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Found 10 papers, 4 papers with code
An Evaluation of Bitcoin Address Classification based on Transaction History Summarization
Bitcoin is a cryptocurrency that features a distributed, decentralized and trustworthy mechanism, which has made Bitcoin a popular global transaction platform.
RelGAN: Multi-Domain Image-to-Image Translation via Relative Attributes
Our method is capable of modifying images by changing particular attributes of interest in a continuous manner while preserving the other attributes.
An Uncoupled Training Architecture for Large Graph Learning
Instead of directly processing the coupled nodes as GCNs, Node2Grids supports a more efficacious method in practice, mapping the coupled graph data into the independent grid-like data which can be fed into the efficient Convolutional Neural Network (CNN).
QR-MIX: Distributional Value Function Factorisation for Cooperative Multi-Agent Reinforcement Learning
Existing methods such as Value Decomposition Network (VDN) and QMIX estimate the value of long-term returns as a scalar that does not contain the information of randomness.
Rethinking the Implementation Matters in Cooperative Multi-Agent Reinforcement Learning
Multi-Agent Reinforcement Learning (MARL) has seen revolutionary breakthroughs with its successful application to multi-agent cooperative tasks such as computer games and robot swarms.
Revisiting the Monotonicity Constraint in Cooperative Multi-Agent Reinforcement Learning
QMIX, a popular MARL algorithm based on the monotonicity constraint, has been used as a baseline for the benchmark environments, such as Starcraft Multi-Agent Challenge (SMAC), Predator-Prey (PP).
Deep-Q Learning with Hybrid Quantum Neural Network on Solving Maze Problems
This study uses a trainable variational quantum circuit (VQC) on a gate-based quantum computing model to investigate the potential for quantum benefit in a model-free reinforcement learning problem.
BLIP-Adapter: Parameter-Efficient Transfer Learning for Mobile Screenshot Captioning
This study aims to explore efficient tuning methods for the screenshot captioning task.
Hybrid Quantum Neural Network in High-dimensional Data Classification
The research explores the potential of quantum deep learning models to address challenging machine learning problems that classical deep learning models find difficult to tackle.
Quantum Embedding with Transformer for High-dimensional Data
Quantum embedding with transformers is a novel and promising architecture for quantum machine learning to deliver exceptional capability on near-term devices or simulators.
