Search Results for author:
Found 6 papers, 4 papers with code
Topological Data Analysis Guided Segment Anything Model Prompt Optimization for Zero-Shot Segmentation in Biological Imaging
Emerging foundation models in machine learning are models trained on vast amounts of data that have been shown to generalize well to new tasks.
Symbolic Regression via Deep Reinforcement Learning Enhanced Genetic Programming Seeding
Symbolic regression is the process of identifying mathematical expressions that fit observed output from a black-box process.
Symbolic Regression via Neural-Guided Genetic Programming Population Seeding
Symbolic regression is the process of identifying mathematical expressions that fit observed output from a black-box process.
Improving exploration in policy gradient search: Application to symbolic optimization
Many machine learning strategies designed to automate mathematical tasks leverage neural networks to search large combinatorial spaces of mathematical symbols.
Hybrid Information-driven Multi-agent Reinforcement Learning
Information theoretic sensor management approaches are an ideal solution to state estimation problems when considering the optimal control of multi-agent systems, however they are too computationally intensive for large state spaces, especially when considering the limited computational resources typical of large-scale distributed multi-agent systems.
Increasing performance of electric vehicles in ride-hailing services using deep reinforcement learning
New forms of on-demand transportation such as ride-hailing and connected autonomous vehicles are proliferating, yet are a challenging use case for electric vehicles (EV).
