Welcome to Machine Intelligence for Medical Engineering Team
Our goal is to invent intelligent systems assisting medical diagnosis and treatment by extracting useful features from medical information, and combining them with powerful computational resources and vast amount of data in the cyber space. To tackle this challenging problem, we utilize all resources in the area of computer science including mathematical basis and robotics.
For more information, please refer to Machine Intelligence Lab.
Model-Induced Generalization Error Bound for Information-Theoretic Representation Learning in Source-Data-Free Unsupervised Domain Adaptation (IEEE Transactions on Image Processing)
Fully Spiking Variational Autoencoder[https://arxiv.org/abs/2110.00375] (AAAI 2022)
EtinyNet: Extremely Tiny Network for TinyML (AAAI 2022)
Towards an Effective Orthogonal Dictionary Convolution Strategy (AAAI 2022)
Neural Articulated Radiance Field (ICCV 2021)
Semantic Mapping of Construction Site from Multiple Daily Airborne LiDAR Data (IEEE Robotics and Automation Letters)
Humor meets morality: Joke generation based on moral judgement (Information Processing & Management)
Hyperbolic Neural Networks++ (ICLR 2021)
The etiology of auditory hallucinations in schizophrenia: from multidimensional levels (Frontiers in Neuroscience)
Multiresolution Discriminative Mixup Network for Fine-Grained Visual Categorization (IEEE Transactions on Neural Networks and Learning Systems)
Explainable Diabetic Retinopathy Detection and Retinal Image Generation (IEEE Journal of Biomedical and Health Informatics)
Beyond Triplet Loss: Person Re-identification with Fine-grained Difference-aware Pairwise Loss (IEEE Transactions on Multimedia)