ML Research

machine learning papers, deep learning, model training, benchmarks

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Scoured 295 posts in 11.4 ms

Flatland: The Adventures of Gradient Descent with Large Step Sizes

 🧠LLMs  Content type: Academic
arxiv.org·

Machine learning from scratch, what to build before using scikit-learn

 🤖AI Models  Content type: Tutorial
iwtlp.com··DEV

Backpropagation Without the Magic: A First-Principles Derivation

 🤖AI Models  Content type: Blog
medium.com
·

Welcome to Machine Learning With Manya: The Ultimate Adventure Map!

 🤖AI Models  Content type: Blog
medium.com·

Forgis-Labs/HEPA: HEPA: Self-supervised horizon-conditioned event predictive architecture for time series. Spotlight at FMSD @ ICML 2026.

 📏Model Evaluation  Content type: Code
github.com··Hacker News

Ultrafast machine learning on FPGAs via Kolmogorov-Arnold Networks

 🤖AI Models

Learning Fuzzy Logic: Automatic Rule Discovery Through Differentiable Circuits

 🧠LLMs
metafunctor.com··DEV

Timing Trick Cuts Energy Used in LLM Training by Up to 14 Percent

 📝Prompt Engineering  Content type: News
spectrum.ieee.org
··Hacker News

Discrete Diffusion Modelling by Estimating the Ratios of the Data Distribution

 🤖AI Models  Content type: News  Content type: Blog

Major Types of Machine Learning

 🤖AI Models  Content type: Blog
medium.com·

Efficient and accurate neural-field reconstruction using resistive memory

 🤖AI Models  Content type: Academic
nature.com
·

SynthonBench: Benchmarking Sample-Efficient Optimization in Combinatorial Chemical Spaces | Miroslav Lzicar

 🧪Property Testing
mireklzicar.com·

AI Detection for Academic Conferences: Paper and Presentation Verification 2026

 💻AI Coding  Content type: Blog
hub.paper-checker.com·

GGUF vs GPTQ vs AWQ: The Plain-English Guide to LLM Quantization (and Which One to Pick)

 📝Prompt Engineering

Towards Diverse Scientific Hypothesis Search with Large Language Models

 📝Prompt Engineering  Content type: Academic
arxiv.org·

The Smallest Brain You Can Build: A Perceptron in Python

 🤖AI Models  Content type: Discussion

ml-from-scratch-book/code: Companion code for Machine Learning From Scratch — 10 core ML algorithms built from scratch with NumPy, compared with Scikit-learn and PyTorch.

 📏Model Evaluation  Content type: Code
github.com··Hacker News

The Untrainable

 📏Model Evaluation  Content type: News  Content type: Blog

Sakana AI launches its Recursive Self-Improvement Lab to build autonomous, self-improving AI systems

 💻AI Coding  Content type: News
digg.com·

Breaking free of a single datacenter: Practical geo-distributed AI operations with the k0smos platforms

 📝Prompt Engineering  Content type: Blog
cncf.io·

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