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Ximalaya Technology Team
Ximalaya Technology Team
Feb 11, 2026 · Artificial Intelligence

How Ximalaya Used Generative AI to Revolutionize Audio Recommendations

This article details Ximalaya's journey from traditional multi‑stage recommendation pipelines to generative AI‑driven models, covering business challenges, architectural and model differences, phased deployments, knowledge distillation, semantic ID encoding, decoder‑only strategies, extensive offline and online evaluations, and future research directions.

Encoder-DecoderRecommendation Systemsaudio recommendation
0 likes · 24 min read
How Ximalaya Used Generative AI to Revolutionize Audio Recommendations
AI Cyberspace
AI Cyberspace
Feb 11, 2026 · Artificial Intelligence

From RNNs to LSTMs and GRUs: A Hands‑On Guide to Sequence Modeling in PyTorch

This tutorial explains the nature of sequential data, why traditional feed‑forward networks struggle with it, and how recurrent architectures such as RNN, LSTM, and GRU capture temporal dependencies, complete with mathematical foundations, training algorithms, and full PyTorch implementations for sentiment analysis, text generation, and encoder‑decoder models.

Encoder-DecoderGRULSTM
0 likes · 57 min read
From RNNs to LSTMs and GRUs: A Hands‑On Guide to Sequence Modeling in PyTorch
JD Tech Talk
JD Tech Talk
Oct 27, 2025 · Artificial Intelligence

How Large Language Models Are Revolutionizing Generative Recommendation Systems

Over the past year, generative recommendation has made substantial progress by leveraging large language models' powerful sequence modeling and reasoning abilities, introducing a new paradigm that replaces complex handcrafted features, addresses traditional recommendation bottlenecks, and outlines the evolution, core technologies, engineering challenges, and future directions of LLM‑based recommendation systems.

AI EngineeringEncoder-DecoderLLM
0 likes · 29 min read
How Large Language Models Are Revolutionizing Generative Recommendation Systems
Wu Shixiong's Large Model Academy
Wu Shixiong's Large Model Academy
Oct 23, 2025 · Artificial Intelligence

Why the Transformer Core Structure Is the Key to AI Interview Success

This article explains the fundamental purpose, architecture, and variants of the Transformer model—including Encoder‑Decoder, Encoder‑only, and Decoder‑only designs—while detailing how attention mechanisms work and why modern large‑language models favor the Decoder‑only approach, providing a concise framework for answering interview questions.

AI InterviewEncoder-DecoderSelf-Attention
0 likes · 10 min read
Why the Transformer Core Structure Is the Key to AI Interview Success
Cognitive Technology Team
Cognitive Technology Team
Jun 29, 2025 · Artificial Intelligence

Understanding Transformers: Core Mechanics Behind Modern AI Models

This article demystifies the Transformer architecture for beginners, explaining its relationship to large models, the self‑attention and multi‑head attention mechanisms, positional encoding, and the roles of Encoder and Decoder components, using clear analogies and visual diagrams to aid comprehension.

Deep LearningEncoder-DecoderPositional Encoding
0 likes · 20 min read
Understanding Transformers: Core Mechanics Behind Modern AI Models
AI Algorithm Path
AI Algorithm Path
Apr 5, 2024 · Artificial Intelligence

Master CNN, RNN, GAN, and Transformer Architectures in One Guide

This article provides a friendly, step‑by‑step overview of five core deep‑learning architectures—CNN, RNN, GAN, Transformers, and encoder‑decoder—explaining their structures, key components, and typical use cases in image and natural‑language processing.

CNNDeep LearningEncoder-Decoder
0 likes · 12 min read
Master CNN, RNN, GAN, and Transformer Architectures in One Guide
Architect
Architect
Mar 19, 2024 · Artificial Intelligence

How Transformers Power Modern NLP: A Deep Dive into Encoder‑Decoder Mechanics

This article explains the core principles of Transformer models—covering input embeddings, self‑attention, multi‑head attention, positional encoding, feed‑forward networks, and decoder strategies—using concrete examples like "The cat sat on the mat" and "The quick brown fox jumps over the lazy dog" to illustrate each step.

Encoder-DecoderFeed-Forward NetworkNLP
0 likes · 13 min read
How Transformers Power Modern NLP: A Deep Dive into Encoder‑Decoder Mechanics
Rare Earth Juejin Tech Community
Rare Earth Juejin Tech Community
Nov 15, 2023 · Artificial Intelligence

Understanding the Transformer Architecture: Encoder, Decoder, and Attention Mechanisms

This article explains the Transformer model, comparing it with RNNs, detailing its encoder‑decoder structure, multi‑head and scaled dot‑product attention, embedding layers, feed‑forward networks, and the final linear‑softmax output, supplemented with diagrams and code examples.

