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Found 8 papers, 1 papers with code
Spike-NeRF: Neural Radiance Field Based On Spike Camera
As a neuromorphic sensor with high temporal resolution, spike cameras offer notable advantages over traditional cameras in high-speed vision applications such as high-speed optical estimation, depth estimation, and object tracking.
AI Agent as Urban Planner: Steering Stakeholder Dynamics in Urban Planning via Consensus-based Multi-Agent Reinforcement Learning
By integrating Multi-Agent Reinforcement Learning, our framework ensures that participatory urban planning decisions are more dynamic and adaptive to evolving community needs and provides a robust platform for automating complex real-world urban planning processes.
Exploring Transformer's potential on automatic piano transcription
Most recent research about automatic music transcription (AMT) uses convolutional neural networks and recurrent neural networks to model the mapping from music signals to symbolic notation.
SceneRec: Scene-Based Graph Neural Networks for Recommender Systems
Collaborative filtering has been largely used to advance modern recommender systems to predict user preference.
Deep Minimax Probability Machine
In a worst-case scenario, MPM tries to minimize an upper bound of misclassification probabilities, considering the global information (i. e., mean and covariance information of each class).
DART: Domain-Adversarial Residual-Transfer Networks for Unsupervised Cross-Domain Image Classification
In this paper, we propose a new unsupervised domain adaptation method named Domain-Adversarial Residual-Transfer (DART) learning of Deep Neural Networks to tackle cross-domain image classification tasks.
Constructing Ontology-Based Cancer Treatment Decision Support System with Case-Based Reasoning
Disease Ontology (DO) that pertains to cancer's clinical stages and their corresponding information components is utilized to improve the reasoning ability of a decision support system (DSS).
Streaming Graph Neural Networks
Current graph neural network models cannot utilize the dynamic information in dynamic graphs.
