Active Learning

tl;dr Pretraining neural networks with massive unlabeled datasets has become popular as it equips the deep models with a better prior to solve downstream tasks. However, this approach generally assumes that for downstream tasks, we have access to annotated data of sufficient size. In this work, we propose ALOE, a…

Learn about the advantages and disadvantages of Active Learning methods and which of them solves your problem. With the advent of deep learning, machine learning models are now trained on huge amounts of data allowing us to solve completely new use cases. However, gaining large labeled datasets comes at a…

Until recently, the only way to deploy software and algorithms for computer vision was through open-source applications or subscribing to proprietary tools (e.g., Software as a Service (SaaS) solutions), such as Encord. Now there’s a third option: low-code and no-code active learning platforms for active learning computer vision projects. …

Labeled data is key to training supervised Machine Learning models, but data annotators often make mistakes. To mitigate annotation mistakes, one can collect multiple annotations per data-point to determine a more reliable consensus label, but this can be expensive! To train the best ML model with the least data labeling…

📬 07-Active Learning is the Future of Generative AI, AI Floods Amazon with Hundreds of Books Written by ChatGPT Transforming AI with Active Learning, ChatGPT Floods Amazon with a Library of Books Hey 👋 folks, this is FundrCap. A weekly newsletter that provides the most recent information and analysis on startup funding and venture capital in artificial intelligence. Let’s get to it. YOU CAN SUBSCRIBE FROM THIS LINK

This article reflects an application of the child-centered practices in my book Problem Solving with Young Children to the crisis of climate change. It introduces play as a mechanism to bring out the roles children can play as the builders of our futures. Introduction How can we, as educators and…

1-Introduction Before we begin our example, let’s give an hint of what active learning is. Classical (or passive) supervised machine learning (ML) algorithms are based on a training dataset (i.e., a set of labeled data). This data may consist of hundreds (or even thousands) of labeled instances. Unlabeled data, however, is…

While fine-tuning pre-trained language models has become a standard NLP practice, data labeling remains a significant bottleneck in NLP. To alleviate this problem, active learning (AL) methods have been used for various NLP tasks such as sentiment analysis and document classification. Even with the state-of-the-art AL approaches, the number of…