Chanhoe Gu

Sun Jae Baek

Kanghyeok Ko

Hyeeun Ku

The Generative Artificial Intelligence Lab (GAIL) at Chung-Ang University focuses on the exploration and development of novel machine learning models and deep learning architectures for diverse data types including biomedical, financial, and image data. The lab's primary focus is on probability estimation machine learning models for large databases and generative deep learning models like Generative Adversarial Networks (GANs) and diffusion models, aiming to capture and emulate human creativity.

생성적 인공지능 연구실에서는 다양한 종류의 빅데이터 처리를 위한 딥 러닝 및 기계학습 모델을 연구하고 있습니다. 특히, 인간의 창의성을 흉내낼 수 있으며 진짜와 같은 가상의 표본을 생성하는 생성적 인공지능 모델을 중심으로 연구하고 있습니다.

Portfolio optimization using predictive auxiliary classifier generative adversarial networks

TextControlGAN: Text-to-Image Synthesis with Controllable Generative Adversarial Networks

SuperstarGAN: Generative adversarial networks for image-to-image translation in large-scale domains

Score-Guided Generative Adversarial Networks

Consensus vs. Controversy: Mapping the Decision Space Where Architectures Diverge

When Do Models Actually Decide? Mapping the Layer-Wise Decision Timeline in Pretrained Neural Networks

Volumetric Plane-based Rendering: A Novel Approach for Internal View Synthesis in 3D Gaussian Splatting

Beyond Top-1: Forensic Analysis of Full Prediction Distributions Reveals Hidden Model Reasoning

K-fold Ensemble of Hiera and VideoMAE for Illegal Waste Dumping Detection

FreqDualNet: Frequency-Aware Vision Transformers for Tumor Segmentation

Fractal-Guided Token Pruning for Efficient Vision Transformers

FracGrad: A Discretized Riemann–Liouville Fractional Integral Approach to Gradient Accumulation for Deep Learning

Recent advances in the inverse design of silicon photonic devices and related platforms using deep generative models

Zero-Shot 3D Scene Representation With Invertible Generative Neural Radiance Fields

Does Low Spoilage Under Cold Conditions Foster Cultural Complexity During the Foraging Era?—Agent-Based Modeling and Reinforcement-Learning Approach

Emergence of self-identity in artificial intelligence: A mathematical framework and empirical study with generative large language models

ZIGNeRF: Zero-shot 3D Scene Representation with Invertible Generative Neural Radiance Fields

Stabilized GAN Models Training with Kernel-Histogram Transformation and Probability Mass Function Distance

OrgUNETR: Utilizing organ information and squeeze and excitation block for improved tumor segmentation

A Multi-Stage Deep Learning Approach Incorporating Text-Image and Image-Image Comparisons for Cheapfake Detection

DuDGAN: Improving Class-Conditional GANs via Dual-Diffusion

Real-world-feature deep transfer learning for medical image classification with a case study on pneumonia X-ray images

MetaSwin: a unified meta vision transformer model for medical image segmentation

Class-Continuous Conditional Generative Neural Radiance Field

ICEGAN: Inverse Covariance Estimating Generative Adversarial Network

Deep Learning in CRISPR-Cas Systems: A Review of Recent Studies

Discovering potential pathways between type 2 diabetes mellitus and diabetic retinopathy: A big data analysis of the South Korean National Sample Cohort

Mathematical Analysis and Performance Evaluation of the GELU Activation Function in Deep Learning

Machine-Learning Big Data Analysis of the Impact of Air Pollutants on Rhinitis-Related Hospital Visits

Recent Advancements in Deep Learning Using Whole Slide Imaging for Cancer Prognosis

Transformer and Attention Mechanisms in Genome Data Analysis: A Comprehensive Review

A Mathematical Interpretation of Autoregressive Generative Pre-Trained Transformer and Self-Supervised Learning

Deep Learning Approaches for lncRNA-mediated Mechanisms: A Comprehensive Review of Recent Developments

A Mathematical Investigation of Hallucination and Creativity in GPT Models

Deep Learning Model with L1 Penalty for Predicting Breast Cancer Metastasis Using Gene Expression Data

Machine Learning for Small Interfering RNAs: A Concise Review of Recent Developments

Multi-Task Deep Learning Games: Investigating Nash Equilibria and Convergence Properties.

Recent Advances in Deep Learning for Protein-Protein Interaction Analysis: A Comprehensive Review

Deep Learning Techniques with Genomic Data in Cancer Prognosis: A Comprehensive Review of the 2021-2023 Literature

Heterogeneous Structure Omnidirectional Strain Sensor Arrays with Cognitively‐Learned Neural Networks

BioEdge: Accelerating Object Detection in Bioimages with Edge-Based Distributed Inference

Machine Learning-Based Analyses of the Effects of Various Types of Air Pollutants on Hospital Visits by Asthma Patients

Estimating the Prognosis of Low-Grade Glioma with Gene Attention using Multi-omics and Multi-modal Schemes

Low-to-Moderate Atmospheric Ozone Levels Are Negatively Correlated with Hospital Visits by Asthma Patients

Inverse design of nanophotonic devices using generative adversarial networks

Deep reinforcement learning with a critic-value-based branch tree for the inverse design of two-dimensional optical devices

An Ensemble Deep Learning Model with a Gene Attention Mechanism for Estimating the Prognosis of Low-grade Glioma

HRGAN: A Generative Adversarial Network Producing Higher-Resolution Images than Training Sets

Estimation with uncertainty via conditional generative adversarial networks

Improved Recurrent Generative Adversarial Networks with Regularization Techniques and a Controllable Framework

Portfolio Management via Two-stage Deep Learning with A Joint Cost.

Serum biomarkers from cell-based assays for AhRL and MIS strongly predicted the future development of diabetes in a large community-based prospective study in Korea.

Clinical Implication of Concordant or Discordant Genomic Profiling Between Primary and Matched Metastatic Tissues in Patients with Colorectal Cancer.

Prediction of Survival Risks with Adjusted Gene Expression through Risk-Gene Networks.

Controllable Generative Adversarial Network.

Regression-based Network Estimation for High-dimensional Genetic Data

The Estimation of Probability Distribution for Factor Variables with Many Categorical Values.

A Comparison of Two-Stage Approaches Based on Penalized Regression for Estimating Gene Networks.

Generative Artificial Intelligence Laboratory (GAIL)

교수님 소개

이민혁

연구실 소개

연구실 키워드

알럼나이가 간 회사

연구실 지원 방법

대우 조건

연도별 논문 인용수

저널 & 학회 논문 출판 수

연구실 논문 목록

Consensus vs. Controversy: Mapping the Decision Space Where Architectures Diverge

When Do Models Actually Decide? Mapping the Layer-Wise Decision Timeline in Pretrained Neural Networks

Volumetric Plane-based Rendering: A Novel Approach for Internal View Synthesis in 3D Gaussian Splatting

연구실 보유 특허

대학별 입시끈

우리 연구실 모집

교수님 소개

이민혁

연구실 키워드

알럼나이가 간 회사

연구실 지원 방법

대우 조건

연도별 논문 인용수

저널 & 학회 논문 출판 수

연구실 논문 목록

Consensus vs. Controversy: Mapping the Decision Space Where Architectures Diverge

When Do Models Actually Decide? Mapping the Layer-Wise Decision Timeline in Pretrained Neural Networks

Volumetric Plane-based Rendering: A Novel Approach for Internal View Synthesis in 3D Gaussian Splatting

연구실 보유 특허

최근 3년간 연구실적 높은 연구실

대학별 입시끈

우리 연구실 모집