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Found 5 papers, 1 papers with code
Variational Information Pursuit with Large Language and Multimodal Models for Interpretable Predictions
Variational Information Pursuit (V-IP) is a framework for making interpretable predictions by design by sequentially selecting a short chain of task-relevant, user-defined and interpretable queries about the data that are most informative for the task.
Image Clustering via the Principle of Rate Reduction in the Age of Pretrained Models
In this paper, we propose a novel image clustering pipeline that leverages the powerful feature representation of large pre-trained models such as CLIP and cluster images effectively and efficiently at scale.
Implicit Bias of Projected Subgradient Method Gives Provable Robust Recovery of Subspaces of Unknown Codimension
In particular, we show that 1) all of the problem instances will converge to a vector in the null space of the subspace and 2) the ensemble of problem instance solutions will be sufficiently diverse to fully span the null space of the subspace (and thus reveal the true codimension of the subspace) even when the true subspace dimension is unknown.
Quantifying Task Complexity Through Generalized Information Measures
In this paper, we propose to measure the complexity of a learning task by the minimum expected number of questions that need to be answered to solve the task.
Implicit Acceleration of Gradient Flow in Overparameterized Linear Models
More precisely, gradient flow preserves the difference of the Gramian~matrices of the input and output weights and we show that the amount of acceleration depends on both the magnitude of that difference (which is fixed at initialization) and the spectrum of the data.
