| Differentiable Monte Carlo ray tracing through edge sampling TM Li, M Aittala, F Durand, J Lehtinen ACM Transactions on Graphics (TOG) 37 (6), 222, 2018 | 457 | 2018 |
| Difftaichi: Differentiable programming for physical simulation Y Hu, L Anderson, TM Li, Q Sun, N Carr, J Ragan-Kelley, F Durand arXiv preprint arXiv:1910.00935, 2019 | 324 | 2019 |
| Learning to optimize halide with tree search and random programs A Adams, K Ma, L Anderson, R Baghdadi, TM Li, M Gharbi, B Steiner, ... ACM Transactions on Graphics (TOG) 38 (4), 1-12, 2019 | 226 | 2019 |
| Taichi: a language for high-performance computation on spatially sparse data structures Y Hu, TM Li, L Anderson, J Ragan-Kelley, F Durand ACM Transactions on Graphics (TOG) 38 (6), 1-16, 2019 | 183 | 2019 |
| Differentiable programming for image processing and deep learning in Halide TM Li, M Gharbi, A Adams, F Durand, J Ragan-Kelley ACM Transactions on Graphics (TOG) 37 (4), 139, 2018 | 136 | 2018 |
| Differentiable vector graphics rasterization for editing and learning TM Li, M Lukáč, M Gharbi, J Ragan-Kelley ACM Transactions on Graphics (TOG) 39 (6), 1-15, 2020 | 128 | 2020 |
| SURE-based optimization for adaptive sampling and reconstruction TM Li, YT Wu, YY Chuang ACM Transactions on Graphics (TOG) 31 (6), 1-9, 2012 | 125 | 2012 |
| Inverse path tracing for joint material and lighting estimation D Azinovic, TM Li, A Kaplanyan, M Nießner Proceedings of the IEEE/CVF conference on computer vision and pattern …, 2019 | 110 | 2019 |
| Sample-based Monte Carlo denoising using a kernel-splatting network M Gharbi, TM Li, M Aittala, J Lehtinen, F Durand ACM Transactions on Graphics (TOG) 38 (4), 1-12, 2019 | 75 | 2019 |
| Unbiased warped-area sampling for differentiable rendering SP Bangaru, TM Li, F Durand ACM Transactions on Graphics (TOG) 39 (6), 1-18, 2020 | 65 | 2020 |
| Anisotropic Gaussian mutations for Metropolis light transport through Hessian-Hamiltonian dynamics TM Li, J Lehtinen, R Ramamoorthi, W Jakob, F Durand ACM Transactions on Graphics (TOG) 34 (6), 209, 2015 | 49 | 2015 |
| Physics-based differentiable rendering: from theory to implementation S Zhao, W Jakob, TM Li ACM siggraph 2020 courses, 1-30, 2020 | 33 | 2020 |
| Systematically differentiating parametric discontinuities SP Bangaru, J Michel, K Mu, G Bernstein, TM Li, J Ragan-Kelley ACM Transactions on Graphics (TOG) 40 (4), 1-18, 2021 | 31 | 2021 |
| Aether: An embedded domain specific sampling language for Monte Carlo rendering L Anderson, TM Li, J Lehtinen, F Durand ACM Transactions on Graphics (TOG) 36 (4), 1-16, 2017 | 25 | 2017 |
| Differentiable rendering of neural sdfs through reparameterization SP Bangaru, M Gharbi, F Luan, TM Li, K Sunkavalli, M Hasan, S Bi, Z Xu, ... SIGGRAPH Asia 2022 Conference Papers, 1-9, 2022 | 17 | 2022 |
| Efficient automatic scheduling of imaging and vision pipelines for the GPU L Anderson, A Adams, K Ma, TM Li, T Jin, J Ragan-Kelley Proceedings of the ACM on Programming Languages 5 (OOPSLA), 1-28, 2021 | 12 | 2021 |
| Differentiable visual computing TM Li arXiv preprint arXiv:1904.12228, 2019 | 11 | 2019 |
| Designing perceptual puzzles by differentiating probabilistic programs K Chandra, TM Li, J Tenenbaum, J Ragan-Kelley ACM SIGGRAPH 2022 Conference Proceedings, 1-9, 2022 | 10 | 2022 |
| Small in-distribution changes in 3D perspective and lighting fool both CNNs and Transformers S Madan, T Sasaki, TM Li, X Boix, H Pfister arXiv preprint arXiv:2106.16198, 2021 | 10 | 2021 |
| Learning to Schedule Halide Pipelines for the GPU L Anderson, A Adams, K Ma, TM Li, J Ragan-Kelley arXiv preprint arXiv:2012.07145, 2020 | 10 | 2020 |
Jonathan Ragan-KelleyMIT CSAILVerified email at mit.edu
