36 citations · 90 across the 21 of their papers we have counts for
11 papers · 1 filter
EPIC: Graph Augmentation with Edit Path Interpolation via Learnable Cost
Jaeseung Heo, Seungbeom Lee, Sungsoo Ahn +1
Data augmentation plays a critical role in improving model performance across various domains, but it becomes challenging with graph data due to their complex and irregular structu…
Substructure-Atom Cross Attention for Molecular Representation Learning
Jiye Kim, Seungbeom Lee, Dongwoo Kim +2
Designing a neural network architecture for molecular representation is crucial for AI-driven drug discovery and molecule design. In this work, we propose a new framework for molec…
Hyperbolic VAE via Latent Gaussian Distributions
Seunghyuk Cho, Juyong Lee, Dongwoo Kim
We propose a Gaussian manifold variational auto-encoder (GM-VAE) whose latent space consists of a set of Gaussian distributions. It is known that the set of the univariate Gaussian…
MetaSSD: Meta-Learned Self-Supervised Detection
Moon Jeong Park, Jungseul Ok, Yo-Seb Jeon +1
Deep learning-based symbol detector gains increasing attention due to the simple algorithm design than the traditional model-based algorithms such as Viterbi and BCJR. The supervis…
Few-Shot Unlearning by Model Inversion
Youngsik Yoon, Jinhwan Nam, Hyojeong Yun +3
We consider a practical scenario of machine unlearning to erase a target dataset, which causes unexpected behavior from the trained model. The target dataset is often assumed to be…
A Rotated Hyperbolic Wrapped Normal Distribution for Hierarchical Representation Learning
Seunghyuk Cho, Juyong Lee, Jaesik Park +1
We present a rotated hyperbolic wrapped normal distribution (RoWN), a simple yet effective alteration of a hyperbolic wrapped normal distribution (HWN). The HWN expands the domain…
Robust Deep Learning from Crowds with Belief Propagation
Hoyoung Kim, Seunghyuk Cho, Dongwoo Kim +1
Crowdsourcing systems enable us to collect large-scale dataset, but inherently suffer from noisy labels of low-paid workers. We address the inference and learning problems using su…
Neural Network Classifier as Mutual Information Evaluator
Zhenyue Qin, Dongwoo Kim, Tom Gedeon
Cross-entropy loss with softmax output is a standard choice to train neural network classifiers. We give a new view of neural network classifiers with softmax and cross-entropy as…
Position-Sensing Graph Neural Networks: Proactively Learning Nodes Relative Positions
Zhenyue Qin, Yiqun Zhang Saeed Anwar, Dongwoo Kim +3
Most existing graph neural networks (GNNs) learn node embeddings using the framework of message passing and aggregation. Such GNNs are incapable of learning relative positions betw…
Rethinking Softmax with Cross-Entropy: Neural Network Classifier as Mutual Information Estimator
Zhenyue Qin, Dongwoo Kim, Tom Gedeon
Mutual information is widely applied to learn latent representations of observations, whilst its implication in classification neural networks remain to be better explained. We sho…
Softmax Is Not an Artificial Trick: An Information-Theoretic View of Softmax in Neural Networks
Zhenyue Qin, Dongwoo Kim
Despite great popularity of applying softmax to map the non-normalised outputs of a neural network to a probability distribution over predicting classes, this normalised exponentia…