Search Results for author: Hongjoon Ahn

Found 5 papers, 2 papers with code

Descent Steps of a Relation-Aware Energy Produce Heterogeneous Graph Neural Networks

1 code implementation22 Jun 2022 Hongjoon Ahn, Yongyi Yang, Quan Gan, Taesup Moon, David Wipf

Moreover, the complexity of this trade-off is compounded in the heterogeneous graph case due to the disparate heterophily relationships between nodes of different types.

Bilevel Optimization Classification +1

SS-IL: Separated Softmax for Incremental Learning

no code implementations ICCV 2021 Hongjoon Ahn, Jihwan Kwak, Subin Lim, Hyeonsu Bang, Hyojun Kim, Taesup Moon

To that end, we analyze that computing the softmax probabilities by combining the output scores for all old and new classes could be the main cause of the bias.

Class Incremental Learning Incremental Learning +1

Continual Learning with Node-Importance based Adaptive Group Sparse Regularization

no code implementations NeurIPS 2020 Sangwon Jung, Hongjoon Ahn, Sungmin Cha, Taesup Moon

We propose a novel regularization-based continual learning method, dubbed as Adaptive Group Sparsity based Continual Learning (AGS-CL), using two group sparsity-based penalties.

Continual Learning

Uncertainty-based Continual Learning with Adaptive Regularization

1 code implementation NeurIPS 2019 Hongjoon Ahn, Sungmin Cha, DongGyu Lee, Taesup Moon

We introduce a new neural network-based continual learning algorithm, dubbed as Uncertainty-regularized Continual Learning (UCL), which builds on traditional Bayesian online learning framework with variational inference.

Continual Learning Variational Inference

Iterative Channel Estimation for Discrete Denoising under Channel Uncertainty

no code implementations24 Feb 2019 Hongjoon Ahn, Taesup Moon

We propose a novel iterative channel estimation (ICE) algorithm that essentially removes the critical known noisy channel assumption for universal discrete denoising problem.


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