Interpretable neural networks help reveal the nature of dark matter

A research team from the Xinjiang Astronomical Observatory (XAO) of the Chinese Academy of Sciences has developed an ...

This New AI Is Cracking the Hidden Laws of Nature

This new AI acts like a digital scientist, turning messy data into simple rules that explain how the world really works.
IEEE

Large-Scale Completion of Ionospheric TEC Maps Using Machine Learning Models With Constraints Conditions

Abstract: Total electron content (TEC) is a key ionospheric parameter, but data gaps, especially over oceans, remain challenging due to sparse receiver coverage. Deep learning offers promising ...
GitHub

Tro-fish/Decoding-Dense-Embeddings

To train the SAE, you need embeddings generated by a DPR model. We use dense embeddings from SimLM for all 8.8M MSMARCO passages and train queries. After training the SAE, you can reconstruct the ...
GitHub

Variational autoencoder with Kolmogorov-Arnold Network for spatial domain detection.

Experiments were executed on NVIDIA A40 of 46068MiB memory in linux with torch==2.1.0+cu121, torch_geometric==2.3.1, torch-sparse==0.6.18+pt21cu121, and torchvision==0.16.0+cu121. The stkan is an ...
IEEE

Selective Imitation Enhanced Deep Reinforcement Learning for AAV Navigation and Obstacle Avoidance With Sparse Rewards

Abstract: Deep reinforcement learning (DRL) has emerged as a promising solution for autonomous operations of autonomous aerial vehicles (AAVs) in unknown environments. However, learning to navigate ...