Semi-Supervised Learning

Semi-supervised learning presents a significant challenge, particularly when working with limited labeled data and abundant unlabeled samples. The gap in performance between supervised and semi-supervised methods remains substantial, especially on large datasets like ImageNet. Capturing uncertainty throughout all layers of a model, rather than just at the output, is crucial for improving accuracy and understanding variability in data.

In this clip
From this podcast
The TWIML AI Podcast (formerly This Week in Machine Learning & Artificial Intelligence)
Trends in Machine Learning with Anima Anandkumar - TWiML Talk #215
Related Questions
- Is less labeled data needed for training machine learning models?
- What are the challenges in machine learning as discussed in the episode Behind the Scenes at NeurIPS with NVIDIA and CalTech’s Anima Anandkumar - Ep. 131 and the clip Benchmarking AI Learning?

Semi-Supervised Learning

In this clip

From this podcast

The TWIML AI Podcast (formerly This Week in Machine Learning & Artificial Intelligence)

Trends in Machine Learning with Anima Anandkumar - TWiML Talk #215

Related Questions

Is less labeled data needed for training machine learning models?

What are the challenges in machine learning as discussed in the episode Behind the Scenes at NeurIPS with NVIDIA and CalTech’s Anima Anandkumar - Ep. 131 and the clip Benchmarking AI Learning?