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06/30/2023
Class-Incremental Learning using Diffusion Model for Distillation and Replay
Class-incremental learning aims to learn new classes in an incremental f...
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06/30/2022
Modularity Optimization as a Training Criterion for Graph Neural Networks
Graph convolution is a recent scalable method for performing deep featur...
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12/11/2021
Leaping Through Time with Gradient-based Adaptation for Recommendation
Modern recommender systems are required to adapt to the change in user p...
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11/12/2021
Simplifying approach to Node Classification in Graph Neural Networks
Graph Neural Networks have become one of the indispensable tools to lear...
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10/18/2021
Natural Image Reconstruction from fMRI using Deep Learning: A Survey
With the advent of brain imaging techniques and machine learning tools, ...
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09/09/2021
Cross-lingual Transfer for Text Classification with Dictionary-based Heterogeneous Graph
In cross-lingual text classification, it is required that task-specific ...
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05/17/2021
Improving Graph Neural Networks with Simple Architecture Design
Graph Neural Networks have emerged as a useful tool to learn on the data...
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03/23/2021
Balanced Softmax Cross-Entropy for Incremental Learning
Deep neural networks are prone to catastrophic forgetting when increment...
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11/22/2020
Stacked Graph Filter
We study Graph Convolutional Networks (GCN) from the graph signal proces...
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07/22/2020
MetAL: Active Semi-Supervised Learning on Graphs via Meta Learning
The objective of active learning (AL) is to train classification models ...
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07/09/2020
Graph Convolutional Networks for Graphs Containing Missing Features
Graph Convolutional Network (GCN) has experienced great success in graph...
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05/05/2019
Learning Graph Neural Networks with Noisy Labels
We study the robustness to symmetric label noise of GNNs training proced...
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04/19/2018