5 papers · 1 filter
MPFlow: Learning Budgeted Max-Flow Optimization on the Lightning Network with Deep Graph Reinforcement Learning
Harrison Rush, Vincent Davis, Simone Antonelli +3
We address liquidity placement in the Bitcoin Lightning Network (LN): given a fixed budget, which channels should a node open to maximize its routing capacity? We cast this as a bu…
Test-Time Training Undermines Safety Guardrails
Simone Antonelli, Sadegh Akhondzadeh, Aleksandar Bojchevski
Test-Time Training (TTT) is an emerging paradigm that enables models to adapt their parameters during inference, improving performance on tasks such as few-shot learning, retrieval…
TOAST: Transformer Optimization using Adaptive and Simple Transformations
Irene Cannistraci, Simone Antonelli, Emanuele Palumbo +4
Foundation models achieve state-of-the-art performance across different tasks, but their size and computational demands raise concerns about accessibility and sustainability. Exist…
Predicting Channel Closures in the Lightning Network with Machine Learning
Simone Antonelli, Vincent Davis, Harrison Rush +4
The Lightning Network (LN) is a second-layer protocol for Bitcoin designed to enable fast and cost-efficient off-chain transactions. Channels in the LN can be closed either by mutu…
Metric Based Few-Shot Graph Classification
Donato Crisostomi, Simone Antonelli, Valentino Maiorca +3
Many modern deep-learning techniques do not work without enormous datasets. At the same time, several fields demand methods working in scarcity of data. This problem is even more c…