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Found 7 papers, 1 papers with code
Tight Continuous Relaxation of the Balanced k-Cut Problem
Spectral Clustering as a relaxation of the normalized/ratio cut has become one of the standard graph-based clustering methods.
Tight Continuous Relaxation of the Balanced $k$-Cut Problem
Spectral Clustering as a relaxation of the normalized/ratio cut has become one of the standard graph-based clustering methods.
It was the training data pruning too!
The large impact on the performance of the KDG model suggests that the pruning may be a useful pre-processing step in training other semantic parsers as well.
Did the Model Understand the Question?
Our strongest attacks drop the accuracy of a visual question answering model from $61. 1\%$ to $19\%$, and that of a tabular question answering model from $33. 5\%$ to $3. 3\%$.
K for the Price of 1: Parameter-efficient Multi-task and Transfer Learning
We introduce a novel method that enables parameter-efficient transfer and multi-task learning with deep neural networks.
K For The Price Of 1: Parameter Efficient Multi-task And Transfer Learning
We introduce a novel method that enables parameter-efficient transfer and multitask learning.
Asymmetric Multiresolution Matrix Factorization
Multiresolution Matrix Factorization (MMF) was recently introduced as an alternative to the dominant low-rank paradigm in order to capture structure in matrices at multiple different scales.
