Robotics & Perception/Multi-task Learning & Meta-learning
[CS330] 03. Supervised solution of Meta-learning problem: Black-Box vs. Optimization-based vs. Non-Parametric
The Meta-Learning Problem Given data from $\mathcal{T}_1, \cdots, \mathcal{T}_n$, quickly solve new task $\mathcal{T}_\textrm{test}$. Assume that meta-training tasks and meta-test task drawn i.i.d. from same task distribution. $\mathcal{T}_1, \cdots, \mathcal{T}_n \sim p(\mathcal{T}), \mathcal{T}_j \sim p(\mathcal{T})$ For example, the task can be: a robot performing different tasks or giving fe..
[CS330] 02. Multi-Task Learning & Transfer learning Basics
What is "Task"? More formally, a task can be described as this format: $\mathcal{T}_i \equiv \{p_i(\textbf{x}), p_i (\textbf{y|x}), \mathcal{L}_i\}$, based on data generating distribution. Multi-task learning: Learn $\mathcal{T}_1, \mathcal{T}_2, \cdots, \mathcal{T}_T$ at once Transfer learning: Solve target task $\mathcal{T}_b$ after solving source task $\mathcal{T}_a$ by transferring knowledge..
[CS330] 01. Course Introduction
Their point of view of why multi-task learning and meta-learning are important Robots can teach us things about intelligence. Faced with the real world Must generalize across tasks, objects, environments, etc Need some common sense understanding to do well Supervision can't be taken for granted Specialists vs. Generalists Specialist: Learn one task in one environment, starting from scratch using..
[CS330 Chelsea Finn] Deep Multi-task learning and Meta-learning Contents
Goal: Check a higher version of the perception for robotics Contents Course introduction & start of multi-task learning 43m Supervised multi-task learning, transfer learning 1h 19m Meta-learning problem statement, black-box meta-learning 1h 18m Optimization-based meta-learning 1h 18m Few-shot learning via metric learning 1h 25m Advanced meta-learning topics 1h 28m Bayesian meta-learning 1h 27m R..