Highlighted Projects
-
Regularized Generative Adversarial Nets (GANs)
-
Theory and Principles for Regularization, Generalization and Semi-Supervised Learning of GANs
- LS-GAN: Loss-Sensitive GANs (more...)
- GLSAL: Generalized Loss-Sensitive Adversarial Learning with Manifold Margins (more...)
- LGAN: Localized GANs for Semi-Supervised Learning with Laplace-Beltrami Operators (more...)
-
Applications
- HTG: An Adversarial Approach to Hard Triplet Generation (more...)
-
Learning Transformation Equivariant Representations (TERs)
-
Theory, Principles and Models for TERs
- AET: AutoEncoding Transformations rather than Data (more...)
- AVT: Autoencoding Variational Transformations (more...)
-
Small Data Challenges in Big Data Era
-
Read Our survey of
- Recent progress on unsupervised and semi-supervised learning (more...)
-
Small Data Methods
- Unsupervised Learning & Semi-Supervised Learning
- Few-Shot Learning & Zero-Shot Learning
-
Time-Series Data Analysis
- SFM: State-Frequency Memory Recurrent Neural Networks
- Stock Price Prediction via Discovering Multi-Frequency Trading Patterns
- Mixture Factorized Ornstein-Uhlenbeck Processes
- First-Take All: Hashing Time-Series Data
-
Perception, Control, Decision-Making and Optimization in Smart City
- Machine Perception for Smart City (e.g., surveillance cameras and IoT)
- AI for Traffic system diagnostics and traffic signal optimization
- AI for scheduling and optimization of civil utilities