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Alimama Tech

Official Alimama tech channel, showcasing all of Alimama's technical innovations.

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Recent Articles

Latest from Alimama Tech

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Alimama Tech
Alimama Tech
Oct 29, 2025 · Artificial Intelligence

LLM Breakthroughs at EMNLP 2025: Embedding Compression, Complex Instructions, Knowledge Scaling

EMNLP 2025 in Suzhou showcases Taobao's booth featuring four cutting‑edge AI papers that introduce a novel embedding compression framework, an automatic iterative refinement method for complex instruction generation, a knowledge infusion scaling law for large language models, and a video caption optimization approach for text‑to‑video generation.

embedding compressioninstruction generationknowledge infusion
0 likes · 7 min read
LLM Breakthroughs at EMNLP 2025: Embedding Compression, Complex Instructions, Knowledge Scaling
Alimama Tech
Alimama Tech
Oct 22, 2025 · Artificial Intelligence

How Alibaba’s AIGC Model Revolutionizes Virtual Fashion Try‑On

This article details Alibaba’s Taobao Star fashion AIGC model, explaining its data pipeline, captioning strategy, multi‑stage training, and impressive virtual try‑on results for users and merchants, while showcasing model‑based and model‑free generation and pose‑transfer capabilities.

AIAIGCModel Training
0 likes · 11 min read
How Alibaba’s AIGC Model Revolutionizes Virtual Fashion Try‑On
Alimama Tech
Alimama Tech
Oct 15, 2025 · Artificial Intelligence

How Alibaba’s Taobao Starry Model Delivers Precise, Consistent E‑commerce Image Edits

Alibaba’s Taobao Starry Image Editing model tackles the e‑commerce challenge of maintaining visual consistency by introducing a high‑fidelity, plug‑in architecture, a million‑scale consistency dataset, and multi‑stage multilingual training, enabling precise, controllable edits without altering product layout or background.

Data engineeringImage editingconsistency
0 likes · 10 min read
How Alibaba’s Taobao Starry Model Delivers Precise, Consistent E‑commerce Image Edits
Alimama Tech
Alimama Tech
Oct 1, 2025 · Artificial Intelligence

How RecIS Revolutionizes Large‑Scale Sparse‑Dense Recommendation Training

RecIS is an open‑source, PyTorch‑based unified framework designed for ultra‑large‑scale sparse‑dense computation in recommendation systems, offering a full solution for training models with massive samples, multimodal inputs, and large embeddings, and demonstrating significant performance gains over TensorFlow and TorchRec in production deployments.

PyTorchdeep learning frameworklarge-scale AI
0 likes · 24 min read
How RecIS Revolutionizes Large‑Scale Sparse‑Dense Recommendation Training
Alimama Tech
Alimama Tech
Sep 24, 2025 · Information Security

Differential Privacy Explained: Theory, Techniques, and Real-World AI Deployments

This article provides a comprehensive overview of differential privacy, covering its mathematical foundations, evolution from theory to engineering, classification of privacy mechanisms, practical implementation cases such as Alibaba's Secure Data Hub, and diverse application scenarios across healthcare, finance, location analytics, and energy forecasting.

AI ComplianceData SecurityNoise Mechanisms
0 likes · 23 min read
Differential Privacy Explained: Theory, Techniques, and Real-World AI Deployments
Alimama Tech
Alimama Tech
Sep 17, 2025 · Artificial Intelligence

How Federated Learning Balances Privacy and Collaboration in AI

Federated Learning enables multiple parties to collaboratively train a global AI model without sharing raw data, using techniques like local training, encrypted parameter exchange, and secure aggregation, while addressing privacy, communication efficiency, heterogeneity, and incentive challenges across horizontal, vertical, and transfer learning scenarios.

Horizontal FLSecure AggregationVertical FL
0 likes · 24 min read
How Federated Learning Balances Privacy and Collaboration in AI
Alimama Tech
Alimama Tech
Sep 3, 2025 · Artificial Intelligence

Privacy-Preserving Machine Learning: Balancing Data Utility and Confidentiality

Privacy-Preserving Machine Learning (PPML) integrates cryptographic techniques such as federated learning, differential privacy, homomorphic encryption, and secure multi-party computation to enable model training and inference on encrypted or distributed data, thereby breaking data silos while safeguarding privacy across sectors like healthcare, finance, and advertising.

Homomorphic Encryptionfederated learningmachine learning
0 likes · 18 min read
Privacy-Preserving Machine Learning: Balancing Data Utility and Confidentiality
Alimama Tech
Alimama Tech
Aug 27, 2025 · Artificial Intelligence

How Multi-Attribution Learning Boosts Conversion Rate Prediction in Display Ads

This article introduces Multi-Attribution Learning (MAL), a novel paradigm that jointly models multiple attribution labels to overcome the single-attribution bottleneck in conversion rate (CVR) prediction, detailing its architecture, auxiliary tasks, extensive offline and online experiments, and significant business gains.

advertising systemsconversion rate predictionmulti-attribution learning
0 likes · 24 min read
How Multi-Attribution Learning Boosts Conversion Rate Prediction in Display Ads
Alimama Tech
Alimama Tech
Aug 20, 2025 · Information Security

How Private Information Retrieval Secures Data Queries in Modern Applications

Private Information Retrieval (PIR) is a core privacy-preserving technique that enables users to query databases without revealing their query content or access patterns, and its evolution—from early theoretical models to efficient, real‑world deployments across blockchain, cloud, and advertising—makes it essential for secure data collaboration.

Private Information Retrievaldata privacysecure multi-party computation
0 likes · 18 min read
How Private Information Retrieval Secures Data Queries in Modern Applications