bolt
draco
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bolt | draco | |
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6 | 4 | |
2,463 | 6,251 | |
- | 1.7% | |
0.0 | 4.5 | |
over 1 year ago | 4 days ago | |
C++ | C++ | |
Mozilla Public License 2.0 | Apache License 2.0 |
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bolt
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Show HN: Want something better than k-means? Try BanditPAM
> frown on that sort of dataset
That example was definitely contrived and designed to strongly illustrate the point. I'll counter slightly that non-peaky topologies aren't uncommon, but they're unlikely to look anything that would push KMedoids to a pathological state rather than just a slightly worse state ("worse" assuming that KMeans is the right choice for a given problem).
> worth pointing out .. data reference
Totally agreed. I hope my answer didn't come across as too negative. It's good work, and everyone else was talking about the positives, so I just didn't want to waste too much time echoing again that while getting the other points across.
> bolt reference
https://github.com/dblalock/bolt
They say as much in their paper, but they aren't the first vector quantization library by any stretch. Their contributions are, roughly:
1. If you're careful selecting the right binning strategy then you can cancel out a meaningful amount of discretization error.
2. If you do that, you can afford to choose parameters that fit everything nicely into AVX2 machine words, turning 100s of branching instructions into 1-4 instructions.
3. Doing some real-world tests to show that (1-2) matter.
Last I checked their code wasn't very effective for the places I wanted to apply it, but the paper is pretty solid. I'd replace it with a faster KMeans approximation less likely to crash on big data (maybe even initializing with KMedoids :) ), and if the thing you're quantizing is trainable with some sort of gradient update step then you should do a few optimization passes in the discretized form as well.
- Bolt: Faster matrix and vector operations that run on compressed data
- 10x faster matrix and vector operations
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[R] Multiplying Matrices Without Multiplying
Code: https://github.com/dblalock/bolt
draco
- how can I reduce size of a gltf file for web use?
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How are rust devs doing?
File size is important on the web, so I wanted to use Draco, which doesn't have good support for Rust (though I'm sure clean bindings are possible).
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WebAssembly vs. JavaScript: The Complete Guide
No direct DOM access is a bit of a pain, you're definitely not going to write your whole app in WASM anytime soon - it makes for decently good modules though, for example the Draco 3D geometry library provides a nice and clean WASM encoder/decoder module.
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webVR ThreeJS games, hosting assets, cost-effective solutions?
This is not a direct solution to your problem. But you could use a compression tool like https://github.com/google/draco to reduce the total filesize.
What are some alternatives?
composer - Supercharge Your Model Training
meshlab - The open source mesh processing system
halutmatmul - Hashed Lookup Table based Matrix Multiplication (halutmatmul) - Stella Nera accelerator
PCL - Point Cloud Library (PCL)
PGM-index - 🏅State-of-the-art learned data structure that enables fast lookup, predecessor, range searches and updates in arrays of billions of items using orders of magnitude less space than traditional indexes
PhysicsFS - PhysFS++ is a C++ wrapper for the PhysicsFS library.
LightGBM - A fast, distributed, high performance gradient boosting (GBT, GBDT, GBRT, GBM or MART) framework based on decision tree algorithms, used for ranking, classification and many other machine learning tasks.
meshoptimizer - Mesh optimization library that makes meshes smaller and faster to render
heavydb - HeavyDB (formerly OmniSciDB)
ozz-animation - Open source c++ skeletal animation library and toolset
Snappy - A fast compressor/decompressor
gpr - General Purpose Raw image format