New Submissions to TMLRJul 25, 2023

One-Round Active Learning through Data Utility Learning and Proxy Models

https://openreview.net/forum?id=8HQCOMRa7g

#labeled #labeling #annotators

One-Round Active Learning through Data Utility Learning and Proxy...

While active learning (AL) techniques have demonstrated the potential to produce high-performance models with fewer labeled data, their application remains limited due to the necessity for multiple...

OpenReview
beSpacificJun 13, 2023
The secret to making #AIChatbots sound #smart and #spew less #toxic nonsense is to use a technique called reinforcement learning from #HumanFeedback, which uses input from people to improve the model’s answers. It relies on a small army of #human #data #annotators who evaluate whether a string of text makes sense and sounds fluent and natural. They decide whether a response should be kept in the AI model’s database or removed. https://www.technologyreview.com/2023/06/13/1074560/we-are-all-ais-free-data-workers
We are all AI’s free data workers

Plus: DeepMind’s game-playing AI just found another way to make code faster.

MIT Technology Review
New Submissions to TMLRFeb 19, 2023

Pareto Optimization for Active Learning under Out-of-Distribution Data Scenarios

https://openreview.net/forum?id=dXnccpSSYF

#labeling #sampling #annotators

Pareto Optimization for Active Learning under Out-of-Distribution...

Pool-based Active Learning (AL) has proven successful in minimizing labeling costs by sequentially selecting the most informative unlabeled data from large pool and querying their labels from an...

OpenReview