Boost Large‑Model Fine‑Tuning with Low‑Cost Data Selection and Construction

The article explains practical techniques for choosing and constructing fine‑tuning data for large language models, covering data diversity through similarity‑based clustering, semi‑supervised filtering with binary classifiers, and uncertainty‑driven sampling using perplexity or reward models to build an efficient, low‑cost pipeline.

Large ModelReward modelactive learning

0 likes · 9 min read

Boost Large‑Model Fine‑Tuning with Low‑Cost Data Selection and Construction

Python Crawling & Data Mining

Mar 11, 2023 · Artificial Intelligence

How to Overcome Data Scarcity in Machine Learning: Strategies and Techniques

Facing data scarcity in machine learning, this article explores why large datasets are essential, categorizes missing data and label gaps, and presents practical solutions such as dataset reuse, augmentation, multimodal learning, curriculum learning, semi‑supervised methods, active learning, transfer and meta‑learning to mitigate the problem.

Meta Learningdata augmentationdata scarcity

0 likes · 19 min read

How to Overcome Data Scarcity in Machine Learning: Strategies and Techniques

DataFunTalk

Aug 12, 2022 · Artificial Intelligence

Multi‑Task Learning for Sample Selection Bias in Financial Risk Control

This article presents a comprehensive study on addressing sample selection bias in credit risk modeling by applying multi‑task learning techniques, including MoE/MMoE, ESMM, hierarchical attention, and semi‑supervised loss, and demonstrates their effectiveness through two real‑world application cases and experimental results.

Financial AIMoErisk control

0 likes · 14 min read

Multi‑Task Learning for Sample Selection Bias in Financial Risk Control

Alibaba Cloud Developer

Sep 18, 2019 · Artificial Intelligence

Mastering Video Object Segmentation: 3 Research Paths & Alibaba’s Latest Advances

This article explains video object segmentation, outlines the three main research directions—semi‑supervised, interactive, and unsupervised—describes Alibaba’s Moku Lab breakthroughs and competition results, and discusses future plans to improve segmentation in complex scenes.

Alibaba ResearchComputer Visioninteractive segmentation

0 likes · 12 min read

Mastering Video Object Segmentation: 3 Research Paths & Alibaba’s Latest Advances

Alibaba Cloud Developer

Aug 8, 2019 · Artificial Intelligence

Alibaba VOS Innovations: Semi-supervised, Interactive & Unsupervised Segmentation

Video Object Segmentation (VOS) is essential for content creation, and Alibaba’s research outlines three main approaches—semi-supervised, interactive, and unsupervised—detailing their algorithms, challenges, evaluation metrics, recent breakthroughs, and future plans to improve accuracy in complex scenes.

AIComputer Visioninteractive

0 likes · 12 min read

Alibaba VOS Innovations: Semi-supervised, Interactive & Unsupervised Segmentation

Youku Technology

Jul 31, 2019 · Artificial Intelligence

Exploring the Three Key Research Directions in Video Object Segmentation

The article outlines video object segmentation (VOS), its importance for content creation, and details the three primary research avenues—semi‑supervised, interactive, and unsupervised—while reviewing benchmark metrics, algorithm categories, challenges, and recent advances from Alibaba’s MoKu Lab, including their competition results and future plans.

AIComputer Visioninteractive

0 likes · 14 min read

Exploring the Three Key Research Directions in Video Object Segmentation