Alternatives
Products that do what Automated smooth Nth order derivatives of noisy data does
This little project came about because I kept running into the same problem: cleanly differentiating sensor data before doing analysis. There are a ton of ways to solve this problem, I've always personally been a fan of using kalman filters for the job as its easy to get the double whammy of resampling/upsampling to a fixed consistent rate and also smoothing/outlier rejection. I wrote a little numpy only bayesian filtering/smoothing library recently (https://github.com/hugohadfield/bayesfilter/) so this felt like a fun and very useful first thing to…
- 1FP
2021 · floptimal.com
- 2BG
Trying to do gradient descent using automatic differentiation over branchy programs? Or to combine them with neural networks for end-to-end training? Then this might be interesting to you. We develped DiscoGrad, a tool for automatic differentiation through C++ programs involving input-dependent control flow (e.g., "if (f(x) < c) { ... }", differentiating wrt. x) and randomness. Our initial motivation was to enable the use of gradient descent with simulations, which often rely heavily on such discrete branching. The latter makes plain autodiff mostly useless, since it can only account for the…
2024 · github.com
- 3PI
Hi HN! I’m Alex from Parabola (https://parabola.io). Parabola is a visual programming tool for creating functional data flows that everyone can use. It’s entirely drag-and-drop, handles data sizes much larger than a traditional spreadsheet, calculates everything live, and can run your flows on a schedule of your choosing. I used to work in strategy consulting, doing data analytics for SMBs and Fortune 500 companies. The amount of time wasted on menial tasks was astounding. Things like cleaning data, generating custom reports, creating human workflows to solve shortcomings in third…
2018 · parabola.io
- 4CF
2016 · github.com
- 5AR
2018 · github.com
- 6MT
2019 · github.com
- 7EA
2019 · empirical-soft.com
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- 9BB
I wanted to record the aurora last weekend, but I only have a Blackmagic Design video camera which is clearly not made for this purpose. Recording a video of the night sky results in extreme noise to the point that you don't really see anything, so I wrote a tool to significantly reduce noise in such video recordings. Essentially it computes a moving average across video frames which significantly reduces the random sensor noise. This works because aurora changes very slowly, and it's roughly comparable to a long exposure time computed out of a video file where the individual frames have a…
2024 · github.com
- 10AS
2018 · github.com
- 11MD
Hello everyone! I've been working on this project for a few months as part of my thesis in Machine Learning. It's meant to be a library that provides an easy-to-use but flexible API to design and train Diffusion Models. I decided to make it because I wanted to quickly prototype a Diffusion Model but there were no good tools to do it with. I think it really can help people prototype their own Diffusion Models a lot faster and only in a few lines of code. The base idea is to have a Model class that takes different modules corresponding to the different aspects of the Diffusion Model process…
2023 · github.com
- 12SJ
2014 · github.com
- 13MN
2019 · github.com
- 14IS
2022 · tonic.ai
- 15CQ
2017 · github.com
- 16IW
2024 · gitlab.com
- 17CF
2017 · github.com
- 18IO
Just small prototype showcasing diffusion models
2025 · github.com
- 194M
Hi, I've been working with other colleagues at University of Bristol for the last year to build an R-package that makes bayesian spatio-temporal modeling easy. That is, if you have some problem like predicting sea level rise over time, disease transmissions, etc., this package may be useful for you. It centers on a collection of Shiny apps that guide you through using the various pieces of spatio-temporal modeling packages like R-INLA or inlabru. Hopefully people find this useful. I think the nice part is that we really tried to design the package so you can add it to your modeling tool kit…
2023 · 4dmodeller.github.io
- 20FD
I'm not a geospatial expert — I work in AI/ML. This started when I was exploring LiDAR data with agentic assitince and noticed that different signal decomposition methods revealed different terrain features. The core idea: if you systematically combine decomposition methods (Gaussian, bilateral, wavelet, morphological, etc.) with different upsampling techniques, each combination has characteristic "failure modes" that selectively preserve or eliminate certain features. The differences between outputs become feature-specific filters. The framework tests 25 decomposition × 19 upsampling…
Jan 2026 · github.com
- 21TA
2019 · github.com
- 22DM
2018 · producthunt.com
- 23DA
2015 · getdatajoy.com
- 24E0
2017 · github.com
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