57 citations · 159 across the 10 of their papers we have counts for
10 papers
Transformers meet Stochastic Block Models: Attention with Data-Adaptive Sparsity and Cost
Sungjun Cho, Seonwoo Min, Jinwoo Kim +3
To overcome the quadratic cost of self-attention, recent works have proposed various sparse attention modules, most of which fall under one of two groups: 1) sparse attention under…
Grounding Visual Representations with Texts for Domain Generalization
Seonwoo Min, Nokyung Park, Siwon Kim +2
Reducing the representational discrepancy between source and target domains is a key component to maximize the model generalization. In this work, we advocate for leveraging natura…
Pure Transformers are Powerful Graph Learners
Jinwoo Kim, Tien Dat Nguyen, Seonwoo Min +4
We show that standard Transformers without graph-specific modifications can lead to promising results in graph learning both in theory and practice. Given a graph, we simply treat…
Supervised Neural Discrete Universal Denoiser for Adaptive Denoising
Sungmin Cha, Seonwoo Min, Sungroh Yoon +1
We improve the recently developed Neural DUDE, a neural network-based adaptive discrete denoiser, by combining it with the supervised learning framework. Namely, we make the superv…
TargetNet: Functional microRNA Target Prediction with Deep Neural Networks
Seonwoo Min, Byunghan Lee, Sungroh Yoon
Motivation: MicroRNAs (miRNAs) play pivotal roles in gene expression regulation by binding to target sites of messenger RNAs (mRNAs). While identifying functional targets of miRNAs…
Pre-Training of Deep Bidirectional Protein Sequence Representations with Structural Information
Seonwoo Min, Seunghyun Park, Siwon Kim +3
Bridging the exponentially growing gap between the numbers of unlabeled and labeled protein sequences, several studies adopted semi-supervised learning for protein sequence modelin…
Polyphonic Music Generation with Sequence Generative Adversarial Networks
Sang-gil Lee, Uiwon Hwang, Seonwoo Min +1
We propose an application of sequence generative adversarial networks (SeqGAN), which are generative adversarial networks for discrete sequence generation, for creating polyphonic…
Neural Universal Discrete Denoiser
Taesup Moon, Seonwoo Min, Byunghan Lee +1
We present a new framework of applying deep neural networks (DNN) to devise a universal discrete denoiser. Unlike other approaches that utilize supervised learning for denoising, w…
deepMiRGene: Deep Neural Network based Precursor microRNA Prediction
Seunghyun Park, Seonwoo Min, Hyunsoo Choi +1
Since microRNAs (miRNAs) play a crucial role in post-transcriptional gene regulation, miRNA identification is one of the most essential problems in computational biology. miRNAs ar…
Deep Learning in Bioinformatics
Seonwoo Min, Byunghan Lee, Sungroh Yoon
In the era of big data, transformation of biomedical big data into valuable knowledge has been one of the most important challenges in bioinformatics. Deep learning has advanced ra…