Search Results for author:
Found 8 papers, 2 papers with code
Virtual Human Generative Model: Masked Modeling Approach for Learning Human Characteristics
In this paper, we propose Virtual Human Generative Model (VHGM), a machine learning model for estimating attributes about healthcare, lifestyles, and personalities.
When is invariance useful in an Out-of-Distribution Generalization problem ?
Popular approaches in this field use the hypothesis that such a predictor shall be an \textit{invariant predictor} that captures the mechanism that remains constant across environments.
MANGA: Method Agnostic Neural-policy Generalization and Adaptation
Efficiently transferring learned policies to an unknown environment with changes in dynamics configurations in the presence of motor noise is very important for operating robots in the real world, and our work is a novel attempt in that direction.
Motion Generation Considering Situation with Conditional Generative Adversarial Networks for Throwing Robots
The appropriate motions can be found efficiently by searching the latent space of the trained cGANs instead of the motion space, while avoiding poor local optima.
Data Interpolating Prediction: Alternative Interpretation of Mixup
Data augmentation by mixing samples, such as Mixup, has widely been used typically for classification tasks.
Semi-flat minima and saddle points by embedding neural networks to overparameterization
We theoretically study the landscape of the training error for neural networks in overparameterized cases.
DISTRIBUTIONAL CONCAVITY REGULARIZATION FOR GANS
We propose Distributional Concavity (DC) regularization for Generative Adversarial Networks (GANs), a functional gradient-based method that promotes the entropy of the generator distribution and works against mode collapse.
A Wrapped Normal Distribution on Hyperbolic Space for Gradient-Based Learning
Hyperbolic space is a geometry that is known to be well-suited for representation learning of data with an underlying hierarchical structure.
