Nature

Novel concept-based image captioning models using LSTM and multi-encoder transformer architecture

Captioning an image involves using a combination of vision and language models to describe the image in an expressive and concise sentence. Successful captioning task requires extracting as much ...
inc42

What Is Encoder-Decoder Architecture? Here’s All You Need to Know

What Is An Encoder-Decoder Architecture? An encoder-decoder architecture is a powerful tool used in machine learning, specifically for tasks involving sequences like text or speech. It’s like a ...
Nature

Driver drowsiness estimation using EEG signals with a dynamical encoder–decoder modeling framework

Drowsiness is a leading cause of accidents on the road as it negatively affects the driver’s ability to safely operate a vehicle. Neural activity recorded by EEG electrodes is a widely used ...

Context compression finally works in production: new research cuts LLM input 16x without the accuracy hit

LCLMs compress LLM context before decode — 8.8x faster at 16x compression, beating every KV cache method tested. Open-sourced by NYU and Columbia.