Federated Learning

Federated Learning

Federated learning is a machine learning technique that trains a shared model across multiple decentralized devices or servers holding local data, without the…

One-Shot Learning

One-shot learning is a machine learning approach in which a model learns to correctly classify or recognize new categories from only a single labeled example p…

Semi-Supervised Learning

Semi-supervised learning is a machine learning approach that trains models using a combination of a small amount of labeled data and a much larger amount of un…

Multi-Task Learning

Multi-task learning is a machine learning technique in which a single model is trained simultaneously on multiple related tasks, sharing internal representatio…

Quantization (ML)

Quantization is a model compression technique that reduces the numerical precision used to represent a neural network's weights and activations (e.g., from 32-…

Mixed Precision Training

Mixed precision training is a technique for training neural networks that uses lower-precision numerical formats (such as 16-bit floating point) for most compu…