Hosted on MSN

Transformer encoder architecture explained simply

We break down the Encoder architecture in Transformers, layer by layer! If you've ever wondered how models like BERT and GPT process text, this is your ultimate guide. We look at the entire design of ...
Hosted on MSN

Transformers’ Encoder Architecture Explained — No Phd Needed!

We break down the Encoder architecture in Transformers, layer by layer! If you've ever wondered how models like BERT and GPT process text, this is your ultimate guide. We look at the entire design of ...
blockchain

NVIDIA Riva TTS Enhances Multilingual Speech and Voice Cloning

NVIDIA has unveiled its latest advancements in text-to-speech (TTS) technology with the introduction of Riva TTS models, designed to enhance multilingual speech synthesis and voice cloning ...
blockchain

NVIDIA Riva TTS Enhances Multilingual Speech and Voice Cloning

NVIDIA introduces Riva TTS models enhancing multilingual speech synthesis and voice cloning, with applications in AI agents, digital humans, and more, featuring advanced architecture and preference ...
InfoQ

Microsoft Introduces Mu: a Lightweight On-Device Language Model for Windows Settings

A monthly overview of things you need to know as an architect or aspiring architect. Unlock the full InfoQ experience by logging in! Stay updated with your favorite authors and topics, engage with ...
9to5Mac

Apple Research just unearthed a forgotten AI technique and is using it to generate images

Today, most generative image models basically fall into two main categories: diffusion models, like Stable Diffusion, or autoregressive models, like OpenAI’s GPT-4o. But Apple just released two papers ...
Ars Technica

Apple Videotoolbox AV1 video encoding is almost too good to be true

I've been transcoding videos on handbrake using AV1 which I think is the latest encoder. AV1 on the Mac is often incredibly efficient. I'm talking 3gb -> 300mb efficient. Even tougher material with ...
GitHub

Understanding Self-Attention(Encoder's Self-Attention and Decoder's Masked Self-Attention) in Transformers

- Driven by the **output**, attending to the **input**. - Each word in the output sequence determines which parts of the input sequence to attend to, forming an **output-oriented attention** mechanism ...
ascopubs.org

Next-generation U-Net Encoder: Decoder for accurate, automated CTC detection from images of peripheral blood nucleated cells stained with EPCAM and DAPI.

Beyond tumor-shed markers: AI driven tumor-educated polymorphonuclear granulocytes monitoring for multi-cancer early detection. Clinical outcomes of a prospective multicenter study evaluating a ...
IEEE

Non-Autoregressive Transformer with Unified Bidirectional Decoder for Automatic Speech Recognition

Abstract: Non-autoregressive (NAR) transformer models have been studied intensively in automatic speech recognition (ASR), and many NAR transformer models is to use the causal mask to limit token ...
IEEE

Hybrid Autoregressive and Non-Autoregressive Transformer Models for Speech Recognition

Abstract: The autoregressive (AR) models, such as attention-based encoder-decoder models and RNN-Transducer, have achieved great success in speech recognition. They predict the output sequence ...