parameters. Next, the actual task is performed with supervised or unsupervised learning. Self-supervised learning has produced promising results in recent years...

Weak supervision (also known as semi-supervised learning) is a paradigm in machine learning, the relevance and notability of which increased with the advent...

perform a specific task. Feature learning can be either supervised or unsupervised. In supervised feature learning, features are learned using labelled...

explicit algorithms. Feature learning can be either supervised, unsupervised, or self-supervised: In supervised feature learning, features are learned using...

Unsupervised learning is a framework in machine learning where, in contrast to supervised learning, algorithms learn patterns exclusively from unlabeled...

Artificial neural networks (ANNs) are models created using machine learning to perform a number of tasks. Their creation was inspired by biological neural...

requiring learning rate warmup. Transformers typically are first pretrained by self-supervised learning on a large generic dataset, followed by supervised fine-tuning...

are language models with many parameters, and are trained with self-supervised learning on a vast amount of text. The largest and most capable LLMs are...

models primarily employed supervised learning from large amounts of manually labeled data. This reliance on supervised learning limited their use of datasets...

are language models with many parameters, and are trained with self-supervised learning on a vast amount of text. This page lists notable large language...

Attention is a machine learning method that determines the relative importance of each component in a sequence relative to the other components in that...

was trained using a combination of first supervised learning on a large dataset, then reinforcement learning using both human and AI feedback, it did...

Reinforcement learning is one of the three basic machine learning paradigms, alongside supervised learning and unsupervised learning. Reinforcement learning differs...

next driver of machine learning commercial success after supervised learning. In the 2020 paper, "Rethinking Pre-Training and self-training", Zoph et al...

Mamba is a deep learning architecture focused on sequence modeling. It was developed by researchers from Carnegie Mellon University and Princeton University...

2024. "Curriculum learning with diversity for supervised computer vision tasks". Retrieved March 29, 2024. "Self-paced Curriculum Learning". Retrieved March...

Google. It learns to represent text as a sequence of vectors using self-supervised learning. It uses the encoder-only transformer architecture. BERT dramatically...

feedback, learning a reward model, and optimizing the policy. Compared to data collection for techniques like unsupervised or self-supervised learning, collecting...

models commonly employed supervised learning from large amounts of manually-labeled data. The reliance on supervised learning limited their use on datasets...

(and multi-lingual) corpora, also providing an early example of self-supervised learning of word embeddings. Word embeddings come in two different styles...

International Conference on Machine Learning (ICML) is a leading international academic conference in machine learning. Along with NeurIPS and ICLR, it is...

much more flexible structure to exist among those alternatives. Supervised learning algorithms search through a hypothesis space to find a suitable hypothesis...

of outputs via an artificial neural network. Deep learning methods, often using supervised learning with labeled datasets, have been shown to solve tasks...

The International Conference on Learning Representations (ICLR) is a machine learning conference typically held in late April or early May each year....

classification and regression algorithms. Hence, it is prevalent in supervised learning for converting weak learners to strong learners. The concept of boosting...

A self-organizing map (SOM) or self-organizing feature map (SOFM) is an unsupervised machine learning technique used to produce a low-dimensional (typically...

activation map use the same set of parameters that define the filter. Self-supervised learning has been adapted for use in convolutional layers by using sparse...

Learning to rank or machine-learned ranking (MLR) is the application of machine learning, typically supervised, semi-supervised or reinforcement learning...

radial basis networks, another class of supervised neural network models). In recent developments of deep learning the rectified linear unit (ReLU) is more...

are language models with many parameters, and are trained with self-supervised learning on a vast amount of text. The largest and most capable LLMs are...