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Few-shot Regression

5 papers with code · Methodology
Subtask of Meta-Learning

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Greatest papers with code

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

ICML 2017 cbfinn/maml

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 ONE-SHOT LEARNING

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

11 Oct 2019BayesWatch/deep-kernel-transfer

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 FEW-SHOT IMAGE CLASSIFICATION FEW-SHOT REGRESSION GAUSSIAN PROCESSES

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

ACL 2019 acbull/HiCE

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

Learning to Learn Kernels with Variational Random Features

ICML 2020 Yingjun-Du/MetaVRF

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

Boosting on the shoulders of giants in quantum device calibration

13 May 2020a-wozniakowski/scikit-physlearn

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 MULTI-TARGET REGRESSION