End-to-end (E2E) models, including the attention-based encoder-decoder (AED) models, have achieved promising performance on the automatic speech recognition (ASR) task. However, the supervised ...

Decoder-based LLMs can be broadly classified into three main types: encoder-decoder, causal decoder, and prefix decoder. Each architecture type exhibits distinct attention patterns. Encoder-Decoder ...

I have implemented encoder-decoder based seq2seq models with attention. The first aim for this implementation is to work towards computational grounded theory and thematic analysis using NLP and deep ...

Quantile-Based Encoder-Decoder Deep Learning Models for Multi-Step Ahead Hydrological Forecasting [Conference presentation]. American Geophysical Union (AGU) Fall Meeting 2022, Online. Recent ...

Large language models (LLMs) have changed the game for machine translation (MT). LLMs vary in architecture, ranging from decoder-only designs to encoder-decoder frameworks. Encoder-decoder models, ...

This repository provides an Encoder-Decoder Sequence-to-Sequence model to generate captions for input videos. Moreover, pre-Trained VGG16 model is being used to extract features for every frame of the ...

Encoder-Decoder Based Route Generation Model for Flexible Travel Recommendation Abstract: Travel route recommendation is an important part of electronic tour guides and map applications. It aims to ...

NVIDIA's TensorRT-LLM now supports encoder-decoder models with in-flight batching, offering optimized inference for AI applications. Discover the enhancements for generative AI on NVIDIA GPUs. The IRS ...