It inherits metric- based meta-learning methods, and only introduces a small overhead to an encoder for parameterizing its distributions, thus is efficient for ...
Feb 1, 2023 · This paper proposes a Hierarchical Gaussian Mixture method to parametrize the generation process of tasks in meta-learning. The proposed model ...
HTGM extends the widely used empirical process of sampling tasks to a theoretical model, which learns task embeddings, fits the mixture distribution of tasks, ...
May 30, 2024 · In this paper, we demonstrate these two challenges can be solved jointly by modeling the density of task instances. We develop a metatraining ...
Nov 14, 2023 · HTGM extends the widely used empirical process of sampling tasks to a theoretical model, which learns task embeddings, fits the mixture ...
GENERATIVE MODEL FOR ROBUST META-LEARNING ... Meta-learning enables quick adaptation of machine learning models to new tasks ... new Hierarchical Gaussian Mixture ...
Haifeng Chen · Hierarchical Gaussian Mixture based Task Generative Model for Robu… · InfoGCL: Information-Aware Graph Contrastive Learning · Parameterized ...
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Hierarchical Gaussian Mixture based Task Generative Model for Robust Meta-Learning Yizhou Zhang☆, Jingchao Ni, Wei Cheng, Zhengzhang Chen, Liang Tong ...
We develop a meta training framework underlain by a novel Hierarchical Gaussian Mixture based Task Generative Model (HTGM). HTGM extends the widely used ...
Hierarchical Gaussian Mixture based Task Generative Model for Robust Meta-Learning ... Hierarchical Deep Generative Models for Multi-Rate Multivariate Time Series