Tech Times

DeepSeek V4 Architecture: How Sparse Attention Cuts Inference Costs, What NIST Found

DeepSeek V4 architecture uses sparse attention to cut inference costs 73% at one-million-token contexts, but a NIST ...
GitHub

Sparse Autoencoders Learn Monosemantic Features in Vision-Language Models

Sparse Autoencoders (SAEs) have recently gained attention as a means to improve the interpretability and steerability of Large Language Models (LLMs), both of which are essential for AI safety. In ...
GitHub

InterPLM: Discovering Interpretable Features in Protein Language Models via Sparse Autoencoders

🧬 Extract SAE features from protein language models (PLMs) 📊 Analyze and interpret learned features through association with protein annotations 🎨 Visualize feature patterns and relationships 🤗 ...
archive

Learning data mining with Python : harness the power of Python to analyze data and create insightful predictive models

remove-circle Internet Archive's in-browser bookreader "theater" requires JavaScript to be enabled. It appears your browser does not have it turned on. Please see ...
PNAS

Linear Recursive Feature Machines provably recover low-rank matrices

Understanding the mechanism of how neural networks learn features from data is a fundamental problem in machine learning. Our work explicitly connects the mechanism of neural feature learning to a ...
IEEE

Shift-invariant sparse representation of images using learned dictionaries

Abstract: Sparse approximations that are evaluated using over complete learned dictionaries are useful in many image processing applications such as compression, denoising and feature extraction.
InfoWorld

8 great little Python web frameworks

Python’s convenience and versatility mean that it’s used to build software in nearly every walk of IT life. One major niche is web services, where Python’s speed of development and flexible metaphors ...