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09/13/2023
Domain-Aware Augmentations for Unsupervised Online General Continual Learning
Continual Learning has been challenging, especially when dealing with un...
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09/01/2023
New metrics for analyzing continual learners
Deep neural networks have shown remarkable performance when trained on i...
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06/06/2023
Learning Representations on the Unit Sphere: Application to Online Continual Learning
We use the maximum a posteriori estimation principle for learning repres...
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07/12/2022
Contrastive Learning for Online Semi-Supervised General Continual Learning
We study Online Continual Learning with missing labels and propose SemiC...
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09/09/2021
FGOT: Graph Distances based on Filters and Optimal Transport
Graph comparison deals with identifying similarities and dissimilarities...
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11/09/2020
SuperDeConFuse: A Supervised Deep Convolutional Transform based Fusion Framework for Financial Trading Systems
This work proposes a supervised multi-channel time-series learning frame...
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11/09/2020
DeConFuse : A Deep Convolutional Transform based Unsupervised Fusion Framework
This work proposes an unsupervised fusion framework based on deep convol...
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11/09/2020
ConFuse: Convolutional Transform Learning Fusion Framework For Multi-Channel Data Analysis
This work addresses the problem of analyzing multi-channel time series d...
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10/02/2020
Deep Convolutional Transform Learning – Extended version
This work introduces a new unsupervised representation learning techniqu...
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03/12/2020
Wasserstein-based Graph Alignment
We propose a novel method for comparing non-aligned graphs of different ...
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06/05/2019
GOT: An Optimal Transport framework for Graph comparison
We present a novel framework based on optimal transport for the challeng...
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05/25/2019
Ultrametric Fitting by Gradient Descent
We study the problem of fitting an ultrametric distance to a dissimilari...
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12/13/2018
Stochastic Gradient Descent for Spectral Embedding with Implicit Orthogonality Constraint
In this paper, we propose a scalable algorithm for spectral embedding. T...
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05/23/2018
A Two-Stage Subspace Trust Region Approach for Deep Neural Network Training
In this paper, we develop a novel second-order method for training feed-...
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12/25/2017
