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Found 10 papers, 4 papers with code
Offline Reinforcement Learning with Behavioral Supervisor Tuning
Offline reinforcement learning (RL) algorithms are applied to learn performant, well-generalizing policies when provided with a static dataset of interactions.
Market Misconduct in Decentralized Finance (DeFi): Analysis, Regulatory Challenges and Policy Implications
The rise of blockchain and Decentralized Finance (DeFi) underscores this intertwined evolution of technology and finance.
Leverage Staking with Liquid Staking Derivatives (LSDs): Opportunities and Risks
Lido, the leading Liquid Staking Derivative (LSD) provider on Ethereum, allows users to stake an arbitrary amount of ETH to receive stETH, which can be integrated with Decentralized Finance (DeFi) protocols such as Aave.
zkFL: Zero-Knowledge Proof-based Gradient Aggregation for Federated Learning
Federated Learning (FL) is a machine learning paradigm, which enables multiple and decentralized clients to collaboratively train a model under the orchestration of a central aggregator.
Defending Against Poisoning Attacks in Federated Learning with Blockchain
In the era of deep learning, federated learning (FL) presents a promising approach that allows multi-institutional data owners, or clients, to collaboratively train machine learning models without compromising data privacy.
A Scalable Inference Method For Large Dynamic Economic Systems
The nature of available economic data has changed fundamentally in the last decade due to the economy's digitisation.
Neural NILM: Deep Neural Networks Applied to Energy Disaggregation
Energy disaggregation estimates appliance-by-appliance electricity consumption from a single meter that measures the whole home's electricity demand.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+4
Demo Abstract: NILMTK v0.2: A Non-intrusive Load Monitoring Toolkit for Large Scale Data Sets
In this demonstration, we present an open source toolkit for evaluating non-intrusive load monitoring research; a field which aims to disaggregate a household's total electricity consumption into individual appliances.
Other Computer Science
NILMTK: An Open Source Toolkit for Non-intrusive Load Monitoring
We demonstrate the range of reproducible analyses which are made possible by our toolkit, including the analysis of six publicly available data sets and the evaluation of both benchmark disaggregation algorithms across such data sets.
Applications
Metadata for Energy Disaggregation
Energy disaggregation is the process of estimating the energy consumed by individual electrical appliances given only a time series of the whole-home power demand.
Databases H.3