Deep LearningEncoder-DecoderNeural Networks
0 likes · 10 min read
Understanding the Transformer Architecture: Encoder, Decoder, and Attention Mechanisms
Sohu Tech Products
Sohu Tech Products
Jul 26, 2023 · Artificial Intelligence

Attention Mechanism, Transformer Architecture, and BERT: An In-Depth Overview

This article provides a comprehensive overview of the attention mechanism, its mathematical foundations, the transformer model architecture—including encoder and decoder components—and the BERT pre‑training model, detailing their principles, implementations, and applications in natural language processing.

Attention MechanismBERTEncoder-Decoder
0 likes · 13 min read
Attention Mechanism, Transformer Architecture, and BERT: An In-Depth Overview
Rare Earth Juejin Tech Community
Rare Earth Juejin Tech Community
Oct 11, 2022 · Artificial Intelligence

GANomaly: Theory and Source Code Analysis

This article explains the GANomaly model for semi‑supervised anomaly detection, detailing its generator‑encoder‑discriminator architecture, loss functions, testing phase scoring, and provides annotated PyTorch source code to help readers implement and understand the approach.

Deep LearningEncoder-DecoderGAN
0 likes · 15 min read
GANomaly: Theory and Source Code Analysis
Alimama Tech
Alimama Tech
Jun 22, 2022 · Artificial Intelligence

Learning Pixel-Level Distinctions for Video Highlight Detection

The Alibaba Mom Creative & Video Platform team introduces PLD‑VHD, a pixel‑level distinction learning framework that uses a 3D CNN encoder‑decoder with temporal and saliency modules to detect highlights, achieving state‑of‑the‑art results on public benchmarks and a 4,724‑video e‑commerce dataset, and boosting ad revenue through precise clipping and cropping.

Encoder-Decodereye trackingpixel-level distinction
0 likes · 11 min read
Learning Pixel-Level Distinctions for Video Highlight Detection
Youku Technology
Youku Technology
Apr 2, 2022 · Artificial Intelligence

Constrained Sequence-to-Tree Generation for Hierarchical Text Classification

At SIGIR 2022, the authors present a constrained Seq2Tree model that transforms hierarchical label taxonomies into preorder sequences and applies dynamic‑dictionary decoding to ensure label consistency, achieving superior hierarchical text classification performance on benchmark datasets and real‑world deployment within Alibaba Entertainment’s AI Brain.

Encoder-DecoderHierarchical Text ClassificationNLP
0 likes · 5 min read
Constrained Sequence-to-Tree Generation for Hierarchical Text Classification
DataFunTalk
DataFunTalk
Jun 22, 2021 · Artificial Intelligence

Survey of Graph Neural Networks for Natural Language Processing

This comprehensive survey reviews the latest research on graph neural networks applied to natural language processing, covering graph construction methods, graph representation learning techniques, encoder‑decoder models, static and dynamic graph building, and discusses challenges, benchmarks, and future directions in the field.

Encoder-DecoderGraph ConstructionNLP
0 likes · 57 min read
Survey of Graph Neural Networks for Natural Language Processing
Meituan Technology Team
Meituan Technology Team
May 27, 2021 · Artificial Intelligence

Technical Solutions for Meal Combo Recommendation in Food Delivery

Meituan Waimai tackles long ordering decisions and low merchant combo creation by deploying an offline‑real‑time hybrid system that generates high‑quality meal combos using graph‑label induction, encoder‑decoder, and attention models, reinforced with quality classification and constraint pruning, boosting combo coverage and user experience.

AIEncoder-DecoderKnowledge Graph
0 likes · 20 min read
Technical Solutions for Meal Combo Recommendation in Food Delivery
Cyber Elephant Tech Team
Cyber Elephant Tech Team
Apr 28, 2021 · Artificial Intelligence

Understanding BERT: From Encoder-Decoder to Transformer and Attention

This article explains the BERT model by first reviewing the Encoder-Decoder framework, then detailing the attention mechanism—including self-attention and multi-head attention—before describing the full Transformer architecture and finally outlining BERT’s encoder-only design, training stages, and fine-tuning applications.

BERTEncoder-DecoderNLP
0 likes · 15 min read
Understanding BERT: From Encoder-Decoder to Transformer and Attention
Hulu Beijing
Hulu Beijing
Dec 14, 2017 · Artificial Intelligence

Understanding Seq2Seq: Framework, Advantages, and Decoding Techniques

This article explains the Seq2Seq encoder‑decoder framework, its benefits for various sequence modeling tasks, and compares common decoding strategies such as greedy search and beam search, while also introducing attention and other enhancements for improved performance.

Beam SearchEncoder-Decoderattention
0 likes · 9 min read
Understanding Seq2Seq: Framework, Advantages, and Decoding Techniques