Eos

Comparing Machine Learning Models of Raindrop Formation

Better simulations of raindrop formation could help improve climate and weather models. This newsletter rocks. Get the most ...
IEEE

DSFedCon: Dynamic Sparse Federated Contrastive Learning for Data-Driven Intelligent Systems

Abstract: Federated learning (FL) makes it possible for multiple clients to collaboratively train a machine-learning model through communicating models instead of data, reducing privacy risk. Thus, FL ...
IEEE

Building a Robust and Efficient Defensive System Using Hybrid Adversarial Attack

Abstract: Adversarial attack is a method used to deceive machine learning models, which offers a technique to test the robustness of the given model, and it is vital to balance robustness with ...
CNX Software

BitNetMCU project enables Machine Learning on CH32V003 RISC-V MCU

Neural networks and other machine learning processes are often associated with powerful processors and GPUs. However, as we’ve seen on the page, AI is also moving to the very edge, and the BitNetMCU ...
GitHub

YAMLE (Yet Another Machine Learning Environment)

YAMLE: Yet Another Machine Learning Environment is an open-source framework that facilitates rapid prototyping and experimentation with machine learning models and methods. The key motivation is to ...
GitHub

The MNIST-1D Dataset

Most machine learning models get around the same ~99% test accuracy on MNIST. Our dataset, MNIST-1D, is 100x smaller (default sample size: 4000+1000; dimensionality: 40) and does a better job of ...
The Conversation

We built a ‘brain’ from tiny silver wires. It learns in real time, more efficiently than computer‑based AI

The world is infatuated with artificial intelligence (AI), and for good reason. AI systems can process vast quantities of data in a seemingly superhuman way. However, current AI systems rely on ...