Tagged articles

multi‑modal fusion

3 articles · Page 1 of 1

Jul 14, 2026 · Artificial Intelligence

How Agents and Native Liquid Cooling Redefine AI Compute Architecture with Millisecond Latency

The article analyzes the shift from single-model inference to massive, continuously running AI agents, explains why CPU power now rivals GPU, describes Inspur's native liquid‑cooled 0.5U servers that pack 384 CPUs per rack, and shows how multi‑modal fusion cuts token latency to 4.77 ms, heralding an industrial‑scale, collaborative AI era.

AI agentsInspurcompute architecture

0 likes · 11 min read

How Agents and Native Liquid Cooling Redefine AI Compute Architecture with Millisecond Latency

DataFunTalk

Jul 31, 2024 · Artificial Intelligence

Decentralized Distribution in Xiaohongshu Recommendation System: Sideinfo, Multi‑modal Fusion, Interest Exploration and Future Directions

This article presents Xiaohongshu's technical solutions for decentralized content distribution, covering the definition of the problem, fast and accurate learning, side‑information modeling, graph‑based multi‑modal fusion, interest exploration and protection, and future research directions such as generative recommendation and large‑model driven user profiling.

decentralized-distributioninterest-explorationmulti‑modal fusion

0 likes · 25 min read

Decentralized Distribution in Xiaohongshu Recommendation System: Sideinfo, Multi‑modal Fusion, Interest Exploration and Future Directions

DataFunSummit

Oct 8, 2023 · Artificial Intelligence

NLP Techniques for Financial Risk Control: Text Modeling, Non‑Text Modeling, Long‑Text Handling, Multi‑Modal Fusion and Sample Optimization

This article presents a comprehensive overview of how natural language processing is applied to financial risk control, covering text and non‑text sequence modeling, tokenization strategies, transformer‑based long‑text architectures, multi‑modal fusion methods, pre‑training techniques and practical sample‑optimization approaches.

AINLPText Modeling

0 likes · 22 min read

NLP Techniques for Financial Risk Control: Text Modeling, Non‑Text Modeling, Long‑Text Handling, Multi‑Modal Fusion and Sample Optimization