6 papers · 1 filter
Eigenfunction Extraction for Ordered Representation Learning
Burak Varıcı, Che-Ping Tsai, Ritabrata Ray +2
Recent advances in representation learning reveal that widely used objectives, such as contrastive and non-contrastive, implicitly perform spectral decomposition of a contextual ke…
Contextures: Representations from Contexts
Runtian Zhai, Kai Yang, Che-Ping Tsai +3
Despite the empirical success of foundation models, we do not have a systematic characterization of the representations that these models learn. In this paper, we establish the con…
Sample based Explanations via Generalized Representers
Che-Ping Tsai, Chih-Kuan Yeh, Pradeep Ravikumar
We propose a general class of sample based explanations of machine learning models, which we term generalized representers. To measure the effect of a training sample on a model's…
Representer Point Selection for Explaining Regularized High-dimensional Models
Che-Ping Tsai, Jiong Zhang, Eli Chien +3
We introduce a novel class of sample-based explanations we term high-dimensional representers, that can be used to explain the predictions of a regularized high-dimensional model i…
Order-free Learning Alleviating Exposure Bias in Multi-label Classification
Che-Ping Tsai, Hung-Yi Lee
Multi-label classification (MLC) assigns multiple labels to each sample. Prior studies show that MLC can be transformed to a sequence prediction problem with a recurrent neural net…
Adversarial Learning of Label Dependency: A Novel Framework for Multi-class Classification
Che-Ping Tsai, Hung-Yi Lee
Recent work has shown that exploiting relations between labels improves the performance of multi-label classification. We propose a novel framework based on generative adversarial…