Few-shot Regression

7 papers with code • 0 benchmarks • 0 datasets

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Greatest papers with code

Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks

cbfinn/maml ICML 2017

We propose an algorithm for meta-learning that is model-agnostic, in the sense that it is compatible with any model trained with gradient descent and applicable to a variety of different learning problems, including classification, regression, and reinforcement learning.

Few-Shot Image Classification Few-shot Regression +2

Bayesian Meta-Learning for the Few-Shot Setting via Deep Kernels

BayesWatch/deep-kernel-transfer NeurIPS 2020

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 to a specific task.

Bayesian Inference Domain Adaptation +3

Learning To Count Everything

cvlab-stonybrook/LearningToCountEverything 16 Apr 2021

We also present a novel adaptation strategy to adapt our network to any novel visual category at test time, using only a few exemplar objects from the novel category.

Few-shot Regression

Few-Shot Representation Learning for Out-Of-Vocabulary Words

acbull/HiCE ACL 2019

Existing approaches for learning word embeddings often assume there are sufficient occurrences for each word in the corpus, such that the representation of words can be accurately estimated from their contexts.

Few-shot Regression Learning Word Embeddings

A Closer Look at the Training Strategy for Modern Meta-Learning

jiaxinchen666/meta-theory NeurIPS 2020

The support/query (S/Q) episodic training strategy has been widely used in modern meta-learning algorithms and is believed to improve their generalization ability to test environments.

Few-Shot Learning Few-shot Regression

Learning to Learn Kernels with Variational Random Features

Yingjun-Du/MetaVRF ICML 2020

We propose meta variational random features (MetaVRF) to learn adaptive kernels for the base-learner, which is developed in a latent variable model by treating the random feature basis as the latent variable.

Few-Shot Learning Few-shot Regression +1

Boosting on the shoulders of giants in quantum device calibration

a-wozniakowski/scikit-physlearn 13 May 2020

Here we introduce a new approach to machine learning that is able to leverage prior scientific discoveries in order to improve generalizability over a scientific model.

Few-Shot Learning Few-shot Regression +2