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The goal of MLVR lab. is to study and develop various models applicable to various domains, such as visual recognition, natural language understanding, healthcare, financial prediction, based upon the general machine learning approaches including deep learning.
- Multi-task deep learning for avoiding negative transfer on shared representations.
- Bayesian deep learning based on approximate variational inference.
- Network structure estimation and optimization for lifelong learning.
- Zero-/Few-shot Learning for Unseen Category Prediction.
- Deep-learning based survivor detection system on UAVs.
- Active incremental learning with model uncertainty for autonomous driving.
- Deep generative model based controllable text generation.
- Personalized conversation model using memory-augmented continual learning.
- Explainable AI. Uncertainty and attention mechanism based reliable prediction research.
- Physiological symptom prediction models in intensive care unit and ward environment.
- Deep gaussian process and variational approach based machine learning algorithms.
- Deep probabilistic models for algorithmic stock trading, real estate price prediction.