1 code implementation • NeurIPS 2019 • Akshay Agrawal, Brandon Amos, Shane Barratt, Stephen Boyd, Steven Diamond, Zico Kolter
In this paper, we propose an approach to differentiating through disciplined convex programs, a subclass of convex optimization problems used by domain-specific languages (DSLs) for convex optimization.
no code implementations • 20 Nov 2017 • Felix Heide, Matthew O'Toole, Kai Zang, David Lindell, Steven Diamond, Gordon Wetzstein
Imaging objects obscured by occluders is a significant challenge for many applications.
1 code implementation • 13 Sep 2017 • Akshay Agrawal, Robin Verschueren, Steven Diamond, Stephen Boyd
We describe a modular rewriting system for translating optimization problems written in a domain-specific language to forms compatible with low-level solver interfaces.
Optimization and Control Mathematical Software
no code implementations • CVPR 2017 • Matthew O'Toole, Felix Heide, David B. Lindell, Kai Zang, Steven Diamond, Gordon Wetzstein
Computer vision algorithms build on 2D images or 3D videos that capture dynamic events at the millisecond time scale.
2 code implementations • 22 May 2017 • Steven Diamond, Vincent Sitzmann, Felix Heide, Gordon Wetzstein
A broad class of problems at the core of computational imaging, sensing, and low-level computer vision reduces to the inverse problem of extracting latent images that follow a prior distribution, from measurements taken under a known physical image formation model.
3 code implementations • 29 Apr 2017 • Stephen Boyd, Enzo Busseti, Steven Diamond, Ronald N. Kahn, Kwangmoo Koh, Peter Nystrup, Jan Speth
The methods we describe in this paper can be thought of as good ways to exploit predictions, no matter how they are made.
1 code implementation • 23 Jan 2017 • Steven Diamond, Vincent Sitzmann, Frank Julca-Aguilar, Stephen Boyd, Gordon Wetzstein, Felix Heide
As such, conventional imaging involves processing the RAW sensor measurements in a sequential pipeline of steps, such as demosaicking, denoising, deblurring, tone-mapping and compression.
3 code implementations • 12 Sep 2016 • Xinyue Shen, Steven Diamond, Madeleine Udell, Yuantao Gu, Stephen Boyd
A multi-convex optimization problem is one in which the variables can be partitioned into sets over which the problem is convex when the other variables are fixed.
Optimization and Control
no code implementations • ICCV 2015 • Steven Diamond, Stephen Boyd
We introduce a convex optimization modeling framework that transforms a convex optimization problem expressed in a form natural and convenient for the user into an equivalent cone program in a way that preserves fast linear transforms in the original problem.
1 code implementation • 17 Oct 2014 • Madeleine Udell, Karanveer Mohan, David Zeng, Jenny Hong, Steven Diamond, Stephen Boyd
This paper describes Convex, a convex optimization modeling framework in Julia.