Continual Learning has been challenging, especially when dealing with
un...
Deep neural networks have shown remarkable performance when trained on
i...
We use the maximum a posteriori estimation principle for learning
repres...
We study Online Continual Learning with missing labels and propose SemiC...
Graph comparison deals with identifying similarities and dissimilarities...
This work proposes a supervised multi-channel time-series learning frame...
This work proposes an unsupervised fusion framework based on deep
convol...
This work addresses the problem of analyzing multi-channel time series d...
This work introduces a new unsupervised representation learning techniqu...
We propose a novel method for comparing non-aligned graphs of different
...
We present a novel framework based on optimal transport for the challeng...
We study the problem of fitting an ultrametric distance to a dissimilari...
In this paper, we propose a scalable algorithm for spectral embedding. T...
In this paper, we develop a novel second-order method for training
feed-...
In this paper, we propose a new optimization algorithm for sparse logist...