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

Abstract: In this work, we propose a Wavelet-based Deep Auto Encoder-Decoder Network (WDAED) based image compression which takes care of the various frequency components present in an image.

PtychoNN is a two-headed encoder-decoder network that simultaneously predicts sample amplitude and phase from input diffraction data alone. PtychoNN is 100s of times faster than iterative phase ...

This tutorial shows how to use Keras library to build deep neural network for ultrasound image nerve segmentation. More info on this Kaggle competition can be found ...

Abstract: In this letter, we propose a deep learning-based iterative residual encoder-decoder method (IRED), which provides an efficient deep learning framework for electromagnetic modeling over a ...

Artificial Intelligence (AI) is rapidly taking over industries. The fear of job displacements is palpable; however, as companies around the world are scrambling to automate various processes, ...

Autoencoders are a class of neural networks that aim to learn efficient representations of input data by encoding and then reconstructing it. They comprise two main parts: the encoder, which ...

Quality of life is greatly affected by chronic wounds. It requires more intensive care than acute wounds. Schedule follow-up appointments with their doctor to track healing. Good wound treatment ...