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Accelerated Sampling of Rare Events using a Neural Network Bias Potential
In particular, we approximate the variance-free bias potential function with DNNs which is trained to maximize the probability of rare event transition under the importance potential function.
Dynamic Flows on Curved Space Generated by Labeled Data
The scarcity of labeled data is a long-standing challenge for many machine learning tasks.
Human Imperceptible Attacks and Applications to Improve Fairness
However, small perturbations which are imperceptible to humans may significantly degrade the performance of well-trained deep neural networks.
Gradient flows on the feature-Gaussian manifold
The scarcity of labeled data is a long-standing challenge for cross-domain machine learning tasks.
Improved Search Strategies with Application to Estimating Facial Blendshape Parameters
It is well known that popular optimization techniques can lead to overfitting or even a lack of convergence altogether; thus, practitioners often utilize ad hoc regularization terms added to the energy functional.
