I always used
aspell to spell check papers. I did not care about setting up a dictionary of words that aspell does not recognize due to time pressure. As papers usually involve few LaTeX files, this was an OK process. For my PhD thesis, however, I needed a more automatic and thorough process. This is because more files are involved and I had to spell check multiple times, several weeks or months apart, throughout the process. In this article, I want to share a semi-automatic but thorough process based on aspell and TeXtidote that worked well for me.
Generally, papers are written to be published at conferences or journals. While some journals care about the LaTeX source used to compile the submitted papers, most venues just expect compiled PDFs to be submitted. However, ArXiv always requires the full LaTeX source to be compiled on the ArXiv servers. As the LaTeX source of every ArXiv paper can be downloaded, this usually involves removing all comments, unused figures/files and “flattening” the directoy structure as ArXiv does not handle subdirectories well. In this article, I want to share two simple scripts that take care of the latter two problems: removing unused files and flattening.
An example of a custom TensorFlow operation implemented in C++.
OPEN SOURCE blenderpy Mesh/Voxel Visualization Figure 1 (click to enlarge): Visualization examples of an occupancy grid (left) and a mesh (right) of a chair. The right visualization also shows a point cloud observation (in red). Blender is an open-source “3D creation suite” — a tool for creating and manipulating 3D shapes and scenes. While I […]
In 2019, I interviewed for research internships at DeepMind and Google AI. I have been asked repeatedly about my preparation for and experience with these interviews. As internship applications at DeepMind have been opened recently, I thought it could be valuable to summarize my experience and recommendations in this article.
Examples, tools and resources for using Caffe’s Python interface pyCaffe.
A template for extending PyTorch using C/CUDA operations.
The code for our ICLR’22 paper on learning optimal conformal classifiers is now available on GitHub. The repository not only includes our implementation of conformal training but also relevant baselines such as coverage training and several conformal predictors for evaluation. Furthermore, it allows to reproduce the majority of experiments from the paper.
Python implementation of probabilistic principal component analysis (PPCA).
3D mesh fusion, voxelization and evaluation for computer vision research.