Abstract: Transfer learning in robotics aims to transfer knowledge across different robot agents or tasks. Current methods in trajectory tracking problems leverage ...

Abstract: Multi-task learning constitutes the prevalent paradigm in numerous vision applications that cast an eye on runtime efficiency. At present however, deep multi-task networks are limited in ...

Welcome to the Zero to Mastery Learn PyTorch for Deep Learning course, the second best place to learn PyTorch on the internet (the first being the PyTorch documentation). 00 - PyTorch Fundamentals ...

Most robot headlines follow a familiar script: a machine masters one narrow trick in a controlled lab, then comes the bold promise that everything is about to change. I usually tune those stories out.

Learn how Network in Network (NiN) architectures work and how to implement them using PyTorch. This tutorial covers the concept, benefits, and step-by-step coding examples to help you build better ...

Learn about DenseNet, one of the most powerful deep learning architectures, in this beginner-friendly tutorial. Understand its structure, advantages, and how it’s used in real-world AI applications.

High-dimensional data often contain noisy and redundant features, posing challenges for accurate and efficient feature selection. To address this, a dynamic multitask learning framework is proposed, ...

Article subjects are automatically applied from the ACS Subject Taxonomy and describe the scientific concepts and themes of the article. A few public databases provide biological activity data for ...

Multi-task Reinforcement Learning (MTRL) has emerged as a critical trainingparadigm for applying reinforcement learning (RL) to a set of complex real-worldrobotic tasks, which demands a generalizable ...