Below are the abstract, introduction, and conclusion of a computer science research paper. Please summarize the main contribution of the work in a single sentence. Your response should include the summary and no additional text.

Paper text:

The recent rise in popularity of few-shot learning algorithms has enabled models to quickly adapt to new tasks based on only a few training samples.
Previous few-shot learning works have mainly focused on classification and reinforcement learning. 

In this paper, we propose a few-shot meta-learning system that focuses exclusively on regression tasks.
Our model is based on the idea that the degree of freedom of the unknown function can be significantly reduced if it is represented as a linear combination of a set of appropriate basis functions.
This enables a few labelled samples to approximate the function.
We design a Feature Extractor network to encode basis functions for a task distribution, and a  Weights Generator to generate the weight vector for a novel task.
We show that our model outperforms the current state of the art meta-learning methods in various regression tasks.
Regression deals with the problem of learning a model relating a set of inputs to a set of outputs.
The learned model can be thought as function y = F (x) that will give a prediction y ∈ R dy given input x ∈ R dx where d y and d x are dimensions of the output and input respectively.
Typically, a regression model is trained on a large number of data points to be able to provide accurate predictions of new inputs.
Recently, there have been a surge in popularity on few-shot learning methods (Vinyals et al., 2016; BID7 BID4 .
Few-shot learning methods require only a few examples from each task to be able to quickly adapt and perform well on a new task.
The fewshot learning model in essence is learning to learn i.e. the model learns to quickly adapt itself to new tasks rather than just learning to give the correct prediction for a particular input sample.In this work, we propose a few shot learning model that targets few-shot regression tasks.
We evaluate our model on the sinusoidal regression tasks and compare our model's performance to several meta-learning algorithms.
We further introduce two more regression tasks, namely the 1D heat equation task modeled by partial differential equations and the 2D Gaussian distribution task.
We propose a few-shot meta learning system that focuses exclusively on regression tasks.
Our model is based on the idea of linear representation of basis functions.
We design a Feature extractor network to encode the basis functions for the entire task distribution.
We design a Weight generator network to generate the weights from the K training samples of a novel task drawn from the same task distribution.
We show that our model has competitive performance in in various few short regression tasks.
A TECHNICAL DETAILS
We propose a few-shot learning model that is tailored specifically for regression tasks