Google and Cornell Researchers Introduce FLASH: A Machine Learning Model That can Achieve High Transformer Quality in Linear Time

The introduction of attention-based transformer architectures has permitted numerous language and vision tasks improvements. However, their use is limited to small context sizes due to their quadratic complexity over the input length. Many scientists have been working on strategies to develop more efficient attention mechanisms and decrease complexity to linear to speedup transformers. So far, […]

The post Google and Cornell Researchers Introduce FLASH: A Machine Learning Model That can Achieve High Transformer Quality in Linear Time appeared first on …

ai paper summary ai shorts applications artificial intelligence cornell university country deep learning editors pick featured google guest post learning machine machine learning machine learning model researchers staff tech tech news technology time transformer unicorns university research usa