Search Results for author:
Found 6 papers, 3 papers with code
Learning with Limited Samples -- Meta-Learning and Applications to Communication Systems
This review monograph provides an introduction to meta-learning by covering principles, algorithms, theory, and engineering applications.
Understanding Benign Overfitting in Gradient-Based Meta Learning
While the conventional statistical learning theory suggests that overparameterized models tend to overfit, empirical evidence reveals that overparameterized meta learning methods still work well -- a phenomenon often called "benign overfitting."
Sharp-MAML: Sharpness-Aware Model-Agnostic Meta Learning
Model-agnostic meta learning (MAML) is currently one of the dominating approaches for few-shot meta-learning.
Is Bayesian Model-Agnostic Meta Learning Better than Model-Agnostic Meta Learning, Provably?
In this paper, we aim to provide theoretical justifications for Bayesian MAML's advantageous performance by comparing the meta test risks of MAML and Bayesian MAML.
Deep Structured Prediction for Facial Landmark Detection
Existing deep learning based facial landmark detection methods have achieved excellent performance.
Ranked #6 on
Facial Landmark Detection
on 300W
Face Alignment With Kernel Density Deep Neural Network
Specifically, for face alignment, we adapt state-of-the-art hourglass neural network into a probabilistic neural network framework with landmark probability map as its output.
Ranked #3 on
Face Alignment
on COFW-68 (300WLP)
