Policy-Based Bayesian Experimental Design for Non-Differentiable Implicit Models

no code implementations • 8 Mar 2022 • Vincent Lim, Ellen Novoseller, Jeffrey Ichnowski, Huang Huang, Ken Goldberg

For applications in healthcare, physics, energy, robotics, and many other fields, designing maximally informative experiments is valuable, particularly when experiments are expensive, time-consuming, or pose safety hazards.

Experimental Design reinforcement-learning +1

A matrix math facility for Power ISA(TM) processors

no code implementations • 7 Apr 2021 • José E. Moreira, Kit Barton, Steven Battle, Peter Bergner, Ramon Bertran, Puneeth Bhat, Pedro Caldeira, David Edelsohn, Gordon Fossum, Brad Frey, Nemanja Ivanovic, Chip Kerchner, Vincent Lim, Shakti Kapoor, Tulio Machado Filho, Silvia Melitta Mueller, Brett Olsson, Satish Sadasivam, Baptiste Saleil, Bill Schmidt, Rajalakshmi Srinivasaraghavan, Shricharan Srivatsan, Brian Thompto, Andreas Wagner, Nelson Wu

Power ISA(TM) Version 3. 1 has introduced a new family of matrix math instructions, collectively known as the Matrix-Multiply Assist (MMA) facility.

Math