Bayesian model-agnostic meta-learning
Due to the inherent model uncertainty, learning to infer Bayesian posterior from a few-shot
dataset is an important step towards robust meta-learning. In this paper, we propose a novel …
dataset is an important step towards robust meta-learning. In this paper, we propose a novel …
Bayesian model-agnostic meta-learning
J Yoon, T Kim, O Dia, S Kim, Y Bengio… - Proceedings of the 32nd …, 2018 - dl.acm.org
Due to the inherent model uncertainty, learning to infer Bayesian posterior from a few-shot
dataset is an important step towards robust meta-learning. In this paper, we propose a novel …
dataset is an important step towards robust meta-learning. In this paper, we propose a novel …
Bayesian model-agnostic meta-learning
J Yoon, T Kim, O Dia, S Kim… - Advances in Neural …, 2018 - researchwithrutgers.com
Due to the inherent model uncertainty, learning to infer Bayesian posterior from a few-shot
dataset is an important step towards robust meta-learning. In this paper, we propose a novel …
dataset is an important step towards robust meta-learning. In this paper, we propose a novel …
B-Small: A Bayesian Neural Network Approach to Sparse Model-Agnostic Meta-Learning
A Madan, R Prasad - ICASSP 2021-2021 IEEE International …, 2021 - ieeexplore.ieee.org
There is a growing interest in the learning-to-learn paradigm, also known as meta-learning,
where models infer on new tasks using a few training examples. Recently, meta-learning …
where models infer on new tasks using a few training examples. Recently, meta-learning …
[PDF][PDF] Bayesian Model-Agnostic Meta-Learning
J Yoon, T Kim, O Dia, S Kim, Y Bengio, S Ahn - papers.neurips.cc
Due to the inherent model uncertainty, learning to infer Bayesian posterior from a few-shot
dataset is an important step towards robust meta-learning. In this paper, we propose a novel …
dataset is an important step towards robust meta-learning. In this paper, we propose a novel …
B-SMALL: A Bayesian Neural Network approach to Sparse Model-Agnostic Meta-Learning
A Madan, R Prasad - arXiv preprint arXiv:2101.00203, 2021 - arxiv.org
There is a growing interest in the learning-to-learn paradigm, also known as meta-learning,
where models infer on new tasks using a few training examples. Recently, meta-learning …
where models infer on new tasks using a few training examples. Recently, meta-learning …
Bayesian Model-Agnostic Meta-Learning
J Yoon, T Kim, O Dia, S Kim… - Advances in Neural …, 2018 - proceedings.neurips.cc
Due to the inherent model uncertainty, learning to infer Bayesian posterior from a few-shot
dataset is an important step towards robust meta-learning. In this paper, we propose a novel …
dataset is an important step towards robust meta-learning. In this paper, we propose a novel …
B-SMALL: A Bayesian Neural Network approach to Sparse Model-Agnostic Meta-Learning
A Madan, R Prasad - arXiv e-prints, 2021 - ui.adsabs.harvard.edu
There is a growing interest in the learning-to-learn paradigm, also known as meta-learning,
where models infer on new tasks using a few training examples. Recently, meta-learning …
where models infer on new tasks using a few training examples. Recently, meta-learning …