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.
Memeburn

Google's Gemma 4 12B Runs AI Natively on Your Laptop — No Cloud Needed

Google's Gemma 4 12B brings multimodal AI — audio, video, and text — to a standard 16GB laptop in 2026. No cloud required. Here's what it does and why it matters.
Tech Times

Google Gemma 4 12B Brings Multimodal AI to 16GB Laptops, Free Under Apache 2.0

Google Gemma 4 12B, released June 3, is an open-weight multimodal model that processes text, images, audio, and video in a ...
TMCnet

WiMi Proposes a New High-Performance Fault-Tolerant Quantum Computing Technology Based on Multi-Hypercube Codes

WiMi Hologram Cloud Inc. (NASDAQ: WiMi) ("WiMi" or the "Company"), a leading global Hologram Augmented Reality ("AR") Technology provider, proposes a new high-performance fault-tolerant quantum ...
AV Network

InfoComm 2026 Product Watch: Setting the Standard

There will be many trends circulating the InfoComm show floor in 2026, and standards are always front and center. Whether it ...
GitHub

encoder-decoder-architecture

Add a description, image, and links to the encoder-decoder-architecture topic page so that developers can more easily learn about it.
marktechpost

This AI Paper Proposes a Novel Dual-Branch Encoder-Decoder Architecture for Unsupervised Speech Enhancement (SE)

Most learning-based speech enhancement pipelines depend on paired clean–noisy recordings, which are expensive or impossible to collect at scale in real-world conditions. Unsupervised routes like ...
The Indian Express

7 free Google AI courses: Master LLMs, ML, and more in under an hour

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, ...
IEEE

Improved Encoder-Decoder Architecture With Human-Like Perception Attention for Monaural Speech Enhancement

Abstract: Speech enhancement (SE) models based on deep neural networks (DNNs) have shown excellent denoising performance. However, mainstream SE models often have high structural complexity and large ...
IEEE

SVInvNet: A Densely Connected Encoder-Decoder Architecture for Seismic Velocity Inversion

Abstract: This study presents a deep learning (DL)-based approach to the seismic velocity inversion problem, focusing on both noisy and noiseless training datasets of varying sizes. Our seismic ...