Search Results for author: Jonathan Ragan-Kelley

Found 8 papers, 6 papers with code

Designing Perceptual Puzzles by Differentiating Probabilistic Programs

no code implementations26 Apr 2022 Kartik Chandra, Tzu-Mao Li, Joshua Tenenbaum, Jonathan Ragan-Kelley

We design new visual illusions by finding "adversarial examples" for principled models of human perception -- specifically, for probabilistic models, which treat vision as Bayesian inference.

Color Constancy Probabilistic Programming

Differentiable Vector Graphics Rasterization for Editing and Learning

1 code implementation ACM Transactions on Graphics 2020 Tzu-Mao Li, Michal Lukáč, Michaël Gharbi, Jonathan Ragan-Kelley

We introduce a differentiable rasterizer that bridges the vector graphics and raster image domains, enabling powerful raster-based loss functions, optimization procedures, and machine learning techniques to edit and generate vector content.

Vector Graphics

Neural Kernels Without Tangents

1 code implementation ICML 2020 Vaishaal Shankar, Alex Fang, Wenshuo Guo, Sara Fridovich-Keil, Ludwig Schmidt, Jonathan Ragan-Kelley, Benjamin Recht

We investigate the connections between neural networks and simple building blocks in kernel space.

DiffTaichi: Differentiable Programming for Physical Simulation

2 code implementations ICLR 2020 Yuanming Hu, Luke Anderson, Tzu-Mao Li, Qi Sun, Nathan Carr, Jonathan Ragan-Kelley, Frédo Durand

We present DiffTaichi, a new differentiable programming language tailored for building high-performance differentiable physical simulators.

Physical Simulations

Programming Heterogeneous Systems from an Image Processing DSL

3 code implementations28 Oct 2016 Jing Pu, Steven Bell, Xuan Yang, Jeff Setter, Stephen Richardson, Jonathan Ragan-Kelley, Mark Horowitz

We address this problem by extending the image processing language, Halide, so users can specify which portions of their applications should become hardware accelerators, and then we provide a compiler that uses this code to automatically create the accelerator along with the "glue" code needed for the user's application to access this hardware.

Software Engineering

A Systematic Approach to Blocking Convolutional Neural Networks

1 code implementation14 Jun 2016 Xuan Yang, Jing Pu, Blaine Burton Rister, Nikhil Bhagdikar, Stephen Richardson, Shahar Kvatinsky, Jonathan Ragan-Kelley, Ardavan Pedram, Mark Horowitz

Convolutional Neural Networks (CNNs) are the state of the art solution for many computer vision problems, and many researchers have explored optimized implementations.

Opt: A Domain Specific Language for Non-linear Least Squares Optimization in Graphics and Imaging

no code implementations22 Apr 2016 Zachary DeVito, Michael Mara, Michael Zollhöfer, Gilbert Bernstein, Jonathan Ragan-Kelley, Christian Theobalt, Pat Hanrahan, Matthew Fisher, Matthias Nießner

Many graphics and vision problems can be expressed as non-linear least squares optimizations of objective functions over visual data, such as images and meshes.

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