Dev tools · alternatives · 2026
24 alternatives to Beating Hinton et al.'s capsule net with fewer params and less training
Hello HN, I recently posted a work-in-progress paper, along with code necessary for replicating all its results, at: https://github.com/glassroom/heinsen_routing Among other things, the code in this repo outperforms Hinton et al.'s recent state-of-the-art result in visual recognition[0] while requiring fewer parameters and an order-of-magnitude fewer training epochs. Most of the original research we do at work tends to be either proprietary in nature or tightly coupled to internal code, so we cannot share it with the world. In this case, however, I was able to remove all…
This is an open-source implementation of a capsule network architecture for visual recognition that achieves better performance than Hinton et al.'s recent benchmark while using fewer parameters and significantly less… Below are 24 products that do a similar job, ranked by how close each is in meaning and then by launch-day votes. Beating Hinton et al.'s capsule net with fewer params and less training launched in 2019; newer entries below may have overtaken it.
- 1
2021 · nn.labml.ai · its alternatives →
- 2BV
Vision models have been gaining popularity as a replacement for traditional OCR. Especially with Gemini 2.0 becoming cost competitive with the cloud platforms. We've been continuously evaluating different models since we released the Zerox package last year (https://github.com/getomni-ai/zerox). And we wanted to put some numbers behind it. So we’re open sourcing our internal OCR benchmark + evaluation datasets. Full writeup + data explorer here: https://getomni.ai/ocr-benchmark Github: https://github.com/getomni-ai/benchmark Huggingface:…
2025 · getomni.ai · its alternatives →
- 3
Thinc is a lightweight type-checked deep learning library for composing models, with support for layers defined in frameworks like PyTorch and TensorFlow.
2020 · thinc.ai · its alternatives →
- 4IC
I spent the past week implementing a 1 Layer Neural Net and training it on MNIST within the visual scripting language provided by scratch.mit.edu. It was tedious, but ultimately not too difficult. The code runs incredibly slowly, so much so that 64 samples of MNIST takes 5+ hours to train on my machine. There were a lot of little mini challenges that were fun to overcome (implementing softmax was very tricky). If you're interested, I encourage you to try and improve on it! More details in the linked blog post.
2024 · bell-boy.github.io · its alternatives →
- 5
I am excited to announce the release of TabPFN v2, a tabular foundation model that delivers state-of-the-art predictions on small datasets in just 2.8 seconds for classification and 4.8 seconds for regression compared to strong baselines tuned for 4 hours. Published in Nature, this model outperforms traditional methods on datasets with up to 10,000 samples and 500 features. The model is available under an open license: a derivative of the Apache 2 license with a single modification, adding an enhanced attribution requirement inspired by the Llama 3 license:…
2025 · nature.com · its alternatives →
- 6AV
Discussions: Hacker News (63 points, 8 comments), Reddit r/programming (312 points, 37 comments) Translations: Arabic, French, Spanish Update: Part 2 is now live: A Visual And Interactive Look at Basic Neural Network Math Motivation I’m not a machine learning expert. I’m a software engineer by train
2016 · jalammar.github.io · its alternatives →
- 7GC
Many important real-world datasets come in the form of graphs or networks: social networks, knowledge graphs, protein-interaction networks, the World Wide Web, etc. (just to name a few). Yet, until recently, very little attention has been devoted to the generalization of neural...
2016 · tkipf.github.io · its alternatives →
- 8IC
A friend and I wrote a book on how to build and train Deep Learning models in Go. We wanted it to be a useful reference for deep learning basics for Go programmers. Deep Learning is slowly seeping into everything we use every day and we thought it would be great if more people could do it in Go. The book is available here and on Amazon as well. https://www.packtpub.com/big-data-and-business-intelligence/hands-deep-learning-go We would appreciate any feedback and we're always looking to improve.
