Results 31 to 40 of about 7,740,063 (200)

Memory-augmented meta-learning on meta-path for fast adaptation cold-start recommendation

Connection Science, 2022
Personalised recommendation is a difficult problem that has received a lot of attention to academia and industry. Because of the sparse user–item interaction, cold-start recommendation has been a particularly difficult problem.
Tianyuan Li, Xin Su, Wei Liu, Wei Liang, Meng-Yen Hsieh, Zhuhui Chen, XuChong Liu, Hong Zhang   +7 more
doaj   +1 more source

Meta-Learning to Cluster

CoRR, 2019
Clustering is one of the most fundamental and wide-spread techniques in exploratory data analysis. Yet, the basic approach to clustering has not really changed: a practitioner hand-picks a task-specific clustering loss to optimize and fit the given data to reveal the underlying cluster structure.
Yibo Jiang, Nakul Verma
openaire   +2 more sources

A Multi-Constraint Guidance and Maneuvering Penetration Strategy via Meta Deep Reinforcement Learning

Drones, 2023
In response to the issue of UAV escape guidance, this study proposed a unified intelligent control strategy synthesizing optimal guidance and meta deep reinforcement learning (DRL).
Sibo Zhao, Jianwen Zhu, Weimin Bao, Xiaoping Li, Haifeng Sun   +4 more
doaj   +1 more source

Meta-Learning [PDF]

, 2019
Meta-learning, or learning to learn, is the science of systematically observing how different machine learning approaches perform on a wide range of learning tasks, and then learning from this experience, or meta-data, to learn new tasks much faster than otherwise possible.
openaire   +2 more sources

Meta-learning of Textual Representations [PDF]

, 2020
Recent progress in AutoML has lead to state-of-the-art methods (e.g., AutoSKLearn) that can be readily used by non-experts to approach any supervised learning problem. Whereas these methods are quite effective, they are still limited in the sense that they work for tabular (matrix formatted) data only. This paper describes one step forward in trying to
Jorge G. Madrid, Hugo Jair Escalante, Eduardo F. Morales 0001   +2 more
openaire   +3 more sources

Learn to Few-Shot Segment Remote Sensing Images from Irrelevant Data

Remote Sensing, 2023
Few-shot semantic segmentation (FSS) is committed to segmenting new classes with only a few labels. Generally, FSS assumes that base classes and novel classes belong to the same domain, which limits FSS’s application in a wide range of areas.
Qingwei Sun, Jiangang Chao, Wanhong Lin, Zhenying Xu, Wei Chen, Ning He   +5 more
doaj   +1 more source

MANFAAT METODE INQUIRY TERHADAP PENGAMATAN HASIL PRAKTIKUM LAS LISTRIK DI LABORATORIUM MANUFAKTUR POLITEKNIK META INDUSTRI CIKARANG [PDF]

, 2022
The purpose of this study was to determine student achievement in the manufacturing process subject before giving treatment, to determine student achievement in the manufacturing process course after treatment, to determine whether there was an effect of
Rivandi, Merdieco, Widodo, Agus, Merdieco Rivandi, Agus Widodo   +3 more
core   +1 more source

Deep Meta-Learning Energy-Aware Path Planner for Unmanned Ground Vehicles in Unknown Terrains

IEEE Access, 2022
This paper presents an adaptive energy-aware prediction and planning framework for vehicles navigating over terrains with varying and unknown properties.
Marco Visca, Roger Powell, Yang Gao, Saber Fallah   +3 more
doaj   +1 more source

Meta-Learning

, 2013
In: Encyclopedia of Systems Biology, W. Dubitzky, O. Wolkenhauer, K-H Cho, H.
  +5 more sources

Meta Learning for Causal Direction

Proceedings of the AAAI Conference on Artificial Intelligence, 2021
The inaccessibility of controlled randomized trials due to inherent constraints in many fields of science has been a fundamental issue in causal inference. In this paper, we focus on distinguishing the cause from effect in the bivariate setting under limited observational data.
Ton, J-F, Sejdinovic, D, Fukumizu, K
openaire   +5 more sources