Meta-learning is widely used in few-shot classification and function regression due to its
ability to quickly adapt to unseen tasks. However, it has not yet been well explored on …

The standard approaches to neural network implementation yield powerful function
approximation capabilities but are limited in their abilities to learn meta representations and …

Uncertainty estimation is an important research area to make deep neural networks (DNNs)
more trustworthy. While extensive research on uncertainty estimation has been conducted …

Grasping inhomogeneous objects in real-world applications remains a challenging task due
to the unknown physical properties such as mass distribution and coefficient of friction. In …

Accurate interpolation algorithms are highly desired in various theoretical and engineering
scenarios. Unlike the traditional numerical algorithms that have exact zero-residual …

Interpolation for scattered data is a classical problem in numerical analysis, with a long
history of theoretical and practical contributions. Recent advances have utilized deep neural …

We present a novel meta-learning approach for 6D pose estimation on unknown objects. In
contrast to" instance-level" pose estimation methods, our algorithm learns object …

Abstract Neural Processes (NPs) families encode distributions over functions to a latent
representation given a set of context data, and decode posterior mean and variance at …

Since the advent of deep learning and deep reinforcement learning, there have been
numerous empirical successes that employ some sort of artificial neural network for a given …

Neural Processes (NPs) families encode distributions over functions to a latent
representation given a set of context data, and decode posterior mean and variance at …