Few-Shot Learning refers to the problem of learning the underlying pattern in the data just
from a few training samples. Requiring a large number of data samples, many deep learning …

The field of meta-learning, or learning-to-learn, has seen a dramatic rise in interest in recent
years. Contrary to conventional approaches to AI where tasks are solved from scratch using …

In few-shot learning scenarios, the challenge is to generalize and perform well on new
unseen examples when only very few labeled examples are available for each task. Model …

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 …

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 meta-learning technique for offline discovery of physics-informed neural
network (PINN) loss functions. We extend earlier works on meta-learning, and develop a …

Recently, different machine learning methods have been introduced to tackle the
challenging few-shot learning scenario that is, learning from a small labeled dataset related …

Inspired by optimization techniques, we propose a novel meta-learning algorithm with
gradient modulation to encourage fast-adaptation of neural networks in the absence of …

Progress in machine learning (ML) stems from a combination of data availability,
computational resources, and an appropriate encoding of inductive biases. Useful biases …

What makes free-hand sketches appealing for humans lies with its capability as a universal
tool to depict the visual world. Such flexibility at human ease, however, introduces abstract …