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Found 6 papers, 3 papers with code
Learning Functional Transduction
Research in Machine Learning has polarized into two general regression approaches: Transductive methods derive estimates directly from available data but are usually problem unspecific.
Xplique: A Deep Learning Explainability Toolbox
Today's most advanced machine-learning models are hardly scrutable.
Meta-Reinforcement Learning with Self-Modifying Networks
Deep Reinforcement Learning has demonstrated the potential of neural networks tuned with gradient descent for solving complex tasks in well-delimited environments.
Look at the Variance! Efficient Black-box Explanations with Sobol-based Sensitivity Analysis
We describe a novel attribution method which is grounded in Sensitivity Analysis and uses Sobol indices.
KuraNet: Systems of Coupled Oscillators that Learn to Synchronize
Here, we present a single approach to both of these problems in the form of "KuraNet", a deep-learning-based system of coupled oscillators that can learn to synchronize across a distribution of disordered network conditions.
Go with the Flow: Adaptive Control for Neural ODEs
Despite their elegant formulation and lightweight memory cost, neural ordinary differential equations (NODEs) suffer from known representational limitations.