2019 · its alternatives →
- 9
2016 · github.com · its alternatives →
- 10
A minimalist deep learning library written from scratch in Python - sradc/SmallPebble
2021 · github.com · its alternatives →
- 11AI
- 12
Most inference UIs that I've come across pretty much just give us a chat-like interface to toy around with models in a single visual conversation thread. Given the fact that we are limited to seeing only one output at a time, it's kind of hard to compare outputs from different models, adjustments made to the prompting, and sampler settings. But even when keeping the generation parameters the same (e.g., to test for reliability in the output) and just going for multiple passes, there is no easy way to have a side-by-side comparison to keep track of the outputs from the multiple "rounds". I…
2024 · github.com · its alternatives →
- 13
Implementation of some interesting ideas of deeplearning. - DeepLearningForFun/Mxnet-Scala/Pix2Pix at master · Ldpe2G/DeepLearningForFun
2017 · github.com · its alternatives →
- 14
Face recognition with deep neural networks. Contribute to cmusatyalab/openface development by creating an account on GitHub.
2015 · github.com · its alternatives →
- 15W1
Hey HackerNews, I built this project over the last few weeks as a palette cleanser from a failed game launch. I wanted to learn a bit about AI/Neural-Networks and naively thought I could build a tiny maze-solving AI in a weekend with a 100% solve rate. Well - I couldn't, but I got pretty close. 14 Bytes total model size, and a 96.5% solve rate on unseen mazes. Trained across 46 phases experimenting with different ideas to improve the model (better performance, smaller size). Its quite fun to watch the model attempt to solve the maze, when they fail its usually due to getting stuck in a…
Jul 2026 · con-dog.github.io · its alternatives →
- 16
I made this tool to get some better intuition on how neural networks/backpropagation worked, but I'm really unsure what to do with it now, so I've put it up on github, and I wrote a little primer on backprop and neural networks to showcase it. Really curious to hear any thoughts you might have, or anything I got wrong in the write up!
2024 · github.com · its alternatives →
- 17IB
Hey HN community, I built a tool that helps optimize your post for hitting the first page of Show HN. How it works: I used a Hugging Face dataset of all Hacker News posts from the past 3 years and trained a model that predicts how successful your post might be. There's still a lot of randomness on HN, so nothing is guaranteed, but the tool helps optimize your post for higher odds. A couple of interesting findings: - GitHub repo links work x3 better than regular domains - Open-source tools have a steady virality rate (13.9% - one of the highest) - "I built" outperforms "We built" - Using…
May 2026 · wannalaunch.com · its alternatives →
- 18
Hi HN, Can old dogs learn new tricks? We've been running clippingmagic.com since we launched it here on hn in May 2013 [0]. It was originally based on a purely "classical" algorithm, but since then Deep Learning has taken over the field, leaving us with the simple choice to adapt or die. Most of our old competitors shut down. The new competition is "a bunch of kids". We're two 40+ year olds with young kids, mortgages, and a 15 year investment in classical image processing algorithms rendered largely obsolete by Deep Learning. Fun times. So it's been challenging, interesting, humbling &…
2022 · pixian.ai · its alternatives →
- 19LT
I’m excited to share a project I’ve been working on for over a year, which I believe will fundamentally change our approach to language models. We’ve designed a new architecture, which replaces the hidden state of an RNN with a machine learning model. This model compresses context through actual gradient descent on input tokens. We call our method “Test-Time-Training layers.” TTT layers directly replace attention, and unlock linear complexity architectures with expressive memory, allowing us to train LLMs with millions (someday billions) of tokens in context. Our instantiations, TTT-Linear…
2024 · its alternatives →
- 20
An implementation of "MixHop: Higher-Order Graph Convolutional Architectures via Sparsified Neighborhood Mixing" (ICML 2019). - benedekrozemberczki/MixHop-and-N-GCN
2019 · github.com · its alternatives →
- 21
Minimal, readable LLM post-training experiments on one 8GB GPU. Measures forgetting, seed variance, and RL emergence. - pochenai/nano-llm-posttraining
Aug 2026 · github.com · its alternatives →
- 22
An implementation of "MixHop: Higher-Order Graph Convolutional Architectures via Sparsified Neighborhood Mixing" (ICML 2019). - benedekrozemberczki/MixHop-and-N-GCN
2019 · github.com · its alternatives →
- 23
Tensorflow implementation of Graph Convolutional Network - shagunsodhani/pregel
2017 · github.com · its alternatives →
- 24
A lightweight C library for artificial neural networks - attractivechaos/kann
2018 · github.com · its alternatives →
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Ranked by how close each launch is in meaning, then by votes. Prices were read from each product’s own site when checked and can change. Refine with your own description →