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Found 8 papers, 4 papers with code
Leveraging Low-Rank and Sparse Recurrent Connectivity for Robust Closed-Loop Control
In this work, we explore the application of recurrent neural networks to tasks of this nature and understand how a parameterization of their recurrent connectivity influences robustness in closed-loop settings.
On the Size and Approximation Error of Distilled Sets
Despite significant empirical progress in recent years, there is little understanding of the theoretical limitations/guarantees of dataset distillation, specifically, what excess risk is achieved by distillation compared to the original dataset, and how large are distilled datasets?
Dataset Distillation with Convexified Implicit Gradients
We propose a new dataset distillation algorithm using reparameterization and convexification of implicit gradients (RCIG), that substantially improves the state-of-the-art.
Understanding Reconstruction Attacks with the Neural Tangent Kernel and Dataset Distillation
We show that both theoretically and empirically, reconstructed images tend to "outliers" in the dataset, and that these reconstruction attacks can be used for \textit{dataset distillation}, that is, we can retrain on reconstructed images and obtain high predictive accuracy.
Efficient Dataset Distillation Using Random Feature Approximation
Dataset distillation compresses large datasets into smaller synthetic coresets which retain performance with the aim of reducing the storage and computational burden of processing the entire dataset.
Evolution of Neural Tangent Kernels under Benign and Adversarial Training
In this limit, the kernel is frozen, and the underlying feature map is fixed.
Generalized Variational Continual Learning
One strand of research has used probabilistic regularization for continual learning, with two of the main approaches in this vein being Online Elastic Weight Consolidation (Online EWC) and Variational Continual Learning (VCL).
Combining Variational Continual Learning with FiLM Layers
The standard architecture for continual learning is a multi-headed neural network, which has shared body parameters and task-specific heads.
