#SparseModels
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benkemp.bsky.social
✂️ How much of a neural network can you delete?

I prune 80% of the weights from a trained model using magnitude-based pruning.

Result?
Surprisingly small performance drop.

Full Python implementation included 👇
solvewithpython.com/sparse-neura...

#MachineLearning #DeepLearning #AI #SparseModels
Magnitude‑Based Pruning in Python: Removing Weights That Don’t Matter
In Article #1, we introduced sparsity by design using fixed masks. That raised the obvious next question: If we want a sparse network, which weights should we remove — and why? This article answers…
solvewithpython.com
January 31, 2026 at 8:21 AM
aidailypost.com
OpenAI just showed that pruning networks into sparse models makes debugging a breeze and could finally crack mechanistic interpretability. Curious how this changes AI research? Dive in for the details. #SparseModels #MechanisticInterpretability #OpenAI

🔗 aidailypost.com/news/openai-...
November 14, 2025 at 8:42 PM
cristinamolerorio.bsky.social
A special thanks to #NeEDSproject for making this collaboration possible.

#RuleSets #InterpretableMachineLearning #RashomonSets #SparseModels
July 3, 2026 at 8:25 AM
aicompetence.org
When it comes to machine learning and AI models, there’s a familiar challenge: scaling. #AIbottlenecks #AImodeloptimization #AIscaling #efficientAItraining #GRINMoE #scalingchallenges #sparsemodels #SparseMixerv2
aicompetence.org/grin-moe-spa...
Overcoming AI Bottlenecks: GRIN-MoE & SparseMixer-v2 Solutions
GRIN-MoE and SparseMixer-v2 overcome AI scaling bottlenecks, offering innovative solutions for faster and more efficient model training.
aicompetence.org
September 22, 2024 at 10:50 AM