WARNING This list outdated, for the up to date version visit https://haskellcosm.com
Types of work:
- RD - research&development
- PR - product
- IP - in-house product
- CO - consulting
| #!/bin/sh | |
| # OpenStack bzr to github mirror | |
| # | |
| # Requirements: | |
| # git >= 1.7.0, bzr >= 2.0.0, | |
| # git-bzr-ng, python-fastimport, patched bzr-fastimport | |
| ##### | |
| # Set up unpackaged source: | |
| # mkdir ~/src |
WARNING This list outdated, for the up to date version visit https://haskellcosm.com
Types of work:
Here's a simple implementation of bilinear interpolation on tensors using PyTorch.
I wrote this up since I ended up learning a lot about options for interpolation in both the numpy and PyTorch ecosystems. More generally than just interpolation, too, it's also a nice case study in how PyTorch magically can put very numpy-like code on the GPU (and by the way, do autodiff for you too).
For interpolation in PyTorch, this open issue calls for more interpolation features. There is now a nn.functional.grid_sample() feature but at least at first this didn't look like what I needed (but we'll come back to this later).
In particular I wanted to take an image, W x H x C, and sample it many times at different random locations. Note also that this is different than upsampling which exhaustively samples and also doesn't give us fle
This is an OPML version of the HN Popularity Contest results for 2025, for importing into RSS feed readers.
Plug: if you want to find content related to your interests from thousands of obscure blogs and noisy sources like HN Newest, check out Scour. It's a free, personalized content feed I work on where you define your interests in your own words and it ranks content based on how closely related it is to those topics.