul 2020 Ari Seff, Yaniv Ovadia, Wenda Zhou, Ryan P. Adams

Parametric computer-aided design (CAD) is the dominant paradigm in mechanical engineering for physical design.

Program Synthesis

Can You Trust Your Model's Uncertainty? Evaluating Predictive Uncertainty Under Dataset Shift

2 code implementations NeurIPS 2019 Yaniv Ovadia, Emily Fertig, Jie Ren, Zachary Nado, D. Sculley, Sebastian Nowozin, Joshua V. Dillon, Balaji Lakshminarayanan, Jasper Snoek

Modern machine learning methods including deep learning have achieved great success in predictive accuracy for supervised learning tasks, but may still fall short in giving useful estimates of their predictive {\em uncertainty}.

Probabilistic Deep Learning

DPPNet: Approximating Determinantal Point Processes with Deep Networks

no code implementations ICLR 2019 Zelda Mariet, Yaniv Ovadia, Jasper Snoek

Determinantal Point Processes (DPPs) provide an elegant and versatile way to sample sets of items that balance the point-wise quality with the set-wise diversity of selected items.

Point Processes

Estimating the Spectral Density of Large Implicit Matrices

no code implementations9 Feb 2018 Ryan P. Adams, Jeffrey Pennington, Matthew J. Johnson, Jamie Smith, Yaniv Ovadia, Brian Patton, James Saunderson

However, naive eigenvalue estimation is computationally expensive even when the matrix can be represented; in many of these situations the matrix is so large as to only be available implicitly via products with vectors.

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