Search Results for author: Seonwoo Min

Found 11 papers, 7 papers with code

Transformers meet Stochastic Block Models: Attention with Data-Adaptive Sparsity and Cost

1 code implementation27 Oct 2022 Sungjun Cho, Seonwoo Min, Jinwoo Kim, Moontae Lee, Honglak Lee, Seunghoon Hong

The forward and backward cost are thus linear to the number of edges, which each attention head can also choose flexibly based on the input.

Stochastic Block Model

Grounding Visual Representations with Texts for Domain Generalization

1 code implementation21 Jul 2022 Seonwoo Min, Nokyung Park, Siwon Kim, Seunghyun Park, Jinkyu Kim

In this work, we advocate for leveraging natural language supervision for the domain generalization task.

Domain Generalization

Pure Transformers are Powerful Graph Learners

1 code implementation6 Jul 2022 Jinwoo Kim, Tien Dat Nguyen, Seonwoo Min, Sungjun Cho, Moontae Lee, Honglak Lee, Seunghoon Hong

We show that standard Transformers without graph-specific modifications can lead to promising results in graph learning both in theory and practice.

Graph Learning Graph Regression +1

Supervised Neural Discrete Universal Denoiser for Adaptive Denoising

no code implementations24 Nov 2021 Sungmin Cha, Seonwoo Min, Sungroh Yoon, Taesup Moon

Namely, we make the supervised pre-training of Neural DUDE compatible with the adaptive fine-tuning of the parameters based on the given noisy data subject to denoising.

Denoising

TargetNet: Functional microRNA Target Prediction with Deep Neural Networks

1 code implementation23 Jul 2021 Seonwoo Min, Byunghan Lee, Sungroh Yoon

Results: In this paper, we introduce TargetNet, a novel deep learning-based algorithm for functional miRNA target prediction.

Pre-Training of Deep Bidirectional Protein Sequence Representations with Structural Information

1 code implementation25 Nov 2019 Seonwoo Min, Seunghyun Park, Siwon Kim, Hyun-Soo Choi, Byunghan Lee, Sungroh Yoon

Bridging the exponentially growing gap between the numbers of unlabeled and labeled protein sequences, several studies adopted semi-supervised learning for protein sequence modeling.

Ranked #18 on Only Connect Walls Dataset Task 1 (Grouping) on OCW (using extra training data)

Language Modelling Masked Language Modeling +1

Deep Recurrent Neural Network-Based Identification of Precursor microRNAs

1 code implementation NeurIPS 2017 Seunghyun Park, Seonwoo Min, Hyun-Soo Choi, Sungroh Yoon

MicroRNAs (miRNAs) are small non-coding ribonucleic acids (RNAs) which play key roles in post-transcriptional gene regulation.

Polyphonic Music Generation with Sequence Generative Adversarial Networks

1 code implementation31 Oct 2017 Sang-gil Lee, Uiwon Hwang, Seonwoo Min, Sungroh Yoon

We propose an application of sequence generative adversarial networks (SeqGAN), which are generative adversarial networks for discrete sequence generation, for creating polyphonic musical sequences.

Sound Audio and Speech Processing

Neural Universal Discrete Denoiser

no code implementations NeurIPS 2016 Taesup Moon, Seonwoo Min, Byunghan Lee, Sungroh Yoon

We present a new framework of applying deep neural networks (DNN) to devise a universal discrete denoiser.

Denoising

deepMiRGene: Deep Neural Network based Precursor microRNA Prediction

no code implementations29 Apr 2016 Seunghyun Park, Seonwoo Min, Hyun-Soo Choi, Sungroh Yoon

Since microRNAs (miRNAs) play a crucial role in post-transcriptional gene regulation, miRNA identification is one of the most essential problems in computational biology.

Feature Engineering

Deep Learning in Bioinformatics

no code implementations21 Mar 2016 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.

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