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Found 6 papers, 2 papers with code
Hierarchical Joint Graph Learning and Multivariate Time Series Forecasting
Multivariate time series is prevalent in many scientific and industrial domains.
Can We Utilize Pre-trained Language Models within Causal Discovery Algorithms?
Scaling laws have allowed Pre-trained Language Models (PLMs) into the field of causal reasoning.
Neural Volumetric Reconstruction for Coherent Synthetic Aperture Sonar
However, image formation is typically under-constrained due to a limited number of measurements and bandlimited hardware, which limits the capabilities of existing reconstruction methods.
IBL-NeRF: Image-Based Lighting Formulation of Neural Radiance Fields
However, they are limited to representing isolated objects with a shared environment lighting, and suffer from computational burden to aggregate rays with Monte Carlo integration.
Classical-to-quantum convolutional neural network transfer learning
We perform numerical simulations of QCNN models with various sets of quantum convolution and pooling operations for MNIST data classification under transfer learning, in which a classical CNN is trained with Fashion-MNIST data.
Optimizing Large-Scale Fleet Management on a Road Network using Multi-Agent Deep Reinforcement Learning with Graph Neural Network
We propose a novel approach to optimize fleet management by combining multi-agent reinforcement learning with graph neural network.
