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Found 4 papers, 3 papers with code
Breaking Neural Network Scaling Laws with Modularity
Using recent theoretical progress in explaining neural network generalization, we investigate how the amount of training data required to generalize on a task varies with the intrinsic dimensionality of a task's input.
Towards Exact Computation of Inductive Bias
Our approach involves modeling the loss distribution of random hypotheses drawn from a hypothesis space to estimate the required inductive bias for a task relative to these hypotheses.
Resampling-free Particle Filters in High-dimensions
State estimation is crucial for the performance and safety of numerous robotic applications.
t-DGR: A Trajectory-Based Deep Generative Replay Method for Continual Learning in Decision Making
Deep generative replay has emerged as a promising approach for continual learning in decision-making tasks.
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