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DeWu Technology
DeWu Technology
Feb 11, 2026 · Artificial Intelligence

How Generative Models Transform Re‑ranking Architecture for Faster, More Diverse Recommendations

This article examines the evolution of re‑ranking systems from traditional pointwise models to a two‑stage generation‑evaluation framework, compares autoregressive and non‑autoregressive generative approaches, details inference speed optimizations with GPU and model‑server upgrades, and outlines a future end‑to‑end sequence generation architecture enhanced by reinforcement learning and contrastive learning.

AIGenerative ModelsInference Optimization
0 likes · 14 min read
How Generative Models Transform Re‑ranking Architecture for Faster, More Diverse Recommendations
HyperAI Super Neural
HyperAI Super Neural
Jan 3, 2026 · Artificial Intelligence

Clone a Voice in 5 seconds with One‑Step Generation: Inside Chatterbox‑Turbo’s High‑Fidelity TTS

Resemble AI’s open‑source Chatterbox‑Turbo reduces TTS generation from ten steps to one, enabling high‑sample‑rate, lossless voice cloning from a 5‑10 second reference while supporting emotional control, side‑language tags, and embedded watermarking for real‑time applications across chatbots, games, podcasts, and education.

Chatterbox‑TurboReal-time inferenceknowledge distillation
0 likes · 7 min read
Clone a Voice in 5 seconds with One‑Step Generation: Inside Chatterbox‑Turbo’s High‑Fidelity TTS
DataFunTalk
DataFunTalk
Sep 23, 2023 · Artificial Intelligence

Paraformer: An Industrial Non‑Autoregressive End‑to‑End Speech Recognition Model and Its Deployment on ModelScope

This article introduces the Paraformer non‑autoregressive end‑to‑end speech recognition model released by Alibaba DAMO Academy, details its architecture, training strategies, large‑scale performance, and provides step‑by‑step guidance for using and fine‑tuning the model on the ModelScope platform with the FunASR toolkit.

ASRModelScopeParaformer
0 likes · 13 min read
Paraformer: An Industrial Non‑Autoregressive End‑to‑End Speech Recognition Model and Its Deployment on ModelScope
DataFunSummit
DataFunSummit
Jul 18, 2022 · Artificial Intelligence

Advances in Natural Language Generation: ProphetNet, Knowledge‑Enhanced Generation, Non‑Autoregressive Pre‑training, Long‑Text Modeling, and Efficient Attention

This talk presents recent year’s research on natural language generation, covering the ProphetNet pre‑trained generation model, external‑knowledge integration for generation, non‑autoregressive pre‑training (BANG), the Poolingformer long‑text architecture, EL‑attention for faster decoding, and a new multi‑task generation benchmark.

efficient attentionknowledge integrationlong‑text modeling
0 likes · 22 min read
Advances in Natural Language Generation: ProphetNet, Knowledge‑Enhanced Generation, Non‑Autoregressive Pre‑training, Long‑Text Modeling, and Efficient Attention
Alibaba Cloud Developer
Alibaba Cloud Developer
Dec 6, 2021 · Artificial Intelligence

Can AI Design Full Clothing Lines? Inside Alibaba’s M6-UFC Generator

Alibaba’s DAMO Academy and Tsinghua University introduced M6‑UFC, a non‑autoregressive multimodal transformer that unifies arbitrary text and image controls to generate high‑quality, editable fashion designs, dramatically reducing carbon emissions and outperforming GAN‑based models in fidelity and relevance while accelerating production speed.

AIM6-UFCfashion design
0 likes · 11 min read
Can AI Design Full Clothing Lines? Inside Alibaba’s M6-UFC Generator
DataFunTalk
DataFunTalk
Apr 6, 2021 · Artificial Intelligence

Advances in Text Summarization: Pointer-Generator, Coverage Mechanisms, Entity Knowledge Integration, and Non-Autoregressive Models

This article reviews recent advances in abstractive summarization, covering pointer‑generator networks with coverage loss, integration of entity knowledge, strategies to mitigate repetition such as unlikelihood training and nucleus sampling, and emerging non‑autoregressive approaches like the Levenshtein Transformer.

NLPPointer-Generatorcoverage
0 likes · 15 min read
Advances in Text Summarization: Pointer-Generator, Coverage Mechanisms, Entity Knowledge Integration, and Non-Autoregressive Models