AI’s Memorization Crisis

O n Tuesday, researchers at Stanford and Yale revealed something that AI companies would prefer to keep hidden. Four popular ...
Science Daily

These mesmerizing patterns are secretly solving hard problems

Tessellations aren’t just eye-catching patterns—they can be used to crack complex mathematical problems. By repeatedly ...

WiMi Releases Next-Generation Quantum Convolutional Neural Network Technology for Multi-Channel Supervised Learning

Beijing, Jan. 05, 2026 (GLOBE NEWSWIRE) -- WiMi Releases Next-Generation Quantum Convolutional Neural Network Technology for Multi-Channel Supervised Learning ...

The Context Crisis: Why AI Projects Are Failing And How To Fix It

The transition from basic RAG to AI Infrastructure powered by Context Engineering is not a future scenario, it is today’s ...
IEEE

Graph Convolution Neural Network Based End-to-End Channel Selection and Classification for Motor Imagery Brain–Computer Interfaces

Abstract: Classification of electroencephalogram-based motor imagery (MI-EEG) tasks is crucial in brain–computer interface (BCI). EEG signals require a large number of channels in the acquisition ...
GitHub

SK-Arafat/Optimizer_kernel

KernelOptimizer is an open-source tool that automates CUDA kernel optimization for PyTorch workloads using large language models (LLMs). Inspired by Stanford CRFM’s fast kernel research, it leverages ...
GitHub

Adaptive Graph Convolution for Point Cloud Analysis

This repository contains the implementation of AdaptConv for point cloud analysis. Adaptive Graph Convolution (AdaptConv) is a point cloud convolution operator presented in our ICCV2021 paper. If you ...
IEEE

Mixing Self-Attention and Convolution: A Unified Framework for Multisource Remote Sensing Data Classification

Abstract: Convolution and self-attention are two powerful techniques for multisource remote sensing (RS) data fusion that have been widely adopted in Earth observation tasks. However, convolutional ...