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DataFunSummit

Sep 11, 2024 · Artificial Intelligence

Weak Supervision Machine Learning in Ant Group Business Scenarios

This article presents an overview of weak supervision machine learning techniques applied to Ant Group’s business scenarios, covering an introduction to weak supervision, challenges of modeling with scarce or noisy labels, detailed methodologies for cross‑domain causal effect estimation, multi‑source noisy label denoising, and real‑world application examples.

Cross-DomainMachine LearningWeak Supervision

0 likes · 18 min read

Weak Supervision Machine Learning in Ant Group Business Scenarios

DataFunTalk

Jul 12, 2024 · Artificial Intelligence

Weak Supervision Machine Learning for Ant Group Business Scenarios: Methods, Experiments, and Applications

This article presents a comprehensive overview of weak supervision machine learning techniques applied to Ant Group's business problems, covering theoretical foundations, cross‑domain causal effect estimation, noisy‑label denoising frameworks, experimental results, and practical use cases such as risk modeling and marketing interventions.

Machine LearningWeak Supervisioncausal inference

0 likes · 16 min read

Weak Supervision Machine Learning for Ant Group Business Scenarios: Methods, Experiments, and Applications

DataFunTalk

Mar 14, 2021 · Artificial Intelligence

A Review of Medical Domain Sentiment Analysis: Interpretability, Contextual Aspect‑Sentiment Relations, Noisy Labels, and Domain Lexicon Construction

This article reviews recent research on medical sentiment analysis, covering interpretability of neural models, contextual aspect‑sentiment interactions, strategies for handling noisy labels, and methods for building domain‑specific sentiment lexicons, highlighting challenges and proposed solutions.

Interpretabilityaspect‑based sentimentdeep learning

0 likes · 19 min read

A Review of Medical Domain Sentiment Analysis: Interpretability, Contextual Aspect‑Sentiment Relations, Noisy Labels, and Domain Lexicon Construction

DataFunTalk

Jul 3, 2020 · Artificial Intelligence

Confident Learning: Detecting and Cleaning Noisy Labels with cleanlab

This article introduces confident learning, a principled framework for identifying and correcting mislabeled data in machine‑learning datasets, explains its three‑step process (count, clean, re‑training), demonstrates usage of the open‑source cleanlab library with code examples, and presents experimental results showing its effectiveness on benchmarks such as CIFAR‑10 and ImageNet.

Machine Learningcleanlabconfident learning

0 likes · 13 min read

Confident Learning: Detecting and Cleaning Noisy Labels with cleanlab