miscellaneous-5

Asset Management

3260 papers • 126 benchmarks • 313 datasets

Asset management in ML is a discipline that offers engineers the necessary management support for processes and operations on different types of ML assets.

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Benchmarks

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Libraries

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Use these libraries to find asset-management-5 models and implementations

firmai/machine-learning-asset-manag…

2 papers 1,632

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Most implemented papers

Semi-supervised Anomaly Detection using AutoEncoders

Manpreet Singh Minhas, J. Zelek•Sun Jan 05 2020

A convolutional auto-encoder architecture for anomaly detection that is trained only on the defect-free (normal) instances and learnt to detect the actual shape of the defects even though no defected images were used during the training.

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Machine Learning in Asset Management—Part 1: Portfolio Construction—Trading Strategies

Derek Snow•Mon Jul 15 2019

This article focuses on portfolio construction using machine learning, highlighting nine different trading varieties each making use of a reinforcement-, supervised-, or unsupervised-learning framework or a combination of these learning frameworks.

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Reap the Harvest on Blockchain: A Survey of Yield Farming Protocols

Jiahua Xu, Yebo Feng•Sat Oct 08 2022

This systematic survey presents yield farming protocols as an aggregation-layer constituent of the wider DeFi ecosystem that interact with primitive-layer protocols such as decentralized exchanges (DEXs) and loanable funds (PLFs) protocol for loanable Funds (PLF).

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SEBERTNets: Sequence Enhanced BERT Networks for Event Entity Extraction Tasks Oriented to the Finance Field

Congqing He, Xiangyu Zhu, Yuquan Le, Yuzhong Liu, Jianhong Yin•Sat Jan 20 2024

A novel model is proposed, Sequence Enhanced BERT Networks (SEBERTNets for short), which can inherit the advantages of the BERT, and while capturing sequence semantic information, and a multi-channel recall method to recall all the corresponding event entity effectively.

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An empirical study of challenges in machine learning asset management

Zhimin Zhao, Yihao Chen, Abdul Ali Bangash, Bram Adams, Ahmed E. Hassan•Sat Feb 24 2024

Critical areas within ML asset management that need further exploration are highlighted, particularly around prevalent macro-topics identified as pain points for ML practitioners, emphasizing the need for collaborative efforts between academia, industry, and the broader research community.

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