What's in the laundromat? Mapping and characterising offshore owned domestic property in London

arXiv:2207.10931 · cs.LG · Submitted 2022-07-22 · Read on arXiv

cs.LG

Submitted: 2022-07-22

Updated: 2022-07-22

Code: https://github.com/JonnoB/empty_homes_london

Terminology

• Machine Learning — 9× in this paper
• F1 Score — 4× in this paper · explained in 4 episodes
• Output Area — 3× in this paper
• Accuracy — 2× in this paper · explained in 1 episode
• Benchmark — 2× in this paper · explained in 2 episodes
• Core — 2× in this paper · explained in 2 episodes
• Embeddings — 2× in this paper · explained in 2 episodes
• Robustness — 2× in this paper · explained in 6 episodes
• Transformer — 2× in this paper · explained in 2 episodes
• Active Learning — 1× in this paper · explained in 6 episodes
• Adam — 1× in this paper · explained in 2 episodes
• False Negatives — 1× in this paper · explained in 2 episodes
• Local Authority District — 1× in this paper
• Lower Super Output Area — 1× in this paper
• Machine Learning (ML) — 1× in this paper · explained in 2 episodes
• Matrix Completion — 1× in this paper · explained in 4 episodes
• Named Entity Recognition — 1× in this paper
• Named Entity Recognition (NER) — 1× in this paper · explained in 2 episodes
• Office for National Statistics — 1× in this paper
• Ontology — 1× in this paper · explained in 2 episodes
• Unique Property Reference Number — 1× in this paper

Sources

• Data Programming: Creating Large Training Sets, Quickly
• Named Entity Recognition without Labelled Data: A Weak Supervision Approach
• Training Complex Models with Multi-Task Weak Supervision
• Pervasive Label Errors in Test Sets Destabilize Machine Learning Benchmarks
• RoBERTa: A Robustly Optimized BERT Pretraining Approach
• Attention Is All You Need

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