Machine learning has been highly successful in data-intensive applications but is often
hampered when the data set is small. Recently, Few-shot Learning (FSL) is proposed to …

Three years ago, we released the Omniglot dataset for one-shot learning, along with five
challenge tasks and a computational model that addresses these tasks. The model was not …

Neural Processes (NPs)(Garnelo et al 2018a; b) approach regression by learning to map a
context set of observed input-output pairs to a distribution over regression functions. Each …

A fundamental assumption of most machine learning algorithms is that the training and test
data are drawn from the same underlying distribution. However, this assumption is violated …

We propose a variational Bayesian framework for enhancing few-shot learning performance.
This idea is motivated by the fact that single point based metric learning approaches are …

Denoising diffusion probabilistic models (DDPM) are powerful hierarchical latent variable
models with remarkable sample generation quality and training stability. These properties …

Learning to classify unseen class samples at test time is popularly referred to as zero-shot
learning (ZSL). If test samples can be from training (seen) as well as unseen classes, it is a …

Neural Ordinary Differential Equations (NODEs) use a neural network to model the
instantaneous rate of change in the state of a system. However, despite their apparent …

Unsupervised learning aims to learn meaningful representations from unlabeled data which
can captures its intrinsic structure, that can be transferred to downstream tasks. Meta …

Meta-learning, or learning to learn, is a machine learning approach that utilizes prior
learning experiences to expedite the learning process on unseen tasks. As a data-driven …