Meta learning aims at learning a model that can quickly adapt to unseen tasks. Widely used meta learning methods include model agnostic meta learning (MAML), implicit MAML, Bayesian MAML. Thanks to its ability of modeling uncertainty, Bayesian MAML often has advantageous empirical performance. Howeve...
This repository contains implementations of the paper, Bayesian Model-Agnostic Meta-Learning (Jaesik Yoon and Taesup Kim et al., NuerIPS 2018). It includes code for running the sinusoid regression task described in the paper. To comparison with MAML and Ensemble MAML, we implemented emaml_main...
研究点推荐 Bayesian Structured Exploration Deep reinforcement learning BSE-MAML Model Agnostic Meta-Reinforcement Learning BSE-MAML) Bayesian structure exploration 站内活动 0关于我们 百度学术集成海量学术资源,融合人工...
The main goal of Few-Shot learning algorithms is to enable learning from small amounts of data. One of the most popular and elegant Few-Shot learning approaches is Model-Agnostic Meta-Learning (MAML). The main idea behind this method is to learn the shared universal weights of a meta-model...
A model-agnostic framework to enhance knowledge graph-based drug combination prediction with drug–drug interaction data and supervised contrastive learning. Brief Bioinform. 2023;24(5):bbab285. Article Google Scholar Liu H, Zhang W, Nie L, et al. Predicting effective drug combinations using ...
†: Make use of additional unlabeled data for semi-supervised learning or transductive inference. Gray: Use fixed pre-trained backbones. Model Backbone 1-shot 5-shot MAML [liu2018transductive] Conv-4 51.67 ± 1.81% 70.30 ± 1.75% MetaSSL† [ren2018meta] Conv-4 52.39 ± 0.44% 70.25 ±...
Because Bayesian statistical methods can be applied to any data, regardless of the type of cognitive model (Bayesian or otherwise) that motivated the data collection, Bayesian methods for data analysis will continue to be appropriate even if Bayesian models of mind lose their appeal. View article ...
Harnesses the power of PyTorch, including auto-differentiation, native support for highly parallelized modern hardware (e.g. GPUs) using device-agnostic code, and a dynamic computation graph. Supports Monte Carlo-based acquisition functions via thereparameterization trick, which makes it straightforward ...
The sparse prior helps the model to learn a given task in the most compact set of neurons. This allows far greater control over the process of learning, and forget- ting. It particularly enables preserving of resources for fu- ture learning, and alleviates...
video-content and human consensus. Shang et al.10developed a novel content-agnostic scheme with the video comments. Different from existing solutions, this approach does not depend on the video content and its meta-data such as title or thumbnail. Jain et al.11proposed a novel model to detect...