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Found 6 papers, 2 papers with code
Towards a Better Evaluation of Out-of-Domain Generalization
In the pursuit of this objective, average measure has been employed as the prevalent measure for evaluating models and comparing algorithms in the existing DG studies.
A Differentiable Framework for End-to-End Learning of Hybrid Structured Compression
In this study, we develop a \textit{Differentiable Framework~(DF)} that can express filter selection, rank selection, and budget constraint into a single analytical formulation.
Towards a Rigorous Analysis of Mutual Information in Contrastive Learning
Contrastive learning has emerged as a cornerstone in recent achievements of unsupervised representation learning.
VNE: An Effective Method for Improving Deep Representation by Manipulating Eigenvalue Distribution
Since the introduction of deep learning, a wide scope of representation properties, such as decorrelation, whitening, disentanglement, rank, isotropy, and mutual information, have been studied to improve the quality of representation.
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Domain Generalization
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Meta-Learning with a Geometry-Adaptive Preconditioner
In this study, we propose Geometry-Adaptive Preconditioned gradient descent (GAP) that can overcome the limitations in MAML; GAP can efficiently meta-learn a preconditioner that is dependent on task-specific parameters, and its preconditioner can be shown to be a Riemannian metric.
A Highly Effective Low-Rank Compression of Deep Neural Networks with Modified Beam-Search and Modified Stable Rank
The resulting BSR (Beam-search and Stable Rank) algorithm requires only a single hyperparameter to be tuned for the desired compression ratio.
